ArticleDiagnostics (Basel, Switzerland)2021
Deep Learning-Based Post-Processing of Real-Time MRI to Assess and Quantify Dynamic Wrist Movement in Health and Disease.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 18 citations in OpenAlex.
- Advances in Artificial Intelligence for Wrist Joint Injury Diagnosis.International journal of medical sciences · 2026Review
- Precision Through Detail: Radiomics and Windowing Techniques as Key for Detecting Dens Axis Fractures in CT Scans.Diagnostics (Basel, Switzerland) · 2025Article
- High-resolution volumetric dynamic magnetic resonance imaging of the wrist using an 8-channel flexible receive coil.Skeletal radiology · 2025Article
- Measurement of Scapholunate Joint Space Width on Real-Time MRI-A Feasibility Study.Diagnostics (Basel, Switzerland) · 2024Article
- Review
- Dynamic assessment of scapholunate ligament status by real-time magnetic resonance imaging: an exploratory clinical study.Skeletal radiology · 2024Article
- Deep Learning-Based Denoising of CEST MR Data: A Feasibility Study on Applying Synthetic Phantoms in Medical Imaging.Diagnostics (Basel, Switzerland) · 2023Article
- AI-driven and automated MRI sequence optimization in scanner-independent MRI sequences formulated by a domain-specific language.Frontiers in neuroimaging · 2023Article
- Adaptive IoU Thresholding for Improving Small Object Detection: A Proof-of-Concept Study of Hand Erosions Classification of Patients with Rheumatic Arthritis on X-ray Images.Diagnostics (Basel, Switzerland) · 2022Article
- Lorentzian-Corrected Apparent Exchange-Dependent Relaxation (LAREX) Ω-Plot Analysis-An Adaptation for qCEST in a Multi-Pool System: Comprehensive In Silico, In Situ, and In Vivo Studies.International journal of molecular sciences · 2022Article
- A review on deep learning MRI reconstruction without fully sampled k-space.BMC medical imaging · 2021Review
Corrections and comments
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Authors and funding
14 authors at 3 institutions in 1 country.
Funding
Abstract
While morphologic magnetic resonance imaging (MRI) is the imaging modality of choice for the evaluation of ligamentous wrist injuries, it is merely static and incapable of diagnosing dynamic wrist instability. Based on real-time MRI and algorithm-based image post-processing in terms of convolutional neural networks (CNNs), this study aims to develop and validate an automatic technique to quantify wrist movement. A total of 56 bilateral wrists (28 healthy volunteers) were imaged during continuous and alternating maximum ulnar and radial abduction. Following CNN-based automatic segmentations of carpal bone contours, scapholunate and lunotriquetral gap widths were quantified based on dedicated algorithms and as a function of wrist position. Automatic segmentations were in excellent agreement with manual reference segmentations performed by two radiologists as indicated by Dice similarity coefficients of 0.96 ± 0.02 and consistent and unskewed Bland-Altman plots. Clinical applicability of the framework was assessed in a patient with diagnosed scapholunate ligament injury. Considerable increases in scapholunate gap widths across the range-of-motion were found. In conclusion, the combination of real-time wrist MRI and the present framework provides a powerful diagnostic tool for dynamic assessment of wrist function and, if confirmed in clinical trials, dynamic carpal instability that may elude static assessment using clinical-standard imaging modalities.
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Registered trials
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