Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
631-03-010 / 631-03-010 ISMRM Abstract

Physics-Informed Harmonisation of RESOLVE and ZOOMit Prostate Diffusion MRI

Primary: Diffusion - Diffusion Reconstruction
Secondary: Acquisition & Reconstruction - AI methods
631-03-010 · Diffusion Acquisition and Reconstruction · Thursday, 14 May, 4:00 PM–5:36 PM · Roof Terrace
631-03-010 · Diffusion Acquisition and Reconstruction · Thursday, 14 May, 4:00 PM–5:36 PM · Roof Terrace
Keywords: Diagnosis/Prediction Diffusion-weighted MRI Physics-informed Deep Learning MRI harmonization Deep learning reconstruction technology
Accepted
Karl Ludger Radke 1, Matthias Boschheidgen1, Thomas A Thiel1, Lars Schimmöller1,2, Hans-Joerg Wittsack1
1Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich Heine University Düsseldorf (DE), Düsseldorf, Germany
2Department of Diagnostic, Interventional Radiology and Nuclear Medicine, Marien Hospital Herne, University Hospital of the Ruhr-University Bochum, Herne, Germany
Presenting Author: Karl Ludger Radke

Synopsis

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References

1. Brendle C, Martirosian P, Schwenzer NF, Kaufmann S, Kruck S, Kramer U, Notohamiprodjo M, Nikolaou K, Schraml C. Diffusion-weighted imaging in the assessment of prostate cancer: Comparison of zoomed imaging and conventional technique. Eur J Radiol. 2016 May;85(5):893-900. doi: 10.1016/j.ejrad.2016.02.020. Epub 2016 Feb 27. PMID: 27130048 [doi] [pmid]
2. Turkbey B, Rosenkrantz AB, Haider MA, Padhani AR, Villeirs G, Macura KJ, Tempany CM, Choyke PL, Cornud F, Margolis DJ, Thoeny HC, Verma S, Barentsz J, Weinreb JC. Prostate Imaging Reporting and Data System Version 2.1: 2019 Update of Prostate Imaging Reporting and Data System Version 2. Eur Urol. 2019 Sep;76(3):340-351. doi: 10.1016/j.eururo.2019.02.033. Epub 2019 Mar 18. PMID: 30898406 [doi] [pmid]
3. Raissi M, Perdikaris P, Karniadakis GE. Physics‑informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J Comput Phys. 2019;378:686‑707. doi:10.1016/j.jcp.2018.10.045. [doi]
4. Fortin JP, Parker D, Tunç B, Watanabe T, Elliott MA, Ruparel K, Roalf DR, Satterthwaite TD, Gur RC, Gur RE, Schultz RT, Verma R, Shinohara RT. Harmonization of multi-site diffusion tensor imaging data. Neuroimage. 2017 Nov 1;161:149-170. doi: 10.1016/j.neuroimage.2017.08.047. Epub 2017 Aug 18. PMID: 28826946; PMCID: PMC5736019. [doi] [pmid]
5. Dewey BE, Zhao C, Reinhold JC, Carass A, Fitzgerald KC, Sotirchos ES, Saidha S, Oh J, Pham DL, Calabresi PA, van Zijl PCM, Prince JL. DeepHarmony: A deep learning approach to contrast harmonization across scanner changes. Magn Reson Imaging. 2019 Dec;64:160-170. doi: 10.1016/j.mri.2019.05.041. Epub 2019 Jul 10. PMID: 31301354; PMCID: PMC6874910. [doi] [pmid]
6. Akiba T, Sano S, Yanase T, et al. Optuna. In: Proc. KDD. 2019:2623‑2631. doi:10.1145/3292500.3330701 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/631-03-010