561-01-005 ISMRM Abstract

Accelerating Prostate Microstructure Mapping: A CycleGAN-Based Framework for HM-MRI Synthesis

Primary: Body - Prostate

Secondary: Analysis Methods - Generative Models

561-01-005 · Generative Models for Image Synthesis · Wednesday, 13 May, 8:20 AM–9:15 AM · Digital Posters Row B

Keywords: Microstructure Prostate cancer Hybrid Multidimensional MRI Cycle Generative Adversarial Network

Accepted

Dianning He ¹, Guantian Huang², Aritrick Chatterjee^3,4, Abel L Campos³, Wei Qian², Shouliang Qi², Gregory S Karczmar^3,4, Aytekin Oto^3,4

¹School of Health Management, China Medical University, China

²College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China

³University of Chicago, Chicago IL, United States of America

⁴Sanford J. Grossman Center of Excellence in Prostate Imaging and Image Guided Therapy, University of Chicago, Chicago, United States of America

Presenting Author: Dianning He

Synopsis

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References

1. Lee GH, Chatterjee A, Karademir I, et al. Comparing Radiologist Performance in Diagnosing Clinically Significant Prostate Cancer with Multiparametric versus Hybrid Multidimensional MRI. Radiology. Nov 2022;305(2):399-407. doi:10.1148/radiol.211895 [doi]

2. Chatterjee A, Antic T, Gallan AJ, et al. Histological validation of prostate tissue composition measurement using hybrid multi-dimensional MRI: agreement with pathologists' measures. Abdom Radiol (NY). Feb 2022;47(2):801-813. doi:10.1007/s00261-021-03371-7 [doi]

3. Jiang X, Devan SP, Xie J, et al. Improving MR cell size imaging by inclusion of transcytolemmal water exchange. NMR in Biomedicine. 2022;35(12)doi:10.1002/nbm.4799 [doi]

4. Zhang H, Wang Q, Shi J, et al. Deep unfolding network with spatial alignment for multi-modal MRI reconstruction. Medical Image Analysis. 2025/01/01/ 2025;99:103331. doi:https://doi.org/10.1016/j.media.2024.103331 [doi]

5. Muglia V. Hybrid Multidimensional MRI: A Step toward the Virtual Assessment of Prostate Histology. Radiology. 2022;302(2):378-379. doi:10.1148/radiol.2021212051 [doi]

6. Gundogdu B, Chatterjee A, Medved M, et al. Physics-Informed Autoencoder for Prostate Tissue Microstructure Profiling with Hybrid Multidimensional MRI. Radiology: Artificial Intelligence. 2025;7(2):e240167. doi:10.1148/ryai.240167 [doi]

7. Fan X, Chatterjee A, Medved M, et al. Introduction to matrix‐based method for analyzing hybrid multidimensional prostate MRI data. Journal of Applied Clinical Medical Physics. 2024;26(1)doi:10.1002/acm2.14544 [doi]

8. Chatterjee A, Yousuf AN, Engelmann R, et al. Prospective Validation of an Automated Hybrid Multidimensional MRI Tool for Prostate Cancer Detection Using Targeted Biopsy: Comparison with PI-RADS-based Assessment. Radiol Imaging Cancer. Jan 2025;7(1):e240156. doi:10.1148/rycan.240156 [doi]

9. Zhu J-Y, Park T, Isola P, et al. Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks. 2017 IEEE International Conference on Computer Vision (ICCV). 2017:2242-2251.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/561-01-005