Better Image Quality for Diffusion-weighted MRI of the Prostate Using Deep Learning

HomeRadiologyVol. 303, No. 2 PreviousNext Reviews and CommentaryFree AccessEditorialBetter Image Quality for Diffusion-weighted MRI of the Prostate Using Deep LearningBaris Turkbey Baris Turkbey Author AffiliationsFrom the Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, 10 Center Dr, Room B3B85, Bethesda, MD 20892.Address correspondence to the author (e-mail: [email protected]).Baris Turkbey Published Online:Feb 1 2022https://doi.org/10.1148/radiol.212078MoreSectionsPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In See also the article by Ueda et al in this issue.Dr Baris Turkbey is a senior clinician at the Molecular Imaging Branch (MIB), National Cancer Institute (NCI), and National Institutes of Health. He currently serves as the section chief of MRI and artificial intelligence in MIB. He is a fellow of Society of Abdominal Radiology, and he serves as a member of the PI-RADS Steering Committee. His main research areas include cancer imaging (MRI, PET/CT), quantitative imaging biomarkers, and artificial intelligence.Download as PowerPointOpen in Image Viewer Prostate MRI has an established benefit of improving clinically significant prostate cancer diagnosis through guiding biopsies (1). In addition, prostate MRI may be useful for ruling out the presence of clinically significant prostate cancer, saving patients from unnecessary biopsies (2). The growing evidence for the benefit of prostate MRI is reported by academic centers. To date, it is unclear if the successful results reported by academic centers can be reproduced in routine community practice. These concerns are closely linked to the nonuniform quality of prostate MRI scans and guided biopsies across various practices (3).The inconsistent quality is likely the result of the complex nature of the MRI-guided localized prostate cancer diagnosis pathway. Multiple key steps are involved: prostate MRI scan acquisition, MRI scan interpretation, data processing for biopsy guidance, biopsy procedure, and biopsy specimen interpretation. These steps are handled by professionals from different disciplines, including an MRI technologist, diagnostic and interventional radiologist, urologist, and pathologist (4). Acquisition of a good-quality MRI scan is the first step of this pathway.To establish a standard for this first step of obtaining a good-quality MRI scan, the Prostate Imaging Reporting and Data System (PI-RADS) committee has established minimum technical requirements in their guideline documents (5). Adherence to these requirements is quite variable and does not necessarily ensure acquiring good-quality prostate MRI scans (6,7). In an attempt to improve the image acquisition quality control process, the PRECISION trial study group introduced the Prostate Imaging Quality (PI-QUAL) system in 2020. PI-QUAL aims to assess prostate MRI quality at the front line and to provide a level of confidence to act on prostate MRI findings and help scan variability through a formal feedback mechanism (8). PI-QUAL and its impact on improving prostate MRI quality are currently being evaluated in a number of studies, and it will be interesting to see outcomes of this research.PI-RADS and PI-QUAL aim to deliver high standards for consistent image acquisition and quality evaluation to improve prostate MRI-guided clinical care. In addition, several proactive research initiatives have been undertaken to improve the quality problem in prostate MRI—mainly by optimizing image reconstruction using deep learning (DL). In a study by Gassenmaier et al (9), with 30 patients, DL-based T2-weighted MRI scan acquisition was much faster than acquisition without DL (1.5 minutes vs 4.5 minutes, respectively), with lower noise levels and superior image quality. Although the impact of this approach on lesion detection and read-out performance was not assessed in a prospective manner, the DL method aims to tackle image quality problems at the level of image acquisition.In this issue of Radiology, Ueda and colleagues (10) evaluate whether DL reconstruction (DLR) can improve the quality of images obtained at diffusion-weighted imaging (DWI) at b values ranging from 1000 to 5000 sec/mm2 in patients with prostate cancer. For DWI, both its apparent diffusion coefficient (ADC) maps and high-b-value (b value = 1400 sec/mm2) components have been reported to be useful for locating prostate cancer lesions and for estimating their aggressiveness (5). In their retrospective study of 60 consecutive patients with biopsy-proven prostate cancer, MRI scans obtained at DWI with b values of 0, 1000, 3000, or 5000 sec/mm2 were reconstructed with and without DLR. Signal-to-noise ratio, contrast-to-noise ratio, and ADCs were compared with and without DLR for each DWI method. Additionally, the authors evaluated image quality with a five-point visual scoring system (1: very poor, 2: poor, 3: equivocal, 4: good, 5: excellent).The key results indicated that DWI with b values of 1000, 3000, and 5000 sec/mm2 had higher signal-to-noise and contrast-to-noise ratios with DLR than without DLR. Closely related to this quantitative analysis, the qualitative evaluation revealed that DWI with DLR had a higher visual quality than DWI without DLR. ADCs of benign versus cancerous foci for DLR-based MRI showed the same trends as MRI without DLR. This is critical to ensure that DLR does not insert adverse features into the images. Finally, the comparisons made by the authors of DLR and conventional MRI had high intra- and interobserver agreement.Ueda et al have thus presented one of the first proactive approaches to improve DWI quality using DLR. This new method uses a convolutional neural network with three layers for denoising images and improving image quality. Despite being a strong pulse sequence for detecting and characterizing prostate cancer, DWI is one of the most fragile techniques of prostate MRI because it can be frequently corrupted by susceptibility artifacts (eg, rectal gas, hip replacement prosthesis). Routine use of DLR can be helpful for improving the image quality of such a useful but nonrobust pulse sequence. This study did not include a patient sample negatively impacted by either rectal gas or prosthesis artifacts on DWI scans. However, a DLR-based denoising strategy can improve image quality in such challenging scenarios.This study by Ueda and colleagues is not without limitations. This is a retrospective study. Thus, actual clinical read-outs of DLR-based DWI scans by radiologists did not happen. Therefore, the actual impact of this approach on prospective lesion detection and delineation and the comfort level of radiologists in reporting the detected lesions must be carefully studied. As previously mentioned, the DLR algorithm must be challenged with nondiagnostic DWI data, which include clinically significant artifacts. If it maintains the good performance defined in this study in terms of quality improvement, it is a clear win for patients and their physicians.In summary, maintaining a uniform, high-quality MRI-guided biopsy pathway for clinical management of localized prostate cancer is critical. Ueda et al demonstrate a new DLR approach for improved DWI quality. This method can potentially be a first step for optimizing the performance of an MRI-guided diagnosis pathway. However, DLR-based DWI first needs validation in prospective studies depicting near real-world situations.Disclosures of Conflicts of Interest: B.T. Cooperative Research and Development Agreement with NVIDIA and Philips; royalties from NIH; patents in the field of AI.References1. Kasivisvanathan V, Rannikko AS, Borghi M, et al. MRI-Targeted or Standard Biopsy for Prostate-Cancer Diagnosis. N Engl J Med 2018;378(19):1767–1777. Crossref, Medline, Google Scholar2. Mehralivand S, Shih JH, Rais-Bahrami S, et al. A Magnetic Resonance Imaging-Based Prediction Model for Prostate Biopsy Risk Stratification. JAMA Oncol 2018;4(5):678–685. Crossref, Medline, Google Scholar3. Schmanke KE, Stiverson JJ, Zackula RE, Srour SG, Lierz MP, Joudi FN. The Role of Multiparametric MRI and MRI-targeted Biopsy in Detecting Clinically Significant Prostate Cancer in the Community Setting: A Retrospective Study. Rev Urol 2020;22(2):57–66. Medline, Google Scholar4. Padhani AR, Schoots IG, Turkbey B, Giannarini G, Barentsz JO. A multifaceted approach to quality in the MRI-directed biopsy pathway for prostate cancer diagnosis. Eur Radiol 2021;31(6):4386–4389. Crossref, Medline, Google Scholar5. 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(3):340–351. Crossref, Medline, Google Scholar6. Esses SJ, Taneja SS, Rosenkrantz AB. Imaging Facilities’ Adherence to PI-RADS v2 Minimum Technical Standards for the Performance of Prostate MRI. Acad Radiol 2018;25(2):188–195. Crossref, Medline, Google Scholar7. Sackett J, Shih JH, Reese SE, et al. Quality of Prostate MRI: Is the PI-RADS Standard Sufficient? Acad Radiol 2021;28(2):199–207. Crossref, Medline, Google Scholar8. Giganti F, Allen C, Emberton M, Moore CM, Kasivisvanathan V; PRECISION study group. Prostate Imaging Quality (PI-QUAL): A New Quality Control Scoring System for Multiparametric Magnetic Resonance Imaging of the Prostate from the PRECISION trial. Eur Urol Oncol 2020;3(5):615–619. Crossref, Medline, Google Scholar9. Gassenmaier S, Afat S, Nickel D, Mostapha M, Herrmann J, Othman AE. Deep learning-accelerated T2-weighted imaging of the prostate: Reduction of acquisition time and improvement of image quality. Eur J Radiol 2021;137109600. Crossref, Medline, Google Scholar10. Ueda T, Ohno Y, Yamamoto K, et al. Deep Learning Reconstruction of Diffusion-weighted MRI Improves Image Quality for Prostatic Imaging. Radiology 2022;303(2):373–381. Link, Google ScholarArticle HistoryReceived: Aug 15 2021Revision requested: Aug 30 2021Revision received: Aug 31 2021Accepted: Sept 1 2021Published online: Feb 01 2022Published in print: May 2022 FiguresReferencesRelatedDetailsCited ByQuality checkpoints in the MRI-directed prostate cancer diagnostic pathwayTristanBarrett, Maartende Rooij, FrancescoGiganti, ClareAllen, Jelle O.Barentsz, Anwar R.Padhani2023 | Nature Reviews Urology, Vol. 20, No. 1Promoting the use of the PI-QUAL score for prostate MRI quality: results from the ESOR Nicholas Gourtsoyiannis teaching fellowshipFrancescoGiganti, Alexander P.Cole, Fiona M.Fennessy, TimothyClinton, Pedro Lopes Da FrotaMoreira, Mariana CostaBernardes, Carl-FredrikWestin, DeepaKrishnaswamy, AndriyFedorov, Daniel A.Wollin, BjoernLangbein, NicolaFrego, MuhieddineLabban, Joy S.Badaoui, Steven L.Chang, Logan G.Briggs, JunichiTokuda, AlessandroAmbrosi, AlexKirkham, MarkEmberton, VeeruKasivisvanathan, Caroline M.Moore, ClareAllen, Clare M.Tempany2022 | European Radiology, Vol. 33, No. 1Accompanying This ArticleDeep Learning Reconstruction of Diffusion-weighted MRI Improves Image Quality for Prostatic ImagingFeb 1 2022RadiologyRecommended Articles Hybrid Multidimensional Prostate MRI: A Quantitative Step in the Right DirectionRadiology2022Volume: 305Issue: 2pp. 408-409PI-RADS: Where Next?Radiology2023Volume: 307Issue: 5Deep Learning Reconstruction of Diffusion-weighted MRI Improves Image Quality for Prostatic ImagingRadiology2022Volume: 303Issue: 2pp. 373-381Evidence Against Routine Use of Glucagon for Prostate MRIRadiology: Imaging Cancer2023Volume: 5Issue: 3Biparametric Prostate MRI Influencing Care Patterns in a Caribbean PopulationRadiology: Imaging Cancer2020Volume: 2Issue: 4See More RSNA Education Exhibits Prostate Cancer: What We Already Know About What Is on the HorizonDigital Posters2020PET/MRI Applications in Abdominopelvic OncologyDigital Posters2020False-Negative Clinically Significant Prostate Cancers on Prostate Multiparametric MR Imaging: What Radiologists Should KnowDigital Posters2020 RSNA Case Collection Anterior transition zone prostate adenocarcinomaRSNA Case Collection2020Hydrogel infiltration into rectal wallRSNA Case Collection2020Radioembolization of Liver Metastasis RSNA Case Collection2020 Vol. 303, No. 2 Metrics Altmetric Score PDF download