Denoising Multiphase Functional Cardiac CT Angiography Using Deep Learning and Synthetic Data

Abstract

Coronary CT angiography is increasingly used for cardiac diagnosis. Dose modulation techniques can reduce radiation dose, but resulting functional images are noisy and challenging for functional analysis. This retrospective study describes and evaluates a deep learning method for denoising functional cardiac imaging, taking advantage of multiphase information in a three-dimensional convolutional neural network. Coronary CT angiograms (n = 566) were used to derive synthetic data for training. Deep learning-based image denoising was compared with unprocessed images and a standard noise reduction algorithm (block-matching and three-dimensional filtering [BM3D]). Noise and signal-to-noise ratio measurements, as well as expert evaluation of image quality, were performed. To validate the use of the denoised images for cardiac quantification, threshold-based segmentation was performed, and results were compared with manual measurements on unprocessed images. Deep learning-based denoised images showed significantly improved noise compared with standard denoising-based images (SD of left ventricular blood pool, 20.3 HU ± 42.5 [SD] vs 33.4 HU ± 39.8 for deep learning-based image denoising vs BM3D; P < .0001). Expert evaluations of image quality were significantly higher in deep learning-based denoised images compared with standard denoising. Semiautomatic left ventricular size measurements on deep learning-based denoised images showed excellent correlation with expert quantification on unprocessed images (intraclass correlation coefficient, 0.97). Deep learning-based denoising using a three-dimensional approach resulted in excellent denoising performance and facilitated valid automatic processing of cardiac functional imaging. Keywords: Cardiac CT Angiography, Deep Learning, Image Denoising Supplemental material is available for this article. © RSNA, 2024.

Keywords: Cardiac CT Angiography; Deep Learning; Image Denoising.

Figure 1:

Example images of a cine series with high-level noise and artifact are shown for unprocessed, block-matching and three-dimensional filtering (BM3D), two-dimensional (2D) U-Net, and 3D U-Net images. The rightmost column shows the corresponding synthetic training data.

Figure 2:

Box-and-whisker plots show results for noise measurements using the SD of the blood pool in Hounsfield units (upper panel) and signal-to-noise ratio (SNR, lower panel) measured within the left ventricular cavity. The left plots show mean measurements including all time frames of the cardiac cycle while the right plots show the results for the most problematic time frame, which usually limits quantitative functional analysis. The box midline represents median, the borders indicate the 1st and 3rd quartiles, and the whisker boundaries extend 1.5 quartiles. BM3D = block-matching and three-dimensional filtering, 2D = two-dimensional.

Figure 3:

Expert evaluations of overall image quality (upper panel), noise-related image quality (middle panel), and artifact-related image quality (lower panel) are shown using a score from 1 (red, unusable) to 5 (dark green, excellent quality). The three-dimensional (3D) U-Net has the highest proportion of high-quality scores in all three categories by a large margin (dark green bar). The block-matching and 3D filtering (BM3D) images show higher quality scores in the noise category compared with the unprocessed images (middle panel). Of note, the quality scores in regard to artifacts are lower for BM3D images compared with unprocessed images (lower panel). The table shows scores as medians with IQRs in parentheses.