Gabriel Dias1, Rodrigo Pommot Berto2-4, Mateus Oliveira1, Lucas Ueda1,5,
Sergio Dertkigil6, Paula D. P. Costa1,7, Amirmohammad Shamaei3,8, Roberto
Souza3,8, Ashley Harris3,4,9, Leticia Rittner1

  1. 1

School of Electrical and Computer Engineering, University of Campinas,
Campinas, Brazil

  2. 2

Department of Biomedical Engineering, University of Calgary, Calgary, Canada

  3. 3

Hotchkiss Brain Institute, University of Calgary, Calgary, Canada

  4. 4

Alberta Children’s Hospital Research Institute, Calgary, Canada

  5. 5

Research and Development Center in Telecommunications, CPQD, Campinas, Brazil

  6. 6

School of Medical Sciences, University of Campinas, Campinas, Brazil

  7. 7

Artificial Intelligence Lab., Recod.ai, University of Campinas, Campinas,
Brazil

  8. 8

Department of Electrical and Software Engineering, University of Calgary,
Calgary, Canada

  9. 9

Department of Radiology, University of Calgary, Calgary, Canada

* Corresponding author:

Name Roberto Souza, PhD Department Electrical and Software Engineering
Institution University of Calgary Address ICT 352C 2500 University Dr NW
Calgary, Alberta Canada T2N 1N4 E-mail<EMAIL_ADDRESS>
Manuscript word count: 4691 Abstract word count: 197

###### Abstract

Purpose: To investigate the use of a Vision Transformer (ViT) to
reconstruct/denoise GABA-edited magnetic resonance spectroscopy (MRS) from a
quarter of the typically acquired number of transients using spectrograms.

Theory and Methods: A quarter of the typically acquired number of transients
collected in GABA-edited MRS scans are pre-processed and converted to a
spectrogram image representation using the Short-Time Fourier Transform
(STFT). The image representation of the data allows the adaptation of a pre-
trained ViT for reconstructing GABA-edited MRS spectra (Spectro-ViT). The
Spectro-ViT is fine-tuned and then tested using in vivo GABA-edited MRS data.
The Spectro-ViT performance is compared against other models in the literature
using spectral quality metrics and estimated metabolite concentration values.

Results: The Spectro-ViT model significantly outperformed all other models in
four out of five quantitative metrics (mean squared error, shape score,
GABA+/water fit error, and full width at half maximum). The metabolite
concentrations estimated (GABA+/water, GABA+/Cr, and Glx/water) were
consistent with the metabolite concentrations estimated using typical GABA-
edited MRS scans reconstructed with the full amount of typically collected
transients.

Conclusion: The proposed Spectro-ViT model achieved state-of-the-art results
in reconstructing GABA-edited MRS, and the results indicate these scans could
be up to four times faster.

Keywords: GABA-edited MRS, MRS Denoising, Deep Learning, Data Reconstruction,
Vision Transformer

##  1 Introduction

Gamma-aminobutyric acid (GABA) is the primary inhibitory neurotransmitter in
the mammalian brain and plays a crucial role in healthy brain function(1).
Understanding GABAergic mechanisms is vital for both normal and pathological
brain states, representing a key area in neuroscience(2). The quantification
of GABA level changes in patients has primarily been investigated using
magnetic resonance spectroscopy (MRS) (3; 4), a non-invasive technique that
allows for the identification and measurement of brain metabolites (5; 6; 7;
8).

The assessment of GABA abnormalities levels through MRS has been observed in
diverse neuropsychiatric disorders(2). These disorders encompass conditions
like schizophrenia (9; 10; 11) and depression (12), neurodevelopmental
disorders, such as autism spectrum disorder (13; 14) and attention deficit
hyperactivity disorder (15; 16), as well as neurological diseases including
Parkinson’s disease(17), amyotrophic lateral sclerosis(18), and diabetic
neuropathy (19).

The most widely used MRS sequence to measure GABA is the J-difference editing
(20; 21; 22) implementation known as MEscher–GArwood Point RESolved
Spectroscopy (MEGA-PRESS)(21; 2). MEGA-PRESS has gained popularity due to
several reasons, including its extensive availability across different scanner
platforms (2), its relatively simple implementation (23), its reproducibility
(24; 25; 26), and ongoing advancements in acquisition techniques and data
processing tools(27; 28).

MEGA-PRESS involves obtaining two sub-signals: one uses specific
radiofrequency pulses (RF) to invert the GABA signal (Edit-ON), while the
other lacks inversion, allowing regular evolution of the spin system
throughout the echo time (Edit-OFF). Subtracting the sub-signal ON from the
sub-signal OFF isolates GABA by subtracting overlapping metabolites and
eliminating stronger signals from creatine-containing compounds.

Although the separability of the GABA signal is significantly improved using
MEGA-PRESS, the method is technically challenging due to a notable limitation:
the time required to obtain high-quality data for accurate detection and
quantification. This challenge is intricately linked to the process of signal
averaging (29), which is a fundamental aspect of MEGA-PRESS. Signal averaging
involves acquiring repeated measurements of the ongoing experiment to enhance
the signal-to-noise ratio (SNR). The obtained signals from these repetitions
are then averaged at the end of the scan. Achieving a significant SNR
improvement can lead to impractical prolongation of scan time, as SNR
increases only with the square root of the number of transients (30; 31; 29).

In the literature, these repeated signals for signal averaging are commonly
referred to by different terms, such as transients, averages, excitations, or
acquisitions (3). Extensive discussions can be found in the literature
regarding the optimal number of transients needed to achieve good signal
quality (23; 2; 32; 33; 34; 26; 35; 36; 3). The recommended range varies from
126 (i.e. 63 Edit-ON + 63 Edit-OFF) (35) to the highest recommended value of
320 (i.e. 160 Edit-ON + 160 Edit-OFF) (2; 32). Current acquisition times can
last over 10 minutes considering the recommendation of 320 transients(3).

In order to enhance the clinical applicability of MRS, it is crucial to
maximize the data quality while minimizing lengthy scan times. The development
and adoption of techniques that could reduce the scan time would not only
enhance the feasibility and practicality of MRS in clinical applications but
would also decrease costs and improve patient scheduling efficiency.

Medical imaging applications have witnessed a growing utilization of Deep
Learning (DL) to enhance the techniques owing to its adaptability and
capability to manage complex data. DL techniques have found application in the
enhancement of data quality and the acceleration of conventional MRS data
acquisition by mitigating noise and reducing scan duration(37; 38; 39; 40). In
the realm of enhancing the MEGA-PRESS, researchers in the field have proposed
DL solutions to tackle the limitations commonly associated with traditional
pre-processing techniques for frequency and phase correction (FPC) (41; 42;
43).

The initial FPC study based on DL by Tapper et al.(41) introduced a multilayer
perceptron (MLP) model with frequency domain input that outperformed
conventional non-DL-based methods in medium to large datasets. Ma et al.(42)
enhanced this MLP by incorporating a convolutional neural network (CNN),
marking the first utilization of CNNs for FPC. This CNN exceeded the previous
MLP in terms of performance and noise resilience. Alternatively, Shamaei et al
(43) employed an unsupervised deep autoencoder with time domain input for FPC.
When compared to five FPC techniques, including the previous DL-based
solutions (41; 42), this method exhibited improved precision and introduced
the similarity index for measuring alignment quality. It also excelled in
linewidth analysis, even when dealing with nuisance peaks, while demonstrating
faster processing times than traditional limited frequency methods.

Despite these efforts, these proposed methods do not resolve the trade-off
between signal quality and scan time. While FPC helps improve signal quality
by minimizing line broadening and subtraction artifacts(33; 22), the
improvement in SNR is inherently related to the amount of collected data
(transients)(29). As a result, the acquisition time when using these FPC
models remains unchanged.

In the context of reducing MEGA-PRESS scan times while maintaining data
quality, Berto et al.’s investigation (40) stands out as first work in the
literature that addressed the trade-off inherent in signal averaging. Berto et
al.(40) introduced a DL model capable of reconstructing MEGA-PRESS scans using
only 80 transients (40 Edit-ON + 40 Edit-OFF), achieving higher SNR and lower
fit error compared to the conventional reconstruction pipeline, which
typically uses the full 320 transients. The study also examined different
training schemes, including using only in-vivo data or pre-training the models
on simulated data and then fine-tuning them with in-vivo data. The results
indicated that the Rdc-UNET2D model, pre-trained on simulated data and fine-
tuned with in-vivo data, outperformed other implemented DL pipelines by the
same study.

A subsequent study in the literature adopted the same investigation of
reconstructing MEGA-PRESS scans using only 80 transients as part of the
Edited-MRS reconstruction challenge (44). This challenge featured four
distinct DL models, each exploring different representations of typically one-
dimensional spectroscopy data in a two-dimensional format. The findings
demonstrated that the leading DL-based edited-MRS reconstruction pipelines
achieved metrics comparable to traditional methods using 320 transients,
despite only utilizing 80 transients. Additionally, the study introduced a
novel metric known as the ‘shape score,’ which exhibited a positive
correlation with the challenge outcomes, indicating its effectiveness in
assessing spectral quality. The model presented in this work secured the first
position in the challenge.

In continuation of recent research efforts in the literature (40; 44), this
study delves deeper into the potential of DL for reducing scan time in MEGA-
PRESS for GABA measurement, introducing an enhanced state-of-the-art DL model
that secured the first-place solution in the edited-MRS reconstruction
challenge (44). The contributions of this paper are as follows:

  * •

State-of-the-art model: A state-of-the-art model that addresses the trade-off
in signal averaging for MEGA-PRESS in GABA measurement. The proposed DL model
has achieved better reconstruction metrics than the one developed by Berto et
al (40). It also secured the first-place solution in the Edited-MRS
reconstruction challenge (44) for reconstructing GABA MEGA-PRESS scans using
only 80 transients.

  * •

Exploration of the spectrogram domain: the successful use of features
extracted from the spectrogram domain to train a DL reconstruction model,
showcasing a promising alternative for MRS related tasks. While previous
models in the MEGA-PRESS literature typically chose either a frequency or time
domain representation as input, the proposed approach explores the
spectrogram’s ability to balance these two aspects.

  * •

Vision Transform (ViT): The use a ViT model (45) to solve a MRS problem. A
recent literature review (46) provides a comprehensive overview of machine
learning applications in MRS, revealing a lack of Transformer models in this
domain. The review emphasizes the importance of exploring different and novel
model types, including Transformers, which have demonstrated potential in
other medical imaging fields (47). Therefore, this work pioneers the use of
the Transformer model, marking its first application in MRS research. By
leveraging the capabilities of the ViT, this study aims to unlock new
possibilities and insights in MRS analysis, potentially enhancing its
performance and opening methods for further advancements.

  * •

Quantification Investigation: This study presents the first investigation of
spectra quantification analysis in GABA MEGA-PRESS DL-based reconstruction and
goes beyond reconstruction metrics. Statistical analysis and data
visualization demonstrate that the proposed model by this work yields
metabolite concentration estimation comparable to those of the conventional
pipeline, which uses 320 transients.

  * •

Transients Subset Analysis: In this study, 320 transients were analyzed by
dividing them into four groups, each representing a quarter of the data
acquisition process. This division allows to assess the robustness of the
innovative ViT model in addressing common challenges in MEGA-PRESS spectral
analysis, such as frequency and phase misalignment caused by subject movement
(23). The model’s stability was evaluated by analyzing transients 1-80,
81-160, 161-240, and 241-320, which simulated different levels of spectral
quality. The consistent results across these groups demonstrated the method’s
robustness.

  * •

Share of code and model: By openly share its findings and code, this work
intends to facilitate easy reuse and to encourage further similar research in
the future. https://github.com/MICLab-Unicamp/Edited_MRS_Challenge

##  2 Methods

###  2.1 Dataset

This study utilized in-vivo data sourced from the Big GABA repository (2; 32;
48).This dataset comprises 144 standard GABA+ edited MEGA-PRESS scans, each
with 320 transients, obtained from GE, Philips, and Siemens scanners across
twelve sites: G4, G5, G7, G8, P4, P6, P8, P10, S1, S3, S5, and S8. The number
of scanners per vendor was the same, equally divided, totaling 48 scans per
vendor. Each site was instructed to comply with the standardized protocol,
maintaining a fixed voxel size of 30x30x30 mm3 in the medial parietal lobe
while also avoiding ventricles and/or the outer surfaces of the brain when
necessary to ensure good data quality. The acquisition parameters are detailed
in Mikkelsen et al.(2; 32). Furthermore, the subjects in the dataset had no
reported neurological or psychiatric illnesses, with a mean age of 26.4 ± 4.1
years. These data correspond to the same in-vivo data used by Berto et al.
(40).

Following the same experimental setup of Berto et al. (40), this research
divided the scans into three subsets: 84 for training, 24 for validation, and
36 for testing. Importantly, each subset maintained an equal number of scans
per site, and the age distribution of subjects in the modelling subsets was
stratified. The extraction of the 160 Edit-On and 160 Edit-Off Free Induction
Decay (FID) signals was performed using Gannet (28), a widely recognized
MATLAB-based open-source software for processing, fitting and quantifying
GABA-edited spectra within the MRS community (49).

A method for accommodating the model’s input limited to 80 transients, while
working with in-vivo data consisting of 320 transients, was implemented,
enabling the effective utilization of the data without discarding the excess
240 transients. The method uses a sliding window with a size of 40, which
moves along the transient axis, creating groups of 40 transients per sub-
signal (ON and OFF). This technique not only addresses the data limitation but
also enhances the model’s learning robustness across different transients,
which may exhibit varying levels of spectral quality.

As a result, this method generated an additional 120 samples, each defined as
a set of 40 transients per sub-signal (Edit-ON and Edit-OFF). These samples
are subsequently used to generate the spectrogram input for the model. After
applying the sliding window method to both the training and validation sets, a
total of 10,080 samples (120$\times$84) were generated for training, and a
total of 2880 samples (120$\times$24) were generated for validation purposes.

The target data for each sample corresponded to the edited spectrum produced
by the Gannet preprocessing pipeline (v.3.3.2) with default settings, which
utilized 320 transients (28) (Fig. 1). The pipeline included eddy current
correction applied to the unsuppressed water signal (50), line-broadening
(apodization) using a 3 Hz weighting constant, robust spectral registration
for both frequency and phase alignment (51; 52), and zero-filling. Signal
averaging employed the weighted averaging method, reducing the contribution of
lower-quality transients to the final edited spectra.

After processing the Gannet data, down-sampling was performed in the edited
spectrum using nearest neighbour interpolation to ensure that all spectra had
a fixed number of data points, specifically 2048 data points. This step
eliminated excessive data points from frequencies that did not significantly
contribute to the overall information, thereby highlighting the relevant
features within the spectra, improving computational storage efficiency, and
simplifying the architecture’s modelling. Also, the target spectra were
normalized by dividing it by its maximum absolute value. One target-edited
spectrum was generated for each scan, with the 120 samples generated by the
sliding window method sharing the same target spectrum (Fig. 1).

Figure 1: The input data for training the model, consisting of 80 transients,
was generated using the sliding window method from the complete set of 320 in-
vivo transients. The target data were obtained from the preprocessing
pipeline, which utilized the full set of transients and the Gannet software.
In this process, the 120 samples generated by the sliding window method shared
the same target spectrum.

###  2.2 Spectrogram Generation

Spectrogram is a common visual representation of the Short-time Fourier
transform (STFT) (53), a technique used to analyze signals in the time-
frequency domain. It involves dividing a time-domain signal into segments and
computing the Fourier Transform (FT) for each time segment (54). This allows
the identification of frequency information that is specific to particular
time intervals, which is especially useful when the frequency components of a
signal change over time. In contrast to the traditional FT, which provides
frequency information averaged over the entire signal duration, the STFT
provides time-localized frequency information. The STFT’s accuracy and
resolution are determined by parameters such as window size, hop size,
sampling rate, window type, and FT length, parameters chosen based on the
desired analysis.

Spectrograms can be seen as one-channel images, where the x-axis represents
time frames, y-axis the frequencies and each pixel represents the magnitude of
each frequency component at each time frame. Thus, it is intuitive to use
vision models on spectrogram to perform different kinds of tasks. In audio
domain this approach is already widely used for processing tasks (55; 56; 57).
Some clinical areas that work with different types of biological signals have
also already shown the effectiveness of using this representation in
classification tasks (58; 59).

In order to generate the spectrogram for each sample, the following steps were
performed (Fig. 2):

  1. 1.

The average was calculated across the 40 Edit-ON FID transients, as well as
for the 40 Edit-OFF FID transients;

  2. 2.

The mean Edit-ON FID was subtracted from the mean Edit-OFF FID;

  3. 3.

The STFT was computed for the resulted FID signal. For signals with 2048 data
points, the window size, hop size, and FFT length were set to 256, 10, and
446, respectively. For signals with 4096 points, only the hop size was changed
to 20;

  4. 4.

The spectrogram was normalized by dividing it by its maximum magnitude value.

  5. 5.

The real values from the spectrogram were computed.

  6. 6.

Zero-padding was performed to increase the spectrogram size to $224\times
224$, preparing it for further use into the Vision Transformer Model, which
was pre-trained on $224\times 224$ images.

Figure 2: Spectrogram generation for each sample involves averaging 40 Edit-ON
and 40 Edit-OFF FID transients, followed by subtracting the mean Edit-ON FID
from the mean Edit-OFF FID. The Short-Time Fourier Transform (STFT) is then
computed on the resulting signal, with spectrogram normalization and zero-
padding enhancing compatibility for Vision Transformer Model (ViT) integration
at a size of $224\times 224$. The output is a 1-channel image (gray-level)
spectrogram. Figure 3: The ViT receives the real-valued spectrogram as input
with 3 replicated channels and outputs a 768-dimensional embedding, that
passes through fully connected layers with ReLU activation ($512\times 1$,
$1024\times 1$, and $2048\times 1$). This final layer generates a GABA-edited
spectrum with 2,048 data points.

###  2.3 The Vision Transformer (ViT) Model

The Vision Transformer (ViT) model, introduced by Dosovitskiy et al. (45),
represents a significant advancement in computer vision by applying
transformer architectures, traditionally used in Natural Language Processing
(NLP), to this field. Distinct from conventional Convolutional Neural Networks
(CNNs), the ViT is a part of the broader category of Transformers, which
utilize attention, residual, and memory techniques for more effective data
modeling. Transformers have demonstrated impressive performance in various
computer vision domains, including image classification (45), video
classification (60), and medical image segmentation (61; 62).

The ViT marks a significant advancement in computer vision by its ability to
prioritize the most important regions of input data, unlike traditional models
that focus on local neighborhood relations in early layers. This is achieved
through several key steps: dividing the input image into smaller, fixed-size
patches for more effective processing; transforming each patch into an
embedded vector representation; and employing a self-attention mechanism on
these embeddings (45). This self-attention, a hallmark of the Transformer
encoder, allows the ViT to discern and interpret the relationships and
interdependencies among different segments of the image, leading to more
insightful image analysis.

A key component of this study is the combination of a pre-trained ViT model
and a spectrogram. Transfer learning from vision tasks to audio tasks
involving spectrograms has been previously studied with good results for CNN
based models(63; 64). Gong et al. (65) introduced the Audio Spectrogram
Transformer (AST), the first convolution-free, purely attention-based model
for audio classification, taking the spectrogram as input. The AST model was
adapted from the ViT architecture pretrained on ImageNet (66). The AST
achieved new state-of-the-art results on three different metrics across audio
classification benchmarks, demonstrating its high performance in extracting
features from spectrograms.

In this research, a pre-trained ViT model on ImageNet (66) was also used. The
ViT model, which processes images at a 224x224 pixel resolution, breaks down
images into 32x32 pixel patches. The ViT model is fed with a real-valued
three-channel spectrogram (Fig.2), producing a 768-dimensional embedding. This
embedding subsequently progresses through fully connected layers, resulting in
a 2048-dimensional output. This final layer contributes to the spectral
reconstruction, resulting in a GABA-edited spectrum containing 2048 data
points (Fig.3).

###  2.4 Training

The model was trained in a supervised manner. Each sample (80 transients) gave
origin to a spectrogram (Fig.2), which is replicated across three channels and
inputted into the model architecture (Fig.3). The model’s output corresponds
to the reconstructed MEGA-PRESS signal, which is compared with its
corresponding target signal (Fig.1) to compute the training loss, which is
supposed to be minimized to enable the model’s learning.

The model was trained with a batch size of 100, a learning rate of 0.0001,
using the Adam optimizer, and for 50 epochs. During training, the dataset was
shuffled once at the start of each epoch. Additionally, data corruption was
applied to each sample individually as it was loaded into the model. This
corruption involved a time-domain corruption by introducing random noise to
the frequency, phase, and amplitude of the transients. This corruption process
utilized a script from the GitHub repository of the Edited-MRS reconstruction
challenge(44). The values of the noisy function parameters used in the script
were uniformly sampled as follows: amplitude noise ranged from 0 (no noise) to
5 for the base level (applied to all transients and scans) and from 0 to 3 for
scan variance (indicating the level of variation between different scans);
frequency noise values ranged from 0 to 4 for the base level and from 0 to 3
for scan variance, while phase noise values ranged from 0 to 3 for both the
base level and scan variance. This method enhances the algorithm’s learning in
realistic noisy scenarios and ensures that samples loaded in the same batch,
generated from the same scan using the sliding window method, exhibit weaker
correlations, thereby making them non-redundant.

The training loss employed was the same used in the study by Berto et al. (40)
(Eq.1). This loss is a combined mean absolute error (MAE) approach,
specifically targeting key spectral regions to prioritize the most significant
attributes in the GABA-difference spectrum, namely the GABA peak at 3 ppm and
the combined Glutamate and Glutamine (Glx) peak at 3.75 ppm.

$\text{Loss}=\frac{(\text{MAE}_{\text{global}}+3\times\text{MAE}_{3.55-3.95ppm}+6\times\text{MAE}_{2.8-3.2ppm})}{10}$
[1]

###  2.5 Evaluation Metrics

As the focus of this work is the GABA metabolite, all the evaluation metrics
employed have been specifically designed to assess the quality of the edited
reconstructed MEGA-PRESS spectrum for GABA measurement. These metrics have
been previously utilized in research and encompass both a DL metric, Mean
Squared Error (MSE), and three traditional MRS metrics: SNR (67; 22; 68),
Linewidth (67; 22; 68), and Fit Error (67; 28). Additionally, a recently
proposed metric from the literature was included, known as Shape Score. The
computation of these metrics is as follows:

\- MSE: Measured between 2.5 ppm and 4 ppm, a min-max normalization was
applied to both the reference and output pipeline spectra to ensure fair
evaluation (44).

\- SNR: Calculated by dividing the GABA peak height by twice the standard
deviation of the 10 ppm to 12 ppm region.

\- Linewidth: Determined as the Full Width at Half Maximum (FWHM), which
denotes the width of the spectral peak at half of its maximum intensity. The
FWHM was computed specifically over the GABA peak.

\- Fit Error: Utilizing Gannet Software, the fit error is calculated during
the fitting of signal functions to metabolite peaks. It is defined as the
ratio of the signal model amplitude to the standard deviation of the model fit
residuals. Importantly, the fit error of interest combines the errors from
both the metabolite and the reference signal model fits, which are summed in
quadrature (28).

\- Shape Score: The shape score is a quantitative metric proposed by Berto et
al. (44). It compares the normalized target with the normalized model’s
reconstruction, focusing on GABA and Glx peaks. It is calculated computing
Pearson’s correlation coefficient and weighting it as 60% for GABA and 40% for
Glx. The signals are normalized using a min-max approach for each peak. The
ppm range for normalizing the GABA peak is 2.8 - 3.2 ppm, while for the Glx
peak, it is 3.55 - 3.9 ppm.

##  3 Experiments

Following the model training phase, a series of evaluation experiments were
conducted on the test set as outlined below:

###  3.1 1\. Reconstruction Quality

The first experiment was to evaluate the reconstruction in the same manner as
reported in the literature in previous studies (40; 44). The results of the
proposed model Spectro-ViT were compared with three other pipelines: the
reconstructions by Gannet when using all the transients (Gannet Full), when
using only a quarter of the transients (Gannet quarter), and with the DL model
Rdc-UNET2D (40).

The edited spectra resulting from each of the pipelines were assessed using
the metrics MSE, SNR, Linewidth, Shape Score, and GABA+/Water Fit Error, as
well as visual inspection. Since each of the test samples contains 320
transients, for the pipelines that use only 80 transients (e.g., Spectro-ViT,
Gannet Quarter, and Rdc-UNET2D), the first 80 transients were used as input
for these pipelines.

###  3.2 2\. Metabolite Quantification

The second experiment involved spectra quantification analysis using the same
pipelines from the previous experiment (Spectro-ViT, Rdc-UNET2D, Gannet
Quarter, Gannet Full). This study presents the first investigation of spectra
quantification analysis in GABA MEGA-PRESS DL-based reconstruction. The
GABA+/Cr, GABA+/Water and Glx/Water measurements was performed using the
Gannet Software (28).

For pipelines that employed a quarter of the conventional number of transients
(Spectro-ViT, Rdc-UNET2D, Gannet Quarter), the first 80 transients from the
test samples were used. Since there are no ground-truth concentration values
for the in-vivo data, the Gannet Full pipeline served as a reference for
calculating the error for these measurements, specifically, the distribution
of the mean absolute error and the mean absolute percentage error.

Furthermore, a Wilcoxon signed-rank test (69) was conducted with the Gannet
Full pipeline as the reference. The null hypothesis tested whether the sample
from each pipeline came from the same distribution as the reference, with a
significance level of $\alpha=0.05$. This significance test was chosen because
it compares paired data and makes no assumptions about the underlying
distribution.

###  3.3 3\. Robustness Evaluation

Finally, an innovative experiment for the existing literature involved the
analysis of the reconstruction of a subset of transients. The robustness of
the proposed Spectro-ViT model was evaluated by analyzing the edited spectra
resulting from the pipeline for different subsets of transients within the
same sample. These groups were created by dividing the 320 transients into
four distinct groups with no overlap: transients 1-80 (Group 1), 81-160 (Group
2), 161-240 (Group 3), and 241-320 (Group 4). Group 1 corresponds to the group
used in experiments 1 and 2. The same reconstruction metrics from experiment 1
(GABA+/Water Fit Error, Linewidth, Shape Score, and SNR) were computed for the
spectra generated for each group, and their distributions were compared.

Regarding quantification, in each group, the distributions and fit errors of
GABA+/Water, Glx/Water, and GABA+/Cr were obtained with the Gannet Software
(28) and analyzed. Also, a comparison of the concentration values with the
reference Gannet Full was made for each group using the Wilcoxon signed-rank
test.

##  4 Results

Figure 4: The test set’s edited spectra, shown in the 2.5 to 4 ppm range,
which is the same range used for the computation of the MSE metric evaluation
(Tab. 1), normalized within this range. Spectra from Siemens, Philips, and GE
scanners are presented. The DL pipelines Spectro-ViT (black) and Rdc-UNET2D
(yellow) reconstructed spectra from the first 80 of 320 transients. The
reference Gannet-Full pipeline (red), using 320 transients, is also displayed.
Both DL pipelines closely match the reference, with Spectro-ViT more
accurately representing GABA’s peak height.

1) Evaluation of Reconstruction: Spectro-ViT has demonstrated its superiority
in most of the reconstruction metrics (Tab.1). Specifically, the proposed
model achieved the best mean shape score, mean MSE, fit error, and linewidth.
The superiority of these two metrics can be visually observed in the
reconstructed spectra (Fig.4). When analyzing the Spectro-ViT results, it
tends to approximate the reconstructed signal to the target better,
particularly in capturing the height and shape of the GABA and Glx peaks.
Spectro-ViT stands out with the GABA+/Water Fit Error metric having the lowest
mean, outperforming even the Gannet Full, with a value approximately 2.6 times
better and also values more consistent with the lowest standard deviation.

Table 1: Mean ± standard deviation of the metrics MSE, SNR, Linewidth (FWHM), Shape Score, and GABA+/Water Fit Error assessed over the edited spectra resulting from the pipelines Gannet Full, Gannet Quarter, Rdc-UNET2D, and Spectro-ViT (the proposed model) for the test set considering 320 transients for the Gannet Full pipeline and the first 80 transients for the others. Best results for each metric are highlighted in bold. Pipeline |  | MSE
---
(2.5 - 4 ppm)
SNR |  | FWHM
---
(ppm)
Shape Score |  | GABA+/Water
---
Fit Error
(%)
Spectro-ViT | 0.0087 ± 0.0154 | 45.1 ± 11.5 | 0.163 ± 0.015 | 0.983 ± 0.019 | 3.056 ± 1.139
Rdc-UNET2D | 0.0105 ± 0.0113 | 45.3 ± 21.5 | 0.166 ± 0.016 | 0.980 ± 0.016 | 4.394 ± 2.382
Gannet Quarter | 0.0090 ± 0.0268 | 10.0 ± 3.1 | 0.182 ± 0.030 | 0.936 ± 0.261 | 7.971 ± 5.184
Gannet Full | N/A | 19.6 ± 5.5 | 0.168 ± 0.013 | N/A | 5.068 ± 2.477

Regarding SNR, the key metric in signal averaging, it is worth noting that
deep learning models can significantly improve this metric compared to the
conventional Gannet Full pipeline. While the Gannet Full pipeline had an
average SNR of 19.6, DL pipelines were able to more than double the value of
the metric. Although the Spectro-ViT model did not outperform the Rdc-UNET2D
model in terms of SNR, the mean difference was less than 0.4%. Furthermore,
the Spectro-ViT model exhibited a considerably lower standard deviation in the
same SNR metric (Tab.1).

2) Spectra Quantification:

In discussing the quantification results, the Gannet Quarter pipeline yielded
the best results for absolute error and MAPE across all the studied relations.
When comparing only DL-based pipelines, the Spectro-ViT model achieved the
best error results for GABA concentrations, while Rdc-UNET2D performed best
for Glx (Tab.2).

Table 2: Quantification results computed over the test set considering 320 transients for the Gannet Full pipeline and the first 80 transients for the others. Distribution of absolute errors (mean ± standard deviation), mean absolute percentage error (MAPE), and Wilcoxon signed-rank test p-values across the pipelines Gannet Full, Gannet Quarter, Rdc-UNET2D, and Spectro-ViT for the quantification values of GABA+/Water, GABA+/Cr, and Glx/Water. The values obtained by the Gannet Full pipeline were used as a reference for the calculation of these statistics. The ‘*’ symbol assigned to the side of the p-value indicates a value below the significance level of 0.05. Metabolite | Pipeline |  | Absolute Error
---
Mean ± STD
| MAPE
---
(%)
| Wilcoxon
---
p-value
| Spectro-ViT | 0.2322 ± 0.1943 | 11.5129 | 0.9073
| Rdc-UNET2D | 0.2605 ± 0.2728 | 13.3162 | *0.0013
GABA+/Water | Gannet Quarter | 0.1933 ± 0.1818 | 10.5654 | *0.0442
| Spectro-ViT | 0.0140 ± 0.0149 | 11.5455 | 0.8584
| Rdc-UNET2D | 0.0160 ± 0.0210 | 13.5265 | *0.0017
GABA+/Cr | Gannet Quarter | 0.0126 ± 0.0144 | 10.7907 | 0.0621
| Spectro-ViT | 0.8281 ± 0.6729 | 12.1099 | 0.6357
| Rdc-UNET2D | 0.7432 ± 0.6402 | 11.1914 | 0.7037
Glx/Water | Gannet Quarter | 0.5385 ± 0.6918 | 7.9911 | 0.9567
Figure 5: Scatter plots displaying concentration distributions computed over
the test set are presented for three pipelines: Spectro-ViT (proposed model)
in the first row (in blue), Rdc-UNET2D in the second row (in green), and
Gannet Quarter in the third row (in yellow). The values for these three
pipelines (Spectro-ViT, Rdc-UNET2D, and Gannet Quarter) were obtained by
considering the first 80 transients of the test set. In all the plots, a red
target line represents the values computed by the Gannet Full pipeline using
320 transients of the test set. Spectro-ViT’s GABA concentrations exhibit an
even distribution around the Gannet Full values, while Rdc-UNET2D tends to
underestimate, and Gannet Quarter tends to overestimate, resulting in shifts
in the distributions. All three pipelines demonstrate a balanced distribution
of Glx/Water without apparent bias.

However, when comparing the distributions to the Full Gannet, Spectro-ViT is
the only pipeline that does not show a statistically significant difference to
the target pipeline in every relation. Rdc-UNET2D has considerably low
p-values for GABA+/Water (0.0013) and GABA+Cr (0.0017), whereas Gannet Quarter
has a slightly lower p-value than 0.05 for GABA+/Water (0.0442) and a slightly
higher value for GABA+/Cr (0.0621). All the pipelines generated high p-values
for Glx/Water (Tab.2). Also, the Spectro-ViT had the lowest coefficient of
variation for both GABA+/Water (22.3%) and GABA+/Cr (13%), while Gannet
Quarter scored 24.2% and 22.6%, and Rdc-UNET2D scored 26% and 22.2%,
respectively.

The scatter plots of the concentrations provide complementary information to
the statistics (Fig.5). GABA concentrations obtained through Spectro-ViT’s
reconstructed spectra are more evenly distributed around the Gannet Full
values, without displaying a bias toward one side of the reference red line.
When examining Rdc-UNET2D data, most values are close to the reference line,
but the model tends to concentrate values and underestimate the target values.
On the other hand, Gannet Quarter tends to overestimate them, resulting in
shifts in the distributions and statistical differences. For Glx/Water, all
pipelines exhibit a balanced distribution without bias towards one side, with
Gannet Quarter having more values closer to the target line, consistent with
the error metrics.

3) Subset Robustness:

Figure 6: Box plots of metrics computed over the spectra resulting from the
Spectro-VIT pipeline (proposed model) for different subsets of transients in
the test set. The Gannet-Full pipeline, computed with 320 transients in the
test set, is also displayed for reference in the SNR and FWHM metrics.
Transients 1-80 (ViT Group 1) are shown in blue, 81-160 (Group 2) in orange,
161-240 (Group 3) in green, 241-320 (Group 4) in red, and Gannet-Full pipeline
in pink. In general, the Spectro-ViT results demonstrate consistency with good
values across the different subsets when considering the quality of
reconstruction. The last transients have shown a higher susceptibility to
frequency shifts, with Group 4 displaying the worst values for MSE and more
dispersed values for Shape Score and SNR. Figure 7: Box plot representations
of the quantification outcomes derived from the Spectro-VIT pipeline (proposed
model) for various sets of transients are presented for the test set.
Additionally, the results obtained from the Gannet-Full pipeline, which was
also applied to the test set with 320 transients, are included for reference.
Transients 1-80 (ViT Group 1) are represented by the color blue, while
transients 81-160 (Group 2) are depicted in orange, followed by transients
161-240 (Group 3) in green, and lastly, transients 241-320 (Group 4) in red,
and the Gannet-Full reference pipeline in pink. Overall, the outcomes of the
Spectro-ViT model showcase a remarkable resemblance to the distribution seen
in the reference Gannet Full across all groups. It is worth noting that the
fitting errors in these subgroups exhibit a superior performance compared to
the Gannet Full.

The investigation of the edited spectra resulting from the Spectro-ViT
pipeline for different subsets of transients demonstrates the consistency of
the proposed model (Fig.6 and Fig.7). The results for the Spectro-ViT Group 1
(transients 1-80) were the ones that have already been discussed previously.

Regarding the reconstruction metrics, all of the other groups of transients
achieved good results as well (Fig.6). The shape score values and SNR results
were found to be high, with the SNR results even surpassing those of the
Gannet Full pipeline. After an investigation of the frequency shifts across
the transients from the dataset, it was shown that there is a higher
susceptibility to frequency shifts for the last transients. The values of
Group 4 (transients 241-320), which displayed the worst values for MSE and
more dispersed values for Shape Score and SNR must be related to this result.

With respect to the quantification of the subsets, the outcomes of the
Spectro-ViT model showcase a remarkable resemblance to the distribution seen
in the reference Gannet Full across all groups, and the fitting errors in
these subgroups also outperform Gannet Full. (Fig.7). The Wilcoxon signed-rank
test comparing the concentration distributions of the subgroups to the
reference Gannet Full pipeline found no statistically significant differences.

##  5 Discussion

Spectro-ViT  outperforms the other compared methods in all reconstruction
metrics, except for SNR (Tab.1). Notably, the fit error values of Spectro-ViT
are significantly better than those of the target, Gannet Full, while
maintaining good proximity to the curve (MSE) and similar peak shapes (shape
score) to the target (Fig.4 and Fig.6). This is an important achievement
because a lower fit error ensures more confident quantification (70).

The SNR values of Spectro-ViT surpass those of the target, with Spectro-ViT’s
mean metric value being nearly 2.30 times higher than the mean value for
Gannet Full. Additionally, the SNR values for Spectro-ViT are more consistent
than those for Rdc-UNET2D (Tab.1).

In terms of quantification, although Gannet-Quarter leads in error metrics
(absolute error distribution and MAE) for quantification (Tab.2), its spectral
quality is suboptimal, as expected. It displays a linewidth nearly 10% greater
than the reference, a high standard deviation, and a GABA+/Water fit error
almost 60% higher than the target, with its SNR mean being almost twice as
poor (Tab.1). These limitations are highlighted by a Wilcoxon signed-rank
test, which shows a statistical difference (p-value=0.0442) between Gannet-
Quarter and Gannet-Full in GABA+/Water concentration distributions, and
scatter plots that indicate an overestimation bias in GABA concentrations
(Fig.5). This reveals that the combination of the metrics in analysis is
necessary for evaluating reconstruction (44). Gannet-Quarter’s best
performance in terms of absolute error distribution and MAE does not lead to
the most reliable quantification. Reliable quantification requires narrow
linewidths, low fit error, and good SNR (22; 70; 3).

Spectro-ViT boasts the best spectral quality metrics (Tab.1) and is the only
pipeline without statistically significant differences between ’predicted’
concentrations and the target across all metabolites (Tab.2). It also has the
lowest coefficient of variation for both GABA+/Water and GABA+/Cr.
Furthermore, scatter plot analysis reveals that, unlike Rdc-UNET2D (which
tends towards underestimation) and Gannet-Quarter (which tends towards
overestimation), Spectro-ViT does not demonstrate such biases (Fig.5).

These findings on Spectro-ViT’s performance are particularly relevant in light
of the study by Dziadosz et al. (71), which revealed intrinsic significant
biases and uncertainties in denoising single MRS spectra using DL. This
underscores the importance of being aware of biases when developing DL-based
denoising techniques for MRS. However, it is important to note that drawing
general conclusions about DL denoising solely from this study is not possible.
The study has limitations: it solely examined the effects of modeling on a
single spectrum and relied on a limited synthetic dataset. Additionally, it
utilized only two implementations of supervised DL denoising, and in-vivo data
was not incorporated.

Furthermore, the Subset Robustness experiment for Spectro-ViT has demonstrated
consistency, considering that the transients from the dataset are susceptible
to different intensities of noise. The quality of all the reconstructions in
this study was good distributed, comparable to the Gannet Full (Fig. 6 and
Fig.7). Furthermore, the Wilcoxon signed-rank test, which compared the
concentration distributions of the subgroups to the reference Gannet Full
pipeline, found no statistically significant difference.

However, it is important to note that further studies would be necessary to
enable the clinical application of this method and explore MRS distributions
beyond what was used in this work in order to observe how the model will
behave. Although only in-vivo data was used in this research and the training
of the model involved on-the-fly augmentations during training, it is not
possible to ensure that it encompassed the MEGA-PRESS distribution for all
potential sources of variance. It is necessary to test if it will also perform
well with data acquired using different protocols, in smaller and more
challenging brain regions, and in a broader population, including subjects
suffering from diseases.

##  6 Conclusions

Experiment results demonstrate that the Spectro-ViT model can reconstruct
GABA-edited MRS spectra using only 80 transients, delivering comparable
outcomes to those achieved by the standard pipeline employing the Gannet
software with 320 transients. Consequently, this study’s results suggest that
the scans conducted herein could be expedited up to four times using this
approach.

The Spectro-ViT’s outcomes, assessed across the quality metrics MSE, SNR,
Linewidth, shape score and fit error, occasionally outperform the spectral
quality of the established pipeline, and the similarities in quantification
distribution to the reference underscore its superior performance, surpassing
the initial efforts of Rdc-UNET2D and securing the top-ranking position in the
Edited-MRS reconstruction challenge.

The Spectro-ViT represents a pioneering effort in MRS research, being the
first to incorporate Transformer models. This innovative approach underlines
the importance of exploring novel model types in advancing the field. The
application of the pretrained ViT, in particular, has shown remarkable
efficiency in extracting features from the time-frequency domain
representation (spectrogram) of the MRS signal. Furthermore, the potential for
simple modifications of the Spectro-ViT architecture opens up new avenues for
research in MRS, where the encoded representation of the spectrogram MRS
signal by the ViT might go beyond reconstruction, also providing good features
for classification and quantification.

In conclusion, the Spectro-ViT model has demonstrated its potential to advance
MRS research by delivering ground-breaking results in GABA-edited MRS spectra
reconstruction. Its performance, surpassing that of previous models,
underscores the value of embracing innovative approaches, such as Transformer
models and new domains to represent the MRS signal such as spectrograms, in
advancing GABA MEGA-PRESS. While these findings are promising, it is important
to acknowledge the need for further research to validate the model’s
applicability across different acquisition protocols and a more diverse
population. Additionally, more research contributions are needed to draw
firmer conclusions about DL denoising in the field. These next steps will be
crucial in realizing the clinical potential of the Spectro-ViT model to
accelerate the Edited-MRS scans.

##  7 Acknowledgments

The researchers from University of Campinas were supported by the DeepMind
Scholarship Program, the National Council for Scientific and Technological
Development (CNPq Process #313598/2020-7), by the BI0S - Brazilian Institute
of Data Science, grant #2020/09838-0, São Paulo Research Foundation (FAPESP) -
BRAINN - Brazilian Institute of Neuroscience and Neurotechnology, grant
#2013/07559-3, São Paulo Research Foundation (FAPESP), and by the Coordenação
de Aperfeiçoamento de Pessoal de Nível Superior - Brasil. The researchers from
the University of Calgary were supported by NSERC Discovery Grant
(#RGPIN-2021-02867), NSERC Discovery Grant (#RGPIN-2017-03875), NSERC Brain
CREATE Award, and Alberta Graduate Excellence Scholarship.

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