Deep learning-based metal artifact reduction in CT for total knee arthroplasty.
Authors
Affiliations (8)
Affiliations (8)
- Program in Biomedical Radiation Sciences, Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea.
- Department of Nuclear Engineering, Ulsan National Institute of Science and Technology, 50 UNIST-gil, Ulju-gun, Ulsan, 44919, Republic of Korea.
- Graduate School of Artificial Intelligence, Ulsan National Institute of Science and Technology, 50 UNIST-gil, Ulju-gun, Ulsan, 44919, Republic of Korea.
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
- Department of Radiology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
- Institute of Radiation Medicine, Seoul National University Medical Research Center, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
- Program in Biomedical Radiation Sciences, Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea. [email protected].
- Institute of Radiation Medicine, Seoul National University Medical Research Center, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea. [email protected].
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.