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) (P<0.01). There was no significant difference between the CT values of the DLMAR group and those of the FBPMAR group (99.8±4.8 HU) and the IRMAR group (99.6±3.4 HU) (P>0.05). The noise of the DLMAR group was found to be significantly lower than that of the other groups (P<0.01). Furthermore, the SNR and CNR of the DLMAR group were also found to be higher than those of the other groups (P<0.01). In the tissue region between the two arms, the differences in CT values among the six groups were not statistically significant (P>0.05). The noise of the DLMAR group was lower than those of the other groups (P<0.01), and the SNR and CNR of the DLMAR group were higher than those of the other groups (P<0.01). In terms of the removal of metal artifacts, the scores of the FBPMAR, IRMAR, and DLMAR groups (4.27±0.32, 4.44±0.34, and 4.61±0.28, respectively) were higher than those of the FBP, IR, and DL groups (1.36±0.54, 1.32±0.45, and 1.24±0.46, respectively) (P<0.01). The DLMAR group also had a higher score of 4.62±0.37 in the visualization of structures between the two arms and 4.53±0.39 in the noise reduction of images, both of which were higher than those of the other groups (P<0.01).

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.