Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
568-04-007 ISMRM Abstract

High-resolution Diffusion-Weighted Imaging of the Prostate using Complex Signal Average: Initial Experience

Primary: Body - Prostate
Secondary: Acquisition & Reconstruction - Image Reconstruction: AI
568-04-007 · Prostate: Technical Applications, Sequences, and Coil Development · Wednesday, 13 May, 2:35 PM–3:30 PM · Digital Posters Row I
Keywords: Deep learning reconstruction Prostate MRI Super-Resolution Deep Learning Reconstruction Complex Signal Averaging Diffusion-weighted imaging (DWI)
Accepted
Atsushi Nakamoto 1, Takashi Ota2, Hideyuki Fukui2, Toru Honda2, Kengo Kiso2, Koki Kaketaka2, Takumi Tanigaki2, Eriko Yoshidome2, Akio Osamura3, Yuki Takai3,4, Masatoshi Hori5, noriyuki tomiyama2
1Department of Future Diagnostic Radiology, The University of Osaka Graduate School of Medicine, Suita, Japan
2Department of Diagnostic and Interventional Radiology, The University of Osaka Graduate School of Medicine, Suita, Japan
3Canon Medical Systems Corporation, Otawara-shi, Japan
4Canon Medical Systems Corporation, Tochigi, Japan
5Department of Artificial Intelligence in Diagnostic Radiology, The University of Osaka Graduate School of Medicine, Suita, Japan
Presenting Author: Atsushi Nakamoto

Synopsis

Motivation:
Goals:
Approach:
Results:
Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.

Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.

To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.

Log in

References

1. Turkbey B, Rosenkrantz AB, Haider MA, et al. Prostate Imaging Reporting and Data System Version 2.1: 2019 Update of Prostate Imaging Reporting and Data System Version 2. Eur Urol. 2019;76:340-351. doi: 10.1016/j.eururo.2019.02.033 [doi]
2. Ueda T, Ohno Y, Yamamoto K, et al. Deep Learning Reconstruction of Diffusion-weighted MRI Improves Image Quality for Prostatic Imaging. Radiology. 2022;303:373-381. doi: 10.1148/radiol.204097. [doi]
3. Prah DE, Paulson ES, Nencka AS, et al. A simple method for rectified noise floor suppression: Phase-corrected real data reconstruction with application to diffusion-weighted imaging. Magn Reson Med. 2010;64:418-29. doi: 10.1002/mrm.22407. [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/568-04-007