Synopsis
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361-02-016
ISMRM Abstract
Into Focus: Super-Resolution of Prostate ADC Maps with Unregistered T2W Images
Accepted
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1. James ND, Tannock I, N’Dow J, et al. The Lancet Commission on prostate cancer: planning for the surge in cases. Lancet. 2024;403(10437):1683-1722. doi:10.1016/S0140-6736(24)00651-2 [doi]
2. Schafer EJ, Laversanne M, Sung H, et al. Recent Patterns and Trends in Global Prostate Cancer Incidence and Mortality: An Update. Eur Urol. 2025;87(3):302-313. doi:10.1016/j.eururo.2024.11.013 [doi]
3. Lemberskiy G, Chandarana H, Bruno M, et al. Feasibility of Accelerated Prostate Diffusion-Weighted Imaging on 0.55 T MRI Enabled With Random Matrix Theory Denoising. Invest Radiol. 2023;58(10):720-729. doi:10.1097/RLI.0000000000000979 [doi]
4. Segeroth M, Breit HC, Wasserthal J, et al. AI-Based Evaluation of Prostate MR Imaging at a Modern Low-field 0.55 T Scanner Compared to 3 T in a Screening Cohort. Acad Radiol. 2025;32(5):2700-2706. doi:10.1016/j.acra.2024.11.024 [doi]
5. Pfaff L, Darwish O, Wagner F, et al. Enhancing diffusion-weighted prostate MRI through self-supervised denoising and evaluation. Sci Rep. 2024;14(1):24292. doi:10.1038/s41598-024-75007-x [doi]
6. Murali S, Ding H, Adedeji F, et al. Bringing MRI to low‐ and middle‐income countries: Directions, challenges and potential solutions. NMR Biomed. 2024;37(7). doi:10.1002/nbm.4992 [doi]
7. Zhao Y, Simpson BS, Morka N, et al. Comparison of Multiparametric Magnetic Resonance Imaging with Prostate-Specific Membrane Antigen Positron-Emission Tomography Imaging in Primary Prostate Cancer Diagnosis: A Systematic Review and Meta-Analysis. Cancers (Basel). 2022;14(14):3497. doi:10.3390/cancers14143497 [doi]
8. Masramon M, Mathew M, Clemente J, et al. Revealing Invisible Prostate Cancer with VERDICT-MRI. ISMRM. 2025.
9. Alexander DC, Zikic D, Ghosh A, et al. Image quality transfer and applications in diffusion MRI. Neuroimage. 2017;152:283-298. doi:10.1016/j.neuroimage.2017.02.089 [doi]
10. Lin H, Figini M, Tanno R, et al. Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulator. September 2019. http://arxiv.org/abs/1909.06763.
11. Figini M, Lin H, Ogbole G, et al. Image Quality Transfer Enhances Contrast and Resolution of Low-Field Brain MRI in African Paediatric Epilepsy Patients. March 2020. http://arxiv.org/abs/2003.07216.
12. Lin H, Figini M, D’Arco F, et al. Low-field magnetic resonance image enhancement via stochastic image quality transfer. April 2023. doi:10.1016/j.media.2023.102807 [doi]
13. Cicimen AG, Tregidgo HFJ, Figini M, et al. Image Quality Transfer of Diffusion MRI Guided By High-Resolution Structural MRI. August 2024. http://arxiv.org/abs/2408.03216.
14. Bourne RM, Bailey C, Johnston EW, et al. Apparatus for histological validation of in vivo and ex vivo magnetic resonance imaging of the human prostate. Front Oncol. 2017;7(MAR). doi:10.3389/fonc.2017.00047 [doi]
15. Hu X, Naiel MA, Wong A, Lamm M, Fieguth P. RUNet: A Robust UNet Architecture for Image Super-Resolution. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE; 2019:505-507. doi:10.1109/CVPRW.2019.00073 [doi]
16. Ho J, Jain A, Abbeel P. Denoising Diffusion Probabilistic Models. ArXiv. 2020.
17. Saha A, Twilt JJ, Bosma JS, et al. The PI-CAI Challenge: Public Training and Development Dataset (v2.0) [Data set]. Zenodo. 2022.
Cite this abstract
http://echo.ismrm.org/p/ISMRM2026/361-02-016