Cycle‐consistent adversarial denoising network for multiphase coronary CT angiography

Abstract

Purpose In multiphase coronary CT angiography (CTA), 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. Recently, deep neural network approaches based on supervised learning technique have demonstrated impressive performance improvement over conventional model‐based iterative methods for low‐dose CT. However, matched low‐ and routine‐dose CT image pairs are difficult to obtain in multiphase CT. To address this problem, we aim at developing a new deep learning framework. Method We propose an unsupervised learning technique that can remove the noise of the CT images in the low‐dose phases by learning from the CT images in the routine dose phases. Although a supervised learning approach is not applicable due to the differences in the underlying heart structure in two phases, the images are closely related in two phases, so we propose a cycle‐consistent adversarial denoising network to learn the mapping between the low‐ and high‐dose cardiac phases. Results Experimental results showed that the proposed method effectively reduces the noise in the low‐dose CT image while 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. Conclusions The proposed network can learn the image distributions from the routine‐dose cardiac phases, which is a big advantage over the existing supervised learning networks that need exactly matched low‐ and routine‐dose CT images. Considering the effectiveness and practicability of the proposed method, we believe that the proposed can be applied for many other CT acquisition protocols.

Generative adversarial network in medical imaging: A review

“…In image reconstruction of moving organs, paired training samples are hard to obtain. Therefore, Ravì et al (2018) proposed a physical acquisition based loss to regulate the generated image structure for endomicroscopy super resolution and Kang et al (2018) proposed to use CycleGAN together with an identity loss in the denoising of cardiac CT. Wolterink et al (2017b) found that in low dose CT denoising, meaningful results can still be achieved when removing the image domain fidelity loss from the pix2pix framework, but the local image structure can be altered. Papers relating to medical image reconstruction are summarized in Table 1.…”

Biomedical Image Reconstruction: From the Foundations to Deep Neural Networks

“…Another way is to build algorithms that do not require a training set, or, at least, not a paired training set (i.e., measurements and their corresponding reconstructions). One way to do this is by enforcing cycle consistency [102], where a pair of algorithms are built, one for reconstruction and one for simulating data. These algorithms are trained together from unpaired data such that they are inverses.…”

Unsupervised Medical Image Translation Using Cycle-MedGAN

“…Cycle-GAN is another unsupervised translation approach which is based on the combination of adversarial losses and the pixel-wise cycle-consistency loss [24]. It has been adapted for medical translation tasks such as CT to MR bidirectionaltranslation [25] and CT denoising [26]. Other unsupervised frameworks exist with an overview available in [27].…”