ArticleEuropean radiology2024
Prospective and multi-reader evaluation of deep learning reconstruction-based accelerated rectal MRI: image quality, diagnostic performance, and reading time.
Article in European radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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
7 citing papers in PubMed.
- Deep learning reconstruction enables accelerated T2-weighted MRI for rectal cancer staging: a prospective study of diagnostic consistency across NEX value reduction.Abdominal radiology (New York) · 2026Article
- Synthetic Phase-Sensitive Inversion Recovery and Extramural Vascular Invasion Scoring Systems for Vascular Invasion Detection and Metastasis Risk in Rectal Cancer.Radiology. Imaging cancer · 2026Article
- Multi-center and multi-reader comparison of abdominopelvic non-contrast and contrast-enhanced CT in the postoperative surveillance of gastrointestinal stromal tumors.Quantitative imaging in medicine and surgery · 2026Article
- Deep learning reconstruction accelerated reduced field-of-view DWI in rectal cancer: mucosa-submucosa-muscularis visualization and T staging.European radiology experimental · 2026Article
- Perilesional dominance: radiomics of multiparametric MRI enhances differentiation of IgG4-Related ophthalmic disease and orbital MALT lymphoma.BMC medical imaging · 2025Article
- Role of Artificial Intelligence in MRI-Based Rectal Cancer Staging: A Systematic Review.Cureus · 2024Review
- Deep Learning-accelerated MRI in Body and Chest.Journal of computer assisted tomographyReview
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
objectivesTo evaluate deep learning reconstruction (DLR)-based accelerated rectal magnetic resonance imaging (MRI) compared with standard MRI. MATERIALS AND
methodsPatients with biopsy-confirmed rectal adenocarcinoma between November/2022 and May/2023 in a single centre were prospectively enrolled for an intra-individual comparison between standard fast spin-echo (FSE
resultsA total of 117 patients were enrolled, with 60 patients undergoing curative surgery. FSE
conclusionFSE CLINICAL RELEVANCE STATEMENT: DLR is clinically applicable for rectal MRI, providing improved image quality with shorter scanning time, which may ease the examination burden. It is beneficial for diagnostic optimisation in improving junior radiologists' T-staging accuracy and reading time. KEY POINTS: The rising incidence of rectal cancer has demanded enhanced efficiency and quality in imaging examinations. FSE
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.