This study compared the image quality, metal artifacts, and diagnostic confidence of conventional 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). They found that DL-MAR showed not only higher image quality but also diagnostic confidence and superior metal artifact reduction compared to conventional CT images and 130-keV monoenergetic images with and without O-MAR in unilateral THA patients. Key points: Metal artifacts introduced by total hip arthroplasty hamper radiologic assessment on CT. A deep-learning algorithm (DL-MAR) was compared to dual-layer CT images with O-MAR. DL-MAR showed best image quality and diagnostic confidence. Highest contrast-to-noise ratios were observed on the DL-MAR images. Article: Image quality and metal artifact reduction in total hip arthroplasty CT: deep learning-based algorithm versus virtual monoenergetic imaging and orthopedic metal artifact reduction Authors: Mark Selles, Ruud H. H. Wellenberg, Derk J. Slotman, Ingrid M. Nijholt, Jochen A. C. van Osch, Kees F. van Dijke, Mario Maas & Martijn F. Boomsma
Impact of deep learning reconstruction on radiation dose reduction and cancer risk in CT examinations
Deep‑learning reconstruction (DLR) shifts CT image formation from a hardware‑limited process to a data‑driven one. In our real‑world cohort of >10,000 body scans, we observed a
28. April 2025
