Impact of Super-Resolution Deep Learning Reconstruction on Low-Dose CT in Patients with Central Venous Catheter or Central Venous Port

CT imaging is useful for evaluating complications related to central venous catheter insertion; however, it involves exposure to ionizing radiation. This study aimed to evaluate the impact of super-resolution deep learning reconstruction (SR-DLR) on the quality of low-dose CT imaging compared to hybrid iterative reconstruction (HIR) in patients with central venous catheter or central venous port. In this retrospective study, chest CT images were reconstructed using SR-DLR and HIR from source data acquired with a three-dimensional landmark scan during scout imaging. Three readers independently assessed the quality of images in terms of noise, artifacts, depiction of the brachiocephalic vein and superior vena cava, and ease of evaluating hematoma and catheter location. The standard deviation of CT attenuation within a region of interest placed in the right atrium was recorded as quantitative image noise. All readers rated noise, artifacts, and depiction of the brachiocephalic vein as significantly improved in SR-DLR compared to HIR (p ≤ 0.042). Two out of three readers rated depiction of the superior vena cava, ease of evaluating hematoma, and catheter location as significantly improved in SR-DLR compared to HIR (p ≤ 0.016). Quantitative image noise in SR-DLR was 10.2 Hounsfield unit, which was significantly reduced compared to HIR (24.0 Hounsfield unit) (p < 0.001). When sufficient information can be obtained from these images, the main CT scans may be omitted. In conclusion, SR-DLR enhanced the quality of low-dose CT imaging compared to HIR in patients with central venous catheter or central venous port.