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

Paper Details

Date Published: 1 March 2019
PDF: 6 pages
Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 1094846 (1 March 2019); doi: 10.1117/12.2513144
Show Author Affiliations
Chul Kyun Ahn, Seoul National Univ. (Korea, Republic of)
Hyeongmin Jin, Seoul National Univ. (Korea, Republic of)
Changyong Heo, Seoul National Univ. (Korea, Republic of)
Jong Hyo Kim, Seoul National Univ. (Korea, Republic of)
Seoul National Univ. Hospital (Korea, Republic of)


Published in SPIE Proceedings Vol. 10948:
Medical Imaging 2019: Physics of Medical Imaging
Taly Gilat Schmidt; Guang-Hong Chen; Hilde Bosmans, Editor(s)

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