Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
661-03-008 ISMRM Abstract

Pre-Scan Noise Map Guided Deep Learning for Denoising and Rician Bias Correction in Prostate DWI

Primary: Acquisition & Reconstruction - AI methods
Secondary: Diffusion - Diffusion Modeling
661-03-008 · Noise and Artifact Mitigation in MRI · Thursday, 14 May, 1:40 PM–2:35 PM · Digital Posters Row B
Keywords: Prostate Diffusion MRI Deep learning Denoising Rician bias correction
Accepted
Mustafa Abbas1, Fredrik Langkilde1, Stephan E Maier1,2, Stefan Kuczera 1
1Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
2Department of Radiology, Brigham and Women's Hospital, Boston, United States of America
Presenting Author: Stefan Kuczera

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References

1. Jakub Jurek, et al. Supervised denoising of diffusion-weighted magnetic resonance images using a convolutional neural network and transfer learning. Biocybernetics and Biomedical Engineering. 2023;43(1):206-232. https://doi.org/10.1016/j.bbe.2022.12.006 [doi]
2. Kuczera Stefan, Alipoor Mohammad, Langkilde Fredrik, Maier Stephan E. Optimized Bias and Signal Inference in Diffusion-weighted Image Analysis (OBSIDIAN). Magnetic Resonance in Med. 2021;86(5):2716–2732. https://doi.org/10.1002/mrm.28773 [doi]
3. Kuczera Stefan, Langkilde Fredrik, Maier Stephan E. Truly Reproducible Uniform Estimation of the ADC with Multi-b Diffusion Data— Application in Prostate Diffusion Imaging. Magnetic Resonance in Med. 2023;89(4):1586–1600. https://doi.org/10.1002/mrm.29533 [doi]
4. Pfaff Laura, et al. Enhancing Diffusion-Weighted Prostate MRI through Self-Supervised Denoising and Evaluation. Sci Rep. 2024;14(1):24292. https://doi.org/10.1038/s41598-024-75007-x [doi]
5. Vasylechko Serge, Afacan Onur, Kurugol Sila. Self-Supervised Denoising Diffusion Probabilistic Models for Abdominal DW-MRI. 2023;14328:80–91. https://doi.org/10.1007/978-3-031-47292-3_8 [doi]
6. Gundogdu Batuhan, Chatterjee Aritrick, Medved Milica, Bagci Ulas, Karczmar Gregory S., Oto Aytekin. Physics-Informed Autoencoder for Prostate Tissue Microstructure Profiling with Hybrid Multidimensional MRI. Radiology: Artificial Intelligence. 2025;7(2):e240167. https://doi.org/10.1148/ryai.240167 [doi]
7. Ni, Zhen-Liang, et al. RAUNet: Residual Attention U-Net for Semantic Segmentation of Cataract Surgical Instruments. Lect Notes Comput Sci (Neural Inf Process). 2019;11954:139-149. https://doi.org/10.1007/978-3-030-36711-4_13 [doi]
8. Alexey Dosovitskiy, et al. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. 2021. https://doi.org/10.48550/arXiv.2010.11929 [doi]
9. Pruessmann, K. P., Weiger, M., Scheidegger, M. B., & Boesiger, P. (1999). SENSE: sensitivity encoding for fast MRI. Magnetic resonance in medicine, 42(5), 952–962. PMID: 10542355 [pmid]

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http://echo.ismrm.org/p/ISMRM2026/661-03-008