Impact of Deep Learning-based Optimization Algorithm on Image Quality of Low-dose Coronary CT Angiography with Noise Reduction: A Prospective Study

Rationale and Objectives: To evaluate deep learning (DL)-based optimization algorithm for low-dose coronary CT angiography (CCTA) image noise reduction and image quality (IQ) improvement. Materials and Methods: A postprocessing platform for the CCTA image was built using a DL-based algorithm. Seventy subjects referred for CCTA were randomly divided into two groups (study group A with 80 kVp and control group B with 100 kVp). Group C was obtained by DL optimization of group A. Subjective IQ was blindly graded by two experienced radiologists on a four-point scale (4-excellent,1-poor). The image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were calculated to evaluate IQ objectively. The difference between the time consumed of iterative reconstruction and DL algorithm was also recorded. Results: The subjective IQ score of group C using the DL algorithm was significantly better than that of group A (p = 0.005). The noise of group C was significantly decreased, while SNR and CNR were significantly increased compared to group A (p < 0.001). The subjective IQ scores were lower in group A compared to group B (p = 0.037), whereas subjective IQ scores in group C were not significantly different (p = 0.874). For objective IQ, the noise of group A was significantly higher, while SNR and CNR were significantly lower than that of group B (p < 0.05). There was no significant difference in noise and SNR between group C and group B (p < 0.05), but CNR in group C was significantly higher than that in group B (p < 0.05). The DL algorithm processes the image twice as fast as the iterative reconstruction speed. Conclusion: The DL-based optimization algorithm could effectively improve the IQ of low-dose CCTA by noise reduction.