[Deep Learning Reconstruction Algorithm Combined With Smart Metal Artifact Reduction Technique Improves Image Quality of Upper Abdominal CT in Critically Ill Patients].

Objective: To evaluate the effect of deep learning reconstruction algorithm combined with smart metal artifact reduction (DLMAR) on the quality of abdominal CT images in critically ill patients who are unable to raise their arms and require electrocardiographic (ECG) monitoring. Methods: A total of 102 patients were retrospectively enrolled. All subjects were critically ill patients who were unable to raise their arms and required ECG monitoring. Images were reconstructed using 6 algorithms, including filtered back projection (FBP), iterative reconstruction (IR), deep learning (DL), FBP combined with smart metal artifact reduction (FBPMAR), adaptive statistical iterative reconstruction-V combined with smart metal artifact reduction (IRMAR), and DLMAR. A quantitative analysis of CT values, noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) was conducted in regions without metal artifacts and regions with metal artifacts in the liver, as well as the tissues, including those from the liver, spleen, pancreas, and aorta, between the two arms. Qualitative analysis of electrode metal artifacts, the visualization of the structures between the two arms, and image noise was performed with a 5-point scoring system (1=worst and 5=best). Results: In the regions of the liver with metal artifacts, there was a significant difference between the CT values of the DLMAR group ([98.5±9.8] Hounsfield units [HU]) and those of the FBP group ([73.7±5.6] HU), the IR group ([75.3±7.5] HU), and the DL group ([66.3±11.4] HU) ( Conclusion: DLMAR reduces artifacts, decreases noise, and improves the quality of abdominal CT imaging in critically ill patients who are unable to raise their arms and require ECG monitoring.