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Session: Poster session 2 (Odd numbers)
Session starts: Friday 27 January, 10:00
Presentation starts: 10:00

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Majid Mohammad Sadeghi (Maastricht University Medical Center)
Erkan Aşık (Eindhoven University of Technology)
Pieter Emans (Maastricht University Medical Center)
Nazli Tümer (Delft University of Technology)
Gabrielle Tuijthof (University of Twente)
Alex Roth (Maastricht University Medical Center)

Abstract:
Focal knee resurfacing implants (FKRIs) are an emerging alternative treatment for knee focal cartilage injuries and are typically intended for the middle-aged population where biological cartilage procedures and total knee replacement are not proper options due to longevity concerns. The knowledge of the distribution of the exact location and size of the focal cartilage defects and the morphology of healthy cartilage at expected defect locations is a key requirement in designing off-the-shelf FKRIs. Current literature on cartilage defect characteristics is limited by the lack of detail and the subjective nature in which location and size are described, whereas little data is available on the local joint morphology. The objective of this work is to describe a statistical shape modelling (SSM)-based workflow for systematically and accurately describing the cartilage defect characteristics observed in patients targeted for FKRIs. Magnetic resonance imaging (MRI) scans of 50 patients treated with FKRIs were included in this study. Cartilage samples were segmented from these scans and were aligned into a common coordinate system using a registration algorithm. Cartilage defect zones were determined on MRIs and co-registered to the same coordinate system using the transformation found for the cartilage models transferred onto aligned cartilage samples using the corresponding transformation matrices obtained following their registration. The distribution of these defect properties, including their exact location in the coordinate system, and their sizes were determined using a dedicated algorithm. In parallel, a SSM of the cartilage was built from the cartilage samples. The mean and variations of morphological properties of the cartilage surface at the locations of the defects were found using the SSM model. The combined information of the distribution of the exact size and shape parameters of cartilage defects and the local cartilage morphology acquired from the SSM of the cartilage serve as important inputs for designing the resurfacing implants. This workflow proves to provide information on the isolated focal knee cartilage defects with the required level of detail currently missing in the literature for enabling FKRI design. This work may also be useful for guiding regenerative medicine or allografting approaches in cartilage repair.