463-06-008 ISMRM Abstract

Automated Lesion Detection of Deep Learning-based Phase Corrected Single-Shot rFOV Diffusion Images of the Prostate

Primary: Analysis Methods - Segmentation and Detection

Secondary: Body - Prostate

463-06-008 · Image Analysis for Cancer · Tuesday, 12 May, 4:55 PM–5:50 PM · Digital Posters Row D

Keywords: Automated segmentation Lesion Detection Prostate DWI Deep learning-based phase correction Deep Learning Image Reconstruction

Accepted

Eugene Milshteyn ¹, Xinzeng Wang², Patricia Lan³, Arnaud Guidon¹, Adele Courot⁴, Nicolas Gogin⁴, Nabih Nakrour⁵, Ranjodh S Dhami⁵, William R Bradley⁵, Mukesh G Harisinghani⁵, Rory L Cochran⁵

¹GE HealthCare, San Ramon, United States of America

²GE Healthcare, Houston, United States of America

³GE HealthCare, Menlo Park, United States of America

⁴GE HealthCare, Buc, France

⁵Department of Radiology, Massachusetts General Hospital, Boston, United States of America

Presenting Author: Eugene Milshteyn

Synopsis

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References

1. 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. Am J Roentgenol. 2021;216(4):943-951. doi:10.2214/AJR.20.23985 [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. Eur Urol Focus. 2021;7(1):86-94. doi:10.1016/j.euf.2019.03.016 [doi]

3. Siddiqui MR, Ansbro B, Shah PV, et al. Real-world use of MRI for risk stratification prior to prostate biopsy. Prostate Cancer Prostatic Dis. 2023;26(2):353-359. doi:10.1038/s41391-022-00543-4 [doi]

4. Cochran RL, Milshteyn E, Ghosh S, et al. Minimizing prostate diffusion weighted MRI examination time through deep learning reconstruction. Clin Imaging. 2025;117:110341. doi:10.1016/j.clinimag.2024.110341 [doi]

5. 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.

6. Wang X, Litwiller D, Guidon A, Lan P, Sprenger T. Robust Complex Signal Averaging for Diffusion Weighted Imaging. In: Proceedings of the 31st Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM). Toronto, ON, Canada; 2023:3963.

7. Wang, Xinzeng, Lan, Patricia, Guidon, Arnaud. DL-based Phase Correction Enables Robust Real Diffusion-Weighted MRI with Increased Diffusion Contrast. In: Proceedings of the 32nd Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM). Singapore; 2024:2414.

8. Milshteyn E, Ghosh S, Wang X, et al. Analysis of Deep Learning-based Phase Correction Applied to Single-Shot rFOV Diffusion Images of the Prostate at 1.5T. In: Proceedings of the 33rd Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM). Honolulu, Hawaii, USA; 2025:2810. doi:10.58530/2025/2810 [doi]

9. Engel H, Nedelcu A, Grimm R, et al. Diagnostic performance of a fully automated AI algorithm for lesion detection and PI-RADS classification in patients with suspected prostate cancer. Radiol Med (Torino). 2025;130(7):1039-1049. doi:10.1007/s11547-025-02003-0 [doi]

10. Lebel RM. Performance characterization of a novel deep learning-based MR image reconstruction pipeline. August 2020. doi:10.48550/arXiv.2008.06559 [doi]

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http://echo.ismrm.org/p/ISMRM2026/463-06-008