Deep learning-based metal artifact reduction in CT for total knee arthroplasty

Abstract

In this study, we investigated the metal artifact reduction (MAR) performance of a deep learning (DL)-based technique in the evaluation of postoperative CT after total knee arthroplasty (TKA). For the development dataset, we collected CT scans from fifty patients without a metal prosthesis, and for the clinical test dataset, we collected CT scans from 44 patients with a previous history of TKA. We developed a DL-based knee MAR network (KMAR-Net) using 25,000 pairs of simulated images generated from 50 patients using the sinogram handling method. Regarding quantitative analysis, the area, mean attenuation, and standard deviation were calculated for Non-MAR, MAR algorithm for orthopedic implants (O-MAR), and KMAR-Net. For qualitative analysis, overall artifact, bone conspicuity, and soft tissue were compared using visual grading analysis. To additionally validate the feasibility of KMAR-Net under controlled conditions, a phantom study using a CTDI phantom with various metallic inserts and scanning parameters was conducted. KMAR-Net outperformed the projection-completion method regarding the area of dark streak artifacts, mean attenuation, and standard deviation within the artifacts. In the qualitative analysis, KMAR-Net was superior to O-MAR in the overall artifact and soft tissue evaluation, and one of the two readers evaluated it as superior for bone conspicuity (P = 0.080 for reader 1 and P < 0.001 for reader 2). In summary, DL-based KMAR-Net showed superior MAR performance in CT compared to the conventional projection-based method.