ArticleEuropean radiology2024
Application research of AI-assisted compressed sensing technology in MRI scanning of the knee joint: 3D-MRI perspective.
Article in European radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07164651 (Prospective Application Study of Intelligent Accelerated Magnetic Resonance Imaging in Children With Abdominal Pain), which is not on this map. Cited by 9 papers.
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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.
Prospective Application Study of Intelligent Accelerated Magnetic Resonance Imaging in Children With Abdominal Pain
Who cites it
9 citing papers in PubMed, 12 citations in OpenAlex.
- Application of 3T contrast-enhanced Dixon water-fat separation coronary magnetic resonance angiography with artificial intelligence-assisted compressed sensing on coronary artery aneurysm in children with Kawasaki disease.Translational pediatrics · 2026Article
- Accelerated MRI in temporomandibular joints using AI-assisted compressed sensing technique: a feasibility study.European radiology · 2025Article
- [Effect of AI-assisted compressed sensing acceleration on MRI radiomic feature extraction and staging model performance for nasopharyngeal carcinoma].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025Article
- Deep Learning-Based Image Reconstruction in Musculoskeletal MRI.Journal of the Korean Society of Radiology · 2025Review
- Free-breathing pediatric cardiac dark-blood imaging with reverse double inversion-recovery and single-shot deep learning reconstruction.Quantitative imaging in medicine and surgery · 2025Article
- AI-assisted compressed sensing MRI improves imaging quality in rectal cancer: a comparative study with conventional acceleration techniques.Quantitative imaging in medicine and surgery · 2025Article
- Accuracy and Reliability of Multimodal Imaging in Diagnosing Knee Sports InjuriesCurrent medical imaging · 2025Article
- Comparison of different acceleration factors of artificial intelligence-compressed sensing for brachial plexus MRI imaging: scanning time and image quality.BMC medical imaging · 2024Article
- Compressed Sensitivity Encoding Artificial Intelligence Accelerates Brain Metastasis Imaging by Optimizing Image Quality and Reducing Scan Time.AJNR. American journal of neuroradiology · 2024Article
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Authors and funding
17 authors at 4 institutions in 1 country.
Funding
Abstract
objectiveTo investigate the potential applicability of AI-assisted compressed sensing (ACS) in knee MRI to enhance and optimize the scanning process.
methodsVolunteers and patients with sports-related injuries underwent prospective MRI scans with a range of acceleration techniques. The volunteers were subjected to varied ACS acceleration levels to ascertain the most effective level. Patients underwent scans at the determined optimal 3D-ACS acceleration level, and 3D compressed sensing (CS) and 2D parallel acquisition technology (PAT) scans were performed. The resultant 3D-ACS images underwent 3.5 mm/2.0 mm multiplanar reconstruction (MPR). Experienced radiologists evaluated and compared the quality of images obtained by 3D-ACS-MRI and 3D-CS-MRI, 3.5 mm/2.0 mm MPR and 2D-PAT-MRI, diagnosed diseases, and compared the results with the arthroscopic findings. The diagnostic agreement was evaluated using Cohen's kappa correlation coefficient, and both absolute and relative evaluation methods were utilized for objective assessment.
resultsThe study involved 15 volunteers and 53 patients. An acceleration factor of 10.69 × was identified as optimal. The quality evaluation showed that 3D-ACS provided poorer bone structure visualization, and improved cartilage visualization and less satisfactory axial images with 3.5 mm/2.0 mm MPR than 2D-PAT. In terms of objective evaluation, the relative evaluation yielded satisfactory results across different groups, while the absolute evaluation revealed significant variances in most features. Nevertheless, high levels of diagnostic agreement (κ: 0.81-0.94) and accuracy (0.83-0.98) were observed across all diagnoses.
conclusionACS technology presents significant potential as a replacement for traditional CS in 3D-MRI knee scans, allowing thinner MPRs and markedly faster scans without sacrificing diagnostic accuracy. CLINICAL RELEVANCE STATEMENT: 3D-ACS-MRI of the knee can be completed in the 160 s with good diagnostic consistency and image quality. 3D-MRI-MPR can replace 2D-MRI and reconstruct images with thinner slices, which helps to optimize the current MRI examination process and shorten scanning time. KEY POINTS: • AI-assisted compressed sensing technology can reduce knee MRI scan time by over 50%. • 3D AI-assisted compressed sensing MRI and related multiplanar reconstruction can replace traditional accelerated MRI and yield thinner 2D multiplanar reconstructions. • Successful application of 3D AI-assisted compressed sensing MRI can help optimize the current knee MRI process.
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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.