Sparse-view computed tomography (CT) is a promising solution for expediting
the scanning process and mitigating radiation exposure to patients, the
reconstructed images, however, contain severe streak artifacts, compromising
subsequent screening and diagnosis. Recently, deep learning-based image
post-processing methods along with their dual-domain counterparts have shown
promising results. However, existing methods usually produce over-smoothed
images with loss of details due to (1) the difficulty in accurately modeling
the artifact patterns in the image domain, and (2) the equal treatment of each
pixel in the loss function. To address these issues, we concentrate on the
image post-processing and propose a simple yet effective FREquency-band-awarE
and SElf-guidED network, termed FreeSeed, which can effectively remove artifact
and recover missing detail from the contaminated sparse-view CT images.
Specifically, we first propose a frequency-band-aware artifact modeling network
(FreeNet), which learns artifact-related frequency-band attention in Fourier
domain for better modeling the globally distributed streak artifact on the
sparse-view CT images. We then introduce a self-guided artifact refinement
network (SeedNet), which leverages the predicted artifact to assist FreeNet in
continuing to refine the severely corrupted details. Extensive experiments
demonstrate the superior performance of FreeSeed and its dual-domain
counterpart over the state-of-the-art sparse-view CT reconstruction methods.
Source code is made available at this https URL

提出了一种基于深度学习和自适应模型的方法，名为 FreeSeed，该方法可以弥补片段图像的缺失和伪造，并显示出优于现有技术的表现。