Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition - Quantitative Imaging: Relaxometry, Multiparametric, MR Fingerprinting, and Synthetic MRI

Quantitative Imaging: Relaxometry, Multiparametric, MR Fingerprinting, and Synthetic MRI

Power Pitch

Acquisition & Reconstruction

Wednesday, 13 May 2026

Power Pitch Theatre 2

13:40 - 15:16

Moderators: Jesse Hamilton & Christoph Kolbitsch

Session Number: 552-02

No CME/CE Credit

Similar Sessions

This session covers topics in quantitative imaging, including relaxometry, multi-parametric quantitative imaging, MR Fingerprinting, and synthetic MRI.

13:40		552-02-001. Automated 3D-MRF as a Single-Scan Non-Invasive Biomarker for Alzheimer's Disease Detection Andrew Dupuis, Sree Gongala, Rasim Boyacioglu, Wassim Malak, Jeffrey Sunshine, Mark Griswold, Chaitra Badve Case Western Reserve University, Cleveland, United States of America Impact: Clinical integration of automated 3D-MRF demonstrates distinct regional relaxometry patterns and multiparametric signatures differentiating AD from controls. Elevated T1/T2 detected without volume loss suggests earlier detection capability. Multi-regional classification (AUC=0.900) supports MRF as a fast, non-invasive biomarker for AD diagnosis.
13:42		552-02-002. T1 mapping Based on 3D SPACE Shows the Transfer of Oxygen into Cerebrospinal Fluid during Hyperoxia in the Healthy Brain Emma Biondetti, Davide Di Censo, Sara Pomante, Stefano Censi, Ekaterina Bliakharskaia, Manuela Carriero, Francesca Graziano, Alessandra Caporale, Antonio Chiarelli, Richard Wise University 'G.d'Annunzio' of Chieti-Pescara, Chieti, Italy Impact: We propose a non-invasive method, using 100% hyperoxia and T₁ (longitudinal relaxation time) mapping, to assess oxygen exchange between blood and cerebrospinal fluid. This could serve as a biomarker for assessing vascular permeability based on the diffusion of oxygen.
13:44		552-02-003. Physics-Informed Synthetic MRI Data Enable Accurate and Generalisable Liver Fat Quantification Using Deep Learning Ting-Yu Lin, Sergio Uribe, Zhaolin Chen, Juan Meneses Monash University, Melbourne, Australia Impact: Physics-informed synthetic MRI offers a scalable, privacy-preserving solution for training deep learning models in quantitative liver fat imaging, improving reproducibility and cross-protocol generalisability in MRI-based AI research.
13:46		552-02-004. Fast Whole-Brain Multi-Parameter Mapping with Scan-Specific, Unsupervised Hybrid-Regularized Networks Amir Heydari, Tae Hyung Kim, Yuting Chen, Yohan Jun, Abbas Ahmadi, Berkin Bilgic Amirkabir University of Technology, Tehran, Iran (Islamic Republic of) Impact: FTL-MAPLE enables whole-brain T₁, T₂^*, frequency, and proton density mapping in ~21 minutes-up to 600× faster than prior methods—while preserving accuracy. Through whole-brain training, coil-compression, parameter-specific optimization and hybrid regularization, it enables rapid quantitative MRI reconstruction.
13:48		552-02-005. Dynamic, Motion-Triggered B₀ Estimation for Retrospective SAMER Motion Correction in T₂w SWI and QSM at 3T and 7T Daniel Polak, Jeanette Deck, Josef Pfeuffer, Hongli Fan, Yimeng Lin, Daniel Nicolas Splitthoff, Bryan Clifford, Jonathan Polimeni, Lawrence Wald, Kawin Setsompop, Stephen Cauley, Nan Wang Siemens Medical Solutions USA, Inc., Malvern, United States of America Impact:* Rapid, robust, and reproducible T2w imaging is essential for tracking disease progression in MS, TBI, stroke, and Alzheimer’s disease. The proposed motion-triggered ΔB₀ estimation integrated with retrospective motion correction enables high-quality, motion-robust T2w imaging with minimal additional scan time.
13:50		552-02-006. Imaging of perivascular space abnormality in SVD with high resolution T2* at 3T Sangam Kanekar, Rommy Elyan, Jianli Wang, Anupa Ekanayake, Senal Peiris, Ran Pang, Deepak Kalra, William Jens, Prasanna Karunanayaka, Paul Eslinger, Scott Hwang, Qing Yang The Pennsylvania State University, Hershey, Pennsylvania, United States of America Impact: This research develops an MRI method to image the inflammation surrounding the PVS, which can be used as a sensitive imaging modality for neuroinflammation for clinical applications.
13:52		552-02-007. vNav-QALAS: Prospective Motion-Corrected 3D Multiparametric Mapping with Integrated Volumetric Navigators Yohan Jun, Yuting Chen, Xingwang Yong, Seonghwan Yee, Robert Frost, Andre van der Kouwe, Michael Gee, Patricia Grant, Borjan Gagoski, Berkin Bilgic, Camilo Jaimes Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, United States of America Impact: The vNav-QALAS technique enables accurate, motion-robust 3D T₁ and T₂ mapping without external tracking hardware, enhancing quantitative imaging reliability for uncooperative or clinical populations and advancing practical, high-quality multiparametric MRI in real-world scanning conditions.
13:54		552-02-008. Accelerated Cartesian Dictionary-based Simultaneous Myocardial T1, T2, and T1ρ Mapping Using Combined T2/T1ρ Preparation Zhenfeng Lyu, Hongzhang Huang, Qinfang Miao, Hanxi Liao, Huili Yang, Liwei Hu, Yumin Zhong, Peng Hu, Haikun Qi ShanghaiTech University, Shanghai, China Impact: The proposed mix-T2T1ρ preparation effectively reduces the acquisition time of the free-breathing myocardial multi-parametric mapping technique, enabling simultaneous T1/T2/T1ρ quantification in 11 heartbeats, advancing more efficient and quantitative cardiac MR.
13:56		552-02-009. High-Resolution, Contrast-Efficient 3D Multitasking MRI for Assessment of Right Ventricular Fibrosis and Function Haoran Li, Xinheng Zhang, Hsu-Lei Lee, Leon Riehakainen, João Pedro Torres Neiva Rodrigues, Yibin Xie, Ivan Cokic, Hsin-Jung Yang, Suvai Gunasekaran Cedars-Sinai Medical Center, Los Angeles, United States of America Impact: This high-resolution, contrast-efficient Multitasking sequence enables RV fibrosis visualization and simultaneous functional assessment. It has potential to guide future studies on RV remodeling and staging of RVD.
13:58		552-02-010. Robust T2 Mapping at 7T using Bloch Simulation Based Echo Modulation Curve (EMC) with Reduced Flip Angle Jianxun Qu, Yanglei Wu, anning li MR Research Collaboration Team, Siemens Healthineers Ltd. Shanghai, Shanghai, China Impact: This study shows that echo modulation curve (EMC) is superior to conventional mono-exponential fitting in UHF T2 mapping. The method is reliable and SAR-efficient, making accurate tissue quantification more feasible and facilitating development of robust quantitative biomarkers at UHF.
14:00		552-02-011. Improving knee articular cartilage T2 repeatability with optimized open-source Multi-Spin Echo sequences Tiago Timoteo Fernandes, Andreia Gaspar, Amir Esrafilian, Jochen Schmidt, Jose Coelho, Vasco Mascarenhas, Sairam Geethanath, Rita Nunes Institute for Systems and Robotics – Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal Impact: Open-source accelerated Multi-Spin Echo (MSE) T2 mapping sequences were optimized for knee articular cartilage using the Cramer-Rao Lower Bound formalism. Improved repeatability, with and without repositioning, was demonstrated both in vitro and in vivo against a vendor-MSE gold standard sequence.
14:02		552-02-012. Accelerated and Noise-Robust Carotid T1–T2 Mapping Using Deep Learning Reconstruction Deniz Karakay, Brian Toner, Arunbalaji Pugazhendhi, Kevin Johnson, Kirsten Concha-Moore, Marina Ferguson, Eze Ahanonu, Ute Goerke, Dimitris Mitsouras, Jonas Schollenberger, David Saloner, Seong-Eun Kim, John Roberts, J. Rock Hadley, Dennis Parker, Scott McNally, Gerald Treiman, Yibin Xie, Debiao Li, Kim-Lien Nguyen, Vibhas Deshpande, Herman Morris, J. Kevin DeMarco, Craig Weinkauf, Ali Bilgin, Maria Altbach University of Arizona, Tucson, United States of America Impact: Physics-guided deep‑learning reconstruction of highly undersampled radial qMRI data delivers rapid and motion‑robust carotid T1/T2 maps for the non-invasive characterization of atherosclerotic plaque components that can be used to assess risk of rupture.
14:04		552-02-013. Quantitative Imaging of Ultrashort T2 Components in the Brain using Multiple Echo Times bSSFP UTE MRI Xin Shen, uzay emir, Peder Larson, Jiang Du University of California, San Diego, United States of America Impact: This study quantifies ultrashort T2 components using bSSFP-UTE with varying minimum echo times, yielding fraction values across different ultrashort T2 ranges. Myelin maps can be generated with reduced contamination from other nonaqueous signals.
14:06		552-02-014. Analytical PDFF, water T1, and water T2 quantification using dual-echo phase-cycled bSSFP Nils Plähn, Joseph Woods, Eva Peper, Matteo Tagliabue, Jessica Bastiaansen University of Bern, Bern, Switzerland Impact: The use of dual-echo phase-cycled bSSFP can simultaneously quantify PDFF, water T1, and water T2. The absence of T1 bias in PDFF quantification allows for higher RF excitation angles, increasing the signal-to-noise ratio of the imaging data.
14:08		552-02-015. Cardiac T1-rho Dispersion Mapping Revisited Vincent Vousten, Maximilian Fuetterer, Sebastian Kozerke University and ETH Zürich, Zürich, Switzerland Impact: While our results suggest limited measurable dispersion (with respect to realistic intra-subject standard deviation), further studies in pathological cases, in particular scar tissue, are warranted to consolidate our findings.
14:10		552-02-016. Improved Magnetic Resonance Fingerprinting with Optimized RF Phase Modulation Christopher Keen, Tom Griesler, Nicole Seiberlich, Yun Jiang University of Michigan, Ann Arbor, United States of America Impact: The improvement in Magnetitic Resonance Fingerprinting (MRF) T₂ accuracy, precession, and repeatability and the reduction in scan time enabled by joint flip angle and RF phase optimization is broadly applicable and enables wider adoption of advanced quantitative imaging techniques.
14:12		552-02-017. Synthetic DENSE Data Generation Using Physics-Based Simulation and Conditional DDPM for Improved Echo Suppression Yuxiao Wu, Shu-Fu Shih, Jun Lyu, Tengyue Zhang, Siyue Li, Sile Wang, Kim-Lien Nguyen, Xiaodong Zhong David Geffen School of Medicine, University of California Los Angeles, Los Angeles, United States of America Impact: We implemented a DL framework for synthesizing realistic DENSE data from simulated DENSE signal. We demonstrated that synthesized DENSE data can advance deep learning-based artifact suppression for improved DENSE quality.
14:14		552-02-018. Time-efficient MP2RAGE for T1 imaging and mapping using Poisson disc undersampling and deep learning based reconstruction Mauro Costagli, Emilio Cipriano, Federico Palmacci, Tom Hilbert, Thomas Yu, Graziella Donatelli, DOMENICO ZACA', Lucio Castellan, Matteo Pardini, Matilde Inglese, Fabrizio Levrero, Luca Roccatagliata, Gian Franco Piredda University of Genoa, Genoa, Italy Impact: The proposed MP2RAGE enables high-quality 3D T1w imaging and quantitative T1-mapping in about 2 minutes, thus facilitating the introduction of MP2RAGE in clinical MRI protocols.