Céline Smekens^{1}, Pieter Van Dyck^{2}, Thomas Janssens^{3}, Jan Sijbers^{1}, and Ben Jeurissen^{1}

^{1}imec-Vision Lab, Department of Physics, University of Antwerp, Wilrijk, Belgium, ^{2}Department of Radiology, Antwerp University Hospital and University of Antwerp, Edegem, Belgium, ^{3}Siemens Healthcare NV/SA, Beersel, Belgium

Model-driven methods for knee structure characterization, such as bi-exponential T2* mapping, may result in unreliable parameter estimation. In contrast, data-driven approaches, such as non-negative matrix factorization (NMF), allow to robustly identify multiple compartments. In this work, convexity-constrained NMF was used to decompose quantitative UTE MRI of three asymptomatic and one degenerative knees for structural characterization. Decomposition in four compartments led to minimal residual errors and low inter-subject differences in normalized mean compartment weights. Shifts in compartment weight distributions correlated to structural abnormalities, suggesting that the proposed approach may aid in the detection, grading and monitoring of internal knee derangements.

$$ min_{{\bf W},{\bf H}}{\parallel{{\bf V}-{\bf VWH}} \parallel}_2^2 \ \ \ \ \ subject\ to \ \ \ \ {\bf W}=[w_{ij}] \geq 0,\ {\bf H}= [h_{ij}] \geq 0 $$

$$$ {\bf V} $$$ (

The proposed data-driven approach enables the characterization of knee structures based on quantitative UTE Spiral VIBE MRI, acquired in a clinically acceptable scan time, and thus holds promise as an instrumental aid for detection, grading and monitoring of structural knee abnormalities.

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DOI: https://doi.org/10.58530/2022/1410