Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition - Quantitative and Multi-Contrast MRI: Methods and Applications

Quantitative and Multi-Contrast MRI: Methods and Applications

Oral

Contrast Mechanisms

Thursday, 14 May 2026

Meeting Room 1.40

13:40 - 15:30

Moderators: Marcelo Zibetti & GAURAV RAJ

Session Number: 607-02

No CME/CE Credit

Similar Sessions

This session highlights recent advances in quantitative and multi-contrast MRI, spanning technical development and emerging applications.

13:40		607-02-001. Fat/water separation at 3 T and 7 T using a 3D radial sequence with quasi-continuous echo times Matthias Rohe, Katharina Tkotz, Armin NAGEL, Stefan Sommer, Max Brockmüller, Florian Knoll, Michael Uder, Nico Egger, Frederik Laun, Tobias Wilferth Uniklinikum Erlangen, Erlangen, Germany Impact: Quasi-Continuous acquisition of the fat/water phase evolution allows for a significantly increased temporal resolution when compared to conventional Dixon approaches. This results in a reliable and robust interpretation of fat and water, which benefits especially measurements at 7T and above.
13:51		607-02-002. Flip Angle Modulation with Extended Readout for Co-Localized PDFF, R2, and T1-Weighted Images Jiayi Tang, Daiki Tamada, Amirhossein Roshanshad, Jitka Starekova, Jon-Fredrik Nielsen, Maxim Zaitsev, Scott Reeder, Diego Hernando University of Wisconsin - Madison, Madison, United States of America Impact:* Our proposed method enables concurrent, free-breathing proton-density fat fraction (PDFF) and R2* mapping, and T1-weighted imaging. This may expedite clinical workflows for liver exams and improve evaluation of both diffuse liver diseases (eg. MASLD, iron overload) and focal liver lesions.
14:02		607-02-003. All-in-one Multiparametric T1, MPF, and QSM Mapping of the Human Brain at 3T and 7T using Deep Learning Reconstruction Thomas Troalen, Anita Masliah, Hugo Dary, Thomas Yu, Josef Pfeuffer, Patrick Liebig, Maxime Guye, Guillaume Duhamel, Olivier Girard, Lucas Soustelle Siemens Healthcare SAS, Courbevoie, France Impact: We present a multiparametric protocol for fast quantification of apparent T₁, macromolecular proton fraction and susceptibility at 3T and 7T. Enhanced by deep learning-based reconstruction, it enables highly accelerated biomarkers’ estimation within clinical acquisition time and spatial resolution requirements.
14:13		607-02-004. Mechanical Properties of Repetitive Head Impacts in Soccer Players Using Magnetic Resonance Elastography Teah Serani, Alma Davidson, Ariana Olivares, Adriana Dipple, Orion Valentine, Finn Myrick, Roman Fleysher, Michael Lipton, Grace McIlvain Columbia University, New York, United States of America Impact: Repetitive head impacts in soccer are associated with increased brain stiffness measured using MR elastography, suggesting microstructural changes too subtle to detect with conventional neuroimaging. These findings may inform monitoring, interventions, and help identify microstructural substrates underlying cognitive outcomes.
14:24		607-02-005. Real-time nonlinear inversion of magnetic resonance elastography with operator learning and spatially-adaptive normalization Juampablo Heras Rivera, Caitlin Neher, Mehmet Kurt University of Washington, Seattle, United States of America Impact: This framework rapidly predicts heterogeneous tissue properties and represents progress towards real-time high-fidelity MRE in a clinical diagnostic setting, broadening the diagnostic capability of rapid inversion beyond the liver to more complex organs such as the brain.
14:35		607-02-006. A Novel MR Elastography Device for Multiple Preclinical Applications: Design, Validation, and Performance Evaluation Gabrielle Mangin, Hannah Fels-Palesandro, Valeria BISIO, Inas H Faris Al Azzawi, Asma Boumaza, Marguerite DUCAMP, Roland Zerelles, Giacomo Annio, Katharina Schregel, Ralph Sinkus Center for Research on Inflammation, Inserm / Université Paris Cité U1149, Paris, France Impact: This standardized dual-coil MRE device enables reproducible, high-frequency biomechanical imaging from in vivo rodents to microscale 3D models. Its modular, low-heat design improves data comparability across studies and supports translational research in oncology, regenerative medicine, and tissue engineering.
14:46		607-02-007. 3D Quasi-static elastography on physiological timescales David G.J. Heesterbeek, Max H.C. van Riel, Ray Sheombarsing, Tristan van Leeuwen, Martijn Froeling, Cornelis van den Berg, Alessandro Sbrizzi University Medical Center Utrecht, Utrecht, Netherlands Impact: This proof-of-principle work introduces a noise-robust 3D quasi-static elastography framework enabling reproducible soft tissue stiffness characterization from displacement fields at physiological timescales. As no boundary information is required, it potentially enables elastography of internal organs driven by physiological motion.
14:57		607-02-008. Gadoquatrane: Dose selection and performance of a low dose for contrast-enhanced MRA based on non-clinical and clinical data Birte Hofmann, Gregor Jost, Chenshuang Lu, Viktoriia Fursenko, Alex Liu, Petra Palkowitsch Bayer AG, Berlin, Germany Impact: The novel tetrameric high-relaxivity gadolinium (Gd)-based contrast agent (GBCA) Gadoquatrane proved that a substantially lower Gd dose achieves similarity in non-clinical and clinical diagnostic performance in CE-MRA versus comparators based on quantitative and qualitative analyses.
15:08		607-02-009. Fluorescent Nanodiamonds MRI Contrast Agents to Protect Endothelial Cells via PI3K/Akt-Mediated Antioxidant Activity CUNJING ZHENG, Wanyufei Liu, Yanping Lin, Jinhuan Song, Jun Peng, Xuhao Zhu, zhongbiao xu, Xiumei Tian Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University,, Guangzhou, China Impact: These findings suggest that FNDs function as multifunctional MRI-visible nanozymes that alleviate oxidative stress, restore endothelial function, and delay vascular aging, offering a promising diagnostic and theranostic strategy for cardiovascular protection.
15:19		607-02-010. Towards Universal AIF Detection: Neural Network Trained on Synthetic DCE-MRI Data Samuel Barnes, Lucas Saca Loma Linda University, Loma Linda, United States of America Impact: Accurate AIF extraction improves the reliability of DCE-MRI biomarkers. Training neural networks entirely on synthetic data enables fast, consistent, and broadly generalizable AIF estimation across scanners, field strengths, acquisition protocols, and species—reducing dependence on manual selection and site-specific training data.