ArticleDiagnostics (Basel, Switzerland)2023
Image Quality and Diagnostic Performance of Accelerated 2D Hip MRI with Deep Learning Reconstruction Based on a Deep Iterative Hierarchical Network.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- FOCUS-DWI improves prostate cancer detection through deep learning reconstruction with IQMR technology.Abdominal radiology (New York) · 2026Article
- Deep learning and conventional hip MRI for the detection of labral and cartilage abnormalities using arthroscopy as standard of reference.European radiology · 2025Article
- Deep learning-based acceleration of high-resolution compressed sense MR imaging of the hip.European journal of radiology open · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesHip MRI using standard multiplanar sequences requires long scan times. Accelerating MRI is accompanied by reduced image quality. This study aimed to compare standard two-dimensional (2D) turbo spin echo (TSE) sequences with accelerated 2D TSE sequences with deep learning (DL) reconstruction (TSE MATERIAL AND
methodsIn this prospective, monocentric study, TSE
resultsCompared with TSE
conclusionTSE
Indexed as
Identifiers
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Registered trials
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