Combined low-dose simulation and deep learning for CT denoising: application of ultra-low-dose cardiac CTA

Chul Kyun Ahn; Hyeongmin Jin; Changyong Heo; Jong Hyo Kim

doi:10.1117/12.2513144

1 March 2019 Combined low-dose simulation and deep learning for CT denoising: application of ultra-low-dose cardiac CTA

Chul Kyun Ahn, Hyeongmin Jin, Changyong Heo, Jong Hyo Kim

Author Affiliations +

Proceedings Volume 10948, Medical Imaging 2019: Physics of Medical Imaging; 1094846 (2019) https://doi.org/10.1117/12.2513144
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

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-lowdose CCTA images.

Citation Download Citation

Chul Kyun Ahn, Hyeongmin Jin, Changyong Heo, and Jong Hyo Kim "Combined low-dose simulation and deep learning for CT denoising: application of ultra-low-dose cardiac CTA", Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 1094846 (1 March 2019); https://doi.org/10.1117/12.2513144

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