Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
662-04-013 ISMRM Abstract

Evaluation of a Deep Learning Based Accelerated 3D Acquisition Strategy for T2-Weighted MRI of the Prostate

Primary: Body - Prostate
Secondary: Acquisition & Reconstruction - Image Reconstruction: AI
662-04-013 · Artificial Intelligence-Based Image Reconstruction in the Body · Thursday, 14 May, 2:35 PM–3:30 PM · Digital Posters Row C
Keywords: Prostate MRI Automated segmentation Accelerated MRI reconstruction Deep learning-based reconstruction 3D FSE
Accepted
Eugene Milshteyn 1, Trevor Kolupar2, Arnaud Guidon1, Ty A Cashen3, Ajeetkumar Gaddipati2, Nabih Nakrour4, Mukesh G Harisinghani4, Rory L Cochran4
1GE HealthCare, San Ramon, United States of America
2GE HealthCare, Waukesha, United States of America
3GE HealthCare, Madison, United States of America
4Department of Radiology, Massachusetts General Hospital, Boston, United States of America
Presenting Author: Eugene Milshteyn

Synopsis

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References

1. Siddiqui MR, Ansbro B, Shah PV, et al. Real-world use of MRI for risk stratification prior to prostate biopsy. Prostate Cancer and Prostatic Diseases. 2023;26(2):353-359. doi:10.1038/s41391-022-00543-4 [doi]
2. Kim SP, Karnes RJ, Mwangi R, et al. Contemporary Trends in Magnetic Resonance Imaging at the Time of Prostate Biopsy: Results from a Large Private Insurance Database. European Urology Focus. 2021;7(1):86-94. doi:10.1016/j.euf.2019.03.016 [doi]
3. Giganti F, Kirkham A, Allen C, et al. Update on Multiparametric Prostate MRI During Active Surveillance: Current and Future Trends and Role of the PRECISE Recommendations. American Journal of Roentgenology. 2021;216(4):943-951. doi:10.2214/AJR.20.23985 [doi]
4. O’Shea A, Guidon A, Lebel RM, et al. Initial experience in abbreviated T2-weighted Prostate MRI using a Deep Learning reconstruction.
5. Cochran RL, Milshteyn E, Ghosh S, et al. Minimizing prostate diffusion weighted MRI examination time through deep learning reconstruction. Clinical Imaging. 2025;117:110341. doi:10.1016/j.clinimag.2024.110341 [doi]
6. Ueda T, Ohno Y, Yamamoto K, et al. Deep Learning Reconstruction of Diffusion-weighted MRI Improves Image Quality for Prostatic Imaging. Radiology. 2022;303(2):373-381.
7. Kaye EA, Aherne EA, Duzgol C, et al. Accelerating Prostate Diffusion-weighted MRI Using a Guided Denoising Convolutional Neural Network: Retrospective Feasibility Study. Radiology: Artificial Intelligence. 2020;2(5):e200007. doi:10.1148/ryai.2020200007 [doi]
8. Ursprung S, Herrmann J, Joos N, et al. Accelerated diffusion-weighted imaging of the prostate using deep learning image reconstruction: A retrospective comparison with standard diffusion-weighted imaging. European Journal of Radiology. 2023;165:110953. doi:10.1016/j.ejrad.2023.110953 [doi]
9. Ahn S, Wollner U, McKinnon G, et al. Deep learning-based reconstruction of highly accelerated 3D MRI. March 2022. doi:https://doi.org/10.48550/arXiv.2203.04674 [doi]
10. Cashen T, Ahn S, Wollner U, et al. Variable density Poisson disc acquisition with iterative deep learning reconstruction for highly accelerated 3D T1-weighted abdominal imaging. In: London, England, UK; 2022:2288. doi:10.58530/2022/2288 [doi]
11. Lebel RM. Performance characterization of a novel deep learning-based MR image reconstruction pipeline. August 2020. doi:10.48550/arXiv.2008.06559 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/662-04-013