Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
364-02-008 ISMRM Abstract

Simulation-Based Deep Learning Framework for Mapping Cell Size Distribution from GESFIDE Susceptibility-Contrast MRI

Primary: Neuro - Tumors
Secondary: Contrast Mechanisms - Microstructure
364-02-008 · Innovations in Brain Tumor Imaging: Quantitative MRI, Radiogenomics, and Deep-Learning Approaches · Monday, 11 May, 9:15 AM–10:10 AM · Digital Posters Row E
Keywords: Deep learning Glioblastoma Cell-size distribution GESFIDE Susceptibility-contrast MRI
Accepted
Natenael Semmineh1,2,3, Indranil Guha1,2,3, Jerrold Boxerman4,5, C. Chad Quarles1,2,3
1Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, United States of America
2Cancer Neuroimaging Research Program, The University of Texas MD Anderson Cancer Center, Houston, United States of America
3Cancer Neuroscience Program, The University of Texas MD Anderson Cancer Center, Houston, United States of America
4Department of Diagnostic Imaging, Rhode Island Hospital, Providence, United States of America
5Alpert Medical School, Brown University, Providence, United States of America
Presenting Author: Puneet Kumar

Synopsis

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References

1. Zhang J et al. Clinical applications of contrast-enhanced perfusion MRI techniques in gliomas. Contrast Media Mol Imaging. 2017; 2017:7064120.
2. Hu LS et al. Integrated molecular and multiparametric MRI mapping of high-grade glioma identifies regional biologic signatures. Nat Commun. 2023; 14:6066.
3. Semmineh NB et al. An efficient computational approach to characterize DSC-MRI signals from 3D heterogeneous tissue structures. PLoS One. 2014; 9:e84764.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/364-02-008