A Lightweight Structure Aimed to Utilize Spatial Correlation for Sparse-View CT Reconstruction

TL;DR

This paper introduces a lightweight dual-domain deep learning model, LS-AAE, for sparse-view CT reconstruction that leverages spatial correlation across slices, achieving state-of-the-art results with fewer parameters.

Contribution

The paper proposes a novel cascade model that utilizes spatial continuity in CT slices and adopts a lightweight inverted residual structure, improving performance and efficiency in sparse-view CT reconstruction.

Findings

Achieves high PSNR and SSIM on challenging sparse sampling intervals

Outperforms existing methods with fewer model parameters

Maintains robustness across various sparsity levels

Abstract

Sparse-view computed tomography (CT) is known as a widely used approach to reduce radiation dose while accelerating imaging through lowered projection views and correlated calculations. However, its severe imaging noise and streaking artifacts turn out to be a major issue in the low dose protocol. In this paper, we propose a dual-domain deep learning-based method that breaks through the limitations of currently prevailing algorithms that merely process single image slices. Since the scanned object usually contains a high degree of spatial continuity, the obtained consecutive imaging slices embody rich information that is largely unexplored. Therefore, we establish a cascade model named LS-AAE which aims to tackle the above problem. In addition, in order to adapt to the social trend of lightweight medical care, our model adopts the inverted residual with linear bottleneck in the module design to make it mobile and lightweight (reduce model parameters to one-eighth of its original) without sacrificing its performance. In our experiments, sparse sampling is conducted at intervals of 4{\deg}, 8{\deg} and 16{\deg}, which appears to be a challenging sparsity that few scholars have attempted before. Nevertheless, our method still exhibits its robustness and achieves the state-of-the-art performance by reaching the PSNR of 40.305 and the SSIM of 0.948, while ensuring high model mobility. Particularly, it still exceeds other current methods when the sampling rate is one-fourth of them, thereby demonstrating its remarkable superiority.