ArticleJournal of magnetic resonance imaging : JMRI2022
Convolutional Neural Networks for Automated Classification of Prostate Multiparametric Magnetic Resonance Imaging Based on Image Quality.
Article in Journal of magnetic resonance imaging : JMRI, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Artificial intelligence-assisted reading of non-contrast prostate MRI: application and concordance with expert interpretation in a screening population within the PROSA trial.European radiology · 2026Trial
- Contrast-free MRI-first screening in young asymptomatic men: Prostate characteristics and cancer detection rates.Prostate cancer and prostatic diseases · 2026Article
- Current status and future directions of AI in prostate cancer detection on MRI: a special report from the ESUR prostate MRI working group authors.European radiology · 2026Article
- Integrating Artificial Intelligence into Prostate MR Imaging: Technical Foundations, Clinical Applications, and Workflow Implications.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026Review
- Bias in deep learning-based image quality assessments of T2-weighted imaging in prostate MRI.Abdominal radiology (New York) · 2026Article
- Prostate MRI cancer detection rate by deep learning-assisted image quality categorization: gas-induced susceptibility artifacts in diffusion-weighted imaging.Insights into imaging · 2025Article
- Predicting ADC map quality from T2-weighted MRI: A deep learning approach for early quality assessment to assist point-of-care.European journal of radiology · 2025Article
- Toward a Refined PI-RADS: The Feasibility and Limitations of More Informative Metrics in Reviewing MRI Scans.Journal of magnetic resonance imaging : JMRI · 2025Review
- PI-QUAL version 2 image quality categorisation and inter-reader agreement compared to version 1.European radiology · 2025Article
- A Narrative Review of Artificial Intelligence in MRI-Guided Prostate Cancer Diagnosis: Addressing Key Challenges.Diagnostics (Basel, Switzerland) · 2025Review
- Deep learning for quality assessment of axial T2-weighted prostate MRI: a tool to reduce unnecessary rescanning.European radiology experimental · 2025Article
- FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare.BMJ (Clinical research ed.) · 2025Article
- Interpreting Prostate Multiparametric MRI: Beyond Adenocarcinoma - Anatomical Variations, Mimickers, and Post-Intervention Changes.Seminars in ultrasound, CT, and MR · 2025Review
- Artificial Intelligence and Neuroscience: Transformative Synergies in Brain Research and Clinical Applications.Journal of clinical medicine · 2025Review
- Transforming Medical Imaging: The Role of Artificial Intelligence Integration in PACS for Enhanced Diagnostic Accuracy and Workflow EfficiencyCurrent medical imaging · 2025Review
- PI-QUAL version 2: an update of a standardised scoring system for the assessment of image quality of prostate MRI.European radiology · 2024Article
- Impact of prostate MRI image quality on diagnostic performance for clinically significant prostate cancer (csPCa).Abdominal radiology (New York) · 2024Article
- Recent trends in AI applications for pelvic MRI: a comprehensive review.La Radiologia medica · 2024Review
- Deep learning-based image quality assessment: impact on detection accuracy of prostate cancer extraprostatic extension on MRI.Abdominal radiology (New York) · 2024Article
- What the urologist needs to know before radical prostatectomy: MRI effective support to pre-surgery planning.La Radiologia medica · 2024Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundProstate magnetic resonance imaging (MRI) is technically demanding, requiring high image quality to reach its full diagnostic potential. An automated method to identify diagnostically inadequate images could help optimize image quality. PURPOSE: To develop a convolutional neural networks (CNNs) based analysis pipeline for the classification of prostate MRI image quality. STUDY TYPE: Retrospective. SUBJECTS: Three hundred sixteen prostate mpMRI scans and 312 men (median age 67). FIELD STRENGTH/SEQUENCE: A 3 T; fast spin echo T2WI, echo planar imaging DWI, ADC, gradient-echo dynamic contrast enhanced (DCE). ASSESSMENT: MRI scans were reviewed by three genitourinary radiologists (V.P., M.D.M., S.C.) with 21, 12, and 5 years of experience, respectively. Sequences were labeled as high quality (Q1) or low quality (Q0) and used as the reference standard for all analyses. STATISTICAL TESTS: Sequences were split into training, validation, and testing sets (869, 250, and 120 sequences, respectively). Inter-reader agreement was assessed with the Fleiss kappa. Following preprocessing and data augmentation, 28 CNNs were trained on MRI slices for each sequence. Model performance was assessed on both a per-slice and a per-sequence basis. A pairwise t-test was performed to compare performances of the classifiers.
resultsThe number of sequences labeled as Q0 or Q1 was 38 vs. 278 for T2WI, 43 vs. 273 for DWI, 41 vs. 275 for ADC, and 38 vs. 253 for DCE. Inter-reader agreement was almost perfect for T2WI and DCE and substantial for DWI and ADC. On the per-slice analysis, accuracy was 89.95% ± 0.02% for T2WI, 79.83% ± 0.04% for DWI, 76.64% ± 0.04% for ADC, 96.62% ± 0.01% for DCE. On the per-sequence analysis, accuracy was 100% ± 0.00% for T2WI, DWI, and DCE, and 92.31% ± 0.00% for ADC. The three best algorithms performed significantly better than the remaining ones on every sequence (P-value < 0.05). DATA
conclusionCNNs achieved high accuracy in classifying prostate MRI image quality on an individual-slice basis and almost perfect accuracy when classifying the entire sequences. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 1.
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