has been shown that machine learning methods can lead to systematic and
unpredictable biases in micro-structure mapping from dMRI signal [53, 67]. It
has also been shown that this bias can depend on factors such as SNR, partial
volume effects, and distribution of the micro-structure values in the training
and test data [53, 67]. As a result, predictions of these methods may lead to
erroneous conclusions when studying the impact of pathology on micro-structure
[67]. While such biases may be present in many machine learning methods,
discovering them requires extensive validation and analysis that is missing in
most published works.

###  III-C Modeling considerations

####  III-C1 Data representation

The dMRI data used as input to machine learning models are typically in the
so-called q space, where each of the measurements in a voxel is associated
with a gradient strength and a gradient direction. The gradient
strength/direction are typically different between subjects/scans. Two
approaches that are commonly used to create a uniform representation of the
data include: (1) interpolation unto a fixed spherical grid, where a typical
grid size is approximately 100 [39], and (2) representation in spherical
harmonics. There exists several formulations for extending the spherical
harmonics representation to multi-shell data [347, 348]. Zucchelli et al.
[349, 45] have advocated for signal representation in terms of their
rotationally invariant features [350]. They have shown that, compared with the
two common representations mentioned above, their representation leads to
better estimation of multi-compartment models [349] and FOD [350]. Another
notable example is sparse representation in dictionaries [61]. In [64],
dictionaries that were separable in spatial and angular domains [351] were
used. Compared with non-separable dictionaries, separable formulations
significantly reduce the number of parameters and may improve the analysis
results [64]. Another flexible representation framework is based on graphs.
This framework was successfully used for dMRI signal interpolation in [31],
where the authors constructed the adjacency matrix in terms of the difference
between pairs of measurements. They computed the difference in terms of
spatial distance, angular difference between gradient directions, and
difference in gradient strengths. A similar representation was used in [313]
to extend the non-local means denoising algorithm to dMRI. Another approach,
proposed in [82], consists of projecting the q-space data onto the standard
(xy, xz, and yz) planes and binning them into fixed grids. For multi-shell
data, each shell is binned separately. For applications that require large
spatial fields of view, such as in tract segmentation [27], limited GPU
memories typically do not allow for using the dMRI data as model input.
Instead, as mentioned above in Sections II-C, II-D, and II-E, DTI or FOD
estimations may be computed and used as input for machine learning models.

####  III-C2 Neural network architecture

Most deep learning works discussed in this paper appear to have designed their
network architecture in an ad-hoc way as they have provided little or no
justification for their choices. One study [352] has reported successful
application of network architecture search [353] for dMRI data prediction.

Standard convolutional networks are equivariant to translation, which is one
of the key reasons for their success in computer vision. However, they are not
equivariant to rotation. Rotation-invariance and rotation-equivariance (i.e.,
a rotation of the input to result in an identical rotation in the output) are
highly desired in many applications. Incorporating these properties in the
network architecture removes the need for the model to learn every possible
orientation of the object/scene, which would otherwise require larger datasets
or massive data augmentation. It has been shown to improve the performance in
various computer vision tasks [354]. In dMRI, the q-space data in each voxel
can be represented as spherical maps. Therefore, in addition to the rotations
in the voxel (physical) space, q-space rotations should also be accounted for
[110]. Most standard neural network models such as CNNs have been designed for
signals in Euclidean space such as digital images. An ideal convolutional
operation for q-space data should be either rotation-invariant (e.g., for
estimating scalar maps of micro-structure) or rotation-equivariant (e.g., for
FOD estimation). Moreover, it should allow for irregular sampling on the
sphere.

Muller et al. [355] have argue that standard (i.e., non-rotationally-
equivariant) networks, when applied in dMRI, are at the risk of both under-
fitting and over-fitting. This is because they need to learn the rotation-
equivariance relationships from training data, which may be biased towards
certain orientations. Incorporating rotation-equivariance will eliminate the
need for learning these constraints, enable better weight sharing, improve
generalization to orientations not seen in training data, and allow the model
to concentrate on learning the actual function of interest [109, 355].

Various techniques have been proposed to build rotation-equivariance into
neural networks and to extend standard convolutions to signals on the sphere.
We briefly explain some of these below.

  * •

Some studies have proposed to discretize the surface of the sphere and apply
standard 2D convolutions on the resulting grid [356, 357, 358]. A
straightforward discretization can be achieved by using an equiangular spacing
in the standard spherical-polar coordinate system. Boomsma and Frellsen [357]
have suggested that a better discretization can be achieved using the cubed-
sphere representation originally developed by [359]. In this representation,
each point on the sphere is represented by the coordinates in one of the six
patches of a circumscribed cube. They define cubed-sphere convolution as
applying a standard 2D convolution on a uniformly spaced grid on the six sides
of the cube. This method can be extended to concentric shells in a ball.

  * •

A common approach in computer vision applications is to divide the surface of
the sphere into small regions and process each region as a planar signal with
standard 2D convolutions [360, 361]. This processing is usually followed by
hierarchical pooling. However, this approach does not result in rotation-
equivariance.

  * •

Another approach is based on representing the data as a graph. Perraudin et
al. proposed DeepShere [128], which processes spherical signals by
representing the sphere as a graph. DeepShere relies on filters that are
restricted to being radial. As a result, the convolutional operations are
rotation-equivariant and the network predictions become invariant or
equivariant to input rotations. Moreover, this formulation has a lower
computational cost than performing the convolution with spherical harmonics.

  * •

Various other techniques have been used to develop rotation-equivariant
convolutional operations. The work of Cohen et al. [362] is based on the
spherical Fourier transform, where the convolution is performed in the
spectral domain based on the convolution theorem. Banerjee et al. [363] have
developed a method based on Volterra’s function. Bouza et al. [364] have
proposed a manifold-valued convolution based on a generalization of the
Volterra Series to manifold-valued functions, whereas Esteves et al. [354]
propose a rotation-equivariant 3D convolutional network based on spherical
harmonics. Cohen and Welling [365] have proposed group equivariant
convolutional networks. Group convolutions enable equivariance with regard to
rotation, thereby increasing the expressive power of convolutions without
increasing the number of parameters.

  * •

Muller et al. [355] have generalized rotation-equivariant neural networks to
the 6D space of dMRI data. They have develop a new linear layer that is
equivariant under rotations in the voxel space and q-space and under
translations in the voxel space. Their formulation enables translation-
equivariance in the coordinate space and rotation-invariance in the coordinate
space and q-space.

Numerous works have reported that these specialized network architectures can
improve the performance of deep learning models in different dMRI analysis
tasks [364, 366, 273, 367, 368]. Bouza et al. found that their proposed
convolution operation improved the state of the art in classification of
Parkinson’s Disease patients versus healthy controls and in FOD reconstruction
[364]. Sedlar et al. reported improved estimation of NODDI parameters with a
rotationally-invariant CNN [369], while Liu et al. show that their network
results in a 10% reduction in the number of trainable weights without a
reduction in performance in a tissue segmentation application [370]. Kerkela
et al. [367] found that a rotation invariant spherical CNN performed better
than a standard MLP in estimating a multi-compartment model. For angular
super-resolution of dMRI data, Lyon et al. showed that a parametric continuous
convolution network outperformed a standard CNN while reducing the number of
parameters by an order of magnitude [274]. Other dMRI study have shown that
rotationally-equivariant networks can lead to more accurate tissue
segmentation and lesion segmentation compared with standard networks [368,
355]. Goodwin-Allcock et al. showed that including rotation equivariance could
improve the generalizability of deep learning models to new gradient schemes
and reduce the required training data size [366].

####  III-C3 Loss functions

For tissue micro-structure mapping, one study compared a training loss
function based on the error in micro-structure prediction and another in terms
of the error in signal prediction [85]. The former is by far more common in
machine learning-based micro-structure mapping works, whereas the latter is
the common loss function in standard optimization-based methods. It was shown
that although the former led to lower micro-structure prediction errors when
the noise was modeled accurately, the latter resulted in lower signal
reconstruction error and it was more practical because it eliminated the need
for reference micro-structure and enabled training with synthetic and real
data [85]. The authors speculated that the weakness of a loss function based
on signal reconstruction error could be due to the fact that neural networks
might tend to learn features that originate from the noise floor. In the
context of IVIM model fitting, a similar method of training based on the
signal prediction error was proposed in [34], where the authors used the terms
“unsupervised” and “physics-informed” to describe their method. Parker et al.
argue that the use of root mean square loss function is not consistent with
the measurement noise distribution in dMRI [88]. They propose a loss based on
Rician likelihood and show that, for estimation of apparent diffusion
coefficient (ADC) and IVIM parameters, it leads to lower estimation bias at
lower SNR.

The quest for a proper loss function in micro-structure estimation has also
been pursued in [92], where the authors argue that loss functions based purely
on the predicted signal are inadequate. Instead, they propose two alternative
loss functions that additionally incorporate the error in micro-structure
estimation. One of their loss functions consists of the $\ell_{1}$ norms of
the errors of the dMRI signal and the micro-structure indices. The other loss
function, which they name the spherical variance loss, is based on the
spherical variance of the dMRI signal that is correlated with micro-structure
[371]. Experiments show that, compared with standard loss functions such as
the $\ell_{1}$, $\ell_{2}$, and Huber loss of the signal prediction error,
these two augmented loss functions result in more accurate prediction of a
range of parameters including GFA, DKI, and NODDI. However, they do not
compare these new loss functions with a loss function based on the error in
predicted micro-structure index.

###  III-D Data, ground truth, and evaluation

For many dMRI applications, obtaining data with reliable target/label is
difficult or impossible. In some applications, such as those involving
segmenting or analyzing specific white matter tracts, labels can be obtained
from human experts. However, such labels are known to suffer from significant
inter- and intra-rater variability [171, 156]. In certain applications,
histological data can be used to establish a reasonable ground truth, but at
considerable cost. For estimation of intra-axonal water exchange time, Hill et
al. used histology analysis of an in-vivo mouse model of demyelination using
electron microscopy to generate ground truth [43]. Specifically, they measured
the thickness of the myelin sheath as a measure of exchange time.
Surprisingly, they found that the prediction of their random forest regression
model based on dMRI signal was strongly correlated with histological
measurements, with a Pearson correlation coefficient of 0.98. Nath et al. used
confocal histology to generate data for FOD estimation [111]. Since the data
obtained with this technique was costly and very limited, they performed
massive data augmentation to increase the effective size of their data. For
data harmonization, a large purely dMRI scan-rescan dataset was used to boost
a small dataset of paired histology and dMRI [298, 301]. Another work used co-
registered dMRI and histology data to develop a machine learning method for
estimating several micro-structural indices and showed that it achieved higher
estimation accuracy than standard methods [21]. For validating a dMRI-based
tract segmentation methods, Lucena et al. [195, 194] have advocated for using
navigated transcranial magnetic stimulation to establish a ground truth.

Most commonly, however, high-quality dMRI measurements have been used to
generate the data needed for developing and validating machine learning
methods. For tract segmentation/registration, Li et al. used an automatic
method based on probabilistic tractography and atlas-based segmentation to
generate their training labels [253]. For micro-structure mapping and for FOD
estimation, published studies have either used dense q-space data and
estimated ground truth parameters using standard methods (e.g., [73, 119, 120,
126, 118, 126]) or they have used simulation techniques such as the Monte
Carlo method (e.g., [43, 85, 67]). Despite its limitations, data simulation is
sometimes the only reasonable approach when high-quality data are not
available to compute a reliable ground truth. For micro-structure estimation,
both numerical and physical phantom are considered valid and powerful methods
for developing and validating computational methods [127, 372]. Data
simulation also has potential advantages: (i) it enables exploring the full
range of parameters that may influence the signal, and (ii) the ground truth
target is known accurately [67, 96]. Because of the flexibility of data
simulation methods, they may also be useful for investigating what data
acquisition protocols lead to more accurate reconstruction. This approach was
used by Lee et al. to determine the optimal b values for IVIM reconstruction
with deep learning [373]. Gyori et al. investigated the impact of training
data distribution on the test performance of supervised machine learning
methods for micro-structure mapping [67]. When training on simulated data,
they found that in order for the model to achieve reasonable accuracy on
atypical brain tissue, the training data should be synthesized using the
complete range of plausible parameter values. When the parameter space used to
generate the training data matched that of healthy brains, estimation accuracy
on atypical brains was low, although the estimations showed a deceptively high
precision.

There have been innovative approaches to dMRI data simulation in prior works.
Nedjati et al. used Monte Carlo simulation to synthesize dMRI data based on
histologically valid micro-structure parameters [25]. Ye and Li have developed
a method for synthesizing q-space data that can potentially be used for any
machine learning application in dMRI [374]. Their signal generator is an MLP
that is trained to minimize the difference between the distribution of the
synthesized signal and that of the observed signal. They use a continuous
representation of the dMRI data in the SHORE basis [375]. Their preliminary
experiments have shown promising results for estimation of the NODDI model.
Qin et al. have proposed and validated a knowledge transfer method that relies
on a source domain where high-quality data are available [376]. Using the
SHORE basis, this method interpolates the source data in the q-space and/or
voxel space to synthesize data that match the quality of the target data.
Model training is carried out in the source domain and transferred to the
target domain. For Gibbs artifact suppression, one study trained a CNN on more
than one million natural images and simulated more than 10 million data
instances [332]. Experiments showed that the size and richness of the
collection of natural images used for training was essential to ensuring
generalizability of the model to dMRI test data. Graham et al. developed a
simulation method to enable direct quantitative evaluation of artifact-
correction methods [377], which they subsequently used to develop and validate
a deep learning-based motion artifact detection technique [334]. Their work
showed that with proper data simulation, only a small real dataset was need to
calibrate the method.

Using simulated data is typically much more convenient than real-world data.
Some works have relied solely on simulated data to train and or evaluate their
methods [88, 32, 15, 53]. However, using in-silico simulations to synthesize
training data may be inadequate because some of the factors such as the noise
distribution are difficult to model accurately [85]. For IVIM-DKI estimation,
one study observed that increasing the noise level in the synthesized training
data improved the model’s test accuracy [87]. Masutani [96] analyzed the
impact of matching the noise level between training and test data. He found
that the best test performance was achieved when the noise level in the
training data was close to or slightly higher than the noise level in the test
data. Martins et al. [36], on the other hand, found that training with a much
noisier dataset could still result in superior results on test data with
relatively lower noise. For DKI estimation, another work found that varying
the noise strength in the synthetic training data led to better results on
clinical test scans compared with a model that was trained using a single
noise strength [97]. In some applications, such as cardiac DTI [32] and fetal
imaging [74] that we have mentioned above, obtaining reliable in-vivo data
faces especial difficulties such that using synthetic training data may be
unavoidable.

Another important consideration is the choice of evaluation metrics. It is
often unclear what metrics should be reported to present a comprehensive
assessment of the method and a fair comparison among competing techniques. As
an example, for micro-structure mapping most studies report the RMSE as the
main or the only metric. However, it has been suggested that correlation
analysis and sensitivity analysis are important for revealing the limitations
of machine learning-based methods that cannot be assessed based solely on
estimation error [68]. Moreover, rather than reporting a single global RMSE
value for a method, detailed analysis of the estimation error such as its
variations in different brain regions may reveal biases of machine learning
methods that are not captured by global RMSE [378, 66, 67, 53].

Although for many dMRI computations obtaining the ground truth may be
difficult or impossible, test-retest reproducibility assessments may be far
easier to perform. As a matter of fact, repeatability tests have often been
used to assess machine learning methods in dMRI. For example, Tunc et al. used
repeated scans of the same subjects to assess the repeatability of their tract
segmentation method [211], while Barbieri et al. computed inter-subject
variability to validate a deep learning method for IVIM estimation [35].
Performing such tests has become more feasible with the growing availability
of public datasets that include repeated scans of the same subjects on the
same scanner [379] or on different scanners [380] or on different scanners
with different protocols [381, 293].

Where ground truth is difficult to establish, visual assessment by medical
experts may be a viable alternative. One study quantified the agreement
between two readers in their interpretation of IVIM parameter maps to compare
a deep learning method with conventional estimation methods [35]. However, the
authors have pointed out that this approach may favor methods that wrongly
compute consistent results while failing to account for genuine heterogeneity.
Another work has reported successful validation of a deep learning method for
fetal DTI estimation based on blind randomized assessment and scoring by
neuroanatomists and neuroradiologists [74]. Experts may also be able to help
in generating ground truth labels for training and validation. This has been a
common approach for white matter tract segmentation. This approach, however,
may suffer from difficulty of defining anatomical ground truth [37], high
intra- and inter-expert variability [156], and labeling errors. Such label
errors need to be properly accounted for using advanced machine learning
techniques [268]. In applications such as tractography and tract analysis,
obtaining labels from multiple experts may be useful but costly [173, 22,
151].

Lack of reliable and standardized data for developing and validating machine
learning methods may also be one of the main barriers to quick and widespread
adoption of these methods. Despite repeated claims that machine learning
methods can outperform classical techniques, these methods have not seen
widespread adoption. For tractography, for example, Poulin et al. point out
that despite repeated demonstration of the capabilities of machine learning
methods, none of these methods have been adopted for real-world applications
[22]. They attribute this, in part, to the lack of well-defined test
benchmarks to allow conclusive performance evaluation and comparison.

A useful approach to assessing the potential of machine learning for dMRI
analysis is via open competitions, where research teams are free to develop
and apply standard non-learning methods and machine learning techniques. A
good example is a recent open challenge where research teams were invited to
develop deep learning methods for estimating diffusion tensor parameters from
reduced number of measurements [77]. Specifically, focusing on distinguishing
between episodic and chronic migraine patients, the goal was to investigate
whether deep learning methods were able to achieve the same level of
sensitivity and specificity when using 21 measurements, compared with 61
measurements typically used with standard methods for this analysis. With
conventional estimation methods, 60% of the differences detected by TBSS with
61 measurements are missed when using 21 measurements. A total of 14 research
teams participated in the challenge. Results showed that deep learning methods
improved the global image metrics such as PSNR and SSIM. They also improved
sensitivity of TBSS. However, these improvements came at the cost of higher
false positive rates, which increased linearly with the true positive rate.
The teams had used different deep learning models and had adopted various
approaches, such as mapping the reduced measurements directly to the target
DTI parameters or estimating dense dMRI measurements first. The study
concluded that the results of deep learning methods should be interpreted with
caution even when global image quality metrics are improved.

As we mentioned above, an appealing approach to assessing the new methods is
to use measures of patient outcome as the evaluation target. This approach has
been successfully attempted in a few works, where the predicted micro-
structure indices have been correlated with post‐stroke outcome [70] and
outcome of pancreatic cancer patients [34]. Another study used acute-phase
dMRI (as well as anatomical MRI) data as input to a small neural network to
directly predict the chronic size of ischemic lesions, where the T2 images at
3 months after stroke were used to establish the ground truth [382]. This
method achieved an Area Under the Receiver Operator Characteristic curve
(AUROC) of 0.89.

###  III-E Explainability, uncertainty, and reliability

Advanced machine learning methods such as deep neural networks are
increasingly used in safety-critical applications such as medicine. However,
they are very complex and hard to interpret [383, 340]. It is well documented
that deep learning models produce overconfident and poorly-calibrated
predictions [384, 385], they can be fooled into making erroneous predictions
[386, 387], and they fail silently on OOD data [388, 389]. There have been
much effort to address these issues [390, 389]. The majority of these efforts
have focused on classification problems. Deep learning-based regression has
received much less attention. For example, while for classification there is a
widely-accepted definition of uncertainty calibration [384, 385], for
regression there is much disagreement [391, 392].

The significance of these issues for machine learning-based dMRI analysis is
clear. Even though very few studies have investigated these issues, they have
reported important results. For example, it has been reported that machine
learning methods display deceptively confident (i.e., high-precision)
predictions on noisy test data, while with classical estimation methods higher
measurement noise levels are properly reflected in predictions as higher
variance [67]. Unfortunately, very little work has been devoted to
characterize these issues and to devise effective solutions. A few studies
have attempted to incorporate uncertainty estimation in their methods. For
micro-structure mapping, Ye et al. quantified the uncertainty of their neural
network-based micro-structure estimation using a residual bootstrap technique
[64]. They validated their uncertainty computation method by assessing the
correlation with estimation error and inspecting the confidence intervals, and
they concluded that this method could compute practically useful uncertainty
estimates. For diffusion tensor estimation, another work computed data-
dependent uncertainty via loss attenuation and model uncertainty using Monte
Carlo dropout [378]. Their extensive experiments showed that estimation
uncertainties computed by their proposed methods could highlight the model’s
biases, detect domain shift, and reflect the strength of noise in the
measurements. Avci et al. have shown that a simple uncertainty quantification
method based on dropout can reduce the prediction error and also serve as an
indicator of pathologies or measurement artifacts [393]. For IVIM estimation,
one study proposed to estimate the full Gaussian posteriors (i.e., mean and
variance) for the parameters and to use the standard deviation of the
posterior as a proxy for estimation uncertainty [89]. Experiments with
simulated and real data showed that the computed uncertainties were
qualitatively meaningful. For tract segmentation, one work has used dropout
and test-time augmentation to compute epistemic and aleatoric uncertainties
[194]. After applying post-hoc uncertainty calibration, the computed
uncertainty is shown to be well correlated with segmentation error. The
authors argue that such uncertainty computations can be useful in surgical
planning applications such as for reliable delineation of eloquent areas. In
general, it may be easier to probe the explainability of conventional machine
learning models than large neural networks. For example, for micro-structure
estimation, one study was able to extensively examine the importance of
different feature sets and different measurement shells in the q-space for a
random forest model [21]. Performing a similar analysis for a deep learning
model should be more challenging due to the longer training times for these
models.

In the context of dMRI super-resolution, Tanno et al. estimated the
uncertainty for CNN and random forest models [282, 394]. For a CNN estimator,
they employed a heteroscedastic noise model to compute the intrinsic
uncertainty and variational dropout to compute the model uncertainty [395,
282]. However, their evaluation of estimation uncertainty relied on a
qualitative visual inspection of the correlation between uncertainty and
estimation error. They showed that the uncertainty was higher for brain
pathologies not observed in the training data and that the computed estimation
uncertainty could be useful for downstream computations such as tractography.
In another work [394], they employed similar techniques to compute the
estimation uncertainty for dMRI super-resolution based on the IQT approach
discussed above. They also developed methods to propagate the uncertainty
estimates to downstream computations such as computation of micro-structure
indices. Through experiments they showed that uncertainty quantification could
highlight model’s failures, explain its prediction performance, and improve
prediction accuracy. Also in the context of super-resolution, Qin et al. [283]
used a method based on model ensembles [396] to quantify the uncertainty for
estimation of NODDI parameters. They showed that the computed uncertainties
had moderate correlation with estimation error and could be used to improve
estimation accuracy.

Model interpretability has also received little attention. Xu et al. [181]
used attention maps similar to [397] in order to interpret the decision
mechanism of a tractography-based tract segmentation model. They showed that
the attention maps provided useful insights and were consistent with the
anatomy of the fiber tracts connecting different brain regions. Varadarajan
and Haldar criticized nonlinear machine learning FOD estimation methods for
their black-box nature and difficulty of predicting their performance on
unseen measurement schemes [398]. Instead, they formulated FOD computation as
a linear estimation problem and achieved prediction accuracy on par with or
better than standard methods. One can argue that works of Ye et al. that
involve unfolding the iterative algorithms for micro-structure estimation [63,
61] also represent a small but significant step towards improving the
explainability of deep learning models. Rensonnet et al. [344] have further
improved Ye’s work in this direction by proposing a hybrid method that
involves the use of a physics-based fingerprinting dictionary within a deep
learning framework. Their method first projects the dMRI signal into a
dictionary containing physics-based responses. An MLP network then uses the
sparse representations computed in the first stage to compute the target
micro-structure indices. Compared with an end-to-end trained MLP, this new
method showed competitive accuracy while having qualitatively more
interpretable features.

Better model explainability may also aid in designing more effective
computational methods. For example, it is unclear why machine learning methods
often achieve more accurate micro-structure estimation than standard
optimization-based methods. The shortcomings of optimization methods are due
to several factors including sensitivity to measurement noise, poor
initialization, and degeneracy (i.e., presence of multiple equally-optimal
solutions). For DTI estimation, which consists in a linear or non-linear least
squares problem, the advantage of deep learning models are likely only due to
their ability to use the measurements in neighboring voxels to reduce the
impact of measurement noise. For multi-compartment models with a complex
optimization landscape, on the other hand, machine learning models may
actually be able to learn to avoid local minima and compute better estimates.

##  IV Conclusions

Machine learning has the potential to usher in a new generation of
computational techniques for dMRI processing and analysis. Some of the
capabilities that have been demonstrated by multiple studies include:
estimation of micro-structural indices, tractography and tract analysis tasks
such as segmentation and registration, denoising, artifact correction, and
data harmonization and enhancement. Compared with conventional computational
techniques in dMRI, the new machine learning methods may offer improved
performance in several aspects including: faster computation, ability to
handle imperfect data, more flexible modular design as well as end-to-end
optimization, seamless and easy integration of spatial information and other
sources of information such as anatomical MRI. However, in order to realize
the full potential of these methods, we need to overcome several critical
limitations and remaining issues. More rigorous validation on rich and
heterogeneous datasets in necessary. Standardized data pre-processing
pipelines, validation benchmarks, and evaluation metrics can dramatically help
the research community in developing and assessing more effective methods and
discovering truly meritorious techniques. Finally, enabling model
explainability and proper uncertainty estimation may greatly enhance and
expedite the adoption of these methods in clinical and scientific
applications.

##  V Acknowledgements

This research was supported in part by the National Institute of Neurological
Disorders and Stroke and Eunice Kennedy Shriver National Institute of Child
Health and Human Development of the National Institutes of Health (NIH) under
award numbers and R01HD110772 R01NS128281. The content of this publication is
solely the responsibility of the author and does not necessarily represent the
official views of the NIH.

## References

  * [1] G. Theaud, J.-C. Houde, A. Boré, F. Rheault, F. Morency, and M. Descoteaux, “Tractoflow: A robust, efficient and reproducible diffusion mri pipeline leveraging nextflow & singularity,” Neuroimage, vol. 218, p. 116889, 2020\.
  * [2] J.-D. Tournier, R. Smith, D. Raffelt, R. Tabbara, T. Dhollander, M. Pietsch, D. Christiaens, B. Jeurissen, C.-H. Yeh, and A. Connelly, “Mrtrix3: A fast, flexible and open software framework for medical image processing and visualisation,” Neuroimage, vol. 202, p. 116137, 2019.
  * [3] E. Garyfallidis, M. Brett, B. Amirbekian, A. Rokem, S. Van Der Walt, M. Descoteaux, I. Nimmo-Smith, and D. Contributors, “Dipy, a library for the analysis of diffusion mri data,” Frontiers in neuroinformatics, vol. 8, p. 8, 2014.
  * [4] C. M. Tax, M. Bastiani, J. Veraart, E. Garyfallidis, and M. O. Irfanoglu, “What’s new and what’s next in diffusion mri preprocessing,” NeuroImage, vol. 249, p. 118830, 2022.
  * [5] M. Bach et al., “Methodological considerations on tract-based spatial statistics (tbss),” Neuroimage, vol. 100, pp. 358–369, 2014.
  * [6] D. K. Jones and M. Cercignani, “Twenty-five pitfalls in the analysis of diffusion mri data,” NMR in Biomedicine, vol. 23, no. 7, pp. 803–820, 2010\.
  * [7] C. Pierpaoli, “Artifacts in diffusion mri,” Diffusion MRI: theory, methods and applications, pp. 303–318, 2010.
  * [8] D. K. Jones and P. J. Basser, ““squashing peanuts and smashing pumpkins”: how noise distorts diffusion-weighted mr data,” Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, vol. 52, no. 5, pp. 979–993, 2004.
  * [9] D. R. Roalf, M. Quarmley, M. A. Elliott, T. D. Satterthwaite, S. N. Vandekar, K. Ruparel, E. D. Gennatas, M. E. Calkins, T. M. Moore, R. Hopson, et al., “The impact of quality assurance assessment on diffusion tensor imaging outcomes in a large-scale population-based cohort,” Neuroimage, vol. 125, pp. 903–919, 2016.
  * [10] A. Yendiki, K. Koldewyn, S. Kakunoori, N. Kanwisher, and B. Fischl, “Spurious group differences due to head motion in a diffusion mri study,” Neuroimage, vol. 88, pp. 79–90, 2014.
  * [11] S. Oldham, A. Arnatkeviciūtė, R. E. Smith, J. Tiego, M. A. Bellgrove, and A. Fornito, “The efficacy of different preprocessing steps in reducing motion-related confounds in diffusion mri connectomics,” NeuroImage, vol. 222, p. 117252, 2020.
  * [12] G. L. Baum, D. R. Roalf, P. A. Cook, R. Ciric, A. F. Rosen, C. Xia, M. A. Elliott, K. Ruparel, R. Verma, B. Tunç, et al., “The impact of in-scanner head motion on structural connectivity derived from diffusion mri,” Neuroimage, vol. 173, pp. 275–286, 2018.
  * [13] D. S. Novikov, V. G. Kiselev, and S. N. Jespersen, “On modeling,” Magnetic resonance in medicine, vol. 79, no. 6, pp. 3172–3193, 2018.
  * [14] D. S. Novikov, E. Fieremans, S. N. Jespersen, and V. G. Kiselev, “Quantifying brain microstructure with diffusion mri: Theory and parameter estimation,” NMR in Biomedicine, vol. 32, no. 4, p. e3998, 2019.
  * [15] L. Kerkelä, K. Seunarine, R. N. Henriques, J. D. Clayden, and C. A. Clark, “Improved reproducibility of diffusion kurtosis imaging using regularized non-linear optimization informed by artificial neural networks,” arXiv preprint arXiv:2203.07327, 2022.
  * [16] A. Tabesh, J. H. Jensen, B. A. Ardekani, and J. A. Helpern, “Estimation of tensors and tensor-derived measures in diffusional kurtosis imaging,” Magnetic resonance in medicine, vol. 65, no. 3, pp. 823–836, 2011.
  * [17] R. Neto Henriques, Advanced methods for diffusion MRI data analysis and their application to the healthy ageing brain.  PhD thesis, Ph.D. thesis, University of Cambridge, 2018.
  * [18] D. K. Jones, T. R. Knösche, and R. Turner, “White matter integrity, fiber count, and other fallacies: the do’s and don’ts of diffusion mri,” Neuroimage, vol. 73, pp. 239–254, 2013.
  * [19] K. H. Maier-Hein et al., “The challenge of mapping the human connectome based on diffusion tractography,” Nature communications, vol. 8, no. 1, pp. 1–13, 2017.
  * [20] C. Thomas, Q. Y. Frank, M. O. Irfanoglu, P. Modi, K. S. Saleem, D. A. Leopold, and C. Pierpaoli, “Anatomical accuracy of brain connections derived from diffusion mri tractography is inherently limited,” Proceedings of the National Academy of Sciences, vol. 111, no. 46, pp. 16574–16579, 2014.
  * [21] R. H. Fick, N. Sepasian, M. Pizzolato, A. Ianus, and R. Deriche, “Assessing the feasibility of estimating axon diameter using diffusion models and machine learning,” in 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), pp. 766–769, IEEE, 2017.
  * [22] P. Poulin, D. Jörgens, P.-M. Jodoin, and M. Descoteaux, “Tractography and machine learning: Current state and open challenges,” Magnetic resonance imaging, vol. 64, pp. 37–48, 2019.
  * [23] K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural networks, vol. 2, no. 5, pp. 359–366, 1989.
  * [24] G. L. Nedjati-Gilani, T. Schneider, M. G. Hall, C. A. Wheeler-Kingshott, and D. C. Alexander, “Machine learning based compartment models with permeability for white matter microstructure imaging,” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014: 17th International Conference, Boston, MA, USA, September 14-18, 2014, Proceedings, Part III 17, pp. 257–264, Springer, 2014.
  * [25] G. L. Nedjati-Gilani, T. Schneider, M. G. Hall, N. Cawley, I. Hill, O. Ciccarelli, I. Drobnjak, C. A. G. Wheeler-Kingshott, and D. C. Alexander, “Machine learning based compartment models with permeability for white matter microstructure imaging,” NeuroImage, vol. 150, pp. 119–135, 2017\.
  * [26] V. Golkov, A. Dosovitskiy, J. I. Sperl, M. I. Menzel, M. Czisch, P. Sämann, T. Brox, and D. Cremers, “Q-space deep learning: twelve-fold shorter and model-free diffusion mri scans,” IEEE transactions on medical imaging, vol. 35, no. 5, pp. 1344–1351, 2016.
  * [27] J. Wasserthal, P. Neher, and K. H. Maier-Hein, “Tractseg-fast and accurate white matter tract segmentation,” NeuroImage, vol. 183, pp. 239–253, 2018\.
  * [28] Q. Tian, B. Bilgic, Q. Fan, C. Liao, C. Ngamsombat, Y. Hu, T. Witzel, K. Setsompop, J. R. Polimeni, and S. Y. Huang, “Deepdti: High-fidelity six-direction diffusion tensor imaging using deep learning,” NeuroImage, vol. 219, p. 117017, 2020.
  * [29] Y. Qin, Y. Li, Z. Zhuo, Z. Liu, Y. Liu, and C. Ye, “Multimodal super-resolved q-space deep learning,” Medical Image Analysis, vol. 71, p. 102085, 2021\.
  * [30] Z. Hu, Y. Wang, Z. Zhang, J. Zhang, H. Zhang, C. Guo, Y. Sun, and H. Guo, “Distortion correction of single-shot epi enabled by deep-learning,” Neuroimage, vol. 221, p. 117170, 2020.
  * [31] Y. Hong, J. Kim, G. Chen, W. Lin, P.-T. Yap, and D. Shen, “Longitudinal prediction of infant diffusion mri data via graph convolutional adversarial networks,” IEEE transactions on medical imaging, vol. 38, no. 12, pp. 2717–2725, 2019.
  * [32] J. Weine, R. J. van Gorkum, C. T. Stoeck, V. Vishnevskiy, and S. Kozerke, “Synthetically trained convolutional neural networks for improved tensor estimation from free-breathing cardiac dti,” Computerized Medical Imaging and Graphics, vol. 99, p. 102075, 2022.
  * [33] D. Karimi and A. Gholipour, “Diffusion tensor estimation with transformer neural networks,” Artificial Intelligence in Medicine, vol. 130, p. 102330, 2022.
  * [34] M. P. Kaandorp, S. Barbieri, R. Klaassen, H. W. van Laarhoven, H. Crezee, P. T. While, A. J. Nederveen, and O. J. Gurney-Champion, “Improved unsupervised physics-informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patients,” Magnetic resonance in medicine, vol. 86, no. 4, pp. 2250–2265, 2021.
  * [35] S. Barbieri, O. J. Gurney-Champion, R. Klaassen, and H. C. Thoeny, “Deep learning how to fit an intravoxel incoherent motion model to diffusion-weighted mri,” Magnetic resonance in medicine, vol. 83, no. 1, pp. 312–321, 2020.
  * [36] J. P. de Almeida Martins, M. Nilsson, B. Lampinen, M. Palombo, P. T. While, C.-F. Westin, and F. Szczepankiewicz, “Neural networks for parameter estimation in microstructural mri: Application to a diffusion-relaxation model of white matter,” NeuroImage, vol. 244, p. 118601, 2021.
  * [37] J. Wasserthal, P. F. Neher, and K. H. Maier-Hein, “Tract orientation mapping for bundle-specific tractography,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part III 11, pp. 36–44, Springer, 2018.
  * [38] H. Li, Z. Liang, C. Zhang, R. Liu, J. Li, W. Zhang, D. Liang, B. Shen, X. Zhang, Y. Ge, et al., “Superdti: Ultrafast dti and fiber tractography with deep learning,” Magnetic resonance in medicine, vol. 86, no. 6, pp. 3334–3347, 2021.
  * [39] P. F. Neher, M. Götz, T. Norajitra, C. Weber, and K. H. Maier-Hein, “A machine learning based approach to fiber tractography using classifier voting,” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part I 18, pp. 45–52, Springer, 2015.
  * [40] T. Gong, Q. Tong, H. He, Z. Li, and J. Zhong, “Robust diffusion parametric mapping of motion-corrupted data with a three-dimensional convolutional neural network,” arXiv preprint arXiv:1905.13075, 2019.
  * [41] T. Gong, Q. Tong, Z. Li, H. He, H. Zhang, and J. Zhong, “Deep learning-based method for reducing residual motion effects in diffusion parameter estimation,” Magnetic Resonance in Medicine, vol. 85, no. 4, pp. 2278–2293, 2021.
  * [42] V. Wegmayr, G. Giuliari, S. Holdener, and J. Buhmann, “Data-driven fiber tractography with neural networks,” in 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018), pp. 1030–1033, IEEE, 2018.
  * [43] I. Hill, M. Palombo, M. Santin, F. Branzoli, A.-C. Philippe, D. Wassermann, M.-S. Aigrot, B. Stankoff, A. Baron-Van Evercooren, M. Felfli, et al., “Machine learning based white matter models with permeability: An experimental study in cuprizone treated in-vivo mouse model of axonal demyelination,” NeuroImage, vol. 224, p. 117425, 2021.
  * [44] L. Y. Cai, H. H. Lee, N. R. Newlin, C. I. Kerley, P. Kanakaraj, Q. Yang, G. W. Johnson, D. Moyer, K. G. Schilling, F. Rheault, et al., “Convolutional-recurrent neural networks approximate diffusion tractography from t1-weighted mri and associated anatomical context,” bioRxiv, pp. 2023–02, 2023.
  * [45] M. Zucchelli, S. Deslauriers-Gauthier, and R. Deriche, “Brain tissue microstructure characterization using dmri based autoencoder neural-networks,” in Computational Diffusion MRI: 12th International Workshop, CDMRI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings 12, pp. 48–57, Springer, 2021.
  * [46] V. Nath, K. Ramadass, K. G. Schilling, C. B. Hansen, R. Fick, S. K. Pathak, A. W. Anderson, and B. A. Landman, “Dw-mri microstructure model of models captured via single-shell bottleneck deep learning,” in Computational Diffusion MRI: International MICCAI Workshop, Lima, Peru, October 2020, pp. 147–157, Springer, 2021.
  * [47] R. L. Harms, F. Fritz, A. Tobisch, R. Goebel, and A. Roebroeck, “Robust and fast nonlinear optimization of diffusion mri microstructure models,” Neuroimage, vol. 155, pp. 82–96, 2017.
  * [48] I. O. Jelescu, J. Veraart, E. Fieremans, and D. S. Novikov, “Degeneracy in model parameter estimation for multi-compartmental diffusion in neuronal tissue,” NMR in Biomedicine, vol. 29, no. 1, pp. 33–47, 2016.
  * [49] D. C. Alexander, P. L. Hubbard, M. G. Hall, E. A. Moore, M. Ptito, G. J. Parker, and T. B. Dyrby, “Orientationally invariant indices of axon diameter and density from diffusion mri,” Neuroimage, vol. 52, no. 4, pp. 1374–1389, 2010.
  * [50] D. C. Alexander, “A general framework for experiment design in diffusion mri and its application in measuring direct tissue-microstructure features,” Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, vol. 60, no. 2, pp. 439–448, 2008\.
  * [51] G. Rensonnet, B. Scherrer, G. Girard, A. Jankovski, S. K. Warfield, B. Macq, J.-P. Thiran, and M. Taquet, “Towards microstructure fingerprinting: estimation of tissue properties from a dictionary of monte carlo diffusion mri simulations,” NeuroImage, vol. 184, pp. 964–980, 2019.
  * [52] A. Daducci, E. J. Canales-Rodríguez, H. Zhang, T. B. Dyrby, D. C. Alexander, and J.-P. Thiran, “Accelerated microstructure imaging via convex optimization (amico) from diffusion mri data,” Neuroimage, vol. 105, pp. 32–44, 2015.
  * [53] T. Gong, F. Grussu, C. A. Wheeler-Kingshott, D. C. Alexander, and H. Zhang, “Machine-learning-informed parameter estimation improves the reliability of spinal cord diffusion mri,” arXiv preprint arXiv:2301.12294, 2023.
  * [54] M. Mozumder, J. M. Pozo, S. Coelho, and A. F. Frangi, “Population-based bayesian regularization for microstructural diffusion mri with noddida,” Magnetic resonance in medicine, vol. 82, no. 4, pp. 1553–1565, 2019.
  * [55] M. Reisert, E. Kellner, B. Dhital, J. Hennig, and V. G. Kiselev, “Disentangling micro from mesostructure by diffusion mri: a bayesian approach,” NeuroImage, vol. 147, pp. 964–975, 2017.
  * [56] J. H. Jensen, J. A. Helpern, A. Ramani, H. Lu, and K. Kaczynski, “Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging,” Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, vol. 53, no. 6, pp. 1432–1440, 2005.
  * [57] H. Zhang, T. Schneider, C. A. Wheeler-Kingshott, and D. C. Alexander, “Noddi: practical in vivo neurite orientation dispersion and density imaging of the human brain,” Neuroimage, vol. 61, no. 4, pp. 1000–1016, 2012.
  * [58] S. Lathuilière, P. Mesejo, X. Alameda-Pineda, and R. Horaud, “A comprehensive analysis of deep regression,” IEEE transactions on pattern analysis and machine intelligence, vol. 42, no. 9, pp. 2065–2081, 2019\.
  * [59] A. Faiyaz, M. Doyley, G. Schifitto, J. Zhong, and M. N. Uddin, “Single-shell noddi using dictionary-learner-estimated isotropic volume fraction,” NMR in Biomedicine, vol. 35, no. 2, p. e4628, 2022.
  * [60] S. Liu, Y. Liu, X. Xu, R. Chen, D. Liang, Q. Jin, H. Liu, G. Chen, and Y. Zhu, “Accelerated cardiac diffusion tensor imaging using deep neural network,” Physics in Medicine & Biology, vol. 68, no. 2, p. 025008, 2023.
  * [61] C. Ye, X. Li, and J. Chen, “A deep network for tissue microstructure estimation using modified lstm units,” Medical image analysis, vol. 55, pp. 49–64, 2019.
  * [62] C. Ye, “Estimation of tissue microstructure using a deep network inspired by a sparse reconstruction framework,” in Information Processing in Medical Imaging: 25th International Conference, IPMI 2017, Boone, NC, USA, June 25-30, 2017, Proceedings 25, pp. 466–477, Springer, 2017.
  * [63] C. Ye, “Tissue microstructure estimation using a deep network inspired by a dictionary-based framework,” Medical image analysis, vol. 42, pp. 288–299, 2017.
  * [64] C. Ye, Y. Li, and X. Zeng, “An improved deep network for tissue microstructure estimation with uncertainty quantification,” Medical image analysis, vol. 61, p. 101650, 2020.
  * [65] M. P. Kaandorp, F. Zijlstra, C. Federau, and P. T. While, “Deep learning intravoxel incoherent motion modeling: Exploring the impact of training features and learning strategies,” Magnetic Resonance in Medicine, vol. 90, no. 1, pp. 312–328, 2023.
  * [66] S. C. Epstein, T. J. Bray, M. Hall-Craggs, and H. Zhang, “Choice of training label matters: how to best use deep learning for quantitative mri parameter estimation,” arXiv preprint arXiv:2205.05587, 2022.
  * [67] N. G. Gyori, M. Palombo, C. A. Clark, H. Zhang, and D. C. Alexander, “Training data distribution significantly impacts the estimation of tissue microstructure with machine learning,” Magnetic resonance in medicine, vol. 87, no. 2, pp. 932–947, 2022.
  * [68] J. P. de Almeida Martins, M. Nilsson, B. Lampinen, M. Palombo, C.-F. Westin, and F. Szczepankiewicz, “On the use of neural networks to fit high-d imensional microstructure models,” in Proceedings of the ISMRM, vol. 401, 2021.
  * [69] S. Jung, H. Lee, K. Ryu, J. E. Song, M. Park, W.-J. Moon, and D.-H. Kim, “Artificial neural network for multi-echo gradient echo–based myelin water fraction estimation,” Magnetic resonance in medicine, vol. 85, no. 1, pp. 380–389, 2021.
  * [70] E. K. Gibbons, K. K. Hodgson, A. S. Chaudhari, L. G. Richards, J. J. Majersik, G. Adluru, and E. V. DiBella, “Simultaneous noddi and gfa parameter map generation from subsampled q-space imaging using deep learning,” Magnetic resonance in medicine, vol. 81, no. 4, pp. 2399–2411, 2019.
  * [71] S. HashemizadehKolowri, R.-R. Chen, G. Adluru, and E. V. DiBella, “Jointly estimating parametric maps of multiple diffusion models from undersampled q-space data: A comparison of three deep learning approaches,” Magnetic Resonance in Medicine, vol. 87, no. 6, pp. 2957–2971, 2022.
  * [72] E. Aliotta, H. Nourzadeh, and S. H. Patel, “Extracting diffusion tensor fractional anisotropy and mean diffusivity from 3-direction dwi scans using deep learning,” Magnetic Resonance in Medicine, vol. 85, no. 2, pp. 845–854, 2021.
  * [73] T. Zheng, G. Yan, H. Li, W. Zheng, W. Shi, Y. Zhang, C. Ye, and D. Wu, “A microstructure estimation transformer inspired by sparse representation for diffusion mri,” Medical Image Analysis, vol. 86, p. 102788, 2023.
  * [74] D. Karimi, C. Jaimes, F. Machado-Rivas, L. Vasung, S. Khan, S. K. Warfield, and A. Gholipour, “Deep learning-based parameter estimation in fetal diffusion-weighted mri,” Neuroimage, vol. 243, p. 118482, 2021.
  * [75] D. Karimi, O. Afacan, C. Velasco-Annis, C. Jaimes, C. Rollins, S. Warfield, and A. Gholipour, “Robust estimation of the fetal brain architecture from in-utero diffusion-weighted imaging,” in 2020 ISMRM & SMRT Annual Meeting & Exhibition, 2020.
  * [76] E. Aliotta, H. Nourzadeh, J. Sanders, D. Muller, and D. B. Ennis, “Highly accelerated, model-free diffusion tensor mri reconstruction using neural networks,” Medical physics, vol. 46, no. 4, pp. 1581–1591, 2019.
  * [77] S. Aja-Fernández, C. Martín-Martín, Á. Planchuelo-Gómez, A. Faiyaz, M. N. Uddin, G. Schifitto, A. Tiwari, S. J. Shigwan, R. K. Singh, T. Zheng, et al., “Validation of deep learning techniques for quality augmentation in diffusion mri for clinical studies,” NeuroImage: Clinical, vol. 39, p. 103483, 2023.
  * [78] S. M. Smith et al., “Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data,” Neuroimage, vol. 31, no. 4, pp. 1487–1505, 2006.
  * [79] D. Karimi, L. Vasung, F. Machado-Rivas, C. Jaimes, S. Khan, and A. Gholipour, “Accurate parameter estimation in fetal diffusion-weighted mri-learning from fetal and newborn data,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part VII 24, pp. 487–496, Springer, 2021.
  * [80] D. Karimi and A. Gholipour, “Atlas-powered deep learning (adl)-application to diffusion weighted mri,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 123–132, Springer, 2022.
  * [81] S. Skare, M. Hedehus, M. E. Moseley, and T.-Q. Li, “Condition number as a measure of noise performance of diffusion tensor data acquisition schemes with mri,” Journal of magnetic resonance, vol. 147, no. 2, pp. 340–352, 2000.
  * [82] J. Park, W. Jung, E.-J. Choi, S.-H. Oh, J. Jang, D. Shin, H. An, and J. Lee, “Diffnet: diffusion parameter mapping network generalized for input diffusion gradient schemes and b-value,” IEEE Transactions on Medical Imaging, vol. 41, no. 2, pp. 491–499, 2021.
  * [83] I. Hill, M. Palombo, M. D. Santin, F. Branzoli, A.-C. Philippe, D. Wassermann, M.-S. Aigrot, B. Stankoff, H. Zhang, S. Lehericy, et al., “Deep neural network based framework for in-vivo axonal permeability estimation,” in Proceedings of the Joint Annual Meeting ISMRM-ESMRMB 2018, ISMRM (International Society for Magnetic Resonance in Medicine), 2018.
  * [84] C. Ye, “Learning-based ensemble average propagator estimation,” in Medical Image Computing and Computer Assisted Intervention- MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part I 20, pp. 593–601, Springer, 2017.
  * [85] F. Grussu, M. Battiston, M. Palombo, T. Schneider, C. A. G. Wheeler-Kingshott, and D. C. Alexander, “Deep learning model fitting for diffusion-relaxometry: a comparative study,” in Computational Diffusion MRI: International MICCAI Workshop, Lima, Peru, October 2020, pp. 159–172, Springer, 2021.
  * [86] C. M. Pirk, P. A. Gómez, I. Lipp, G. Buonincontri, M. Molina-Romero, A. Sekuboyina, D. Waldmannstetter, J. Dannenberg, S. Endt, A. Merola, et al., “Deep learning-based parameter mapping for joint relaxation and diffusion tensor mr fingerprinting,” in Medical Imaging with Deep Learning, pp. 638–654, PMLR, 2020.
  * [87] M. Bertleff, S. Domsch, S. Weingärtner, J. Zapp, K. O’Brien, M. Barth, and L. R. Schad, “Diffusion parameter mapping with the combined intravoxel incoherent motion and kurtosis model using artificial neural networks at 3 t,” NMR in Biomedicine, vol. 30, no. 12, p. e3833, 2017.
  * [88] C. S. Parker, A. Schroder, S. C. Epstein, J. Cole, D. C. Alexander, and H. Zhang, “Rician likelihood loss for quantitative mri using self-supervised deep learning,” arXiv preprint arXiv:2307.07072, 2023.
  * [89] L. Zhang, V. Vishnevskiy, A. Jakab, and O. Goksel, “Implicit modeling with uncertainty estimation for intravoxel incoherent motion imaging,” in 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 1003–1007, IEEE, 2019.
  * [90] J. Huang, P. F. Ferreira, L. Wang, Y. Wu, A. I. Aviles-Rivero, C.-B. Schonlieb, A. D. Scott, Z. Khalique, M. Dwornik, R. Rajakulasingam, et al., “Deep learning-based diffusion tensor cardiac magnetic resonance reconstruction: A comparison studies,” arXiv preprint arXiv:2304.00996, 2023.
  * [91] T. Goodwin-Allcock, T. Gong, R. Gray, P. Nachev, and H. Zhang, “Patch-cnn: Training data-efficient deep learning for high-fidelity diffusion tensor estimation from minimal diffusion protocols,” arXiv preprint arXiv:2307.01346, 2023.
  * [92] G. Chen, Y. Hong, K. M. Huynh, and P.-T. Yap, “Deep learning prediction of diffusion mri data with microstructure-sensitive loss functions,” Medical image analysis, vol. 85, p. 102742, 2023.
  * [93] C. Ye, Y. Qin, C. Liu, Y. Li, X. Zeng, and Z. Liu, “Super-resolved q-space deep learning,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part III 22, pp. 582–589, Springer, 2019\.
  * [94] Y. Li, Y. Qin, Z. Liu, and C. Ye, “Pretraining improves deep learning based tissue microstructure estimation,” in Computational Diffusion MRI: International MICCAI Workshop, Lima, Peru, October 2020, pp. 173–185, Springer, 2021.
  * [95] Z. Li, T. Gong, Z. Lin, H. He, Q. Tong, C. Li, Y. Sun, F. Yu, and J. Zhong, “Fast and robust diffusion kurtosis parametric mapping using a three-dimensional convolutional neural network,” IEEE Access, vol. 7, pp. 71398–71411, 2019.
  * [96] Y. Masutani, “Noise level matching improves robustness of diffusion mri parameter inference by synthetic q-space learning,” in 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 139–142, IEEE, 2019.
  * [97] Y. Masutani, T. Fujiwara, and K. Sasaki, “Synthetic q-space learning with mixture distribution noise for robust dki parameter inference,” in International Forum on Medical Imaging in Asia 2021, vol. 11792, pp. 181–185, SPIE, 2021.
  * [98] K. G. Schilling, V. Janve, Y. Gao, I. Stepniewska, B. A. Landman, and A. W. Anderson, “Histological validation of diffusion mri fiber orientation distributions and dispersion,” Neuroimage, vol. 165, pp. 200–221, 2018\.
  * [99] K. Schilling, V. Janve, Y. Gao, I. Stepniewska, B. A. Landman, and A. W. Anderson, “Comparison of 3d orientation distribution functions measured with confocal microscopy and diffusion mri,” Neuroimage, vol. 129, pp. 185–197, 2016.
  * [100] V. Prčkovska, P. Rodrigues, A. Puigdellivol Sanchez, M. Ramos, M. Andorra, E. Martinez-Heras, C. Falcon, A. Prats-Galino, and P. Villoslada, “Reproducibility of the structural connectome reconstruction across diffusion methods,” Journal of Neuroimaging, vol. 26, no. 1, pp. 46–57, 2016.
  * [101] M. Bucci, M. L. Mandelli, J. I. Berman, B. Amirbekian, C. Nguyen, M. S. Berger, and R. G. Henry, “Quantifying diffusion mri tractography of the corticospinal tract in brain tumors with deterministic and probabilistic methods,” NeuroImage: Clinical, vol. 3, pp. 361–368, 2013.
  * [102] S. Peled, O. Friman, F. Jolesz, and C.-F. Westin, “Geometrically constrained two-tensor model for crossing tracts in dwi,” Magnetic resonance imaging, vol. 24, no. 9, pp. 1263–1270, 2006.
  * [103] D. S. Tuch, T. G. Reese, M. R. Wiegell, N. Makris, J. W. Belliveau, and V. J. Wedeen, “High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity,” Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, vol. 48, no. 4, pp. 577–582, 2002.
  * [104] B. Scherrer, M. Taquet, and S. K. Warfield, “Reliable selection of the number of fascicles in diffusion images by estimation of the generalization error,” in International Conference on Information Processing in Medical Imaging, pp. 742–753, Springer, 2013.
  * [105] T. Schultz, C.-F. Westin, and G. Kindlmann, “Multi-diffusion-tensor fitting via spherical deconvolution: a unifying framework,” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2010: 13th International Conference, Beijing, China, September 20-24, 2010, Proceedings, Part I 13, pp. 674–681, Springer, 2010.
  * [106] J.-D. Tournier, F. Calamante, and A. Connelly, “Robust determination of the fibre orientation distribution in diffusion mri: non-negativity constrained super-resolved spherical deconvolution,” Neuroimage, vol. 35, no. 4, pp. 1459–1472, 2007.
  * [107] B. Jeurissen, J.-D. Tournier, T. Dhollander, A. Connelly, and J. Sijbers, “Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion mri data,” NeuroImage, vol. 103, pp. 411–426, 2014\.
  * [108] K. Patel, S. Groeschel, and T. Schultz, “Better fiber odfs from suboptimal data with autoencoder based regularization,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part III 11, pp. 55–62, Springer, 2018.
  * [109] A. Elaldi, N. Dey, H. Kim, and G. Gerig, “Equivariant spherical deconvolution: learning sparse orientation distribution functions from spherical data,” in Information Processing in Medical Imaging: 27th International Conference, IPMI 2021, Virtual Event, June 28–June 30, 2021, Proceedings 27, pp. 267–278, Springer, 2021.
  * [110] A. Elaldi, G. Gerig, and N. Dey, “$e(3)\times so(3)$-equivariant networks for spherical deconvolution in diffusion mri,” arXiv preprint arXiv:2304.06103, 2023.
  * [111] V. Nath, K. G. Schilling, P. Parvathaneni, C. B. Hansen, A. E. Hainline, Y. Huo, J. A. Blaber, I. Lyu, V. Janve, Y. Gao, et al., “Deep learning reveals untapped information for local white-matter fiber reconstruction in diffusion-weighted mri,” Magnetic resonance imaging, vol. 62, pp. 220–227, 2019.
  * [112] V. Nath, I. Lyu, K. G. Schilling, P. Parvathaneni, C. B. Hansen, Y. Huo, V. A. Janve, Y. Gao, I. Stepniewska, A. W. Anderson, et al., “Enabling multi-shell b-value generalizability of data-driven diffusion models with deep shore,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part III 22, pp. 573–581, Springer, 2019\.
  * [113] H. Kebiri, A. Gholipour, R. Lin, L. Vasung, D. Karimi, and M. Bach Cuadra, “Robust estimation of the microstructure of the early developing brain using deep learning,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 293–303, Springer, 2023.
  * [114] S. Koppers and D. Merhof, “Direct estimation of fiber orientations using deep learning in diffusion imaging,” in Machine Learning in Medical Imaging: 7th International Workshop, MLMI 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Proceedings 7, pp. 53–60, Springer, 2016.
  * [115] Z. Lin, T. Gong, K. Wang, Z. Li, H. He, Q. Tong, F. Yu, and J. Zhong, “Fast learning of fiber orientation distribution function for mr tractography using convolutional neural network,” Medical physics, vol. 46, no. 7, pp. 3101–3116, 2019.
  * [116] C. Ye and J. L. Prince, “Fiber orientation estimation guided by a deep network,” in Medical Image Computing and Computer Assisted Intervention- MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part I, pp. 575–583, Springer, 2017\.
  * [117] D. Karimi, L. Vasung, C. Jaimes, F. Machado-Rivas, S. K. Warfield, and A. Gholipour, “Learning to estimate the fiber orientation distribution function from diffusion-weighted mri,” NeuroImage, vol. 239, p. 118316, 2021.
  * [118] S. Koppers, C. Haarburger, J. C. Edgar, and D. Merhof, “Reliable estimation of the number of compartments in diffusion mri,” in Bildverarbeitung für die Medizin 2017: Algorithmen-Systeme-Anwendungen. Proceedings des Workshops vom 12. bis 14. März 2017 in Heidelberg, pp. 203–208, Springer, 2017.
  * [119] S. Koppers, M. Friedrichs, and D. Merhof, “Reconstruction of diffusion anisotropies using 3d deep convolutional neural networks in diffusion imaging,” in Modeling, analysis, and visualization of anisotropy, pp. 393–404, Springer, 2017.
  * [120] T. Yao, N. Newlin, P. Kanakaraj, L. Y. Cai, K. Ramadass, K. Schilling, B. A. Landman, Y. Huo, et al., “A unified single-stage learning model for estimating fiber orientation distribution functions on heterogeneous multi-shell diffusion-weighted mri,” arXiv preprint arXiv:2303.16376, 2023\.
  * [121] D. Karimi, L. Vasung, C. Jaimes, F. Machado-Rivas, S. Khan, S. K. Warfield, and A. Gholipour, “A machine learning-based method for estimating the number and orientations of major fascicles in diffusion-weighted magnetic resonance imaging,” Medical image analysis, vol. 72, p. 102129, 2021.
  * [122] T. Yao, F. Rheault, L. Y. Cai, Z. Asad, N. Newlin, C. Cui, R. Deng, K. Ramadass, A. Shafer, S. Resnick, et al., “Robust fiber odf estimation using deep constrained spherical deconvolution for diffusion mri,” arXiv preprint arXiv:2306.02900, 2023.
  * [123] J. Bartlett, C. Davey, L. Johnston, and J. Duan, “Recovering high-quality fods from a reduced number of diffusion-weighted images using a model-driven deep learning architecture,” arXiv preprint arXiv:2307.15273, 2023.
  * [124] T. Schultz, “Learning a reliable estimate of the number of fiber directions in diffusion mri,” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2012: 15th International Conference, Nice, France, October 1-5, 2012, Proceedings, Part III 15, pp. 493–500, Springer, 2012.
  * [125] M. Taquet, B. Scherrer, N. Boumal, B. Macq, and S. K. Warfield, “Estimation of a multi-fascicle model from single b-value data with a population-informed prior,” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2013: 16th International Conference, Nagoya, Japan, September 22-26, 2013, Proceedings, Part I 16, pp. 695–702, Springer, 2013.
  * [126] V. Nath, S. K. Pathak, K. G. Schilling, W. Schneider, and B. A. Landman, “Deep learning estimation of multi-tissue constrained spherical deconvolution with limited single shell dw-mri,” in Medical Imaging 2020: Image Processing, vol. 11313, pp. 162–171, SPIE, 2020.
  * [127] I. O. Jelescu, M. Palombo, F. Bagnato, and K. G. Schilling, “Challenges for biophysical modeling of microstructure,” Journal of Neuroscience Methods, vol. 344, p. 108861, 2020.
  * [128] N. Perraudin, M. Defferrard, T. Kacprzak, and R. Sgier, “Deepsphere: Efficient spherical convolutional neural network with healpix sampling for cosmological applications,” Astronomy and Computing, vol. 27, pp. 130–146, 2019.
  * [129] C.-H. Yeh, D. K. Jones, X. Liang, M. Descoteaux, and A. Connelly, “Mapping structural connectivity using diffusion mri: Challenges and opportunities,” Journal of Magnetic Resonance Imaging, vol. 53, no. 6, pp. 1666–1682, 2021\.
  * [130] F. Zhang, A. Daducci, Y. He, S. Schiavi, C. Seguin, R. Smith, C.-H. Yeh, T. Zhao, and L. J. O’Donnell, “Quantitative mapping of the brain’s structural connectivity using diffusion mri tractography: a review,” NeuroImage, p. 118870, 2022.
  * [131] A. Lemkaddem, D. Skiöldebrand, A. Dal Palú, J.-P. Thiran, and A. Daducci, “Global tractography with embedded anatomical priors for quantitative connectivity analysis,” Frontiers in neurology, vol. 5, p. 232, 2014.
  * [132] P. J. Basser, S. Pajevic, C. Pierpaoli, J. Duda, and A. Aldroubi, “In vivo fiber tractography using dt-mri data,” Magnetic resonance in medicine, vol. 44, no. 4, pp. 625–632, 2000.
  * [133] R. E. Smith, J.-D. Tournier, F. Calamante, and A. Connelly, “Anatomically-constrained tractography: improved diffusion mri streamlines tractography through effective use of anatomical information,” Neuroimage, vol. 62, no. 3, pp. 1924–1938, 2012.
  * [134] J.-F. Mangin, P. Fillard, Y. Cointepas, D. Le Bihan, V. Frouin, and C. Poupon, “Toward global tractography,” Neuroimage, vol. 80, pp. 290–296, 2013\.
  * [135] H. Takemura, C. F. Caiafa, B. A. Wandell, and F. Pestilli, “Ensemble tractography,” PLoS computational biology, vol. 12, no. 2, p. e1004692, 2016.
  * [136] F.-C. Yeh, “Shape analysis of the human association pathways,” Neuroimage, vol. 223, p. 117329, 2020.
  * [137] R. Smith, D. Raffelt, J.-D. Tournier, and A. Connelly, “Quantitative streamlines tractography: methods and inter-subject normalisation,” Aperture Neuro, vol. 2, 2020.
  * [138] S. N. Sotiropoulos and A. Zalesky, “Building connectomes using diffusion mri: why, how and but,” NMR in Biomedicine, vol. 32, no. 4, p. e3752, 2019.
  * [139] J. Y.-M. Yang, C.-H. Yeh, C. Poupon, and F. Calamante, “Diffusion mri tractography for neurosurgery: the basics, current state, technical reliability and challenges,” Physics in Medicine & Biology, vol. 66, no. 15, p. 15TR01, 2021.
  * [140] F. Rheault, P. Poulin, A. V. Caron, E. St-Onge, and M. Descoteaux, “Common misconceptions, hidden biases and modern challenges of dmri tractography,” Journal of neural engineering, vol. 17, no. 1, p. 011001, 2020.
  * [141] K. G. Schilling, A. Daducci, K. Maier-Hein, C. Poupon, J.-C. Houde, V. Nath, A. W. Anderson, B. A. Landman, and M. Descoteaux, “Challenges in diffusion mri tractography–lessons learned from international benchmark competitions,” Magnetic resonance imaging, vol. 57, pp. 194–209, 2019\.
  * [142] P. F. Neher, M.-A. Côté, J.-C. Houde, M. Descoteaux, and K. H. Maier-Hein, “Fiber tractography using machine learning,” Neuroimage, vol. 158, pp. 417–429, 2017.
  * [143] T. Sarwar, C. Seguin, K. Ramamohanarao, and A. Zalesky, “Towards deep learning for connectome mapping: A block decomposition framework,” NeuroImage, vol. 212, p. 116654, 2020.
  * [144] D. G. Duru and M. Ozkan, “Som based diffusion tensor mr analysis,” in 2007 5th International Symposium on Image and Signal Processing and Analysis, pp. 403–406, IEEE, 2007.
  * [145] D. G. Duru and M. Özkan, “Self-organizing maps for brain tractography in mri,” in 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 1509–1512, IEEE, 2013.
  * [146] O. de Lucena, Deep learning for brain analysis in MR imaging.  PhD thesis, Master’s thesis, Universidade Estadual de Campinas, 2018\.
  * [147] P. Poulin, F. Rheault, E. St-Onge, P.-M. Jodoin, and M. Descoteaux, “Bundle-wise deep tracker: Learning to track bundle-specific streamline paths,” Proc. of the Int. Society for Magnetic Resonance in medicine ISMRM-ESMRMB, 2018.
  * [148] I. Benou and T. Riklin Raviv, “Deeptract: A probabilistic deep learning framework for white matter fiber tractography,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part III 22, pp. 626–635, Springer, 2019.
  * [149] V. Wegmayr and J. M. Buhmann, “Entrack: Probabilistic spherical regression with entropy regularization for fiber tractography,” International Journal of Computer Vision, vol. 129, pp. 656–680, 2021.
  * [150] P. Poulin, M.-A. Côté, J.-C. Houde, L. Petit, P. F. Neher, K. H. Maier-Hein, H. Larochelle, and M. Descoteaux, “Learn to track: deep learning for tractography,” in Medical Image Computing and Computer Assisted Intervention- MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part I 20, pp. 540–547, Springer, 2017.
  * [151] A. Théberge, C. Desrosiers, M. Descoteaux, and P.-M. Jodoin, “Track-to-learn: A general framework for tractography with deep reinforcement learning,” Medical Image Analysis, vol. 72, p. 102093, 2021\.
  * [152] D. Jörgens, Ö. Smedby, and R. Moreno, “Learning a single step of streamline tractography based on neural networks,” in Computational Diffusion MRI: MICCAI Workshop, Québec, Canada, September 2017, pp. 103–116, Springer, 2018.
  * [153] K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning phrase representations using rnn encoder-decoder for statistical machine translation,” arXiv preprint arXiv:1406.1078, 2014.
  * [154] K. H. Maier-Hein, P. Neher, J.-C. Houde, M.-A. Côté, E. Garyfallidis, J. Zhong, M. Chamberland, F.-C. Yeh, Y.-C. Lin, Q. Ji, et al., “Tractography-based connectomes are dominated by false-positive connections,” BioRxiv, p. 084137, 2016.
  * [155] F. Rheault, A. De Benedictis, A. Daducci, C. Maffei, C. M. Tax, D. Romascano, E. Caverzasi, F. C. Morency, F. Corrivetti, F. Pestilli, et al., “Tractostorm: The what, why, and how of tractography dissection reproducibility,” Human brain mapping, vol. 41, no. 7, pp. 1859–1874, 2020\.
  * [156] K. G. Schilling et al., “Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?,” NeuroImage, vol. 243, p. 118502, 2021.
  * [157] B. Wilkins, N. Lee, and M. Singh, “Development and evaluation of a simulated fibercup phantom,” in International Symposium on Magnetic Resonance in Medicine (ISMRM’12), p. 1938, 2012.
  * [158] P. F. Neher, F. B. Laun, B. Stieltjes, and K. H. Maier-Hein, “Fiberfox: facilitating the creation of realistic white matter software phantoms,” Magnetic resonance in medicine, vol. 72, no. 5, pp. 1460–1470, 2014.
  * [159] C. Poupon, L. Laribiere, G. Tournier, J. Bernard, D. Fournier, P. Fillard, M. Descoteaux, and J.-F. Mangin, “A diffusion hardware phantom looking like a coronal brain slice,” in Proceedings of the international society for magnetic resonance in medicine, vol. 18, p. 581, 2010.
  * [160] P. Fillard, M. Descoteaux, A. Goh, S. Gouttard, B. Jeurissen, J. Malcolm, A. Ramirez-Manzanares, M. Reisert, K. Sakaie, F. Tensaouti, et al., “Quantitative evaluation of 10 tractography algorithms on a realistic diffusion mr phantom,” Neuroimage, vol. 56, no. 1, pp. 220–234, 2011.
  * [161] F. L. Sinzinger and R. Moreno, “Reinforcement learning based tractography with so (3) equivariant agents,” in Geometric Deep Learning in Medical Image Analysis (Extended abstracts), 2022.
  * [162] T. Wanyan, L. Liu, and E. Garyfallidis, “Tractography using reinforcement learning and adaptive-expanding graphs,” in International symposium on biomedical imaging, 2018.
  * [163] M. Reisert, V. A. Coenen, C. Kaller, K. Egger, and H. Skibbe, “Hamlet: hierarchical harmonic filters for learning tracts from diffusion mri,” arXiv preprint arXiv:1807.01068, 2018.
  * [164] D. N. Bullock, E. A. Hayday, M. D. Grier, W. Tang, F. Pestilli, and S. R. Heilbronner, “A taxonomy of the brain’s white matter: twenty-one major tracts for the 21st century,” Cerebral Cortex, vol. 32, no. 20, pp. 4524–4548, 2022.
  * [165] S. Wakana, H. Jiang, L. M. Nagae-Poetscher, P. C. Van Zijl, and S. Mori, “Fiber tract–based atlas of human white matter anatomy,” Radiology, vol. 230, no. 1, pp. 77–87, 2004.
  * [166] V. Wycoco, M. Shroff, S. Sudhakar, and W. Lee, “White matter anatomy: what the radiologist needs to know,” Neuroimaging Clinics, vol. 23, no. 2, pp. 197–216, 2013.
  * [167] K. Kamada, T. Todo, Y. Masutani, S. Aoki, K. Ino, T. Takano, T. Kirino, N. Kawahara, and A. Morita, “Combined use of tractography-integrated functional neuronavigation and direct fiber stimulation,” Journal of neurosurgery, vol. 102, no. 4, pp. 664–672, 2005.
  * [168] M. F. Glasser and J. K. Rilling, “Dti tractography of the human brain’s language pathways,” Cerebral cortex, vol. 18, no. 11, pp. 2471–2482, 2008\.
  * [169] E. J. Bubb, C. Metzler-Baddeley, and J. P. Aggleton, “The cingulum bundle: anatomy, function, and dysfunction,” Neuroscience & Biobehavioral Reviews, vol. 92, pp. 104–127, 2018.
  * [170] L. Zhuang, W. Wen, W. Zhu, J. Trollor, N. Kochan, J. Crawford, S. Reppermund, H. Brodaty, and P. Sachdev, “White matter integrity in mild cognitive impairment: a tract-based spatial statistics study,” Neuroimage, vol. 53, no. 1, pp. 16–25, 2010.
  * [171] S. Wakana, A. Caprihan, M. M. Panzenboeck, J. H. Fallon, M. Perry, R. L. Gollub, K. Hua, J. Zhang, H. Jiang, P. Dubey, et al., “Reproducibility of quantitative tractography methods applied to cerebral white matter,” Neuroimage, vol. 36, no. 3, pp. 630–644, 2007.
  * [172] R. O. Suarez, O. Commowick, S. P. Prabhu, and S. K. Warfield, “Automated delineation of white matter fiber tracts with a multiple region-of-interest approach,” Neuroimage, vol. 59, no. 4, pp. 3690–3700, 2012.
  * [173] A. Yendiki et al., “Automated probabilistic reconstruction of white-matter pathways in health and disease using an atlas of the underlying anatomy,” Frontiers in neuroinformatics, vol. 5, p. 23, 2011.
  * [174] S. Warrington, K. L. Bryant, A. A. Khrapitchev, J. Sallet, M. Charquero-Ballester, G. Douaud, S. Jbabdi, R. B. Mars, and S. N. Sotiropoulos, “Xtract-standardised protocols for automated tractography in the human and macaque brain,” Neuroimage, vol. 217, p. 116923, 2020.
  * [175] F. Zhang, S. C. Karayumak, N. Hoffmann, Y. Rathi, A. J. Golby, and L. J. O’Donnell, “Deep white matter analysis (deepwma): Fast and consistent tractography segmentation,” Medical Image Analysis, vol. 65, p. 101761, 2020.
  * [176] F. Liu, J. Feng, G. Chen, Y. Wu, Y. Hong, P.-T. Yap, and D. Shen, “Deepbundle: fiber bundle parcellation with graph convolution neural networks,” in Graph Learning in Medical Imaging: First International Workshop, GLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings 1, pp. 88–95, Springer, 2019.
  * [177] P. D. N. Lam, G. Belhomme, J. Ferrall, B. Patterson, M. Styner, and J. C. Prieto, “Trafic: fiber tract classification using deep learning,” in Medical Imaging 2018: Image Processing, vol. 10574, pp. 257–265, SPIE, 2018\.
  * [178] T. Gupta, S. M. Patil, M. Tailor, D. Thapar, and A. Nigam, “Brainsegnet: A segmentation network for human brain fiber tractography data into anatomically meaningful clusters,” arXiv preprint arXiv:1710.05158, 2017\.
  * [179] V. Gupta, S. I. Thomopoulos, F. M. Rashid, and P. M. Thompson, “Fibernet: An ensemble deep learning framework for clustering white matter fibers,” in Medical Image Computing and Computer Assisted Intervention- MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part I 20, pp. 548–555, Springer, 2017.
  * [180] V. Gupta, S. I. Thomopoulos, C. K. Corbin, F. Rashid, and P. M. Thompson, “Fibernet 2.0: an automatic neural network based tool for clustering white matter fibers in the brain,” in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 708–711, IEEE, 2018.
  * [181] H. Xu, M. Dong, M.-H. Lee, N. O’Hara, E. Asano, and J.-W. Jeong, “Objective detection of eloquent axonal pathways to minimize postoperative deficits in pediatric epilepsy surgery using diffusion tractography and convolutional neural networks,” IEEE transactions on medical imaging, vol. 38, no. 8, pp. 1910–1922, 2019.
  * [182] L. J. O’Donnell and C.-F. Westin, “Automatic tractography segmentation using a high-dimensional white matter atlas,” IEEE transactions on medical imaging, vol. 26, no. 11, pp. 1562–1575, 2007.
  * [183] M. Dayan, V.-M. Katsageorgiou, L. Dodero, V. Murino, and D. Sona, “Unsupervised detection of white matter fiber bundles with stochastic neural networks,” in 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 3513–3517, IEEE, 2018.
  * [184] R. R. Jha, S. Patil, A. Nigam, and A. Bhavsar, “Fs2net: fiber structural similarity network (fs2net) for rotation invariant brain tractography segmentation using stacked lstm based siamese network,” in Computer Analysis of Images and Patterns: 18th International Conference, CAIP 2019, Salerno, Italy, September 3–5, 2019, Proceedings, Part II 18, pp. 459–469, Springer, 2019.
  * [185] Y. Chen, C. Zhang, T. Xue, Y. Song, N. Makris, Y. Rathi, W. Cai, F. Zhang, and L. J. O’Donnell, “Deep fiber clustering: Anatomically informed fiber clustering with self-supervised deep learning for fast and effective tractography parcellation,” NeuroImage, vol. 273, p. 120086, 2023.
  * [186] W. Liu, Q. Lu, Z. Zhuo, Y. Li, Y. Duan, P. Yu, L. Qu, C. Ye, and Y. Liu, “Volumetric segmentation of white matter tracts with label embedding,” Neuroimage, vol. 250, p. 118934, 2022.
  * [187] T. Xue, F. Zhang, C. Zhang, Y. Chen, Y. Song, A. J. Golby, N. Makris, Y. Rathi, W. Cai, and L. J. O’Donnell, “Superficial white matter analysis: An efficient point-cloud-based deep learning framework with supervised contrastive learning for consistent tractography parcellation across populations and dmri acquisitions,” Medical Image Analysis, vol. 85, p. 102759, 2023.
  * [188] V. Siless et al., “Anatomicuts: Hierarchical clustering of tractography streamlines based on anatomical similarity,” NeuroImage, vol. 166, pp. 32–45, 2018.
  * [189] T. Xue, Y. Chen, C. Zhang, A. J. Golby, N. Makris, Y. Rathi, W. Cai, F. Zhang, and L. J. O’Donnell, “Tractcloud: Registration-free tractography parcellation with a novel local-global streamline point cloud representation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 409–419, Springer, 2023.
  * [190] L. Zöllei, C. Jaimes, E. Saliba, P. E. Grant, and A. Yendiki, “Tracts constrained by underlying infant anatomy (traculina): An automated probabilistic tractography tool with anatomical priors for use in the newborn brain,” Neuroimage, vol. 199, pp. 1–17, 2019.
  * [191] K. Hua, J. Zhang, S. Wakana, H. Jiang, X. Li, D. S. Reich, P. A. Calabresi, J. J. Pekar, P. C. van Zijl, and S. Mori, “Tract probability maps in stereotaxic spaces: analyses of white matter anatomy and tract-specific quantification,” Neuroimage, vol. 39, no. 1, pp. 336–347, 2008.
  * [192] S. Mukherjee, N. Paquette, M. D. Nelson, Y. Wang, J. Wallace, A. Panigrahy, and N. Lepore, “Deep-learning based tractography for neonates,” in 16th International Symposium on Medical Information Processing and Analysis, vol. 11583, pp. 85–91, SPIE, 2020.
  * [193] Q. Lu, Y. Li, and C. Ye, “Volumetric white matter tract segmentation with nested self-supervised learning using sequential pretext tasks,” Medical Image Analysis, vol. 72, p. 102094, 2021.
  * [194] O. Lucena, P. Borges, J. Cardoso, K. Ashkan, R. Sparks, and S. Ourselin, “Informative and reliable tract segmentation for preoperative planning,” Frontiers in Radiology, vol. 2, p. 866974, 2022.
  * [195] O. Lucena, J. P. Lavrador, H. Irzan, C. Semedo, P. Borges, F. Vergani, A. Granados, R. Sparks, K. Ashkan, and S. Ourselin, “Assessing informative tract segmentation and ntms for pre-operative planning,” Journal of Neuroscience Methods, vol. 396, p. 109933, 2023.
  * [196] H. Kebiri, A. Gholipour, M. B. Cuadra, and D. Karimi, “Direct segmentation of brain white matter tracts in diffusion mri,” arXiv preprint arXiv:2307.02223, 2023.
  * [197] B. Li, M. De Groot, R. M. Steketee, R. Meijboom, M. Smits, M. W. Vernooij, M. A. Ikram, J. Liu, W. J. Niessen, and E. E. Bron, “Neuro4neuro: A neural network approach for neural tract segmentation using large-scale population-based diffusion imaging,” Neuroimage, vol. 218, p. 116993, 2020\.
  * [198] H. Xu, T. Xue, D. Liu, F. Zhang, C.-F. Westin, R. Kikinis, L. J. O’Donnell, and W. Cai, “A registration-and uncertainty-based framework for white matter tract segmentation with only one annotated subject,” in 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), pp. 1–5, IEEE, 2023.
  * [199] E. Garyfallidis et al., “Recognition of white matter bundles using local and global streamline-based registration and clustering,” NeuroImage, vol. 170, pp. 283–295, 2018.
  * [200] N. Labra et al., “Fast automatic segmentation of white matter streamlines based on a multi-subject bundle atlas,” Neuroinformatics, vol. 15, no. 1, pp. 71–86, 2017.
  * [201] J. D. Clayden, A. J. Storkey, and M. E. Bastin, “A probabilistic model-based approach to consistent white matter tract segmentation,” IEEE transactions on medical imaging, vol. 26, no. 11, pp. 1555–1561, 2007.
  * [202] V. Siless et al., “Registration-free analysis of diffusion mri tractography data across subjects through the human lifespan,” NeuroImage, vol. 214, p. 116703, 2020.
  * [203] D. Wassermann, L. Bloy, E. Kanterakis, R. Verma, and R. Deriche, “Unsupervised white matter fiber clustering and tract probability map generation: Applications of a gaussian process framework for white matter fibers,” NeuroImage, vol. 51, no. 1, pp. 228–241, 2010.
  * [204] E. Garyfallidis, M. Brett, M. M. Correia, G. B. Williams, and I. Nimmo-Smith, “Quickbundles, a method for tractography simplification,” Frontiers in neuroscience, vol. 6, p. 175, 2012.
  * [205] A. Brun, H. Knutsson, H.-J. Park, M. E. Shenton, and C.-F. Westin, “Clustering fiber traces using normalized cuts,” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2004: 7th International Conference, Saint-Malo, France, September 26-29, 2004. Proceedings, Part I 7, pp. 368–375, Springer, 2004.
  * [206] A. Vázquez, N. López-López, A. Sánchez, J. Houenou, C. Poupon, J.-F. Mangin, C. Hernández, and P. Guevara, “Ffclust: Fast fiber clustering for large tractography datasets for a detailed study of brain connectivity,” NeuroImage, vol. 220, p. 117070, 2020.
  * [207] M. Maddah, L. Zollei, W. E. L. Grimson, C.-F. Westin, and W. M. Wells, “A mathematical framework for incorporating anatomical knowledge in dt-mri analysis,” in 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 105–108, IEEE, 2008.
  * [208] H. Li, Z. Xue, L. Guo, T. Liu, J. Hunter, and S. T. Wong, “A hybrid approach to automatic clustering of white matter fibers,” NeuroImage, vol. 49, no. 2, pp. 1249–1258, 2010.
  * [209] D. Wassermann, N. Makris, Y. Rathi, M. Shenton, R. Kikinis, M. Kubicki, and C.-F. Westin, “The white matter query language: a novel approach for describing human white matter anatomy,” Brain Structure and Function, vol. 221, pp. 4705–4721, 2016.
  * [210] F. Zhang, I. Norton, W. Cai, Y. Song, W. M. Wells, and L. J. O’Donnell, “Comparison between two white matter segmentation strategies: an investigation into white matter segmentation consistency,” in 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), pp. 796–799, IEEE, 2017.
  * [211] B. Tunç, W. A. Parker, M. Ingalhalikar, and R. Verma, “Automated tract extraction via atlas based adaptive clustering,” Neuroimage, vol. 102, pp. 596–607, 2014.
  * [212] B. Tunç, M. Ingalhalikar, D. Parker, J. Lecoeur, N. Singh, R. L. Wolf, L. Macyszyn, S. Brem, and R. Verma, “Individualized map of white matter pathways: connectivity-based paradigm for neurosurgical planning,” Neurosurgery, vol. 79, no. 4, p. 568, 2016.
  * [213] V. J. Sydnor, A. M. Rivas-Grajales, A. E. Lyall, F. Zhang, S. Bouix, S. Karmacharya, M. E. Shenton, C.-F. Westin, N. Makris, D. Wassermann, et al., “A comparison of three fiber tract delineation methods and their impact on white matter analysis,” Neuroimage, vol. 178, pp. 318–331, 2018\.
  * [214] M. Catani and M. T. De Schotten, “A diffusion tensor imaging tractography atlas for virtual in vivo dissections,” cortex, vol. 44, no. 8, pp. 1105–1132, 2008.
  * [215] L. J. O’Donnell, A. J. Golby, and C.-F. Westin, “Fiber clustering versus the parcellation-based connectome,” NeuroImage, vol. 80, pp. 283–289, 2013\.
  * [216] A. Chekir, M. Descoteaux, E. Garyfallidis, M.-A. Côté, and F. O. Boumghar, “A hybrid approach for optimal automatic segmentation of white matter tracts in hardi,” in 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES), pp. 177–180, IEEE, 2014.
  * [217] Q. Xu, A. W. Anderson, J. C. Gore, and Z. Ding, “Gray matter parcellation constrained full brain fiber bundling with diffusion tensor imaging,” Medical Physics, vol. 40, no. 7, p. 072301, 2013.
  * [218] P.-L. Bazin et al., “Direct segmentation of the major white matter tracts in diffusion tensor images,” Neuroimage, vol. 58, no. 2, pp. 458–468, 2011.
  * [219] N. Ratnarajah and A. Qiu, “Multi-label segmentation of white matter structures: application to neonatal brains,” NeuroImage, vol. 102, pp. 913–922, 2014.
  * [220] C. Lenglet, M. Rousson, and R. Deriche, “Dti segmentation by statistical surface evolution,” IEEE Transactions on Medical Imaging, vol. 25, no. 6, pp. 685–700, 2006.
  * [221] L. Jonasson, X. Bresson, P. Hagmann, O. Cuisenaire, R. Meuli, and J.-P. Thiran, “White matter fiber tract segmentation in dt-mri using geometric flows,” Medical Image Analysis, vol. 9, no. 3, pp. 223–236, 2005.
  * [222] W. Guo, Y. Chen, and Q. Zeng, “A geometric flow-based approach for diffusion tensor image segmentation,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 366, no. 1874, pp. 2279–2292, 2008.
  * [223] I. Eckstein et al., “Active fibers: Matching deformable tract templates to diffusion tensor images,” Neuroimage, vol. 47, pp. T82–T89, 2009.
  * [224] X. Dong et al., “Multimodality white matter tract segmentation using cnn,” in Proceedings of the ACM Turing Celebration Conference-China, pp. 1–8, 2019.
  * [225] J. Wasserthal, P. F. Neher, D. Hirjak, and K. H. Maier-Hein, “Combined tract segmentation and orientation mapping for bundle-specific tractography,” Medical image analysis, vol. 58, p. 101559, 2019.
  * [226] B. Li, M. de Groot, M. W. Vernooij, M. A. Ikram, W. J. Niessen, and E. E. Bron, “Reproducible white matter tract segmentation using 3d u-net on a large-scale dti dataset,” in Machine Learning in Medical Imaging: 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings 9, pp. 205–213, Springer, 2018\.
  * [227] Q. Lu and C. Ye, “Knowledge transfer for few-shot segmentation of novel white matter tracts,” in Information Processing in Medical Imaging: 27th International Conference, IPMI 2021, Virtual Event, June 28–June 30, 2021, Proceedings 27, pp. 216–227, Springer, 2021.
  * [228] P. Guevara, D. Duclap, C. Poupon, L. Marrakchi-Kacem, P. Fillard, D. Le Bihan, M. Leboyer, J. Houenou, and J.-F. Mangin, “Automatic fiber bundle segmentation in massive tractography datasets using a multi-subject bundle atlas,” Neuroimage, vol. 61, no. 4, pp. 1083–1099, 2012.
  * [229] C. Román, M. Guevara, R. Valenzuela, M. Figueroa, J. Houenou, D. Duclap, C. Poupon, J.-F. Mangin, and P. Guevara, “Clustering of whole-brain white matter short association bundles using hardi data,” Frontiers in neuroinformatics, vol. 11, p. 73, 2017.
  * [230] A. Vázquez, N. López-López, J. Houenou, C. Poupon, J.-F. Mangin, S. Ladra, and P. Guevara, “Automatic group-wise whole-brain short association fiber bundle labeling based on clustering and cortical surface information,” BioMedical Engineering OnLine, vol. 19, no. 1, pp. 1–24, 2020.
  * [231] M. Guevara, P. Guevara, C. Román, and J.-F. Mangin, “Superficial white matter: A review on the dmri analysis methods and applications,” Neuroimage, vol. 212, p. 116673, 2020.
  * [232] T. Xue, F. Zhang, C. Zhang, Y. Chen, Y. Song, N. Makris, Y. Rathi, W. Cai, and L. J. O’Donnell, “Supwma: consistent and efficient tractography parcellation of superficial white matter with deep learning,” in 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), pp. 1–5, IEEE, 2022.
  * [233] S. M. Smith, G. Kindlmann, and S. Jbabdi, “Cross-subject comparison of local diffusion mri parameters,” in Diffusion MRI, pp. 209–239, Elsevier, 2014\.
  * [234] L. Pini et al., “Brain atrophy in alzheimer’s disease and aging,” Ageing research reviews, vol. 30, pp. 25–48, 2016.
  * [235] C. E. Sexton et al., “A meta-analysis of diffusion tensor imaging in mild cognitive impairment and alzheimer’s disease,” Neurobiology of aging, vol. 32, no. 12, pp. 2322–e5, 2011.
  * [236] J. Ashburner and K. J. Friston, “Voxel-based morphometry—the methods,” Neuroimage, vol. 11, no. 6, pp. 805–821, 2000.
  * [237] E. Pagani et al., “A method for obtaining tract-specific diffusion tensor mri measurements in the presence of disease: application to patients with clinically isolated syndromes suggestive of multiple sclerosis,” Neuroimage, vol. 26, no. 1, pp. 258–265, 2005.
  * [238] T. Madhyastha et al., “Longitudinal reliability of tract-based spatial statistics in diffusion tensor imaging,” Human brain mapping, vol. 35, no. 9, pp. 4544–4555, 2014.
  * [239] R. A. Edden and D. K. Jones, “Spatial and orientational heterogeneity in the statistical sensitivity of skeleton-based analyses of diffusion tensor mr imaging data,” Journal of neuroscience methods, vol. 201, no. 1, pp. 213–219, 2011.
  * [240] A. Zalesky, “Moderating registration misalignment in voxelwise comparisons of dti data: a performance evaluation of skeleton projection,” Magnetic resonance imaging, vol. 29, no. 1, pp. 111–125, 2011.
  * [241] D. Raffelt, J.-D. Tournier, J. Fripp, S. Crozier, A. Connelly, and O. Salvado, “Symmetric diffeomorphic registration of fibre orientation distributions,” Neuroimage, vol. 56, no. 3, pp. 1171–1180, 2011.
  * [242] H. Zhang, P. A. Yushkevich, D. C. Alexander, and J. C. Gee, “Deformable registration of diffusion tensor mr images with explicit orientation optimization,” Medical image analysis, vol. 10, no. 5, pp. 764–785, 2006\.
  * [243] I. Grigorescu, A. Uus, D. Christiaens, L. Cordero-Grande, J. Hutter, A. D. Edwards, J. V. Hajnal, M. Modat, and M. Deprez, “Diffusion tensor driven image registration: a deep learning approach,” in International Workshop on Biomedical Image Registration, pp. 131–140, Springer, 2020.
  * [244] F. Zhang, W. M. Wells, and L. J. O’Donnell, “Deep diffusion mri registration (ddmreg): a deep learning method for diffusion mri registration,” IEEE Transactions on Medical Imaging, vol. 41, no. 6, pp. 1454–1467, 2021.
  * [245] J. J. Bouza, C.-H. Yang, and B. C. Vemuri, “Geometric deep learning for unsupervised registration of diffusion magnetic resonance images,” in International Conference on Information Processing in Medical Imaging, pp. 563–575, Springer, 2023.
  * [246] I. Grigorescu, A. Uus, D. Christiaens, L. Cordero-Grande, J. Hutter, D. Batalle, A. David Edwards, J. V. Hajnal, M. Modat, and M. Deprez, “Attention-driven multi-channel deformable registration of structural and microstructural neonatal data,” in International Workshop on Preterm, Perinatal and Paediatric Image Analysis, pp. 71–81, Springer, 2022.
  * [247] G. Balakrishnan, A. Zhao, M. R. Sabuncu, J. Guttag, and A. V. Dalca, “Voxelmorph: a learning framework for deformable medical image registration,” IEEE transactions on medical imaging, vol. 38, no. 8, pp. 1788–1800, 2019.
  * [248] D. C. Alexander, C. Pierpaoli, P. J. Basser, and J. C. Gee, “Spatial transformations of diffusion tensor magnetic resonance images,” IEEE transactions on medical imaging, vol. 20, no. 11, pp. 1131–1139, 2001.
  * [249] G. Prasad, S. H. Joshi, N. Jahanshad, J. Villalon-Reina, I. Aganj, C. Lenglet, G. Sapiro, K. L. McMahon, G. I. de Zubicaray, N. G. Martin, et al., “Automatic clustering and population analysis of white matter tracts using maximum density paths,” Neuroimage, vol. 97, pp. 284–295, 2014.
  * [250] Y. Jin, Y. Shi, L. Zhan, B. A. Gutman, G. I. de Zubicaray, K. L. McMahon, M. J. Wright, A. W. Toga, and P. M. Thompson, “Automatic clustering of white matter fibers in brain diffusion mri with an application to genetics,” Neuroimage, vol. 100, pp. 75–90, 2014.
  * [251] F. Zhang, P. Savadjiev, W. Cai, Y. Song, Y. Rathi, B. Tunç, D. Parker, T. Kapur, R. T. Schultz, N. Makris, et al., “Whole brain white matter connectivity analysis using machine learning: an application to autism,” Neuroimage, vol. 172, pp. 826–837, 2018.
  * [252] B. Li, W. J. Niessen, S. Klein, M. de Groot, M. A. Ikram, M. W. Vernooij, and E. E. Bron, “A hybrid deep learning framework for integrated segmentation and registration: evaluation on longitudinal white matter tract changes,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part III 22, pp. 645–653, Springer, 2019.
  * [253] B. Li, W. J. Niessen, S. Klein, M. de Groot, M. A. Ikram, M. W. Vernooij, and E. E. Bron, “Longitudinal diffusion mri analysis using segis-net: a single-step deep-learning framework for simultaneous segmentation and registration,” NeuroImage, vol. 235, p. 118004, 2021.
  * [254] B. Li, W. J. Niessen, S. Klein, M. A. Ikram, M. W. Vernooij, and E. E. Bron, “Learning unbiased group-wise registration (lugr) and joint segmentation: evaluation on longitudinal diffusion mri,” in Medical Imaging 2021: Image Processing, vol. 11596, pp. 136–144, SPIE, 2021.
  * [255] D. Karimi, H. Kebiri, and A. Gholipour, “Tbss++: A novel computational method for tract-based spatial statistics,” bioRxiv, pp. 2023–07, 2023.
  * [256] M. Chamberland, S. Genc, C. M. Tax, D. Shastin, K. Koller, E. P. Raven, A. Cunningham, J. Doherty, M. B. van den Bree, G. D. Parker, et al., “Detecting microstructural deviations in individuals with deep diffusion mri tractometry,” Nature computational science, vol. 1, no. 9, pp. 598–606, 2021.
  * [257] S. Qi, S. Meesters, K. Nicolay, B. M. ter Haar Romeny, and P. Ossenblok, “The influence of construction methodology on structural brain network measures: A review,” Journal of neuroscience methods, vol. 253, pp. 170–182, 2015\.
  * [258] Y. Wen, L. He, K. M. von Deneen, and Y. Lu, “Brain tissue classification based on dti using an improved fuzzy c-means algorithm with spatial constraints,” Magnetic Resonance Imaging, vol. 31, no. 9, pp. 1623–1630, 2013.
  * [259] P.-T. Yap, Y. Zhang, and D. Shen, “Brain tissue segmentation based on diffusion mri using l0 sparse-group representation classification,” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pp. 132–139, Springer, 2015.
  * [260] A. Ciritsis, A. Boss, and C. Rossi, “Automated pixel-wise brain tissue segmentation of diffusion-weighted images via machine learning,” NMR in Biomedicine, vol. 31, no. 7, p. e3931, 2018.
  * [261] S. Schnell, D. Saur, B. Kreher, J. Hennig, H. Burkhardt, and V. G. Kiselev, “Fully automated classification of hardi in vivo data using a support vector machine,” NeuroImage, vol. 46, no. 3, pp. 642–651, 2009.
  * [262] A. Vasilev, V. Golkov, M. Meissner, I. Lipp, E. Sgarlata, V. Tomassini, D. K. Jones, and D. Cremers, “q-space novelty detection with variational autoencoders,” in Computational Diffusion MRI: MICCAI Workshop, Shenzhen, China, October 2019, pp. 113–124, Springer, 2020.
  * [263] F. Zhang, A. Breger, K. I. K. Cho, L. Ning, C.-F. Westin, L. J. O’Donnell, and O. Pasternak, “Deep learning based segmentation of brain tissue from diffusion mri,” NeuroImage, vol. 233, p. 117934, 2021.
  * [264] F. Zhang, A. Breger, L. Ning, C.-F. Westin, L. O’Donnell, and O. Pasternak, “Deep learning based brain tissue segmentation from novel diffusion kurtosis imaging features,” in Annual Meeting of the International Society for Magnetic Resonance in Medicine, 2020.
  * [265] W. Zhang, R. Li, H. Deng, L. Wang, W. Lin, S. Ji, and D. Shen, “Deep convolutional neural networks for multi-modality isointense infant brain image segmentation,” NeuroImage, vol. 108, pp. 214–224, 2015.
  * [266] R. I. Reid, Z. Nedelska, C. G. Schwarz, C. Ward, C. R. Jack, and A. D. N. Initiative, “Diffusion specific segmentation: skull stripping with diffusion mri data alone,” in Computational Diffusion MRI: MICCAI Workshop, Québec, Canada, September 2017, pp. 67–80, Springer, 2018.
  * [267] X. Wang, X.-H. Li, J. W. Cho, B. E. Russ, N. Rajamani, A. Omelchenko, L. Ai, A. Korchmaros, S. Sawiak, R. A. Benn, et al., “U-net model for brain extraction: Trained on humans for transfer to non-human primates,” Neuroimage, vol. 235, p. 118001, 2021.
  * [268] D. Karimi, H. Dou, S. K. Warfield, and A. Gholipour, “Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis,” Medical image analysis, vol. 65, p. 101759, 2020.
  * [269] R. Faghihpirayesh, D. Karimi, D. Erdoğmuş, and A. Gholipour, “Fetal-bet: Brain extraction tool for fetal mri,” arXiv preprint arXiv:2310.01523, 2023.
  * [270] C. Murray, O. Oladosu, M. Joshi, S. Kolind, J. Oh, and Y. Zhang, “Neural network algorithms predict new diffusion mri data for multi-compartmental analysis of brain microstructure in a clinical setting,” Magnetic Resonance Imaging, vol. 102, pp. 9–19, 2023.
  * [271] S. Koppers, C. Haarburger, and D. Merhof, “Diffusion mri signal augmentation: from single shell to multi shell with deep learning,” in Computational Diffusion MRI: MICCAI Workshop, Athens, Greece, October 2016 19, pp. 61–70, Springer, 2017.
  * [272] S. Yin, Z. Zhang, Q. Peng, and X. You, “Fast and accurate reconstruction of hardi using a 1d encoder-decoder convolutional network,” arXiv preprint arXiv:1903.09272, 2019.
  * [273] S. Koppers and D. Merhof, “Enhancing diffusion signal augmentation using spherical convolutions,” in Computational Diffusion MRI: International MICCAI Workshop, Lima, Peru, October 2020, pp. 189–200, Springer, 2021.
  * [274] M. Lyon, P. Armitage, and M. A. Álvarez, “Spatio-angular convolutions for super-resolution in diffusion mri,” arXiv preprint arXiv:2306.00854, 2023\.
  * [275] M. Lyon, P. Armitage, and M. A. Álvarez, “Angular super-resolution in diffusion mri with a 3d recurrent convolutional autoencoder,” in International Conference on Medical Imaging with Deep Learning, pp. 834–846, PMLR, 2022.
  * [276] M. Ren, H. Kim, N. Dey, and G. Gerig, “Q-space conditioned translation networks for directional synthesis of diffusion weighted images from multi-modal structural mri,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part VII 24, pp. 530–540, Springer, 2021.
  * [277] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,” Communications of the ACM, vol. 63, no. 11, pp. 139–144, 2020.
  * [278] N. M. Elsaid and Y.-C. Wu, “Super-resolution diffusion tensor imaging using srcnn: a feasibility study,” in 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2830–2834, IEEE, 2019.
  * [279] Q. Tian, Z. Li, Q. Fan, C. Ngamsombat, Y. Hu, C. Liao, F. Wang, K. Setsompop, J. R. Polimeni, B. Bilgic, et al., “Srdti: Deep learning-based super-resolution for diffusion tensor mri,” arXiv preprint arXiv:2102.09069, 2021.
  * [280] R. Zeng, J. Lv, H. Wang, L. Zhou, M. Barnett, F. Calamante, and C. Wang, “Fod-net: A deep learning method for fiber orientation distribution angular super resolution,” Medical Image Analysis, vol. 79, p. 102431, 2022.
  * [281] O. Lucena, S. B. Vos, V. Vakharia, J. Duncan, K. Ashkan, R. Sparks, and S. Ourselin, “Enhancing the estimation of fiber orientation distributions using convolutional neural networks,” Computers in Biology and Medicine, vol. 135, p. 104643, 2021.
  * [282] R. Tanno, D. E. Worrall, A. Ghosh, E. Kaden, S. N. Sotiropoulos, A. Criminisi, and D. C. Alexander, “Bayesian image quality transfer with cnns: exploring uncertainty in dmri super-resolution,” in Medical Image Computing and Computer Assisted Intervention- MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part I 20, pp. 611–619, Springer, 2017.
  * [283] Y. Qin, Z. Liu, C. Liu, Y. Li, X. Zeng, and C. Ye, “Super-resolved q-space deep learning with uncertainty quantification,” Medical Image Analysis, vol. 67, p. 101885, 2021.
  * [284] T. Spears and P. T. Fletcher, “Learning spatially-continuous fiber orientation functions,” arXiv preprint arXiv:2312.05721, 2023.
  * [285] D. C. Alexander, D. Zikic, J. Zhang, H. Zhang, and A. Criminisi, “Image quality transfer via random forest regression: applications in diffusion mri,” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014: 17th International Conference, Boston, MA, USA, September 14-18, 2014, Proceedings, Part III 17, pp. 225–232, Springer, 2014\.
  * [286] D. C. Alexander, D. Zikic, A. Ghosh, R. Tanno, V. Wottschel, J. Zhang, E. Kaden, T. B. Dyrby, S. N. Sotiropoulos, H. Zhang, et al., “Image quality transfer and applications in diffusion mri,” NeuroImage, vol. 152, pp. 283–298, 2017.
  * [287] S. B. Blumberg, R. Tanno, I. Kokkinos, and D. C. Alexander, “Deeper image quality transfer: Training low-memory neural networks for 3d images,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part I, pp. 118–125, Springer, 2018.
  * [288] D. C. Alexander, A. Ghosh, S. A. Hurley, and S. N. Sotiropoulos, “Image quality transfer benefits tractography of low-resolution data,” Intl Society for Magnetic Resonance in Medicine (ISMRM), 2016.
  * [289] Y. Hong, G. Chen, P.-T. Yap, and D. Shen, “Multifold acceleration of diffusion mri via deep learning reconstruction from slice-undersampled data,” in International Conference on Information Processing in Medical Imaging, pp. 530–541, Springer, 2019.
  * [290] J.-P. Fortin, D. Parker, B. Tunç, T. Watanabe, M. A. Elliott, K. Ruparel, D. R. Roalf, T. D. Satterthwaite, R. C. Gur, R. E. Gur, et al., “Harmonization of multi-site diffusion tensor imaging data,” Neuroimage, vol. 161, pp. 149–170, 2017.
  * [291] H. Mirzaalian, L. Ning, P. Savadjiev, O. Pasternak, S. Bouix, O. Michailovich, G. Grant, C. E. Marx, R. A. Morey, L. A. Flashman, et al., “Inter-site and inter-scanner diffusion mri data harmonization,” NeuroImage, vol. 135, pp. 311–323, 2016.
  * [292] S. Cetin-Karayumak, K. Stegmayer, S. Walther, P. R. Szeszko, T. Crow, A. James, M. Keshavan, M. Kubicki, and Y. Rathi, “Exploring the limits of combat method for multi-site diffusion mri harmonization,” bioRxiv, pp. 2020–11, 2020.
  * [293] L. Ning, E. Bonet-Carne, F. Grussu, F. Sepehrband, E. Kaden, J. Veraart, S. B. Blumberg, C. S. Khoo, M. Palombo, I. Kokkinos, et al., “Cross-scanner and cross-protocol multi-shell diffusion mri data harmonization: Algorithms and results,” Neuroimage, vol. 221, p. 117128, 2020.
  * [294] S. C. Karayumak, S. Bouix, L. Ning, A. James, T. Crow, M. Shenton, M. Kubicki, and Y. Rathi, “Retrospective harmonization of multi-site diffusion mri data acquired with different acquisition parameters,” Neuroimage, vol. 184, pp. 180–200, 2019.
  * [295] C. M. Tax, F. Grussu, E. Kaden, L. Ning, U. Rudrapatna, C. J. Evans, S. St-Jean, A. Leemans, S. Koppers, D. Merhof, et al., “Cross-scanner and cross-protocol diffusion mri data harmonisation: A benchmark database and evaluation of algorithms,” NeuroImage, vol. 195, pp. 285–299, 2019.
  * [296] C. B. Hansen, K. G. Schilling, F. Rheault, S. Resnick, A. T. Shafer, L. L. Beason-Held, and B. A. Landman, “Contrastive semi-supervised harmonization of single-shell to multi-shell diffusion mri,” Magnetic Resonance Imaging, vol. 93, pp. 73–86, 2022.
  * [297] D. Moyer, G. Ver Steeg, C. M. Tax, and P. M. Thompson, “Scanner invariant representations for diffusion mri harmonization,” Magnetic resonance in medicine, vol. 84, no. 4, pp. 2174–2189, 2020.
  * [298] V. Nath, P. Parvathaneni, C. B. Hansen, A. E. Hainline, C. Bermudez, S. Remedios, J. A. Blaber, K. G. Schilling, I. Lyu, V. Janve, et al., “Inter-scanner harmonization of high angular resolution dw-mri using null space deep learning,” in Computational Diffusion MRI: International MICCAI Workshop, Granada, Spain, September 2018 22, pp. 193–201, Springer, 2019\.
  * [299] N. R. Newlin, L. Y. Cai, T. Yao, D. Archer, K. G. Schilling, T. J. Hohman, K. R. Pechman, A. Jefferson, A. T. Shafer, S. M. Resnick, et al., “Comparing voxel-and feature-wise harmonization of complex graph measures from multiple sites for structural brain network investigation of aging,” in Medical Imaging 2023: Image Processing, vol. 12464, pp. 524–530, SPIE, 2023\.
  * [300] S. B. Blumberg, M. Palombo, C. S. Khoo, C. M. Tax, R. Tanno, and D. C. Alexander, “Multi-stage prediction networks for data harmonization,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part IV 22, pp. 411–419, Springer, 2019.
  * [301] V. Nath, S. Remedios, P. Parvathaneni, C. B. Hansen, R. G. Bayrak, C. Bermudez, J. A. Blaber, K. G. Schilling, V. A. Janve, Y. Gao, et al., “Harmonizing 1.5 t/3t diffusion weighted mri through development of deep learning stabilized microarchitecture estimators,” in Medical Imaging 2019: Image Processing, vol. 10949, pp. 173–182, SPIE, 2019.
  * [302] H. Mirzaalian, L. Ning, P. Savadjiev, O. Pasternak, S. Bouix, O. Michailovich, S. Karmacharya, G. Grant, C. E. Marx, R. A. Morey, et al., “Multi-site harmonization of diffusion mri data in a registration framework,” Brain imaging and behavior, vol. 12, pp. 284–295, 2018.
  * [303] S. Koppers, L. Bloy, J. I. Berman, C. M. Tax, J. C. Edgar, and D. Merhof, “Spherical harmonic residual network for diffusion signal harmonization,” in Computational Diffusion MRI: International MICCAI Workshop, Granada, Spain, September 2018 22, pp. 173–182, Springer, 2019.
  * [304] Q. Tong, T. Gong, H. He, Z. Wang, W. Yu, J. Zhang, L. Zhai, H. Cui, X. Meng, C. W. Tax, et al., “A deep learning–based method for improving reliability of multicenter diffusion kurtosis imaging with varied acquisition protocols,” Magnetic Resonance Imaging, vol. 73, pp. 31–44, 2020.
  * [305] T. Yao, F. Rheault, L. Y. Cai, V. Nath, Z. Asad, N. Newlin, C. Cui, R. Deng, K. Ramadass, K. Schilling, et al., “Deep constrained spherical deconvolution for robust harmonization,” in Medical Imaging 2023: Image Processing, vol. 12464, pp. 169–176, SPIE, 2023.
  * [306] S. St-Jean, M. A. Viergever, and A. Leemans, “Harmonization of diffusion mri data sets with adaptive dictionary learning,” Human brain mapping, vol. 41, no. 16, pp. 4478–4499, 2020.
  * [307] O. Dietrich, J. G. Raya, S. B. Reeder, M. Ingrisch, M. F. Reiser, and S. O. Schoenberg, “Influence of multichannel combination, parallel imaging and other reconstruction techniques on mri noise characteristics,” Magnetic resonance imaging, vol. 26, no. 6, pp. 754–762, 2008.
  * [308] E. J. Canales-Rodríguez, A. Daducci, S. N. Sotiropoulos, E. Caruyer, S. Aja-Fernández, J. Radua, J. M. Y. Mendizabal, Y. Iturria-Medina, L. Melie-García, Y. Alemán-Gómez, et al., “Spherical deconvolution of multichannel diffusion mri data with non-gaussian noise models and spatial regularization,” PloS one, vol. 10, no. 10, p. e0138910, 2015.
  * [309] D. K. Jones, “The effect of gradient sampling schemes on measures derived from diffusion tensor mri: a monte carlo study,” Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, vol. 51, no. 4, pp. 807–815, 2004.
  * [310] J. Veraart, E. Fieremans, and D. S. Novikov, “Diffusion mri noise mapping using random matrix theory,” Magnetic resonance in medicine, vol. 76, no. 5, pp. 1582–1593, 2016.
  * [311] J. V. Manjón, P. Coupé, L. Martí-Bonmatí, D. L. Collins, and M. Robles, “Adaptive non-local means denoising of mr images with spatially varying noise levels,” Journal of Magnetic Resonance Imaging, vol. 31, no. 1, pp. 192–203, 2010.
  * [312] S. St-Jean, P. Coupé, and M. Descoteaux, “Non local spatial and angular matching: Enabling higher spatial resolution diffusion mri datasets through adaptive denoising,” Medical image analysis, vol. 32, pp. 115–130, 2016\.
  * [313] G. Chen, B. Dong, Y. Zhang, D. Shen, and P.-T. Yap, “Neighborhood matching for curved domains with application to denoising in diffusion mri,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 629–637, Springer, 2017.
  * [314] J. Lehtinen, J. Munkberg, J. Hasselgren, S. Laine, T. Karras, M. Aittala, and T. Aila, “Noise2noise: Learning image restoration without clean data,” arXiv preprint arXiv:1803.04189, 2018.
  * [315] A. Krull, T.-O. Buchholz, and F. Jug, “Noise2void-learning denoising from single noisy images,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2129–2137, 2019.
  * [316] J. Batson and L. Royer, “Noise2self: Blind denoising by self-supervision,” in International Conference on Machine Learning, pp. 524–533, PMLR, 2019.
  * [317] S. Fadnavis, J. Batson, and E. Garyfallidis, “Patch2self: Denoising diffusion mri with self-supervised learning,” Advances in Neural Information Processing Systems, vol. 33, pp. 16293–16303, 2020.
  * [318] Q. Tian, Z. Li, Q. Fan, J. R. Polimeni, B. Bilgic, D. H. Salat, and S. Y. Huang, “Sdndti: Self-supervised deep learning-based denoising for diffusion tensor mri,” Neuroimage, vol. 253, p. 119033, 2022.
  * [319] K. G. Schilling, S. Fadnavis, M. Visagie, E. Garyfallidis, B. A. Landman, S. A. Smith, and K. P. O’Grady, “Patch2self denoising of diffusion mri in the cervical spinal cord improves repeatability and feature conspicuity,” in International Society for Magnetic Resonance in Medicine Annual Meeting, 2021.
  * [320] J. L. Andersson, “Diffusion mri artifact correction,” in Advances in Magnetic Resonance Technology and Applications, vol. 4, pp. 123–146, Elsevier, 2021.
  * [321] J. L. Andersson, S. Skare, and J. Ashburner, “How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging,” Neuroimage, vol. 20, no. 2, pp. 870–888, 2003.
  * [322] S. T. Duong, S. L. Phung, A. Bouzerdoum, and M. M. Schira, “An unsupervised deep learning technique for susceptibility artifact correction in reversed phase-encoding epi images,” Magnetic Resonance Imaging, vol. 71, pp. 1–10, 2020.
  * [323] B. Zahneisen, K. Baeumler, G. Zaharchuk, D. Fleischmann, and M. Zeineh, “Deep flow-net for epi distortion estimation,” Neuroimage, vol. 217, p. 116886, 2020.
  * [324] A. Z. Alkilani, T. Çukur, and E. U. Saritas, “Fd-net: An unsupervised deep forward-distortion model for susceptibility artifact correction in epi,” arXiv preprint arXiv:2303.10436, 2023.
  * [325] A. Legouhy, M. Graham, M. Guerreri, W. Stee, T. Villemonteix, P. Peigneux, and H. Zhang, “Correction of susceptibility distortion in epi: a semi-supervised approach with deep learning,” in International Workshop on Computational Diffusion MRI, pp. 38–49, Springer, 2022.
  * [326] K. G. Schilling, J. Blaber, C. Hansen, L. Cai, B. Rogers, A. W. Anderson, S. Smith, P. Kanakaraj, T. Rex, S. M. Resnick, et al., “Distortion correction of diffusion weighted mri without reverse phase-encoding scans or field-maps,” PLoS One, vol. 15, no. 7, p. e0236418, 2020.
  * [327] Y. Qiao, W. Sun, and Y. Shi, “Fod-based registration for susceptibility distortion correction in brainstem connectome imaging,” NeuroImage, vol. 202, p. 116164, 2019.
  * [328] Y. Qiao and Y. Shi, “Unsupervised deep learning for fod-based susceptibility distortion correction in diffusion mri,” IEEE transactions on medical imaging, vol. 41, no. 5, pp. 1165–1175, 2021.
  * [329] K. G. Schilling, J. Blaber, Y. Huo, A. Newton, C. Hansen, V. Nath, A. T. Shafer, O. Williams, S. M. Resnick, B. Rogers, et al., “Synthesized b0 for diffusion distortion correction (synb0-disco),” Magnetic resonance imaging, vol. 64, pp. 62–70, 2019.
  * [330] R. Ayub, Q. Zhao, M. Meloy, E. V. Sullivan, A. Pfefferbaum, E. Adeli, and K. M. Pohl, “Inpainting cropped diffusion mri using deep generative models,” in Predictive Intelligence in Medicine: Third International Workshop, PRIME 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings 3, pp. 91–100, Springer, 2020.
  * [331] Q. Zhang, G. Ruan, W. Yang, Y. Liu, K. Zhao, Q. Feng, W. Chen, E. X. Wu, and Y. Feng, “Mri gibbs-ringing artifact reduction by means of machine learning using convolutional neural networks,” Magnetic resonance in medicine, vol. 82, no. 6, pp. 2133–2145, 2019.
  * [332] M. J. Muckley, B. Ades-Aron, A. Papaioannou, G. Lemberskiy, E. Solomon, Y. W. Lui, D. K. Sodickson, E. Fieremans, D. S. Novikov, and F. Knoll, “Training a neural network for gibbs and noise removal in diffusion mri,” Magnetic resonance in medicine, vol. 85, no. 1, pp. 413–428, 2021.
  * [333] A. Ahmad, D. Parker, S. Dheer, Z. R. Samani, and R. Verma, “3d-qcnet–a pipeline for automated artifact detection in diffusion mri images,” Computerized Medical Imaging and Graphics, vol. 103, p. 102151, 2023.
  * [334] M. S. Graham, I. Drobnjak, and H. Zhang, “A supervised learning approach for diffusion mri quality control with minimal training data,” NeuroImage, vol. 178, pp. 668–676, 2018.
  * [335] C. Kelly, M. Pietsch, S. Counsell, and J.-D. Tournier, “Transfer learning and convolutional neural net fusion for motion artefact detection,” in Proceedings of the Annual Meeting of the International Society for Magnetic Resonance in Medicine, Honolulu, Hawaii, vol. 3523, 2017.
  * [336] Z. R. Samani, J. A. Alappatt, D. Parker, A. A. O. Ismail, and R. Verma, “Qc-automator: Deep learning-based automated quality control for diffusion mr images,” Frontiers in neuroscience, vol. 13, p. 1456, 2020.
  * [337] F. Szczepankiewicz, D. van Westen, E. Englund, C.-F. Westin, F. Ståhlberg, J. Lätt, P. C. Sundgren, and M. Nilsson, “The link between diffusion mri and tumor heterogeneity: Mapping cell eccentricity and density by diffusional variance decomposition (divide),” Neuroimage, vol. 142, pp. 522–532, 2016\.
  * [338] O. Reynaud, “Time-dependent diffusion mri in cancer: tissue modeling and applications,” Frontiers in Physics, vol. 5, p. 58, 2017.
  * [339] J. Tallus, M. Mohammadian, T. Kurki, T. Roine, J. P. Posti, and O. Tenovuo, “A comparison of diffusion tensor imaging tractography and constrained spherical deconvolution with automatic segmentation in traumatic brain injury,” NeuroImage: Clinical, vol. 37, p. 103284, 2023.
  * [340] A. B. Arrieta, N. Díaz-Rodríguez, J. Del Ser, A. Bennetot, S. Tabik, A. Barbado, S. García, S. Gil-López, D. Molina, R. Benjamins, et al., “Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai,” Information Fusion, vol. 58, pp. 82–115, 2020.
  * [341] R. Lin, A. Gholipour, J.-P. Thiran, D. Karimi, H. Kebiri, and M. B. Cuadra, “Cross-age and cross-site domain shift impacts on deep learning-based white matter fiber estimation in newborn and baby brains,” arXiv preprint arXiv:2312.14773, 2023.
  * [342] L. Y. Cai, Q. Yang, C. B. Hansen, V. Nath, K. Ramadass, G. W. Johnson, B. N. Conrad, B. D. Boyd, J. P. Begnoche, L. L. Beason-Held, et al., “Prequal: An automated pipeline for integrated preprocessing and quality assurance of diffusion weighted mri images,” Magnetic resonance in medicine, vol. 86, no. 1, pp. 456–470, 2021.
  * [343] M. Cieslak, P. A. Cook, X. He, F.-C. Yeh, T. Dhollander, A. Adebimpe, G. K. Aguirre, D. S. Bassett, R. F. Betzel, J. Bourque, et al., “Qsiprep: an integrative platform for preprocessing and reconstructing diffusion mri data,” Nature methods, vol. 18, no. 7, pp. 775–778, 2021.
  * [344] G. Rensonnet, L. Adam, and B. Macq, “Solving inverse problems with deep neural networks driven by sparse signal decomposition in a physics-based dictionary,” arXiv preprint arXiv:2107.10657, 2021.
  * [345] L. Ruff, J. R. Kauffmann, R. A. Vandermeulen, G. Montavon, W. Samek, M. Kloft, T. G. Dietterich, and K.-R. Müller, “A unifying review of deep and shallow anomaly detection,” Proceedings of the IEEE, vol. 109, no. 5, pp. 756–795, 2021.
  * [346] W. Liu, X. Wang, J. Owens, and Y. Li, “Energy-based out-of-distribution detection,” Advances in neural information processing systems, vol. 33, pp. 21464–21475, 2020.
  * [347] M. Descoteaux, R. Deriche, D. Le Bihan, J.-F. Mangin, and C. Poupon, “Multiple q-shell diffusion propagator imaging,” Medical image analysis, vol. 15, no. 4, pp. 603–621, 2011.
  * [348] J. Cheng, T. Jiang, and R. Deriche, “Theoretical analysis and practical insights on eap estimation via a unified hardi framework,” in MICCAI Workshop on Computational Diffusion MRI (CDMRI), 2011.
  * [349] M. Zucchelli, S. Deslauriers-Gauthier, and R. Deriche, “Investigating the effect of dmri signal representation on fully-connected neural networks brain tissue microstructure estimation,” in 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 725–728, IEEE, 2021.
  * [350] M. Zucchelli, S. Deslauriers-Gauthier, and R. Deriche, “A computational framework for generating rotation invariant features and its application in diffusion mri,” Medical image analysis, vol. 60, p. 101597, 2020.
  * [351] E. Schwab, R. Vidal, and N. Charon, “Spatial-angular sparse coding for hardi,” in Medical Image Computing and Computer-Assisted Intervention-MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part III 19, pp. 475–483, Springer, 2016.
  * [352] H. Chen, Z. Zhang, M. Jin, and F. Wang, “Prediction of dmri signals with neural architecture search,” Journal of Neuroscience Methods, vol. 365, p. 109389, 2022.
  * [353] B. Zoph and Q. V. Le, “Neural architecture search with reinforcement learning,” arXiv preprint arXiv:1611.01578, 2016.
  * [354] C. Esteves, C. Allen-Blanchette, A. Makadia, and K. Daniilidis, “Learning so (3) equivariant representations with spherical cnns,” in Proceedings of the European Conference on Computer Vision (ECCV), pp. 52–68, 2018.
  * [355] P. Müller, V. Golkov, V. Tomassini, and D. Cremers, “Rotation-equivariant deep learning for diffusion mri,” arXiv preprint arXiv:2102.06942, 2021\.
  * [356] Y.-C. Su and K. Grauman, “Learning spherical convolution for fast features from 360 imagery,” Advances in Neural Information Processing Systems, vol. 30, 2017.
  * [357] W. Boomsma and J. Frellsen, “Spherical convolutions and their application in molecular modelling,” Advances in neural information processing systems, vol. 30, 2017.
  * [358] B. Coors, A. P. Condurache, and A. Geiger, “Spherenet: Learning spherical representations for detection and classification in omnidirectional images,” in Proceedings of the European conference on computer vision (ECCV), pp. 518–533, 2018.
  * [359] C. Ronchi, R. Iacono, and P. S. Paolucci, “The “cubed sphere”: A new method for the solution of partial differential equations in spherical geometry,” Journal of computational physics, vol. 124, no. 1, pp. 93–114, 1996.
  * [360] N. Gillet, A. Mesinger, B. Greig, A. Liu, and G. Ucci, “Deep learning from 21-cm tomography of the cosmic dawn and reionization,” Monthly Notices of the Royal Astronomical Society, vol. 484, no. 1, pp. 282–293, 2019.
  * [361] J. Fluri, T. Kacprzak, A. Refregier, A. Amara, A. Lucchi, and T. Hofmann, “Cosmological constraints from noisy convergence maps through deep learning,” Physical Review D, vol. 98, no. 12, p. 123518, 2018.
  * [362] T. S. Cohen, M. Geiger, J. Köhler, and M. Welling, “Spherical cnns,” arXiv preprint arXiv:1801.10130, 2018.
  * [363] M. Banerjee, R. Chakraborty, J. Bouza, and B. C. Vemuri, “Volterranet: A higher order convolutional network with group equivariance for homogeneous manifolds,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 2, pp. 823–833, 2020.
  * [364] J. J. Bouza, C.-H. Yang, D. Vaillancourt, and B. C. Vemuri, “A higher order manifold-valued convolutional neural network with applications to diffusion mri processing,” in Information Processing in Medical Imaging: 27th International Conference, IPMI 2021, Virtual Event, June 28–June 30, 2021, Proceedings 27, pp. 304–317, Springer, 2021.
  * [365] T. Cohen and M. Welling, “Group equivariant convolutional networks,” in International conference on machine learning, pp. 2990–2999, PMLR, 2016.
  * [366] T. Goodwin-Allcock, J. McEwen, R. Gray, P. Nachev, and H. Zhang, “How can spherical cnns benefit ml-based diffusion mri parameter estimation?,” in International Workshop on Computational Diffusion MRI, pp. 101–112, Springer, 2022.
  * [367] L. Kerkelä, K. Seunarine, F. Szczepankiewicz, and C. A. Clark, “Microstructural neuroimaging using spherical convolutional neural networks,” arXiv preprint arXiv:2211.09887, 2022.
  * [368] R. Liu, F. Lauze, E. Bekkers, K. Erleben, and S. Darkner, “Group convolutional neural networks for dwi segmentation,” in Geometric Deep Learning in Medical Image Analysis, pp. 96–106, PMLR, 2022.
  * [369] S. Sedlar, A. Alimi, T. Papadopoulo, R. Deriche, and S. Deslauriers-Gauthier, “A spherical convolutional neural network for white matter structure imaging via dmri,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part III 24, pp. 529–539, Springer, 2021.
  * [370] R. Liu, F. Lauze, K. Erleben, R. W. Berg, and S. Darkner, “Bundle geodesic convolutional neural network for diffusion-weighted imaging segmentation,” Journal of Medical Imaging, vol. 9, no. 6, pp. 064002–064002, 2022.
  * [371] K. M. Huynh, T. Xu, Y. Wu, G. Chen, K.-H. Thung, H. Wu, W. Lin, D. Shen, P.-T. Yap, and U. B. C. P. Consortium, “Probing brain micro-architecture by orientation distribution invariant identification of diffusion compartments,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part III 22, pp. 547–555, Springer, 2019\.
  * [372] E. Fieremans and H.-H. Lee, “Physical and numerical phantoms for the validation of brain microstructural mri: A cookbook,” Neuroimage, vol. 182, pp. 39–61, 2018.
  * [373] W. Lee, B. Kim, and H. Park, “Quantification of intravoxel incoherent motion with optimized b-values using deep neural network,” Magnetic Resonance in Medicine, vol. 86, no. 1, pp. 230–244, 2021.
  * [374] C. Ye, Y. Cui, and X. Li, “Q-space learning with synthesized training data,” in Computational Diffusion MRI: International MICCAI Workshop, Granada, Spain, September 2018 22, pp. 123–132, Springer, 2019.
  * [375] E. Ozarslan, C. Koay, T. M. Shepherd, S. J. Blackband, and P. J. Basser, “Simple harmonic oscillator based reconstruction and estimation for three-dimensional q-space mri,” in Proc. Intl. Soc. Mag. Reson. Med, vol. 17, p. 1396, Citeseer, 2009.
  * [376] Y. Qin, Y. Li, Z. Liu, and C. Ye, “Knowledge transfer between datasets for learning-based tissue microstructure estimation,” in 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 1530–1533, IEEE, 2020\.
  * [377] M. S. Graham, I. Drobnjak, and H. Zhang, “Realistic simulation of artefacts in diffusion mri for validating post-processing correction techniques,” NeuroImage, vol. 125, pp. 1079–1094, 2016.
  * [378] D. Karimi, S. K. Warfield, and A. Gholipour, “Calibrated diffusion tensor estimation,” arXiv preprint arXiv:2111.10847, 2021.
  * [379] M. Chamberland, M. Bernier, G. Girard, D. Fortin, M. Descoteaux, and K. Whittingstall, “Penthera 1.5 t,” URL: https://doi. org/10.5281/zenodo, vol. 2602022, 2019.