Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition - Neuroinflammation: Metabolites, Function, and AI

Neuroinflammation: Metabolites, Function, and AI

Digital Poster

Neuro A

Thursday, 14 May 2026

Digital Posters Row J

08:30 - 09:25

Session Number: 669-01

No CME/CE Credit

Similar Sessions

This digital poster session highlights emerging methods for characterizing tissue biology, metabolism, and clinical outcomes in multiple sclerosis using advanced MRI contrasts and data-driven approaches. Presentations span innovative techniques such as CEST MRI, MR spectroscopy, MR fingerprinting, calibrated fMRI, ultra-high-field imaging, and AI-accelerated acquisition, alongside machine learning models for disease classification and prognosis. Together, the posters emphasize the integration of biophysical imaging, computational innovation, and multimodal biomarkers to improve understanding of lesion heterogeneity, neuroinflammation, metabolic dysfunction, and individualized outcome prediction in MS.

		669-01-001. Assessing heterogeneity of Multiple Sclerosis lesions by APTw CEST MRI: Insights into lesion biology Evgeny Golubtsov, Nico Sollmann, Isabelle Schmid, Noah Enbergs, Julius Kernbach, Strahinja Stojanovic, Daniel Schwarz, Meinrad Beer, Johannes Rosskopf, Deborah Erhart, Stefanie Jung, Patrick Liebig, Hayrettin Tumani, Laura Jäger, Klemens Ruprecht, Wolfgang Wick, Daniel Paech, Sabine Heiland, Martin Bendszus, Michael Breckwoldt, Kianush Karimian-Jazi University Hospital Heidelberg, Heidelberg, Germany Impact: APTw-CEST MRI enables noninvasive assessment of intralesional heterogeneity in MS, improving lesion characterization and disease monitoring. This biomarker may guide treatment decisions, enhance understanding of lesion dynamics, and stimulate future research on molecular imaging markers in neuroinflammation.
		669-01-002. Quasi-steady-state CEST for rapid and quantitative lesion detection in multiple sclerosis at 3 T Huabin Zhang, Ziyan Wang, Shihao Zeng, Jiawen Wang, PEI CAI, Ka Fung Henry Mak, Koon Chan, BenSheng Qiu, Jianpan Huang The University of Hong Kong, Hong Kong, Hong Kong Impact: QUASS CEST reduced the scan time by 46.7% while maintaining high MS lesion contrast, demonstrating strong potential for MS diagnosis. It delivers rich, quantitative information on lesion pathology, offering valuable molecular insights for MS diagnosis and monitoring.
		669-01-003. Transient Nuclear Overhauser Effect (tNOE) MRI Detects Diffuse Lipid Related Pathology in Multiple Sclerosis Blake Benyard, Dushyant Kumar, Anshuman Swain, neil wilson, Sunil Khokhar, Paul Jacobs, Nikka Bakhtiar, Matthew Schindler, Mohammad Haris, Ravinder Reddy University of Pennsylvania, Philadelphia, United States of America Impact: 3D-tNOE MRI offers a sensitive, lipid-specific biomarker for myelin pathology. It could be applied clinically to longitudinally track the pathological changes in MS and other neurodegenerative disorders, as well as to evaluate treatment efficacy.
		669-01-004. Clinical Validation of Automated MR Fingerprinting in Multiple Sclerosis: A Pilot Study at 1.5T and 3.0T Andrew Dupuis, Sree Gongala, Rasim Boyacioglu, Ari Blitz, Jessie EP Sun, Mark Griswold, Chaitra Badve Case Western Reserve University, Cleveland, United States of America Impact: This first demonstration of online whole-brain MRF integrated in MS clinical workflows across 1.5T/3.0T scanners eliminates manual processing and ROI placement. Automated analysis with PACS integration and field-strength-specific tissue characterization supports feasibility of objective monitoring for MS-related injury.
		669-01-005. Assessing Cerebral Oxygen Metabolism in Multiple Sclerosis Using Breath-Hold single Calibrated fMRI Davide Di Censo, Elizabeth Fear, Alessandra Caporale, Stefano Censi, Francesca Graziano, Emma Biondetti, Lucie Chalet, Giulia Rocco, Manuela Carriero, Sara Pomante, Eleonora Patitucci, Fabrizio Fasano, DOMENICO ZACA', Michael Germuska, Antonio Chiarelli, Valentina Tomassini, Richard Wise University 'G.d'Annunzio' of Chieti-Pescara, Chieti, Italy Impact: This study demonstrates the potential of breath-hold calibrated-fMRI as a practical, non-invasive tool to detect early metabolic dysfunction in multiple sclerosis, offering an accessible method for monitoring neurovascular impairment which may contribute to disease monitoring beyond conventional structural imaging.
		669-01-006. Thalamic MRS Reveals Neuroinflammatory Profiles in Neuropathic Pain and Opioid Use Among People with HIV Atilla Gonenc, Minhae Kim, Shibani Mukerji, Marco Loggia, Eva-Maria Ratai Massachusetts General Hospital, Boston, United States of America Impact: Thalamic ¹H-MRS reveals neuroinflammatory profiles in virally suppressed people with HIV, linked to neuropathic pain but independent of neuronal loss, supporting a potential additive effect across clinical phenotypes and demonstrating MRS sensitivity to subtle, region-specific neuroinflammation.
		669-01-007. Acceleration of Brain 3D Structural MRI with Compressed Sensing Artificial Intelligence in Multiple Sclerosis Chunjie Guo, Dan Liao, Zicheng Wang, simin yang, Zhiwei Shen, Tao Jin, Huimao Zhang the First Hospital of Jilin University, Changchun, China Impact: Accelerated 3D MRI using CS-AI preserves image quality and enables high-resolution visualization of the brain, in conjunction with ON, and UCSC. These methods support faster, more efficient MS evaluation, improving integration into clinical workflows.
		669-01-008. Predicting Multiple Sclerosis with Multimodal Deep Learning Integrating Lesion and Normal-Appearing White Matter Information Jiajian Ma, Valentin Stepanov, Wushuang Rui, Hsuan-Chih Chen, Michael Lan, Jenny Chen, Timothy Liu, Ingrid Littig, Roshi Patel, Matthew Breen, Matthew Lee, Katharina Eikermann-Haerter, Dmitry Novikov, Kimberly ONeill, Els Fieremans, Yiqiu Shen New York University, New York, United States of America Impact: Deep learning models integrating structural and diffusion MRI can detect multiple sclerosis by utilizing signals from both lesions and normal-appearing white matter. This finding may inspire new investigations into subclinical tissue damage and improve early, more accurate MS diagnosis.
		669-01-009. Fuse Cortical Morphometrics and 3D MRI Radiomics to Predict Disability and cognitive Progression in RRMS: A Multicenter study Zhuo Wang, Jing Zhang, Kai AI The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, China Impact: We developed two clinically actionable nomograms fusing cortical morphometric and radiomic features from 3D MRIs to provide individualized prediction of disability progression and cognitive worsening in RRMS. It enables early identification of high-risk patients, facilitating personalized treatment strategies.
		669-01-010. Longitudinal 7T MR Spectroscopic Imaging of Early Relapsing-Remitting Multiple Sclerosis Before and After Therapy Initiation Anna Zöchner, Wolfgang Bogner, Assunta Dal-Bianco, Bernhard Strasser, Lukas Hingerl, Rebeka Rumbak, Paulus Rommer, Eva Niess Medical University of Vienna, Vienna, Austria Impact: Elevated mI/tNAA and reduced tNAA/tCr in normal-appearing white matter are detectable in early-stage, untreated RRMS and persist after one year of therapy, suggesting they may serve as early biomarkers of the disease and aid in monitoring progression and guiding interventions.
		669-01-011. Predicting Neurocognitive Disorder development in Multiple Sclerosis using Artificial Intelligence on MRI and Clinical Data Loredana Storelli, Damiano Mistri, Alice Mastropasqua, Marta Grosselle, Paolo Preziosa, Giulia Mazzetti, Lucrezia Rossi, Paola Valsasina, Elisabetta Pagani, Massimo Filippi, Maria A. Rocca IRCCS San Raffaele Scientific Institute, Milan, Italy Impact: This study demonstrates the potential of explainable AI to accurately predict cognitive decline in MS, offering a step toward personalized monitoring and early intervention based on clinically meaningful and biologically grounded markers.
		669-01-012. Polyvinyl Alcohol Cryogels Doped with Mn²⁺/Ni²⁺: Phantom Study Towards a Multiple Sclerosis Brain Phantom Qizhe Xu, Florian Kehrein, Frank Zoellner Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany Impact: Polyvinyl alcohol (PVA) cryogel tube phantoms doped with Mn²⁺/Ni²⁺ deliver tissue-like strength and precisely tunable T₁/T₂, providing a stable, reproducible platform toward a realistic multiple sclerosis (MS) brain phantom and quantitative MRI calibration and testing.
		669-01-013. Differentiating MS and NMOSD from the Mimics: An Explainable Deep Learning Approach on 3D Brain FLAIR MRI Chunjie Guo, Ziqi Zhao, Yinglu Sun, shuting xu, Feifei Deng, Zhuo Wang, Yan Wang, Tao Jin, Huimao Zhang the First Hospital of Jilin University, Changchun, China Impact: These findings can accelerate the clinical diagnosis of MS and NMOSD from the mimics. There is a significant overlap between the most active regions highlighted by deep learning and the clinically relevant regions derived from physicians' domain knowledge.
		669-01-014. Central Vein Sign Detection: Evaluating the Impact of Lesion Size and Transfer Learning Tommaso Di Noto, Till Huelnhagen, Matej Kudrna, Manuela Vaneckova, Tobias Kober, Jonas Richiardi, Lynn Daboul, Bryan Quah, Jin Jin, Omar Al-Louzi, Daniel Reich, Pascal Sati, Bénédicte Maréchal, Jonathan Disselhorst Siemens Healthineers International AG, Lausanne, Switzerland Impact: This research advances automated detection of Central Vein Sign, a key multiple sclerosis marker, offering insights that may improve diagnosis and consistency across hospitals. It highlights how technical choices, like lesion size, can affect diagnosis and subsequent patient care.
		669-01-015. Evaluating full-fit and single-point quantitative magnetization transfer MRI methods in the lumbosacral spinal cord at 3T Gabriella Dunay, Alicia Cronin, Aimee Salakhov, Seth Smith, Kristin O'Grady Vanderbilt University Medical Center, Nashville, United States of America Impact: Comparing PSR derived from robust full-fit and clinically feasible single-point qMT in healthy controls is essential to validate constrained single-point analysis for future higher-resolution detection of MS demyelination and its correlation with lower-extremity symptoms commonly seen in the disease.