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Abstract #1410

Data-driven characterization of knee structures using non-negative matrix factorization of quantitative UTE Spiral VIBE MRI

Céline Smekens1, Pieter Van Dyck2, Thomas Janssens3, Jan Sijbers1, and Ben Jeurissen1
1imec-Vision Lab, Department of Physics, University of Antwerp, Wilrijk, Belgium, 2Department of Radiology, Antwerp University Hospital and University of Antwerp, Edegem, Belgium, 3Siemens 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.

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