Metal Artifact Reduction Dual-Energy CT as an Accurate and Reliable Method for Measuring Total Knee Arthroplasty Femoral Component Rotation Compared to Conventional CT.
Chankue ParkSang-Min LeeJae Seung SeoTae Woo KimSeung Joon RheeHee Seok JeongPublished in: The journal of knee surgery (2022)
This article determines the accuracy and reliability of dual-energy computed tomography (DECT) with metal artifact reduction (MAR) in the evaluation of femoral component rotation after total knee arthroplasty (TKA), in comparison with conventional CT images. A total of 49 patients (mean age, 69 years; 42 women) who underwent TKA between January 2019 and March 2020 were retrospectively enrolled. Femoral component rotation, including the anatomic and surgical transepicondylar axes, was evaluated with preoperative conventional CT and postoperative conventional CT and DECT with MAR. Surgical femoral component rotation was also assessed as a reference standard. Accuracy was assessed using paired t -test, and inter- and intraobserver reliability using intraclass correlation coefficients (ICCs) based on postoperative conventional CT and DECT with MAR. Clinical outcomes were evaluated using the Knee Society objective and functional scores. Accuracy of femoral component rotation was not significantly different from that of surgical rotation with both conventional CT and DECT with MAR. However, inter- and intraobserver reliability were better for DECT with MAR (ICC: 0.953-0.966) than for conventional CT (ICC: 0.641-0.749). The Knee Society objective and functional scores improved 1 year postoperatively. CONCLUSION: DECT with MAR showed accurate and more reliable results than did conventional CT in the evaluation of femoral component rotation after TKA.
Keyphrases
- dual energy
- computed tomography
- image quality
- total knee arthroplasty
- contrast enhanced
- positron emission tomography
- magnetic resonance imaging
- patients undergoing
- high resolution
- total hip
- metabolic syndrome
- magnetic resonance
- end stage renal disease
- knee osteoarthritis
- type diabetes
- prognostic factors
- chronic kidney disease
- machine learning
- mass spectrometry
- convolutional neural network