Improved image quality in abdominal CT in patients who underwent treatment for hepatocellular carcinoma with small metal implants using a raw data-based metal artifact reduction algorithm

Objectives: To determine the value of a raw data-based metal artifact reduction (SEMAR) algorithm for image quality improvement in abdominal CT for patients with small metal implants. Methods: Fifty-eight patients with small metal implants (3–15 mm in size) who underwent treatment for hepatocellular carcinoma were imaged with CT. CT data were reconstructed by filtered back projection with and without SEMAR algorithm in axial and coronal planes. To evaluate metal artefact reduction, mean CT number (HU and SD) and artefact index (AI) values within the liver were calculated. Two readers independently evaluated image quality of the liver and pancreas and visualization of vasculature using a 5-point visual score. HU and AI values and image quality on images with and without SEMAR were compared using the paired Student’s t-test and Wilcoxon signed rank test. Interobserver agreement was evaluated using linear-weighted κ test. Results: Mean HU and AI on images with SEMAR was significantly lower than those without SEMAR (P < 0.0001). Liver and pancreas image qualities and visualizations of vasculature were significantly improved on CT with SEMAR (P < 0.0001) with substantial or almost perfect agreement (0.62 ≤ κ ≤ 0.83). Conclusions: SEMAR can improve image quality in abdominal CT in patients with small metal implants by reducing metallic artefacts. Key Points: • SEMAR algorithm significantly reduces metallic artefacts from small implants in abdominal CT. • SEMAR can improve image quality of the liver in dynamic CECT. • Confidence visualization of hepatic vascular anatomies can also be improved by SEMAR.