Dual-domain Wasserstein generative adversarial network with hybrid loss for low-dose CT imaging

Objective: Low-dose computed tomography (LDCT) has gained significant attention in hospitals and clinics as a popular imaging modality for reducing the risk of X-ray radiation. However, reconstructed LDCT images often suffer from undesired noise and artifacts, which can negatively impact diagnostic accuracy. This study aims to develop a novel approach to improve LDCT imaging performance.

Approach: A dual-domain Wasserstein generative adversarial network (DWGAN) with hybrid loss is proposed as an effective and integrated deep neural network (DNN) for LDCT imaging. The DWGAN comprises two key components: a generator (G) network
and a discriminator (D) network. The G network is a dual-domain DNN designed to predict high-quality images by integrating three essential components: the projection domain denoising module, filtered back-projection-based reconstruction layer, and image-domain enhancement module. The D network is a shallow convolutional neural network used to differentiate between real (label) and generated images. To prevent the reconstructed images from becoming excessively smooth and to preserve both structural and textural details, a hybrid loss function with weighting coefficients is incorporated into the DWGAN.

Main results: Numerical experiments demonstrate
that the proposed DWGAN can effectively suppress noise and better preserve image details compared with existing methods. Moreover, its application to head CT data confirms the superior performance of the DWGAN in restoring structural and textural details.

Significance: The proposed DWGAN framework exhibits excellent performance in recovering structural and textural details in LDCT images. Furthermore, the framework can be applied to other tomographic imaging techniques that suffer from image distortion problems.