A CNN-based denoising method trained with images acquired with electron density phantoms for thin-sliced coronary artery calcium scans.

Abstract

This work proposed a convolutional neural network (CNN)-based method trained with images acquired with electron density phantoms to reduce quantum noise for coronary artery calcium (CAC) scans reconstructed with slice thickness less than 3mm. A DenseNet model was used to estimate quantum noise for CAC scans reconstructed with slice thickness of 0.5, 1.0 and 1.5mm. Training data was acquired using electron density phantoms in three different sizes. The label images of the CNN model were real noise maps, while the input images of the CNN model were pseudo noise maps. Image denoising was conducted by subtracting the CNN output images from thin-sliced CAC scans. The efficacy of the proposed method was verified through both phantom study and patient study. By means of phantom study, the proposed method was proven effective in reducing quantum noise in CAC scans reconstructed with 1.5-mm slice thickness without causing significant texture change or variation in HU values. With regard to patient study, calcifications were more clear on the denoised CAC scans reconstructed with slice thickness of 0.5, 1.0 and 1.5mm than on 3-mm slice images, while over-smooth changes were not observed in the denoised CAC scans reconstructed with 1.5-mm slice thickness. Our results demonstrated that the electron density phantoms can be used to generate training data for the proposed CNN-based denoising method to reduce quantum noise for CAC scans reconstructed with 1.5-mm slice thickness. Because anthropomorphic phantom is not a necessity, our method could make image denoising more practical in routine clinical practice.