Combined Low-dose Simulation and Deep Learning for CT Denoising: Application of Ultra-low-dose Cardiac CTA

This study presents a novel deep learning approach for denoising of ultra-low-dose cardiac CT angiography (CCTA) by combining a low-dose simulation technique and convolutional neural network (CNN). Twenty-five CT angiography (CTA) scans acquired with ECG gating (70 - 100 kVp, 100 - 200 mAs) were fed into the low-dose simulation tool to generate a paired set of simulated low-dose CTA and synthetic low-dose noise. A modified U-net model with 4x4 kernel size and five layers was trained with these paired dataset to predict the low-dose noise from the given low-dose CCTA image. For generation of simulation low-dose CTA, differing level of low-dose conditions from 10% to 2.5% were applied. Independent 5 ultra-low-dose CTA scans (70 - 100 kVp, 4% dose of full-dose) with ECG gating were used for testing the denoising performance of the trained U-net. A denoised CCTA image was obtained by subtracting the predicted noise image by the U-net from the ultra-low-dose CCTA images. The performance was evaluated quantitatively in terms of noise measurements in ascending aorta, left/right ventricles, and qualitatively by comparing the noise pattern and image quality. Average of image noise in ascending aorta, left/right ventricles were 149 +/- 41HU, 200 +/- 15HU, 164 +/- 21HU in ultra-low-dose, and 46 +/- 14HU, 66 +/- 9HU, 55 +/- 12HU in deep learning-denoised images. The overall noise was significantly reduced by 70%. The noise pattern was indistinguishable from that of real CCTA image, and the image quality of denoised CCTA images was much higher than that of ultra-low-dose CCTA images.