Editorial for “Comparison of a Deep Learning-Accelerated vs. Conventional T2-Weighted Sequence in Biparametric MRI of the Prostate”
Corresponding Author
Zhe Wu PhD
Techna Institute, University Health Network, Toronto, Ontario, Canada
Search for more papers by this authorRajesh Bhayana MD
Department of Medical Imaging, University Health Network, Mount Sinai Hospital, Women's College Hospital, and University of Toronto, Toronto, Ontario, Canada
Search for more papers by this authorKâmil Uludağ PhD
Techna Institute, University Health Network, Toronto, Ontario, Canada
Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea
Search for more papers by this authorCorresponding Author
Zhe Wu PhD
Techna Institute, University Health Network, Toronto, Ontario, Canada
Search for more papers by this authorRajesh Bhayana MD
Department of Medical Imaging, University Health Network, Mount Sinai Hospital, Women's College Hospital, and University of Toronto, Toronto, Ontario, Canada
Search for more papers by this authorKâmil Uludağ PhD
Techna Institute, University Health Network, Toronto, Ontario, Canada
Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea
Search for more papers by this authorEvidence Level: 5
Technical Efficacy: Stage 6
References
- 1Turkbey 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(3): 340-351.
- 2Pahwa S, Schiltz NK, Ponsky LE, Lu Z, Griswold MA, Gulani V. Cost-effectiveness of MR imaging–guided strategies for detection of prostate cancer in biopsy-naive men. Radiology 2017; 285: 157-166.
- 3Woo S, Suh CH, Kim SY, Cho JY, Kim SH, Moon MH. Head-to-head comparison between biparametric and multiparametric MRI for the diagnosis of prostate cancer: A systematic review and meta-analysis. Am J Roentgenol 2018; 211: W226-W241.
- 4Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P. SENSE: Sensitivity encoding for fast MRI. Magn Reson Med 1999; 42: 952-962.
10.1002/(SICI)1522-2594(199911)42:5<952::AID-MRM16>3.0.CO;2-S CAS PubMed Web of Science® Google Scholar
- 5Griswold MA, Jakob PM, Heidemann RM, et al. Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magn Reson Med 2002; 47: 1202-1210.
- 6Hammernik K, Klatzer T, Kobler E, et al. Learning a variational network for reconstruction of accelerated MRI data. Magn Reson Med 2018; 79: 3055-3071.
- 7Gassenmaier S, Afat S, Nickel MD, et al. Accelerated T2-weighted TSE imaging of the prostate using deep learning image reconstruction: A prospective comparison with standard T2-weighted TSE imaging. Cancers (Basel) 2021; 13: 13.
- 8Johnson PM, Tong A, Donthireddy A, et al. Deep learning reconstruction enables highly accelerated biparametric MR imaging of the prostate. J Magn Reson Imaging 2022; 56: 184-195.
- 9 Tong A, Bagga B, Petrocelli R, et al. Comparison of a deep learning-accelerated versus conventional T2-weighted sequence in biparametric MRI of the prostate. J Magn Reson Imaging 2023; 58: 1059-1068.
- 10Montoya Perez I, Merisaari H, Jambor I, et al. Detection of prostate cancer using biparametric prostate MRI, radiomics, and kallikreins: A retrospective multicenter study of men with a clinical suspicion of prostate cancer. J Magn Reson Imaging 2022; 55: 465-477.