ArticleSkeletal radiology2024
Enhanced bone assessment of the shoulder using zero-echo time MRI with deep-learning image reconstruction.
Article in Skeletal radiology, 2024. 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.
- Zero echo time MRI with deep learning reconstruction and chemical shift correction for detecting osteolytic myeloma lesions.European radiology experimental · 2026Trial
- Deep learning-based chemical shift-artifact correction of ZTE MRI for enhanced bone depiction of the lumbar spine.Skeletal radiology · 2026Article
- Deep Learning-Augmented Zero Echo Time MRI Increases Diagnostic Confidence for Osseous Assessment in Hand and Foot MRI Protocols.Diagnostics (Basel, Switzerland) · 2026Article
- ZTE MRI for Rotator Cuff Tear Arthropathy: Integrated Bone-Muscle Analysis and Its Association with Pseudoparesis.Journal of clinical medicine · 2025Article
- Current Landscape of Short-T2 Imaging Techniques in the Musculoskeletal System: The Past, Present and Future.Journal of magnetic resonance imaging : JMRI · 2025Review
- Deep learning reconstruction of zero-echo time sequences to improve visualization of osseous structures and associated pathologies in MRI of cervical spine.Insights into imaging · 2025Article
- Article
- Glenohumeral cartilage lesions: reviewing diagnostic imaging strategies and preoperative evaluation.Radiologia brasileiraReview
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo assess a deep learning-based reconstruction algorithm (DLRecon) in zero echo-time (ZTE) MRI of the shoulder at 1.5 Tesla for improved delineation of osseous findings.
methodsIn this retrospective study, 63 consecutive exams of 52 patients (28 female) undergoing shoulder MRI at 1.5 Tesla in clinical routine were included. Coronal 3D isotropic radial ZTE pulse sequences were acquired in the standard MR shoulder protocol. In addition to standard-of-care (SOC) image reconstruction, the same raw data was reconstructed with a vendor-supplied prototype DLRecon algorithm. Exams were classified into three subgroups: no pathological findings, degenerative changes, and posttraumatic changes, respectively. Two blinded readers performed bone assessment on a 4-point scale (0-poor, 3-perfect) by qualitatively grading image quality features and delineation of osseous pathologies including diagnostic confidence in the respective subgroups. Quantitatively, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of bone were measured. Qualitative variables were compared using the Wilcoxon signed-rank test for ordinal data and the McNemar test for dichotomous variables; quantitative measures were compared with Student's t-testing.
resultsDLRecon scored significantly higher than SOC in all visual metrics of image quality (all, p < 0.03), except in the artifact category (p = 0.37). DLRecon also received superior qualitative scores for delineation of osseous pathologies and diagnostic confidence (p ≤ 0.03). Quantitatively, DLRecon achieved superior CNR (95 CI [1.4-3.1]) and SNR (95 CI [15.3-21.5]) of bone than SOC (p < 0.001).
conclusionDLRecon enhanced image quality in ZTE MRI and improved delineation of osseous pathologies, allowing for increased diagnostic confidence in bone assessment.
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