1. Image quality and metal artifact reduction in total hip arthroplasty CT: deep learning-based algorithm versus virtual monoenergetic imaging and orthopedic metal artifact reduction
- Author
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Mark Selles, Ruud H. H. Wellenberg, Derk J. Slotman, Ingrid M. Nijholt, Jochen A. C. van Osch, Kees F. van Dijke, Mario Maas, and Martijn F. Boomsma
- Subjects
Arthroplasty (replacement, hip) ,Artificial intelligence ,Artifacts ,Deep learning ,Tomography (x-ray computed) ,Medical physics. Medical radiology. Nuclear medicine ,R895-920 - Abstract
Abstract Background To compare image quality, metal artifacts, and diagnostic confidence of conventional computed tomography (CT) images of unilateral total hip arthroplasty patients (THA) with deep learning-based metal artifact reduction (DL-MAR) to conventional CT and 130-keV monoenergetic images with and without orthopedic metal artifact reduction (O-MAR). Methods Conventional CT and 130-keV monoenergetic images with and without O-MAR and DL-MAR images of 28 unilateral THA patients were reconstructed. Image quality, metal artifacts, and diagnostic confidence in bone, pelvic organs, and soft tissue adjacent to the prosthesis were jointly scored by two experienced musculoskeletal radiologists. Contrast-to-noise ratios (CNR) between bladder and fat and muscle and fat were measured. Wilcoxon signed-rank tests with Holm-Bonferroni correction were used. Results Significantly higher image quality, higher diagnostic confidence, and less severe metal artifacts were observed on DL-MAR and images with O-MAR compared to images without O-MAR (p
- Published
- 2024
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