331-02-014 / 331-02-014 ISMRM Abstract

Zero-Shot Physics-Informed Neural Networks for Robust Multi-Vendor DSC-MRI Perfusion Quantification in Glioblastoma Patients

Primary: Neuro - Tumors

Secondary: Analysis Methods - Data Processing

331-02-014 · AI Frontiers in Image Analysis · Monday, 11 May, 1:50 PM–3:06 PM · Roof Terrace

Keywords: Glioblastoma Physics-informed Neural Networks Perfusion quantification DSC-MRI Consensus in Perfusion

Accepted

Ziyu Fu^1,2,3, Puneet Kumar ^2,3,4, Mahsa Servati^2,3,4,5, Chinmay Mokashi^2,3,4, Natenael Semmineh^2,3,4, Nazanin K Majd^2,3,6, Vinaykumar K Puduvalli^2,3,6, C. Chad Quarles^2,3,4

¹Department of Imaging Physics, 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 Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, United States of America

⁵Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, United States of America

⁶Neuro-Oncology, The University of Texas MD Anderson Cancer Center, Houston, United States of America

Presenting Author: Puneet Kumar

Synopsis

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References

1. Quarles CC, Bell LC, Stokes AM. Imaging vascular and hemodynamic features of the brain using dynamic susceptibility contrast and dynamic contrast enhanced MRI. Neuroimage. 2019 Feb 15;187:32–55.

2. Calamante F, Gadian DG, Connelly A. Quantification of bolus-tracking MRI: Improved characterization of the tissue residue function using Tikhonov regularization. Magnetic Resonance in Medicine. 2003;50(6):1237–47.

3. Rotkopf LT, Ziener CH, von Knebel-Doeberitz N, Wolf SD, Hohmann A, Wick W, et al. A physics-informed deep learning framework for dynamic susceptibility contrast perfusion MRI. Medical Physics. 2024;51(12):9031–40.

4. Boxerman JL, Quarles CC, Hu LS, Erickson BJ, Gerstner ER, Smits M, et al. Consensus recommendations for a dynamic susceptibility contrast MRI protocol for use in high-grade gliomas. Neuro Oncol. 2020 Sept 29;22(9):1262–75.

5. Asaduddin M, Kim EY, Park SH. SPINNED: Simulation-based physics-informed neural network for deconvolution of dynamic susceptibility contrast MRI perfusion data. Magnetic Resonance in Medicine. 2024;92(3):1205–18.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/331-02-014