ArticleThe British journal of radiology2022
Convolutional neural network-based reconstruction for acceleration of prostate T
Article in The British journal of radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The scientific evidence of commercial AI products for MRI acceleration: a systematic review.European radiology · 2025Pooled it
- Highly accelerated knee magnetic resonance imaging using deep neural network (DNN)-based reconstruction: prospective, multi-reader, multi-vendor study.Scientific reports · 2023Trial
- Deep learning reconstruction in prostate MRI: technical principles, clinical applications, and future directions.Abdominal radiology (New York) · 2026Review
- Prostate MRI Using Deep Learning Reconstruction in Response to Cancer Screening Demands-A Systematic Review and Meta-Analysis.Journal of personalized medicine · 2025Review
- A Narrative Review of Artificial Intelligence in MRI-Guided Prostate Cancer Diagnosis: Addressing Key Challenges.Diagnostics (Basel, Switzerland) · 2025Review
- Deep learning-accelerated T2WI of the prostate for transition zone lesion evaluation and extraprostatic extension assessment.Scientific reports · 2024Article
- Recent trends in AI applications for pelvic MRI: a comprehensive review.La Radiologia medica · 2024Review
- Deep learning-based reconstruction for acceleration of lumbar spine MRI: a prospective comparison with standard MRI.European radiology · 2023Article
- [Clinical Application and Limitations of Myeloma Response Assessment and Diagnosis System (MY-RADS)].Journal of the Korean Society of Radiology · 2023Review
- Deep Learning-accelerated MRI in Body and Chest.Journal of computer assisted tomographyReview
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe aim of this study was to develop a deep neural network (DNN)-based parallel imaging reconstruction for highly accelerated 2D turbo spin echo (TSE) data in prostate MRI without quality degradation compared to conventional scans.
methods155 participant data were acquired for training and testing. Two DNN models were generated according to the number of acquisitions (NAQ) of input images: DNN-N1 for NAQ = 1 and DNN-N2 for NAQ = 2. In the test data, DNN and TSE images were compared by quantitative error metrics. The visual appropriateness of DNN reconstructions on accelerated scans (DNN-N1 and DNN-N2) and conventional scans (TSE-Conv) was assessed for nine parameters by two radiologists. The lesion detection was evaluated at DNNs and TES-Conv by prostate imaging-reporting and data system.
resultsThe scan time was reduced by 71% at NAQ = 1, and 42% at NAQ = 2. Quantitative evaluation demonstrated the better error metrics of DNN images (29-43% lower NRMSE, 4-13% higher structure similarity index, and 2.8-4.8 dB higher peak signal-to-noise ratio;
conclusionsDNN-based reconstruction in highly accelerated prostate TSE imaging showed comparable quality to conventional TSE. ADVANCES IN KNOWLEDGE: Our framework reduces the scan time by 42% of conventional prostate TSE imaging without sequence modification, revealing great potential for clinical application.
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