Deep Radon Prior: A Fully Unsupervised Framework for Sparse-View CT Reconstruction

TL;DR

This paper introduces Deep Radon Prior (DRP), an unsupervised neural network framework for sparse-view CT reconstruction that reduces artifacts and does not require training data, outperforming some supervised methods.

Contribution

The paper presents a novel unsupervised deep learning framework for CT reconstruction that integrates a neural network as an implicit prior, eliminating the need for training datasets.

Findings

DRP effectively suppresses artifacts in sparse-view CT images.

DRP achieves comparable or superior results to supervised methods.

The method demonstrates good generalization and interpretability.

Abstract

Although sparse-view computed tomography (CT) has significantly reduced radiation dose, it also introduces severe artifacts which degrade the image quality. In recent years, deep learning-based methods for inverse problems have made remarkable progress and have become increasingly popular in CT reconstruction. However, most of these methods suffer several limitations: dependence on high-quality training data, weak interpretability, etc. In this study, we propose a fully unsupervised framework called Deep Radon Prior (DRP), inspired by Deep Image Prior (DIP), to address the aforementioned limitations. DRP introduces a neural network as an implicit prior into the iterative method, thereby realizing cross-domain gradient feedback. During the reconstruction process, the neural network is progressively optimized in multiple stages to narrow the solution space in radon domain for the under-constrained imaging protocol, and the convergence of the proposed method has been discussed in this work. Compared with the popular pre-trained method, the proposed framework requires no dataset and exhibits superior interpretability and generalization ability. The experimental results demonstrate that the proposed method can generate detailed images while effectively suppressing image artifacts.Meanwhile, DRP achieves comparable or better performance than the supervised methods.