AC-IND: Sparse CT Reconstruction Based on Attenuation Coefficient Estimation and Implicit Neural Distribution

Wangduo Xie, Richard Schoonhoven, Tristan van Leeuwen, Matthew B. Blaschko; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 3074-3083

Computed tomography (CT) reconstruction plays a crucial role in industrial nondestructive testing and medical diagnosis. Sparse view CT reconstruction aims to reconstruct high-quality CT images while only using a small number of projections which helps to improve the detection speed of industrial assembly lines and is also meaningful for reducing radiation in medical scenarios. Sparse CT reconstruction methods based on implicit neural representations (INRs) have recently shown promising performance but still produce artifacts because of the difficulty of obtaining useful prior information. In this work we incorporate a powerful prior: the total number of material categories of objects. To utilize the prior we design AC-IND a self-supervised method based on Attenuation Coefficient Estimation and Implicit Neural Distribution. Specifically our method first transforms the traditional INR from scalar mapping to probability distribution mapping. Then we design a compact attenuation coefficient estimator initialized with values from a rough reconstruction and fast segmentation. Finally our algorithm finishes the CT reconstruction by jointly optimizing the estimator and the generated distribution. Through experiments we find that our method not only outperforms the comparative methods in sparse CT reconstruction but also can automatically generate semantic segmentation maps. Code is available at https://github.com/AIIAAI/AC-IND.