461-04-012 ISMRM Abstract

Biparametric MRI with Super-resolution Deep Learning Reconstruction in Bladder Cancer

Primary: Body - Urogenital

Secondary: Acquisition & Reconstruction - Image Reconstruction: AI

461-04-012 · Quantitative MRI Across the Body · Tuesday, 12 May, 2:35 PM–3:30 PM · Digital Posters Row B

Keywords: Deep learning reconstruction Diagnostic performance Bladder cancer Super-Resolution Deep Learning Reconstruction Biparametric Magnetic Resonance Imaging

Accepted

Atsushi Nakamoto ¹, Toru Honda², Koki Kaketaka², Takashi Ota², Hideyuki Fukui², Kengo Kiso², Takumi Tanigaki², Eriko Yoshidome², Masatoshi Hori³, 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

³Department of Artificial Intelligence in Diagnostic Radiology, The University of Osaka Graduate School of Medicine, Suita, Japan

Presenting Author: Atsushi Nakamoto

Synopsis

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References

1. Panebianco V, Narumi Y, Altun E, et al. Multiparametric Magnetic Resonance Imaging for Bladder Cancer: Development of VI-RADS (Vesical Imaging-Reporting And Data System). Eur Urol. 2018;74:294-306. DOI: 10.1016/j.eururo.2018.04.029 [doi]

2. Ye L, Chen Y, Xu H, et al. Biparametric magnetic resonance imaging assessment for detection of muscle-invasive bladder cancer: a systematic review and meta-analysis. Eur Radiol. 2022;32:6480-6492. DOI: 10.1007/s00330-022-08696-5 [doi]

3. Taguchi S, Tambo M, Watanabe M, et al. Prospective Validation of Vesical Imaging-Reporting and Data System Using a Next-Generation Magnetic Resonance Imaging Scanner-Is Denoising Deep Learning Reconstruction Useful? J Urol. 2021;205:686-692. DOI: 10.1097/JU.0000000000001373 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/461-04-012