Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition

Mapping Liver and Pancreas Health

Oral

Body

Monday, 11 May 2026

Hall 1B

16:10 - 18:00

Moderators: Satoshi Kobayashi & Aaryani Tipirneni-Sajja

Session Number: 302-04

No CME/CE Credit

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This session highlights advances in liver and pancreatic MRI and PET/MR, featuring AI-driven diagnostics, non contrast and accelerated imaging, quantitative biomarkers, and emerging contrast agents. These are advances that will help in the management of patients with liver fibrosis, HCC, pancreatic cancer, pancreatic cystic lesions.

Skill Level: Intermediate

16:10		302-04-001. Mapping Liver and Pancreas Health: Integrating Clinico-Pathological Insights with Advanced Imaging Technology Satoshi Kobayashi Kanazawa University Graduate School of Medical Sciences, Kanazawa, Japan
16:21		302-04-002. A Liver-Specific Manganese-Based Contrast Agent Detects MASH by Dual PET/MR Imaging Mohammad Ghaderian, Remy Chiaffarelli, Laura Kübler, Yasmin Al-Moufleh, Julian Brock, Jan Kretschmer, Csaba Tömböly, Alexis Achacoso, Balázs Váradi, Gyula Tircsó, Andre Martins University Hospital Tuebingen, Tuebingen, Germany Impact: In this work, we introduce a safe, liver-specific Mn-based CA candidate that enables quantitative PET/MR assessment of hepatic and renal function, addressing the current lack of liver-targeted MRI agents and advancing multimodal imaging for metabolic liver disorders, such as MASH.
16:32		302-04-003. Quantitative MRI/MRE Assessment of the Liver-Pancreas-axis in MASLD: Effects of Diabetes on Tissue Composition and Mechanics Hao Wu, Caixin Qiu, Jiahui Li, Kevin Glaser, Sudhakar Venkatesh, Armando Manduca, Alina Allen, Vijay Shah, Richard Ehman, Meng Yin Mayo Clinic, Rochester, United States of America Impact: Our study highlights the potential of multiparametric liver–pancreas MRI/MRE to noninvasively quantify tissue compositional and biomechanical alterations during metabolic diseases progression and regression, providing a sensitive imaging framework for evaluating organ interactions and guiding therapeutic strategies in metabolic disorders.
16:43		302-04-004. The Value of Peritumoral Feature on 3.0T MR Susceptibility Weighted Imaging for Predicting Microvascular Invasion in HCC Yuanqiang Xiao, Haimei Chen, Jinhui Zhou, Ying Zeng, Lina Zhang, Ying Deng, Sichi Kuang, Jeremy Heilman, Kay Pepin, Jun Chen, Jin Wang The third affiliated hospital of Sun Yat-sen University, Guangzhou, China, Department of Radiology, China Impact: The percentage value of peritumoral hypointensity length on SWI was an independent predictor of MVI(AUC=0.847, NPV:92.9%) in HCC patients. Combination SWI with peritumoral hypointensity in HBP provided better performance for identifying MVI status in HCC patients (AUC=0.903, 96.9% NPV).
16:54		302-04-005. 2D Biomechanical fingerprinting of liver disease identifies a pre-fibrotic niche permissive for HCC development Gabrielle Mangin, Leon Gruenewald, Nicole Ziegengeist, Katerina Torgashov, Omar Darwish, Giacomo Annio, Valérie Paradis, Jerome Boursier, Valerie Vilgrain, Thomas Vogl, Vitali Koch, Ralph Sinkus Center for Research on Inflammation, Inserm / Université Paris Cité U1149, Paris, France Impact: 3D magnetic resonance elastography enables noninvasive biomechanical fingerprinting of liver disease, revealing a pre-fibrotic niche with early inflammation and tumor-permissive mechanics, extending elastography beyond fibrosis staging toward individualized HCC risk assessment.
17:05		302-04-006. AI-Based Detection of Liver Iron Overload and Steatosis from MRI Localizers Yura Oh, Dheerendranath Battalapalli, Marwa Ismail, Julius Heidenreich, Jitka Starekova, David Harris, Garrett Fullerton, Shreyas Vasanawala, Scott Reeder, Pallavi Tiwari, Diego Hernando University of Wisconsin - Madison, Madison, United States of America Impact: A deep learning method detects liver iron overload and steatosis from the localizers acquired at the beginning of every exam, with radiologist-level performance. This may enable immediate triage to identify patients who would benefit from same-exam specialized iron/fat quantification.
17:16		302-04-007. Deep Learning-Accelerated Non-Contrast-Enhanced Liver MRI in the Detection of Recurrent HCC After Curative Treatment San-Yuan Dong, Caixia Fu, Sheng-Xiang Rao Zhongshan Hospital, Fudan University, Shanghai, China Impact: The DL-accelerated non-contrast-enhanced MRI may serve as an alternative follow-up method which can potentially replace whole contrast-enhanced MRI and conventional non-contrast-enhanced MRI, offering improved accessibility, reduced costs, and enhanced patient safety without compromising diagnostic efficacy.
17:27		302-04-008. Early Therapy Response Assessment in Pancreatic Ductal Adenocarcinoma Using Quantitative DCE-MRI Ezinwanne Onuoha, Martin Holland, Darryl Outlaw, Midhun Malla, Mehmet Akce, Salila Hashmi, Sushanth Reddy, Bart Rose, Desiree Morgan, Grant Williams, Moh'd Khushman, Dana Cardin, Kate Frederick-Dyer, Xiaoyu Jiang, Bassel El-Rayes, Junzhong Xu, Harrison Kim The Ohio State University, Columbus, United States of America Impact: This multi-institutional study shows that qDCE-MRI with P4-based correction enables early, accurate prediction of PDAC treatment response with up to 98% accuracy, outperforming conventional biomarkers and supporting its clinical use for timely, personalized therapy decisions.
17:38		302-04-009. Guided Discussion Aaryani Tipirneni-Sajja University of Houston, Houston, United States of America