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Clémence Ligneul¹, Jesper Andersson¹, Saad Jbabdi¹, Jason Lerch^1,2,3, and William T Clarke^1
1Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom, ²Mouse Imaging Centre, The Hospital for Sick Children, Toronto, ON, Canada, ³Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada

Keywords: Data Processing, Spectroscopy, Diffusion-weighted spectroscopy

Robust processing is crucial for a reliable microstructural interpretation of advanced diffusion-weighted magnetic resonance spectroscopy (DW-MRS) methods. The spectral signal intensity depends on the direction when using strong diffusion-gradients and multiple directions, even more if the voxel content is very anisotropic (e.g. contains a white matter tract). In this study we propose a model-based way of identifying motion corrupted averages that accounts for the voxel anisotropy.

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Leonardo Campos¹, Kelley M. Swanberg¹, and Christoph Juchem^1,2
1Biomedical Engineering, Columbia University, New York, NY, United States, ²Radiology, Columbia University, New York, NY, United States

Keywords: Data Processing, Spectroscopy

There remains controversy surrounding the use of zero filling during spectral quantification of in vivo proton magnetic resonance spectra (¹H-MRS) using linear combination model (LCM) fitting. We examine the potential mixing of real and imaginary information theorized with zero filling, and whether this is demonstrated by a comparable change in accuracy and precision provided by complex fitting. We show that application of zero filling does improve fitting precision when a baseline is not present; with an imperfectly modeled unknown in vivo baseline, however, zero filling does not necessarily reduce error in real relative to complex fits.

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Guodong Weng^1,2, Piotr Radojewski^1,2, and Johannes Slotboom^1,2
1Support Center for Advanced Neuroimaging (SCAN), University of Bern, Bern, Switzerland, ²Translational Imaging Center, sitem-insel, Bern, Switzerland

Keywords: Pulse Sequence Design, Spectroscopy, Spectral editing

2-Hydroxyglutarate (2HG) is a biomarker for IDH-mutant glioma, and previous studies have shown that cystathionine (Cys) can be a potential biomarker for 1q/19q co-deletion of glioma. Previous studies used SVS based MEGA-editing MRS to detect 2HG and co-edited Cys, but editing efficiency of Cys is not optimal due to the narrow bandwidth of the applied MEGA editing-pulses. This work shows that SLOW-editing is able to detect both 2HG and Cys in an optimal way with whole-brain coverage. 

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Stefano Tambalo¹, Sebastian Hübner¹, Francesca Saviola¹, Tobias Kober^2,3,4, and Jorge Jovicich^1
1University of Trento, Trento, Italy, ²Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland, ³Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, ⁴LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

Keywords: Data Processing, Spectroscopy

The automated voxel placement (AVP) framework has been proposed to optimize accuracy and reproducibility in single-voxel brain proton spectroscopy. AVP is based on the affine transformation of brain coordinates from standard to subject-specific space defined on a 3D T1-weighted structural scan. Here we evaluate the robustness of the AVP approach by evaluating the displacement of voxel center coordinates across 3D T1w MPRAGE sequence variants, including compressed sensing (CS) accelerated protocols. We show that AVP gave small but significantly higher voxel displacements for MP2RAGE and CS-MP2RAGE. There were no significant differences between multi-echo MPRAGE (6 min) and CS-MPRAGE (1 min).

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Kyung Min Nam^1,2,3, Guodong Weng^2,3, Nam G Lee⁴, Edwin Versteeg¹, Yeong-Jae Jeon^5,6, Arjan Hendriks¹, Jannie Wijnen¹, Alex Bhogal¹, Dennis Klomp¹, Maxim Zaitsev⁷, and Johannes Slotboom^2,3
1Department of High Field MR, Centre for Image Sciences, University of Medical Centre Utrecht, Utrecht, Netherlands, ²Institute for Diagnostic and Interventional Neuroradiology, Support Center for Advanced Neuroimaging (SCAN), University of Bern, Bern, Switzerland, ³Translational Imaging Center, sitem-insel AG, Bern, Switzerland, ⁴Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States, ⁵Lee Gil Ya Cancer & Diabetes Institute, Gachon University, Incheon, Korea, Republic of, ⁶Department of Health Sciences and Technology, GAIHST, Gachon University, Incheon, Korea, Republic of, ⁷Department of Radiology, Division of Medical Physics, University Medical Center Freiburg, Freiburg, Germany

Keywords: Data Acquisition, Spectroscopy

An MRSI sequence using a pair of chemically selective adiabatic 2𝜋 refocus pulses, referred to as 2𝜋-CSAP, was implemented using the open-source Pulseq framework. This sequence enables full coverage of the frequency spectrum of interest through a shift in carrier frequency, which has been a limitation in rapid MRSI sequences. In addition,  this shifted frequency spectrum also minimizes signal contaminations from residual water signal after water suppression or unsuppressed water signal and strong lipid signals, possibly eliminating the need for additional water suppression pre-pulses. This base sequence can potentially be combined with accelerated acquisition techniques for better scan efficiency.

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Jiying Dai^1,2, Mark Gosselink¹, Alexander J. E. Raaijmakers^1,3, and Dennis W. J. Klomp^1
1UMC Utrecht, Utrecht, Netherlands, ²Tesla Dynamic Coils B.V., Zaltbommel, Netherlands, ³Eindhoven University of Technology, Eindhoven, Netherlands

Keywords: Image Reconstruction, Spectroscopy

We utilized a quintuple-tuned RF head coil array by using the high-SNR ²³Na signals from the brain to optimize the weighting for combining signals from the same coil array elements for the low-concentrated ³¹P metabolites. ²³Na-weighted Roemer combination of ³¹P MRSI signals is verified on EM simulations and MR experiments. Comparing to ³¹P self-weighted combination, ²³Na-weighted combination shows higher SNR and better-combined spectra in regions with low intrinsic SNR. It also shows potential of mitigating the signal contaminations when using ³¹P-self-weights for ³¹P data acquired with large voxels.

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Ronald Instrella¹ and Christoph Juchem^1,2
1Biomedical Engineering, Columbia University, New York, NY, United States, ²Radiology, Columbia University, New York, NY, United States

Keywords: Data Analysis, Spectroscopy, Brain, 1H-MRS, Quantification

The effect of uncertainty propagation for absolute quantification performed on individual single-voxel in vivo ¹H-MRS experiments has yet to be examined. In this case study, we conduct an uncertainty analysis using Monte Carlo simulations on two previously published studies with reported absolute concentrations of metabolites and macromolecules, each employing different field strengths, relative linewidths and internal references (e.g. total creatine, tissue-specific water). The uncertainty from 8 in vivo spectra is simulated and compared to CRLBs from Linear Combination Modeling. A multifold uncertainty increase is observed in both metabolites and macromolecules across datasets, demonstrating that the CRLB systematically overestimates quantification precision.

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Julian P. Merkofer¹, Sina Amirrajab¹, Johan S. van den Brink², Mitko Veta¹, Jacobus F. A. Jansen^1,3, Marcel Breeuwer^1,2, and Ruud J. G. van Sloun^1,4
1Eindhoven University of Technology, Eindhoven, Netherlands, ²Philips Healthcare, Best, Netherlands, ³Maastricht University Medical Center, Maastricht, Netherlands, ⁴Philips Research, Eindhoven, Netherlands

Keywords: Machine Learning/Artificial Intelligence, Spectroscopy, Deep Learning, Uncertainty Prediction, Adaptive Ensemble

Current deep learning methods for metabolite quantification in magnetic resonance spectroscopy do not offer reliable measures for uncertainty. Having a widely applicable measure can aid with the identification of fitting errors and enable uncertainty-based adaptive ensembling of model-based quantification and neural network predictions. In this abstract, we propose a training strategy based on a log-likelihood cost that allows joint optimization of concentration and uncertainty estimation for each individual metabolite. We show that the predicted uncertainties correlate well with the actual estimation errors and that uncertainty-based adaptive ensembling outperforms the individual estimators as well as standard ensembling.

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Yiling Liu^1,2, Zhiyong Zhang¹, and Assaf Tal^2
1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, ²Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot, Israel

Keywords: Data Analysis, Spectroscopy

It has been suggested that fitting dynamic MRS data in tandem (2D fitting) should be more precise than conventional 1D fitting, without impairing its accuracy. Functional MRS (fMRS) is a dynamic method used for detecting endogenous metabolic changes in the brain. In this work, we implemented a 2D spectral-temporal fitting framework for the synthetic fMRS data. Preliminary experiments confirm that 2D fitting improves precision approximately three-fold compared to the conventional 1D fitting in terms of fMRS data.

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Ke Zhou¹, Ziyan Wang¹, Dingyi Lin¹, Yi-Cheng Hsu², and Min Wang^1
1College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China, ²Siemens Healthcare Ltd, Shanghai, China

Keywords: Data Processing, Spectroscopy, Spectra Registration

Diffusion-weighted MRS (DW-MRS) suffers from low signal intensity and large phase errors when applying strong diffusion weighting. Coil combination and spectral registration are vital as spectra with high b-values are usually poorly reconstructed. This work aims at the optimization of post-processing pipeline with singular value decomposition (SVD)-based spectral registration and the comparison of different regimes of coil combination and spectral registration for in-vivo DW-MRS post-processing. The SNR of post-processed spectra and the metabolite diffusivities are assessed. The statistical outcomes suggest that the SVD-based spectral registration could improve the SNR of DW-MRS signal at high b-values and therefore alleviate diffusivity overestimations.

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Jullie W Pan¹, Melissa Terpstra¹, Junghwan Kim¹, and Hoby P Hetherington^2
1Radiology, University of Missouri Columbia, columbia, MO, United States, ²Resonance Research Inc., Billerica, MA, United States

Keywords: Data Acquisition, Spectroscopy, shimming

Because of the small volumes (2 to 15cc) used in single voxel spectroscopy (SVS), field inhomogeneities over such voxels are thought to be well managed by low order shim methods, e.g., FAST(EST)MAP using 1st-2nd spherical harmonic shim terms. In this report performed at 7T (Siemens Terra) we evaluate SVS from a key region of interest (prefrontal cortex PFC) with FAST(EST)MAP in comparison with Bolero shimming, a method that specifically manages high order shim terms. High accuracy field maps and LCModel analysis of STEAM spectroscopy are evaluated.

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Ying Xiao^1,2, Bernard Lanz¹, Songi Lim^1,2, and Lijing Xin^1
1Animal imaging and technology, CIBM, EPFL, Lausanne, Switzerland, ²Laboratory for Functional and Metabolic Imaging (LIFMET), EPFL, Lausanne, Switzerland

Keywords: Data Processing, Spectroscopy, LCModel

This study investigated the spectral linewidth effect on GABA quantification, as well as its correction approach in LCModel with both simulated and in vivo acquired ¹H MR spectra. Using the same basis set to fit spectra with different linewidths results in elevated variations between measurements. An effective method to reduce the variability caused by the linewidth difference effect and more accurately quantify GABA is to use spectral-linewidth-matched basis sets. This study shows the validity of this approach for most of the Voigt-type spectra.

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Sungtak Hong¹ and Jun Shen^1
1National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States

Keywords: Data Analysis, Spectroscopy

Numerical Monte Carlo analysis was performed to quantify metabolite-metabolite correlations of spectral origin in ³¹P MRS spectra acquired from human brain at both 3 and 7 Tesla without any confounding biological correlations. Significant correlations were found for many ³¹P-containing metabolite pairs. In particular, the 3 and 7 Tesla NAD⁺-NADH correlations are significantly different because of the field strength difference. These results demonstrate that it is necessary to incorporate metabolite-metabolite correlations originating from spectral overlap into statistical models that correlate MRS measurements with clinical parameters when overlapping ³¹P signals are of clinical interest. 

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Ankita Singh¹, Aruna Singh¹, Dushyant Kumar¹, and Rama Jayasundar^2
1NMR, All India Institute of Medical Sciences, New Delhi, India, ²Department of NMR, All India Institute of Medical Sciences, New Delhi, India

Keywords: Data Acquisition, Spectroscopy, proton NMR spectroscopy, in vitro spectroscopy, obesity

Obesity, a disorder of lipid metabolism, has become a serious health issue globally. Recently, research on pungent phytochemicals and plants with nutritional and medicinal values has gained interest in obesity management. In this study, phytochemicals (n=21) and medicinal plants (n=38) from pungent and non-pungent groups were studied using proton NMR phytometabolomics for their anti-obesity properties. Multivariate analysis of NMR data demonstrated the potential of proton NMR metabolomics in differentiating medicinal plants and their active phytochemicals with anti-obesity properties. These were further confirmed with anti-lipase assays and alkaloid analysis (indicating presence of pungent molecules) of the medicinal plants.

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Jing Xu¹, Michael C Langham¹, Hengyi Rao², Marianne Nabbout¹, Alessandra S Caporale^1,3,4, Alexander M Barclay¹, John A Detre², and Felix W Wehrli^1
1Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States, ²Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States, ³Department of Neurosciences, ‘G. d’Annunzio University’ of Chieti-Pescara, Chieti, Italy, ⁴Institute for Advanced Biomedical Technologies (ITAB), ‘G. d’Annunzio University’ of Chieti-Pescara, Chieti, Italy

Keywords: Data Analysis, Metabolism

Sleep is fundamental to human health and function. Cerebral metabolism and blood supply are key physiological parameters of brain function, but the manner in which they change during different sleep stages is still largely unknown. In this study, we collected wakefulness and sleep data with concurrent EEG-MRI. We measured CBF, SvO₂ and CMRO₂ with radial OxFlow MRI. Our data show that CMRO₂ is lower during non-REM sleep than during wakefulness and declines progressively as sleep stages become deeper. CBF decreases during non-REM sleep compared with wakefulness, while SvO₂ gradually increases from wakefulness to slow wave sleep.

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Zhiwei Huang¹, Giulio Gambarota², Ying Xiao¹, Daniel Wenz¹, and Lijing Xin^1
1Animal imaging and technology core (AIT), Center for Biomedical Imaging (CIBM), Ecole Polytechnique Fédérale de Lausanne, Ecublens, Switzerland, ²Faculty of Pharmacy, University of Rennes, Rennes, France

Keywords: Data Acquisition, Spectroscopy, Diffusion

³¹P diffusion magnetic resonance spectroscopy could assess the diffusion properties of high energy metabolites. In this study, a diffusion weighted (DW) STEAM sequence was implemented, and spectra were acquired in the human calf muscle of six healthy volunteers. Frequency and phase alignments were applied prior to spectral averaging. The ADC of phosphocreatine (PCr), adenosine triphosphate (ATP), inorganic phosphate (Pi) and glycerol phosphorylcholine (GPC) were (0.24±0.02, 0.15±0.04, 0.43±0.14, 0.40±0.09)×10^-3 mm²/s. To the best of our knowledge, this is the first study reporting the ADCs of ATP, Pi, and GPC, and the second study reporting the ADC of PCr in human.

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Xinyu Liu^1,2,3, Selin Scherrer⁴, Sven Egger⁴, Song-I Lim^1,3, Benedikt Lauber⁴, Wolfgang Taube⁴, and Lijing Xin^1,3
1Animal Imaging and Technology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, ²Laboratory for functional and metabolic imaging (LIFMET), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, ³Center for Biomedical Imaging (CIBM), Lausanne, Switzerland, ⁴Department of Neurosciences and Movement Science, University of Fribourg, Fribourg, Switzerland

Keywords: Data Acquisition, Spectroscopy, Data analysis, motor training, GABA, 7 Tesla

This study aims to examine the modulatory effect of long-term balance training on sensorimotor cortex GABA level in an older population. In-vivo GABA levels were accessed using in-vivo MR spectroscopy (MRS) at 7T. GABA level in sensorimotor cortex was measured in sixteen healthy older adults using MEGA-sSPECIAL sequence, and short echo time semi-sSPECIAL sequence was used for metabolite profiling, before and after a three-months period of balance training. Using edited MEGA-sSPECIAL we detected a significant increase in sensorimotor GABA level after training, indicating potentially enhanced motor inhibition by coordinative balance learning.

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Rajakumar Nagarajan¹, Jane A Kent², and Gwenael Layec^2
1Human Magnetic Resonance Center, Institute for Applied Life Sciences, University of Massachusetts, Amherst MA, Amherst, MA, United States, ²Kinesiology, University of Massachusetts, Amherst MA, Amherst, MA, United States

Keywords: Data Acquisition, Spectroscopy

Using two-dimensional 31-phosphorus spectroscopic imaging (2D-MRSI) in human skeletal muscle, we have demonstrated that proton decoupling and nuclear Overhauser effect sequences improve the coefficient of variation and enhance the resolution of key metabolites in vivo.

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Alicia Plaindoux¹, Yann Le Fur², Jia Guo³, Clothilde Courivaud¹, Julien Valette⁴, Vasile Stupar^1,5, and Florence Fauvelle^1,5
1Grenoble Institute Neurosciences, INSERM, U1216, University Grenoble Alpes, Grenoble, France, ²CRMBM, Aix-Marseille University, Marseille, France, ³Departement of Psychiatry, Columbia University, New York, NY, United States, ⁴Université Paris-Saclay, CEA, CNRS, MIRCen, Laboratoire des Maladies Neurodégénératives, Fontenay-aux-Roses, France, ⁵IRMaGe, INSERM, US17, CNRS, UMS 3552, CHU Grenoble Alpes, University Grenoble Alpes, Grenoble, France

Keywords: Data Acquisition, Spectroscopy, MEGA-LASER, CSI, edition

A surgical resection of the epileptogenic zone (EZ) can be proposed to drug-resistant epileptic patients, mainly suffering from focal epilepsy, in order to free them from their seizures. However, the mean efficiency of surgery is around 50 to 80%, indicating a possible miss-delimitation of the EZ. In recent studies, GABA was found to be a specific biomarker of the EZ in a mouse model of mesio-temporal lobe epilepsy (MTLE). Therefore, in this project, we propose a GABA-edited spectroscopic imaging method to improve the spatial localization of the EZ in this mouse model.

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Ajin Joy¹, Uzay Emir^2,3, Paul M. Macey⁴, and M. Albert Thomas^1
1Radiological Sciences, University of California, Los Angeles, Los Angeles, CA, United States, ²School of Health Sciences, Purdue University, West Lafayette, IN, United States, ³Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States, ⁴School of Nursing and Brain Research Institute, University of California, Los Angeles, Los Angeles, CA, United States

Keywords: Data Processing, Brain

Limiting the total data acquisition time to a clinically feasible runtime has been a major challenge in MR spectroscopic imaging. Recently rosette based non-cartesian encoding of k-space has been used for spectroscopic imaging due to their fast encoding speed and lower gradient/slew rate requirements. While rosette spectroscopic imaging has been attempted for 2D and 3D spectroscopic imaging, feasibility of undersampling the petals in rosette is not shown. In this study, we implemented a rosette 2D and 3D spectroscopic imaging sequence and shown the feasibility of acceleration factors up to 8x using compressed sensing reconstruction.