A total variation prior unrolling approach for computed tomography reconstruction

作者：Pengcheng Zhang, Shuhui Ren, Yi Liu, Zhiguo Gui, Hong Shangguan, Yanling Wang, Huazhong Shu, Yang Chen · 发表于：Medical Physics · 年份：2023 · DOI：10.1002/mp.16307 · 被引用次数：11 · 研究领域：Medical Imaging Techniques and Applications、Advanced X-ray and CT Imaging、Digital Radiography and Breast Imaging

BACKGROUND: With the rapid development of deep learning technology, deep neural networks can effectively enhance the performance of computed tomography (CT) reconstructions. One kind of commonly used method to construct CT reconstruction networks is to unroll the conventional iterative reconstruction (IR) methods to convolutional neural networks (CNNs). However, most unrolling methods primarily unroll the fidelity term of IR methods to CNNs, without unrolling the prior terms. The prior terms are always directly replaced by neural networks. PURPOSE: In conventional IR methods, the prior terms play a vital role in improving the visual quality of reconstructed images. Unrolling the hand-crafted prior terms to CNNs may provide a more specialized unrolling approach to further improve the performance of CT reconstruction. In this work, a primal-dual network (PD-Net) was proposed by unrolling both the data fidelity term and the total variation (TV) prior term, which effectively preserves the image edges and textures in the reconstructed images. METHODS: By further deriving the Chambolle-Pock (CP) algorithm instance for CT reconstruction, we discovered that the TV prior updates the reconstructed images with its divergences in each iteration of the solution process. Based on this discovery, CNNs were applied to yield the divergences of the feature maps for the reconstructed image generated in each iteration. Additionally, a loss function was applied to the predicted divergences of the...