Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition - Quantitative Imaging: MR Fingerprinting

Quantitative Imaging: MR Fingerprinting

Oral

Acquisition & Reconstruction

Tuesday, 12 May 2026

Meeting Room 1.60

16:00 - 17:50

Moderators: Johan Berglund & Emilie Sleight

Session Number: 408-04

No CME/CE Credit

Similar Sessions

This session covers advances in Magnetic Resonance Fingerprinting and applications.

Skill Level: Basic,Intermediate,Advanced

16:00		408-04-001. Diffusion mapping insensitive to relaxation using Multi-Echo BURST Fingerprinting Simran Kukran, Andrew Dupuis, Madison Augelli, Imraj Singh, Anuj Sharma, Jessie EP Sun, Rasim Boyacioglu, Shaihan Malik, Mark Griswold Case Western Reserve University, Cleveland, United States of America Impact: Multi-Echo BURST Fingerprinting could enable reduced model complexity in complex diffusion frameworks given varied diffusion weighting in a single acquisition, insensitive to relaxation. Maximum effective b values of more than 800s/mm² are achieved with a 11.74mT/m diffusion encoding gradient.
16:11		408-04-002. ASTRAD: Acquisition-Sequence and k-Space Trajectory Co-Design for Accelerated MR Fingerprinting Xiang Wang, Qingping Chen, Chenyang Liu, Lu Wang, Peilin Wang, Xiangyu Zhou, Yao Pu, Maximilian Gram, Tom Griesler, Yimin Ni, Peng Cao, Sebastian Littin, Jing Cai, Maxim Zaitsev, Tian Li The Hong Kong Polytechnic University, Hong Kong, Hong Kong Impact: ASTRAD enables fast, accurate quantitative MRF without dedicated reconstruction, improving robustness at high acceleration and demonstrating readiness for routine brain and early-stage abdominal studies. Scanner-transferable parameters lower deployment barriers, supporting broader clinical adoption and standardized multicenter validation of quantitative MRI.
16:22		408-04-003. Fully 3D Unrolled Magnetic Resonance Fingerprinting Reconstruction via Staged Pretraining and Implicit Gridding Yonatan Urman, Mark Nishimura, Daniel Abraham, Xiaozhi Cao, Kawin Setsompop Stanford University, Stanford, United States of America Impact: Rapid and accurate whole-brain quantitative mapping at 1 mm through a fully end-to-end 3D unrolled reconstruction made possible by an efficient progressive training strategy and implicit-gridding–based data-consistency, supporting acquisitions as short as 30-second with reconstruction time under 15 seconds.
16:33		408-04-004. Online 3D-MRF for Population-Scale Quantitative Neuroimaging: A 3,849-Exam Clinical Deployment Andrew Dupuis, Rasim Boyacioglu, Yong Chen, Jeffrey Sunshine, Chaitra Badve, Mark Griswold Case Western Reserve University, Cleveland, United States of America Impact: This 3,849-exam clinical deployment demonstrates population-scale quantitative relaxometry through automated online 3D-MRF integrated with PACS and EHR systems. Linking tissue parameter maps with clinical metadata enables systematic retrospective cohort analysis previously limited by manual workflows and qualitative imaging.
16:44		408-04-005. MR Fingerprinting for All-In-One Parametric Mapping and Multi-Contrast Synthetic LGE in Hypertrophic Cardiomyopathy Zexuan Liu, Calder Sheagren, Nicole Seiberlich, Jacob Richardson, Imran Rashid, William Truesdell, Jesse Hamilton Biomedical Engineering, University of Michigan, Ann Arbor, United States of America Impact: Magnetic Resonance Fingerprinting offers a unified platform for efficient T1 and T2 mapping and synthetic multi-contrast (bright-blood, dark-blood, and patient-optimized) LGE imaging for evaluation of hypertrophic cardiomyopathy. Furthermore, native MRF T1/T2 maps show promise for identifying diffuse abnormalities in HCM.
16:55		408-04-006. Cardiac MRF Optimization at 3T Using Rosette Trajectories and MT Modeling in OpenMRF Sydney Kaplan, Evan Cummings, Tom Griesler, Maximilian Gram, Nicole Seiberlich University of Michigan, Ann Arbor, United States of America Impact: Optimizing cMRF at 3T with rosette trajectories and MT modeling yields robust quantitative mapping, while the OpenMRF framework enables sequence sharing, paving the way for standardized, reproducible myocardial mapping across MRI systems.
17:06		408-04-007. Novel Quantum-Sensing Hyperpolarized 13C Molecular MR Fingerprinting Applications in Abdominal and Pelvic Cancers Hsin-Yu Chen, Charlie Wang, Minjie Zhu, Daniel Gebrezgiabhier, Michael Ohliger, Xiaoxi Lui, Ivan de Kouchkovsky, John Kurhanewicz, Robert Bok, Rahul Aggarwal, Peder Larson, Jeremy Gordon, Zhen Wang, Dan Vigneron University of California San Francisco, San Francisco, United States of America Impact: In human abdominal and pelvic cancer applications, novel quantum-sensing hyperpolarized ¹³C MR fingerprinting techniques offered improved sensitivity that can be leveraged to more accurately assess pyruvate, lactate and alanine metabolic pathophysiology and delineate finer features.
17:17		408-04-008. Fast, reliable, and high-quality χ-separation via EPTI and MRF Hwihun Jeong, Jongho Lee, Yogesh Rathi, Michael Zeineh, May Htwe Han, Kawin Setsompop, Nan Wang Brigham and Women's Hospital and Harvard Medical School, Boston, United States of America Impact: χ-separation with EPTI + MRF provides a fast, reliable, COSMOS-quality χ-separation map, overcoming the reliability issues of deep learning while maintaining clinical efficiency. It enables accurate quantitative neuroimaging of complex pathologies like MS within feasible scan time.
17:28		408-04-009. 3D In-Vivo Brain MR Fingerprinting Optimized for 100mT Gabriel Zihlmann, Najat Salameh, Mathieu Sarracanie University of Aberdeen, Aberdeen, United Kingdom Impact: We demonstrate optimized, robust, gradient-spoiled-MRF at 100 mT with 2.55-mm isotropic resolution and present first 3D in-vivo results in the human brain. Particular emphasis was placed on the effects on $B_1^+$ variability on optimized schedule candidates for best performance.
17:39		408-04-010. UBNAno: Physics-Informed Brain Lesion Synthesis for Generalizable Anomaly Detection Eunate Alzaga Goñi, Rhea Adams, Shahrzad Moinian, Walter Zhao, Pew-Thian Yap, Evan Calabrese, Dan Ma Duke University, Durham, United States of America Impact: Existing brain lesion detection methods are not generalizable across diverse pathologies, MR contrasts, and scanner configurations without requiring labeled clinical data. We introduce UBNAno, a physics-informed approach for robust brain lesion detection across institutions with varying imaging protocols.