Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
602-02-005 ISMRM Abstract

Decoding the Determinants of Prostate MRI Quality: Multicenter Analysis and an AI Model for Early Prediction of Image Failure

Primary: Body - Prostate
Secondary: Analysis Methods - Classification and Prediction
602-02-005 · Prostate MRI: Methodological Developments · Thursday, 14 May, 1:40 PM–3:30 PM · Hall 1B
Keywords: Prostate MRI Diagnostic image quality CNN Artificial Intelligence in MRI
Accepted
jeff brender 1, Mitsuki Ota1, Nathan Nguyen1, Joshua W Ford1, Shun Kishimoto1,2, Murali Krishna3,4, Peter L Choyke1, Baris Turkbey5
1Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, United States of America
2Urologic Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, United States of America
3National Institutes of Health (NIH), Bethesda, United States of America
4Radiation Biology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, United States of America
5Molecular Imaging Branch, National Institutes of Health, Bethesda, United States of America
Presenting Author: jeff brender

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. Windisch O, Benamran D, Dariane C, Favre MM, Djouhri M, Chevalier M, Guillaume B, Oderda M, Gatti M, Faletti R, Colinet V, Lefebvre Y, Bodard S, Diamand R, Fiard G. Role of the Prostate Imaging Quality PI-QUAL Score for Prostate Magnetic Resonance Image Quality in Pathological Upstaging After Radical Prostatectomy: A Multicentre European Study. Eur Urol Open Sci 2023;47:94-101. doi: 10.1016/j.euros.2022.11.013 [doi]
2. Barrett T, de Rooij M, Giganti F, Allen C, Barentsz JO, Padhani AR. Quality checkpoints in the MRI-directed prostate cancer diagnostic pathway. Nat Rev Urol 2023;20(1):9-22. doi: 10.1038/s41585-022-00648-4 [doi]
3. Sackett J, Shih JH, Reese SE, Brender JR, Harmon SA, Barrett T, Coskun M, Madariaga M, Marko J, Law YM, Turkbey EB, Mehralivand S, Sanford T, Lay N, Pinto PA, Wood BJ, Choyke PL, Turkbey B. Quality of Prostate MRI: Is the PI-RADS Standard Sufficient? Acad Radiol 2021;28(2):199-207. doi: 10.1016/j.acra.2020.01.031 [doi]
4. Cipollari S, Guarrasi V, Pecoraro M, Bicchetti M, Messina E, Farina L, Paci P, Catalano C, Panebianco V. Convolutional Neural Networks for Automated Classification of Prostate Multiparametric Magnetic Resonance Imaging Based on Image Quality. J Magn Reson Imaging 2022;55(2):480-490. doi: 10.1002/jmri.27879 [doi]
5. Alis D, Kartal MS, Seker ME, Guroz B, Basar Y, Arslan A, Sirolu S, Kurtcan S, Denizoglu N, Tuzun U, Yildirim D, Oksuz I, Karaarslan E. Deep learning for assessing image quality in bi-parametric prostate MRI: A feasibility study. Eur J Radiol 2023;165:110924. doi: 10.1016/j.ejrad.2023.110924 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/602-02-005