Automatic continuous profiling improves knee joint shape assessment
SA-Profile: Automated Sulcus Angle Profiling from Super-Resolution MRI
Computer Vision and Pattern RecognitionArtificial Intelligence
Summary
Problems with knee pain and instability are linked to a bony shape called the trochlea. Doctors measure an angle in images to see if the shape is abnormal, but this measurement depends a lot on which image slice they pick. The authors created a computer method that builds a sharper 3D image from several scans and then measures this angle all along the area, giving a fuller and more reliable picture. Their method matched well with manual checks and showed clear shape differences in patients with knee problems.
What this means in practice
- •For medical imaging developers: Create tools that automatically generate detailed knee shape profiles from multiple MRI scans for improved diagnosis of trochlear abnormalities.
- •For orthopedic clinicians: Use continuous sulcus angle profiles from enhanced MRI to better characterize and monitor patients with anterior knee pain and patellar instability.
Authors
Michael Wehrli, Leo Widmer, Edwin Li, Noel Fiechter, Lorenzo Pettinari, Sidaty El Hadramy, Carol C. Hasler, Philippe C. Cattin
Abstract
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/SA_Profile.