Implicit Neural Representations for Robust Joint Sparse-View CT Reconstruction

TL;DR

This paper introduces a novel INR-based Bayesian framework for joint sparse-view CT reconstruction that leverages common patterns across multiple objects to improve image quality and robustness to noise.

Contribution

It proposes a new INR-based Bayesian approach with latent variables for joint reconstruction, enhancing quality and efficiency over existing methods.

Findings

Higher reconstruction quality with sparse views

Robustness to measurement noise demonstrated

Latent variables aid in faster learning and initialization

Abstract

Computed Tomography (CT) is pivotal in industrial quality control and medical diagnostics. Sparse-view CT, offering reduced ionizing radiation, faces challenges due to its under-sampled nature, leading to ill-posed reconstruction problems. Recent advancements in Implicit Neural Representations (INRs) have shown promise in addressing sparse-view CT reconstruction. Recognizing that CT often involves scanning similar subjects, we propose a novel approach to improve reconstruction quality through joint reconstruction of multiple objects using INRs. This approach can potentially utilize the advantages of INRs and the common patterns observed across different objects. While current INR joint reconstruction techniques primarily focus on speeding up the learning process, they are not specifically tailored to enhance the final reconstruction quality. To address this gap, we introduce a novel INR-based Bayesian framework integrating latent variables to capture the common patterns across multiple objects under joint reconstruction. The common patterns then assist in the reconstruction of each object via latent variables, thereby improving the individual reconstruction. Extensive experiments demonstrate that our method achieves higher reconstruction quality with sparse views and remains robust to noise in the measurements as indicated by common numerical metrics. The obtained latent variables can also serve as network initialization for the new object and speed up the learning process.