Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
351-03-009 / 351-03-009 ISMRM Abstract

Deep learning-enhanced biparametric prostate MRI for optimized clinical workflows

Primary: Acquisition & Reconstruction - AI methods
Secondary: Body - Prostate
351-03-009 · AI Methods · Monday, 11 May, 4:10 PM–5:46 PM · Power Pitch Theatre 1
351-03-009 · AI Methods · Monday, 11 May, 4:10 PM–5:46 PM · Power Pitch Theatre 1
Keywords: Prostate cancer Prostate MRI Denoising AI-Accelerated MRI Deep-learning-based image reconstruction
Accepted
José de Arcos , María de la Luz Jurado Gómez1, Patricia Lan2, Xinzeng Wang3, Michael Carl4,5, Dan W Rettmann6, Ajeetkumar Gaddipati6, Pablo Garcia-Polo7, Arnaud Guidon8, Polina Rudenko1, Claudia Fontenla Martínez1, Asunción Torregrosa1, Luis Martí-Bonmatí1
1Hospital Universitario y Politécnico La Fe, Valencia, Spain
2GE HealthCare (Menlo Park, US), Menlo Park, United States of America
3GE Healthcare, Houston, United States of America
4MR Clinical Solutions, GE HealthCare, San Ramon, United States of America
5GE HealthCare, San Ramon, United States of America
6GE HealthCare (US), Waukesha, United States of America
7GE HealthCare, Madrid, Spain
8GEHealthCare, Boston, United States of America
Presenting Author: José de Arcos

Synopsis

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References

1. Antonia M. Pausch, et al. "Ultra-fast biparametric MRI in prostate cancer assessment: Diagnostic performance and image quality compared to conventional multiparametric MRI.", European Journal of Radiology Open,Volume 14 (2025). https://doi.org/10.1016/j.ejro.2025.100635. [doi]
2. Lebel, R. Marc. "Performance characterization of a novel deep learning-based MR image reconstruction pipeline." arXiv preprint arXiv:2008.06559 (2020). https://doi.org/10.48550/arXiv.2008.06559 [doi]
3. Gamito, Manuel N., et al. "Accurate multidimensional Poisson-disk sampling." ACM Transactions on Graphics (TOG) 29.1 (2009): 1-19. https://doi.org/10.1145/1640443.1640451 [doi]
4. Feng, Li, et al. "Compressed sensing for body MRI." Journal of Magnetic Resonance Imaging 45.4 (2017): 966-987. https://doi.org/10.1002/jmri.25547 [doi]
5. Ahn, Sangtae, et al. "Deep learning-based reconstruction of highly accelerated 3D MRI." arXiv preprint arXiv:2203.04674 (2022). https://doi.org/10.48550/arXiv.2203.04674 [doi]
6. Saritas, E.U., et al. "DWI of the spinal cord with reduced FOV single-shot EPI". Magn. Reson. Med., 60: 468-473. https://doi.org/10.1002/mrm.21640 [doi]
7. Chen N-K, et al. "A robust multi-shot scan strategy for high-resolution diffusion weighted MRI enabled by multiplexed sensitivity-encoding (MUSE)". Neuroimage (2013); Volume 72: 41-47. https://doi.org/10.1016/j.neuroimage.2013.01.038 [doi]
8. Lan Patricia, et al. "Reduced Noise and Motion Artifacts for MUSE Reconstruction using Deep Learning-based Phase Correction." (2024) ISMRM 2024; Singapore.
9. Chien N., et al. "Deep learning based multi-shot breast diffusion MRI: Improving imaging quality and reduced distortion". Eur J Radiol. (2025) Sep 15;193:112419. https://doi.org/10.1016/j.ejrad.2025.112419 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/351-03-009