Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
361-02-015 ISMRM Abstract

AI accelerated DWI of the Prostate: Preserved Diagnostic Value and ADC Metrics in a Prospective Non-inferiority Study

Primary: Body - Prostate
Secondary: Diffusion - DWI/DTI/DKI
361-02-015 · Prostate: Clinical Applications, AI, and Post-Processing · Monday, 11 May, 9:15 AM–10:10 AM · Digital Posters Row B
Keywords: Diffusion-Weighted Imaging Prostate MRI Apparent diffusion coefficient AI-Accelerated MRI Diagnostic confidence
Accepted
Vlad Sacalean 1, Oliver Gebler1, Wei Liu2,3,4, Ralph Strecker3,5, Elisabeth Weiland6, Fabian Bamberg1, Jakob Weiß1, Maximilian Frederik Russe1, Hannes Engel1
1University Medical Center Freiburg — Department of Diagnostic and Interventional Radiology, Freiburg, Germany
2MR Application Predevelopment, Siemens Healthcare Ltd., Erlangen, Germany
3Siemens Healthineers AG, Erlangen, Germany
4Research & Clinical Translation, Magnetic Resonance, Siemens Healthineers AG, Erlangen, Germany
5MR Application Predevelopment, Siemens Healthineers, Erlangen, Germany
6Siemens Healthineers AG, Forchheim, Germany
Presenting Author: Vlad Sacalean

Synopsis

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References

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2. PI‑RADS AC of RC on (2019) Prostate Imaging Reporting & Data System (PI‑RADS) Version 2.1
3. Dietrich O, Raya JG, Reeder SB, et al (2007) Measurement of signal-to-noise ratios in MR images: influence of multichannel coils, parallel imaging, and reconstruction filters. J Magn Reson Imaging 26:375–385. https://doi.org/10.1002/jmri.20969 [doi]
4. Stocker D, Manoliu A, Becker AS, et al (2018) Image Quality and Geometric Distortion of Modern Diffusion-Weighted Imaging Sequences in Magnetic Resonance Imaging of the Prostate. Invest Radiol 53:200–206. https://doi.org/10.1097/RLI.0000000000000429 [doi]
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8. Ueda T, Ohno Y, Yamamoto K, et al (2022) Deep Learning Reconstruction of Diffusion-weighted MRI Improves Image Quality for Prostatic Imaging. Radiology 303:373–381. https://doi.org/10.1148/radiol.204097 [doi]
9. Ursprung S, Herrmann J, Joos N, et al (2023) Accelerated diffusion-weighted imaging of the prostate using deep learning image reconstruction: A retrospective comparison with standard diffusion-weighted imaging. Eur J Radiol 165:110953. https://doi.org/10.1016/j.ejrad.2023.110953 [doi]
10. Jeong J, Yeom SK, Choi IY, et al (2024) Deep learning image reconstruction of diffusion-weighted imaging in evaluation of prostate cancer focusing on its clinical implications. Quant Imaging Med Surg 14:3432–3446. https://doi.org/10.21037/qims-23-1379 [doi]
11. Nishioka N, Fujima N, Tsuneta S, et al (2024) Enhancing the image quality of prostate diffusion-weighted imaging in patients with prostate cancer through model-based deep learning reconstruction. Eur J Radiol Open 13:100588. https://doi.org/10.1016/j.ejro.2024.100588 [doi]
12. Lemberskiy G, Chandarana H, Bruno M, et al (2023) Feasibility of Accelerated Prostate Diffusion-Weighted Imaging on 0.55 T MRI Enabled With Random Matrix Theory Denoising. Invest Radiol 58:720–729. https://doi.org/10.1097/RLI.0000000000000979 [doi]
13. Zhang P, Feng Z, Chen S, et al (2025) Accelerating prostate rs-EPI DWI with deep learning: Halving scan time, enhancing image quality, and validating in vivo. Magn Reson Imaging 121:110418. https://doi.org/10.1016/j.mri.2025.110418 [doi]
14. Bae SH, Hwang J, Hong SS, et al (2022) Clinical feasibility of accelerated diffusion weighted imaging of the abdomen with deep learning reconstruction: Comparison with conventional diffusion weighted imaging. Eur J Radiol 154:110428. https://doi.org/10.1016/j.ejrad.2022.110428 [doi]
15. W. Liu, O. Darwish, T. Benkert, E. Weiland, and D. Nickel. Improved readout-segmented EPI using deep learning reconstruction. Proc.Intl.Soc.Mag.Reson.Med.32(2024):5106

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http://echo.ismrm.org/p/ISMRM2026/361-02-015