Tuesday, July 27, 2021

Multimodality‐based knee joint modeling method with bone and cartilage structures for total knee arthroplasty

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Abstract

Objective

We propose a robust and accurate knee joint modeling method with bone and cartilage structures to enable accurate surgical guidance for knee surgery.

Methods

A multimodality registration strategy is proposed to fuse MR and CT images of the femur and tibia separately to remove spatial inconsistency caused by knee bending in CT/MR scans. Automatic segmentation of the femur, tibia, and cartilages is carried out with ROI clustering and intensity analysis based on the multimodal fusion of images.

Results

Experimental results show that the registration error is 1.13 ± 0.30 mm. The Dice similarity coefficient (DSC) values of the proposed segmentation method of the femur, tibia, femoral and tibial cartilages are 0.969, 0.966, 0.910, and 0.872, respectively.

Conclusions

This study demonstrates the feasibility and effectiveness of multimodality-based registration and segmentation methods for knee joint modeling. The proposed metho d can provide users with 3D anatomical models of the femur, tibia, and cartilages with few human inputs.

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