ArticleDento maxillo facial radiology2025
Deep learning image enhancement for confident diagnosis of TMJ osteoarthritis in zero-TE MR imaging.
Article in Dento maxillo facial radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic accuracy of artificial intelligence models for temporomandibular joint anomalies on MRI: a systematic review and meta-analysis.Biomedical engineering online · 2026Pooled it
- Zero echo time MRI with deep learning reconstruction and chemical shift correction for detecting osteolytic myeloma lesions.European radiology experimental · 2026Trial
- Zero echo time MRI for detecting osseous anomalies in neurogenic thoracic outlet syndrome.Skeletal radiology · 2026Article
- Deep learning-based chemical shift-artifact correction of ZTE MRI for enhanced bone depiction of the lumbar spine.Skeletal radiology · 2026Article
- Deep learning for automated detection and classification of temporomandibular joint disorders: A scoping review.Imaging science in dentistry · 2026Review
- The Application of Magnetic Resonance Imaging in Dentistry: A Bibliometric Analysis.International dental journal · 2026Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
objectivesThis study aimed to evaluate the effectiveness of deep learning method for denoising and artefact reduction (AR) in zero echo time MRI (ZTE-MRI). Also, clinical applicability was evaluated by comparing image diagnosis to the temporomandibular joint (TMJ) cone-beam CT (CBCT).
methodsCBCT and routine ZTE-MRI data were collected for 30 patients, along with an additional ZTE-MRI obtained with reduced scan time. Scan time-reduced image sets were processed into denoised and AR images based on a deep learning technique. The image quality of the routine sequence, denoised, and AR image sets was compared quantitatively using the signal-to-noise ratio (SNR) and qualitatively using a 3-point grading system (0: poor, 1: good, 2: excellent). The presence of osteoarthritis was assessed in each imaging protocol. Diagnostic accuracy of each protocol was compared against the CBCT results, which served as the reference standard. The SNR and the qualitative scores were compared using analysis of variance test and Kruskal-Wallis test, respectively. The diagnostic accuracy was assessed using Cohen's κ (<0.5 = poor; 0.5 to <0.75 = moderate; 0.75 to <0.9 = good; ≥0.9 = excellent).
resultsBoth the denoised and AR protocols resulted in significantly enhanced SNR compared to the routine protocol, with the AR protocol showing a higher SNR than the denoised one. The qualitative assessment also showed highest grade in AR protocol with statistical significance. The osteoarthritis diagnosis showed enhanced agreement with CBCT in denoised (κ = 0.928) and AR images (κ = 0.929) than routine images (κ = 0.707).
conclusionsA newly developed deep learning technique for both denoising and artefact reduction in ZTE-MRI presented clinical usefulness. Specifically, AR protocol showed significantly improved image quality and comparable diagnostic accuracy comparable to CBCT. It can be expected that this novel technique would help overcome the current limitation of ZTE-MRI for replacing CBCT in bone imaging of TMJ.
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