AC-IND: Sparse CT reconstruction based on attenuation coefficient estimation and implicit neural distribution

TL;DR

AC-IND introduces a self-supervised approach for sparse CT reconstruction that leverages attenuation coefficient estimation and implicit neural distribution, improving image quality and enabling automatic semantic segmentation.

Contribution

The paper presents a novel method combining attenuation coefficient estimation with implicit neural distribution for improved sparse CT reconstruction.

Findings

Outperforms existing sparse CT reconstruction methods

Automatically generates semantic segmentation maps

Enhances reconstruction quality with fewer projections

Abstract

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.