Unsupervised Self-Prior Embedding Neural Representation for Iterative Sparse-View CT Reconstruction

TL;DR

This paper introduces Spener, an unsupervised neural method that uses iterative image priors to improve sparse-view CT reconstruction, especially in noisy and out-of-domain scenarios, matching supervised methods in performance.

Contribution

The novel Spener method leverages imperfect reconstructions as priors within an iterative framework, enhancing INR-based SVCT reconstruction without supervision.

Findings

Spener achieves comparable results to supervised methods on in-domain data.

Spener outperforms supervised methods on out-of-domain datasets.

Spener significantly improves INR-based methods in noisy SVCT scenarios.

Abstract

Emerging unsupervised implicit neural representation (INR) methods, such as NeRP, NeAT, and SCOPE, have shown great potential to address sparse-view computed tomography (SVCT) inverse problems. Although these INR-based methods perform well in relatively dense SVCT reconstructions, they struggle to achieve comparable performance to supervised methods in sparser SVCT scenarios. They are prone to being affected by noise, limiting their applicability in real clinical settings. Additionally, current methods have not fully explored the use of image domain priors for solving SVCsT inverse problems. In this work, we demonstrate that imperfect reconstruction results can provide effective image domain priors for INRs to enhance performance. To leverage this, we introduce Self-prior embedding neural representation (Spener), a novel unsupervised method for SVCT reconstruction that integrates iterative reconstruction algorithms. During each iteration, Spener extracts local image prior features from the previous iteration and embeds them to constrain the solution space. Experimental results on multiple CT datasets show that our unsupervised Spener method achieves performance comparable to supervised state-of-the-art (SOTA) methods on in-domain data while outperforming them on out-of-domain datasets. Moreover, Spener significantly improves the performance of INR-based methods in handling SVCT with noisy sinograms. Our code is available at https://github.com/MeijiTian/Spener.