SA-Profile: Automated Sulcus Angle Profiling from Super-Resolution MRI
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Abstract (arXiv)
Trochlear dysplasia (TD) is an abnormality of the femoral trochlea associated with anterior knee pain and patellar instability. The sulcus angle (SA) is used to assess trochlear morphology, but it is typically measured on a single axial MR slice with no clear guidance on which to select, making it sensitive to slice selection and landmark placement. We propose an automatic framework for continuous SA profiling from super-resolved MR volumes. Clinically acquired axial, coronal, and sagittal MR scans are combined using implicit neural representations to reconstruct a high-resolution volume. SA measurements are computed across the trochlear region using two landmark detection U-Net models. The approach was evaluated on the public fastMRI dataset and a small in-house cohort of patients with TD. Compared with conventional manual single-slice SA measurements, the proposed automated method yielded a mean absolute error of 11.6^circ while providing continuous characterization of trochlear morphology. Population-level analysis demonstrated distinct mean SA profiles between the public cohort and the in-house TD cohort, highlighting the potential of profile-based assessment to characterize TD. By reducing reliance on a single manually selected axial slice, the proposed framework extends conventional SA assessment to a continuous profile-based description of trochlear morphology without additional imaging, while remaining conceptually linked to current clinical assessment. Further validation is required. The code is available: https://github.com/wehrlimi/SAProfile.
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References · 7
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2018fastMRI: An Open Dataset and Benchmarks for Accelerated MRIcited by 470
- 2019Making Convolutional Networks Shift-Invariant Againcited by 391
- 2023WIRE: Wavelet Implicit Neural Representationscited by 220
- 2023Single-subject Multi-contrast MRI Super-resolution via Implicit Neural Representationscited by 32
- 2025Optimizing Rank for High-Fidelity Implicit Neural Representationscited by 8
- 2025Towards MR-Based Trochleoplasty Planningcited by 1
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