Keywords: Software Tools, Software Tools, Spectroscopy

Motivation: Offline MR spectroscopy (MRS) analyses are limited by proprietary software on the console.

Goal(s): This study proposes a novel MATLAB-based toolbox to process the scanner-derived MRS raw data offline.

Approach: Functions in the proposed toolbox were programmed to efficiently perform MRS data extraction, peak detection in the spectral data, and Lorentzian curve fitting to provide a noiseless spectrum.

Results: With the toolbox, raw MRS data can be utilized offline; clinically relevant metabolites whose peaks were originally obscured in console-derived spectra can now be clearly resolved.

Impact: The development of a MATLAB toolbox for MRS data processing streamlines access to valuable MRS information and enhances peak detection, thus potentially increasing the utility and accessibility of MRS clinically and providing medical practitioners with more diagnostic information.

Keywords: Data Processing, Data Analysis

Motivation: Macromolecules have significant spectral overlap with metabolites, confounding accurate quantification of metabolites in ultrashort-TE MRSI.

Goal(s): To develop a novel method for effective and reliable separation of metabolites and macromolecules from ultrashort-TE FID MRSI data.

Approach: We translated auxiliary macromolecule-free SE metabolite signals to FID signals using a learning-based approach. The translated metabolite reference was incorporated in the spectral model of FID MRSI data through generalized series modelling. Macromolecules signals were modelled with probabilistic subspaces.

Results: The proposed method has been validated using numerical simulation and experimental data from healthy subjects and a tumor patient, producing encouraging results.

Impact: This work provides a novel approach to exploiting the characteristic spectral features in FID and SE MRSI experiments for effective separation of metabolites and macromolecules.

Keywords: Data Processing, Spectroscopy, Reproducible Research

Motivation: Many details of in vivo proton magnetic resonance spectroscopy (¹H MRS) data analysis still lack evidence-based standards. 

Goal(s): The ¹H-MRS Validation Effort Resource Initiative (¹H-MRS VERI) is a public repository for sharing spectral datasets useful for formulating such standards.

Approach: ¹H-MRS VERI data sets include measurements from phantoms (VERI_ph), spectral simulations of predefined metabolite scaling, including simulated and/or measured in-vivo-like nuisance signals (VERI_sim), and ¹H-MRS acquisitions from human or other tissues colocalized with supporting measurements from non-¹H-MRS experiments (VERI_vivo).

Results: ¹H-MRS VERI facilitates field-wide efforts toward accurate and precise in vivo ¹H-MRS quantification via transparent, replicable investigation of shared empirical standards.

Impact: ¹H-MRS research marches on, largely uninformed by commonly accepted evidence-based guidelines for many data analysis details from denoising methods to baseline definitions. ¹H-MRS VERI provides a curated collection of data standards to facilitate the shared development of these still-needed guidelines. 

Keywords: Data Processing, Data Analysis, Metabolomics, metabolomic imaging, nuclear magnetic resonance, spectroscopy, biomarkers, data processing, metabolism, metabolites

Motivation: Manual identification of potential metabolites from untargeted MRS-based metabolomics studies is often tedious, labor-intensive, and prone to error. 

Goal(s): To develop an automated and customizable program to systematically identify metabolites from spectral regions of interest (ROIs) based on databases, such as Human Metabolome Database (HMDB) for specific medical conditions. 

Approach: We integrated experimental data — including ROIs, statistical significance, group trends/comparisons, and tissue- and disease-specific information — with automated HMDB searching, to output relevant and significant potential metabolites. 

Results: Given spectral ROIs, and relevant significance and trend data, our program is capable of identifying possible disease- and tissue-specific metabolites.

Impact: Our program automates the manual database-searching process required for metabolite identification in MRS-based metabolomics research, enabling fast, robust, and reliable identification and categorization of metabolites based on user-customizable factors such as significance, trend, and tissue- and disease-specificity.

Keywords: Data Processing, Shims

Motivation: Eddy-currents can bias magnetic field (∆B0) maps.

Goal(s): To assess the impact of eddy-current-biased ∆B0 maps on B0 shimming and T2* measurements.

Approach: Measure the shimmed ∆B0 field after having used a ∆B0 map that is thought to be affected by eddy currents vs. one that is obtained using the gradient-reversal technique for canceling eddy-currents. Measure T2* values after using both shimming conditions.

Results: Eddy-currents arising from spatial-encoding gradients have a measurable impact on shim quality and can reduce T2* values in the brain by up to 10 ms.

Impact: Scientists and clinicians interested in measuring T2* in areas that are affected by strong magnetic field inhomogeneities may want to consider that eddy-current-induced biases in ∆B0 maps could lead to reduced T2* in those regions.

Keywords: Data Processing, Spectroscopy

Motivation: Retrospective methods to correct frequency drifts has been evaluated at 3T systems but not at 7T systems, where the line-broadening effect may degrade the performance of the correction.

Goal(s): To evaluate retrospective frequency drift correction methods at 7T using simulations and human spectra.

Approach: The frequency correction methods were applied to the simulated spectra and human spectra at 7T to evaluate the accuracy of the frequency drift estimates and the spectral linewidth after the correction.

Results: Among these methods, the spectral registration method showed a more accurate estimate of the frequency drift and a larger improvement of spectral linewidth.

Impact: 7T offers a high sensitivity to detect weakly represented metabolites/neurotransmitters that are relevant to studying neurological and mental disorders. This study addresses frequency drift correction for accurate, consistent metabolite/neurotransmitter quantifications and may help expand the applications of MRS at 7T.

Keywords: Data Processing, Machine Learning/Artificial Intelligence, Magnetic Resonance Spectroscopic Imaging (MRSI); Metabolite Quantification; Uncertainty Estimates; Quantitative MRSI Analysis

Motivation: Addressing challenges in ultra-short TE MRSI data quantification of the Human Brain at 7T utilizing deep learning

Goal(s): Develop a Variational Physics-Informed Autoencoder (VPIAE) to enhance MRSI metabolite quantification, ensuring faster, robust, and efficient metabolite mapping with uncertainty estimates.

Approach: Combine a variational autoencoder with a physics-informed decoder, training on 7T MRSI brain data, and benchmark against a traditional method (LCModel)

Results: VPIAE outperforms conventional MRSI methods in speed by 6 orders of magnitude, offers comparable accuracy, and provides uncertainty estimates for reliable interpretation, promising advancements in clinical and research applications.

Impact: VPIAE enables swift MRSI analysis, crucial for clinicians diagnosing neurological conditions and researchers studying metabolic brain changes. It opens avenues for exploring brain metabolite dynamics with greater fidelity and advancing the field's understanding of brain metabolism.

Keywords: Radiomics, Metabolism, 13C NMR, 13C isotopomer analysis

Motivation: The TCGA database reveals that gliomas exhibit the presence of phosphoglycerate dehydrogenase (PHGDH), a crucial enzyme in the generation of serine through de novo biosynthesis.

Goal(s): Our objective was to measure the serine biosynthesis flux in glioma patients.

Approach: We used ¹³C-glucose intravenous infusion during the surgical resection and ¹³C isotopomer analysis to track serine biosynthesis in patients. 

Results: We detected  19.2% ± 6.5% of  serine biosynthetic flux relative to lactate production and the generation of glycine to produce one-carbon units in both low-grade gliomas and GBMs. 

Impact: These results indicate that targeting serine de novo synthesis may be of therapeutic value. We are currently working on developing PHGDH knockdown cell line from a GBM patient to test the effects of serine synthesis on cellular viability.

Keywords: Data Processing, Spectroscopy, Optimization, Torch, Auto-differentiation.

Motivation: This study addresses the time-consuming water residual removal process in MRSI, aiming to expedite it, given the lack of GPU implementation for methods like HLSVD-PRO.

Goal(s): We aim to increase water residual removal speed using Torch for GPU-parallelized linear combination model fitting.

Approach: Our method utilizes Torch to model and optimize water residuals fitting with 7-Lorentzian profiles. Performance is evaluated with in a large in-vivo dataset, comparing our method to HLSVD-PRO both in efficiency and accuracy.

Results: Our approach accelerates water residual removal, outperforming HLSVD-PRO in processing speed by a factor 14x, while mantaining equivalent quantification accuracy, offering promise for MRSI applications.

Impact: This study's accelerated water residual removal method in MRSI can benefit scientists and clinicians by reducing processing time. And it opens avenues for more extensive research in the topic.

Keywords: Software Tools, Spectroscopy

Motivation: Synthetic spectra can be used to investigate modeling assumptions, optimize sequence parameters and for machine learning training data 

Goal(s): To develop a platform that can produce synthetic spectra, and to use it to investigate the effects of ¹⁴N coupling on ¹H quantification

Approach: MARSS was extended to be able to create synthetic spectra, and accommodate spin 1 nuclei 

Results: Using synthetic spectra it was shown that approximating ¹⁴N heteronuclear coupling as weak homonuclear coupling results in small effects on quantification of the prominent metabolites at short echo time for PRESS, however, these effects increase with echo time.

Impact: MARSS was extended to simulate non-¹H in vivo synthetic magnetic resonance spectra. Synthetic spectra can be used to bolster experimental evidence, or to investigate questions which are impossible or infeasible experimentally.

Keywords: Data Processing, Spectroscopy, Organoids

Motivation: 12

Goal(s): 2

Approach: 2

Results: 2

Impact: 2

Keywords: Segmentation, Spectroscopy, Point Resolved Spectroscopy, PRESS

Motivation: Rapid Relaxometry through Acquisition of Multiple Saturated T₂ Curves (RRAMSC) is a time-efficient STEAM compartmental analysis method. While it has been theorized that the technique can also be used in PRESS localization, it has not yet been demonstrated.

Goal(s): Our goal is to examine RRAMSC for separating the tissue water and CSF when using PRESS localized spectroscopy.

Approach: To our knowledge, this is the first study to demonstrate the applicability of RRAMSC for PRESS in vivo. 

Results: Results show excellent agreement between theoretical and actual differences for a PRESS-based RRAMSC, showing negligible differences and a mean error of less than 2%

Impact: This study extends the rapid relaxometry through acquistion of multiple saturated T₂ curves, a time-efficient relaxometry technique for water compartmentalization, to the more commonly used point resolved spectroscopy localization technique. 

Keywords: Data Processing, Sparse & Low-Rank Models, liver, 13C-MRS, denoising, MP-PCA, 7T, human, metabolism

Motivation: Need to improve determination of kinetics for low-concentration metabolites using X-nuclear MRS. 

Goal(s): Investigate potential benefits of denoising by Marchenko-Pastur Principal Component Analysis (MP-PCA) for extracting natural-abundance glycogen kinetics from ¹³C-MRS data.

Approach: MP-PCA applied on synthetic and human in-vivo hepatic ¹³C-MRS time-course datasets.

Results: MP-PCA substantially improves apparent SNR and reduces mean linear regression residuals, without introducing bias in slope estimates. MP-PCA is shown to be valuable for the determination of unknown physiologic time-courses of low-concentrated glycogen signals; here, specifically enabling use of lower D-glucose loads in combined deuterium metabolic imaging and ¹³C-MRS evaluations of hepatic glucose metabolism. 

Impact: Our findings on MP-PCA's efficacy in enhancing the determination of glycogen kinetics by ¹³C-MRS broaden the understanding of denoising techniques in MR spectroscopy and ultimately impact researchers and clinicians who develop, assess, or apply MR techniques suffering from low SNR.  

Keywords: Software Tools, Spectroscopy, quantification; MRS; AMARES; python; FID; MNS; 31P

Motivation: As a mainstream time-domain fitting algorithm for quantifying MRS data, to date, AMARES has been confined to Java and MATLAB, while the dominant language for deep learning, Python, does not yet have an AMARES implementation.

Goal(s): To develop pyAMARES, a python package implementing the AMARES, providing the MRS community with flexible and robust MRS data fitting capabilities. 

Approach: PyAMARES imports prior knowledge from spreadsheets as initial values and constraints for fitting MRS data according to the AMARES model function. 

Results: PyAMARES effectively fits in vivo MRS data with varied algorithms, proving its versatility as a versatile fitting tool.

Impact: PyAMARES provides accessible, flexible, and robust time-domain MRS fitting in Python, bridging the gap between advanced data analysis and deep learning by Python and the AMARES algorithm, previously limited to Java and MATLAB implementations.

Keywords: Segmentation, Kidney

Motivation: The evaluation of diffusion-relaxation correlation spectrum imaging (DR-CSI) without prior knowledge has not been investigated.

Goal(s): To differentiate high-grade from low-grade clear cell renal cell carcinoma using DR-CSI spectra in an equal separating analysis.

Approach: The DR-CSI spectrum was segmented into multiple equal subregions from 2*2 to 9*9 and was evaluated based on their accuracy and repeatability.

Results: Interreader agreement decreased as divisions in the equipartition model increased (from 0.859 to 0.920). Accuracy increased from 2x2 to 9x9 model (0.68 for 2x2, 0.69 for 3x3 and 4x4, 0.70 for 5x5, 0.71 for 6x6, 0.78 for 7x7, and 0.75 for 8x8 and 9x9).

Impact: This study validated an method for extracting information from DR-CSI spectra based on the differentiation of high-grade from low-grade ccRCC without prior knowledge. It provides a reference for processing DR-CSI spectra, helping this technology be applied to other clinical scenarios.

Keywords: Data Processing, Spectroscopy

Motivation: The accuracy of absolute quantitation of metabolites by 1H MRS in grey-matter is compromised in the presence of partial volumes of CSF

Goal(s): To develop a linear model of metabolite concentration estimates as a function of partial volumes

Approach: The effects of varying tissue fractions and relaxation times on metabolite concentrations were modelled to investigate how they affected the variability in metabolite concentration estimates

Results: A general linear model with parameters relating to tissue partial volume as covariates enables statistical comparisons of absolute metabolite levels between patient sub-groups without a priori knowledge of tissue relaxation times.

Impact: A General Linear Model statistical approach enables robust comparison of cortical grey matter metabolite concentrations between patient groups, and minimises the propagation of errors that occurs when the tissue partial volume properties are inaccurately estimated.