Cycle Consistent Adversarial Denoising Network for Multiphase Coronary CT Angiography

TL;DR

This paper introduces a cycle-consistent adversarial network for denoising low-dose coronary CT images, effectively reducing noise while preserving details without creating artificial features, thus improving diagnostic quality.

Contribution

It presents a novel semi-supervised, cycle-consistent adversarial approach for denoising low-dose CT images, addressing the challenge of learning from unpaired data in medical imaging.

Findings

Significant noise reduction in low-dose CT images.

Preservation of detailed textures and edges.

Improved diagnostic image quality.

Abstract

In coronary CT angiography, a series of CT images are taken at different levels of radiation dose during the examination. Although this reduces the total radiation dose, the image quality during the low-dose phases is significantly degraded. To address this problem, here we propose a novel semi-supervised learning technique that can remove the noises of the CT images obtained in the low-dose phases by learning from the CT images in the routine dose phases. Although a supervised learning approach is not possible due to the differences in the underlying heart structure in two phases, the images in the two phases are closely related so that we propose a cycle-consistent adversarial denoising network to learn the non-degenerate mapping between the low and high dose cardiac phases. Experimental results showed that the proposed method effectively reduces the noise in the low-dose CT image while the preserving detailed texture and edge information. Moreover, thanks to the cyclic consistency and identity loss, the proposed network does not create any artificial features that are not present in the input images. Visual grading and quality evaluation also confirm that the proposed method provides significant improvement in diagnostic quality.