IDOL-Net: An Interactive Dual-Domain Parallel Network for CT Metal Artifact Reduction

04/03/2021
by   PetsTime, et al.
6

Due to the presence of metallic implants, the imaging quality of computed tomography (CT) would be heavily degraded. With the rapid development of deep learning, several network models have been proposed for metal artifact reduction (MAR). Since the dual-domain MAR methods can leverage the hybrid information from both sinogram and image domains, they have significantly improved the performance compared to single-domain methods. However,current dual-domain methods usually operate on both domains in a specific order, which implicitly imposes a certain priority prior into MAR and may ignore the latent information interaction between both domains. To address this problem, in this paper, we propose a novel interactive dualdomain parallel network for CT MAR, dubbed as IDOLNet. Different from existing dual-domain methods, the proposed IDOL-Net is composed of two modules. The disentanglement module is utilized to generate high-quality prior sinogram and image as the complementary inputs. The follow-up refinement module consists of two parallel and interactive branches that simultaneously operate on image and sinogram domain, fully exploiting the latent information interaction between both domains. The simulated and clinical results demonstrate that the proposed IDOL-Net outperforms several state-of-the-art models in both qualitative and quantitative aspects.

READ FULL TEXT

page 1

page 4

page 6

page 7

page 8

research
12/13/2020

LEARN++: Recurrent Dual-Domain Reconstruction Network for Compressed Sensing CT

Compressed sensing (CS) computed tomography has been proven to be import...
research
02/16/2021

DAN-Net: Dual-Domain Adaptive-Scaling Non-local Network for CT Metal Artifact Reduction

Metal implants can heavily attenuate X-rays in computed tomography (CT) ...
research
05/12/2021

CT-Net: Complementary Transfering Network for Garment Transfer with Arbitrary Geometric Changes

Garment transfer shows great potential in realistic applications with th...
research
03/08/2021

U-DuDoNet: Unpaired dual-domain network for CT metal artifact reduction

Recently, both supervised and unsupervised deep learning methods have be...
research
08/31/2023

Unsupervised CT Metal Artifact Reduction by Plugging Diffusion Priors in Dual Domains

During the process of computed tomography (CT), metallic implants often ...
research
07/24/2022

FD-MAR: Fourier Dual-domain Network for CT Metal Artifact Reduction

The presence of high-density objects such as metal implants and dental f...

Please sign up or login with your details

Forgot password? Click here to reset