ArticleEuropean radiology experimental2025
Deep learning for quality assessment of axial T2-weighted prostate MRI: a tool to reduce unnecessary rescanning.
Article in European radiology experimental, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Optimal Selection of the Effective Echo Time (TEJournal of magnetic resonance imaging : JMRI · 2026Article
- Deep learning-based prostate cancer diagnosis on MRI with hip prostheses: artifact and sequence effects.Abdominal radiology (New York) · 2026Article
- Prostate MRI quality improvement: a Roadmap from the ESUR Prostate MRI Working Group.European radiology · 2026Review
- Digital twin technologies in prostate cancer as a frontier for precision medicine.European radiology experimental · 2026Review
- Prostate T2-weighted spin-echo MRI with and without glucagon: a paired scan quality assessment.Abdominal radiology (New York) · 2026Article
- Bias in deep learning-based image quality assessments of T2-weighted imaging in prostate MRI.Abdominal radiology (New York) · 2026Article
- Artificial Intelligence in Prostate MRI: Addressing Current Limitations Through Emerging Technologies.Journal of magnetic resonance imaging : JMRI · 2026Review
- Improved image quality and reduced acquisition time in prostate T2-weighted spin-echo MRI using a modified PI-RADS-adherent sequence.European radiology experimental · 2025Article
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Authors and funding
8 authors.
Funding
Abstract
backgroundT2-weighted images are a critical component of prostate magnetic resonance imaging (MRI), and it would be useful to automatically assess image quality (IQ) on a patient-specific basis without radiologist oversight.
methodsThis retrospective study comprised 1,412 axial T2-weighted prostate scans. Four experienced uroradiologists graded IQ using a 0-to-3 scale (0 = uninterpretable; 1 = marginally interpretable; 2 = adequately diagnostic; 3 = more than adequately diagnostic), binarized into nondiagnostic (IQ0 or IQ1), requiring rescanning, and diagnostic (IQ2 or IQ3), not requiring rescanning. The deep learning (DL) model was trained on 1,006 scans; 203 other scans were used for validation of multiple convolutional neural networks; the remaining 203 exams were used as a test set. 3D-DenseNet_169 was chosen among 11 models based on multiple evaluation criteria. The rescan predictions were compared to the number of rescans performed on a subset of 174 exams.
resultsThe model accurately predicts radiologist IQ scores (Cohen κ = 0.658), similar to the human inter-rater reliability (κ = 0.688-0.791). The model also predicts rescanning necessity similarly to radiologists: model κ = 0.537; reviewer κ = 0.577-0.703. The rescan model prediction area under the curve was 0.867.
conclusionThe DL model showed a strong ability to differentiate diagnostic from nondiagnostic axial T2-weighted prostate images, accurately mimicking expert radiologists' IQ scores. Using the model, the clinical unnecessary rescan rate could be reduced from over 50% to less than 30%. RELEVANCE STATEMENT: DL assessment of T2-weighted prostate MRI scans can accurately assess IQ, determining the need to repeat inadequate scans as well as avoiding repeat scans of those with adequate diagnostic quality, resulting in reduced unnecessary rescanning. KEY POINTS: Artificial intelligence assessment of prostate MRI T2-weighted image quality can improve exam time management. The model showed over 75% accuracy in assessing prostate MRI T2-weighted image quality. Expert radiologists have a substantial agreement in evaluating prostate MRI T2-weighted image quality.
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