Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition - Software and Analysis for MR Spectroscopy and Chemical Shift Imaging

Software and Analysis for MR Spectroscopy and Chemical Shift Imaging

Digital Poster

Analysis Methods

Tuesday, 12 May 2026

Digital Posters Row J

13:40 - 14:35

Session Number: 469-03

No CME/CE Credit

Similar Sessions

This session will highlight recent advances in fat–water separation techniques, chemical shift imaging, and MR spectroscopy, with a particular emphasis on software frameworks and quantitative analysis tools. Contributions will cover methodological innovations, validation studies, reflecting current challenges and emerging opportunities.

		469-03-001. Unraveling the Interpretation of Magnetic Susceptibility: Integrating Neurochemistry and Vascular Physiology Julia Huck, Sean McGarry, Tiffany Bell, Ashley Harris University of Calgary, Calgary, Canada Impact: This study will quantify the neurochemical and vascular determinants of magnetic susceptibility, advancing the biological interpretation of quantitative susceptibility mapping (QSM) and supporting its use as a scalable metabolic imaging marker for brain health and disease.
		469-03-002. Optimization of Semi-LASER Coherence Pathway Selection for Robust Water Ghosting and Lipid Artifact Reduction at 7T Wenkai Liang, Manoj Kumar Sarma, Anke Henning University of Texas Southwestern Medical Center, Dallas, United States of America Impact: An optimized coherence pathway selection strategy enhances spectral purity in semi-LASER MRS at 7T without requiring additional OVS modules, thereby reducing SAR and TR.
		469-03-003. In Vivo Fatty Acid Composition of Adipose Tissue Using MR Spectroscopy and Deep Neural Networks Aftar Ahmad Sami, Nakul Gupta, Aaryani Tipirneni-Sajja University of Houston, Houston, United States of America Impact: This work shows that synthetic data augmentation combined with neural networks can accurately quantify fatty acid composition from MR spectroscopy without extensive in vivo data collection, thus facilitating in vivo fat profiling for assessment of metabolic diseases and dietary interventions.
		469-03-004. Impact of MRS Sequence Implementation and Fitting Models on Test-Retest Brain ¹H-MRS Quantification Accuracy ROCIO ARTIGAS, Afonso Simões, Ana Xavier, Viola Hollestein, Pablo Irarrazaval, David Norris, José Marques Biomedical Imaging Center. Pontificia Universidad Católica de Chile, Santiago, Chile, Chile Impact: This study demonstrates that optimizing sequence design and spectral-fitting (including fieldmap-correction or water-referencing) significantly improves metabolite quantification reliability in challenging brain regions with high B0 inhomogeneities, like hippocampus, advancing the potential of single-voxel ¹H-MRS for robust clinical and research applications.
		469-03-005. Value of MRS for fetal brain development in mid-to-late pregnancy Dejuan Shan, Lianxiang Xiao, Jiaxiang xin, Jinfeng Cao, Maobo Wang Shandong Provincial Maternal and Child Health Care Hospital Affiliated to Qingdao University, Ji Nan, China Impact: This study confirms an obvious correlation between fetal brain metabolites and gestational age, supporting the feasibility of establishing standard values for these metabolites in fetal brain assessment.
		469-03-006. Deep Learning water-unsuppressed ultra-high field MRSI for simultaneous metabolic, susceptibility, and myelin water imaging Paul Weiser, Jiye Kim, Jongho Lee, Amirmohammad Shamaei, Gulnur Ungan, Malte Hoffmann, Antoine Klauser, Berkin Bilgic, Ovidiu Andronesi Massachusetts General Hospital and Harvard Medical School, Boston, United States of America Impact: This approach enables fast high-resolution simultaneous metabolic, susceptibility, and myelin imaging at ultra-high field, reducing reconstruction and quantification time from 10h-48h to 7min-52min, which is feasible for clinical applications.
		469-03-007. In Vivo Monitoring of Glycolysis substrates by Enzyme-Mediated Phosphate Transfer (EMPT)-MRS Claudia Quattrociocchi, Cecilia Fiorucci, Steffen Ringgaard, Christoffer Laustsen, Giulia Vassallo, Francesca Garello, Michael Væggemose, Esben Hansen, Silvio Aime, Daniela Delli Castelli University of Turin, Turin, Italy Impact: The EMPT-MRS method enhances sensitivity to key metabolic processes, enabling differentiation between healthy and pathological metabolism within clinically feasible scan times. By characterizing endogenous bioenergetic fluxes, it shows potential to advance metabolic imaging biomarkers and improve cancer diagnosis and monitoring.
		469-03-008. Detection of Macromolecular Changes in Brain Tumor Using 1H FID MRSI at 7T Mahrshi Jani, Andrew Wright, Yeison Rodriguez, Kimberly Chan, Anke Henning University of Texas Southwestern Medical Center, Dallas, United States of America Impact: This study examines alterations in macromolecular intermediates in glioma by comparing tumor regions with contralateral tissue. Regional shifts provide an indirect yet integrative readout of metabolic reprogramming, including branched-chain amino acid pathway upregulation, reflecting biochemical changes within the tumor microenvironment.
		469-03-009. CloudBrain-VisualAI: A Web Visualized Deep Learning Programming for Cloud Magnetic Resonance Image and Spectroscopy Analysis Yirong Zhou, Yanghuang Wu, Hao Gong, Jianshu Chen, Dicheng Chen, Tao Gong, Mengtian Lu, Lianxin Xie, Ji Qi, Zhiguo Huang, Ruibin Ma, Qin Xu, Fan Yang, Di Guo, Xiaobo Qu Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China Impact: CloudBrain-VisualAI simplifies deep learning for medical imaging by providing a web-based platform. It enables non-experts to develop and deploy deep learning models for MRI and MRS analysis, accelerating research and clinical applications in a collaborative environment.
		469-03-010. Short echo 1H CSI of Downfield Metabolites in the Human Brain at 7T and 3T Slimane Tounekti, Sophia Swago, Mark Elliott, RAVI PRAKASH REDDY NANGA, neil wilson, Ravinder Reddy, Walter Witschey University of Pennsylvania, Philadelphia, United States of America Impact: In vivo CSI of downfield metabolites will enable precise quantification of their concentration and spatial distribution, providing regionally localized biomarkers of brain cellular metabolism and overall neural health.
		469-03-011. Chemical-shift Artifacts Suppression for Non-Fat-Suppressed Turbo-Spin-Echo Imaging at 5T Jingzhe Liu, Puzheng Wen, Fan Liu, Hua Guo The First Affiliated Hospital of Tsinghua University, Beijing, China Impact: Our investigation confirmed the chemical-shift artifact in non-fat-suppressed TSE on 5T. Imaging protocols developed in this study can allow us to take full advantage of the high signal-to-noise ratio of 5T for high-resolution, high-fidelity musculoskeletal imaging.
		469-03-012. Research on the Application of Slice-Selective Gradient Reversal Technique at 5.0T Ultra-High Field in Lumbar Spine MR Imagin Zhensong Wang, Jie Gan, Shuo Chen Shandong Provincial Third Hospital, Shandong Universtiy, Jinan, China Impact: SSGR enhances image quality in ultra-high-field MRI, improving diagnosis of musculoskeletal diseases. Its ability to eliminate artifacts and improve fat suppression may extend ultra-high-field MRI applications to other anatomical regions, impacting clinical practice and patient outcomes.
		469-03-013. Neurometabolic Changes in Traumatic Brain Injury patients: Establishing and Enabling 1H-MRS Data Acquisition and Analysis Siddharth Singh, Saipavitra Murali-Manohar, Maninder Singh, BV Rathish Kumar, Sudhir Pathak, Ekta Dixit, Anit Parihar, Anil Chandra, Manoj Kumar, Bal Ojha, Durgesh Dwivedi King George's Medical University, Lucknow, India Impact: We demonstrated a standardized analysis workflow enabling ¹H-MRS quantification in heterogenous traumatic brain injury. This step supports development of metabolic biomarkers for brain injury characterization and recovery monitoring in TBI patients.
		469-03-014. Impact of Unsupervised Learning-Based Denoising on Event-Related MR Spectroscopic Data: Evidence from the vmPFC Michael Burke, Harleen Chhabra, Michael Nitsche, Erhan Genc Leibniz Research Centre for Working Environment and Human Factors, Dortmund, Germany Impact: This work demonstrates that unsupervised denoising substantially improves fMRS data quality and reliability. The approach advances event-related spectroscopy, facilitating detection of rapid neurochemical dynamics in cognitive and clinical neuroscience applications.
		469-03-015. Age- and Region-Specific Water Relaxation Atlases for Metabolite Concentration Correction: Comparison of QALAS and DESPOT Abdelrahman Gad, Yulu Song, Gizeaddis Simegn, Saipavitra Murali-Manohar, Zahra Shams, Helge Zöllner, Christopher Davies-Jenkins, Dunja Simicic, Aaron Gudmundson, Emily Carter, Can Ceritoglu, J Tilak Ratnanather, Vivek Yedavalli, Georg Oeltzschner, Borjan Gagoski, Eric Porges, Richard AE Edden Johns Hopkins University School of Medicine, Baltimore, United States of America Impact: Reliable water relaxation reference values are critical for accurate metabolite quantification using MRS. This study presents a new water relaxation atlas based on QALAS mapping methods and compares it to a previous atlas based on DESPOT.