An Unsupervised Reconstruction Method For Low-Dose CT Using Deep Generative Regularization Prior

TL;DR

This paper introduces an unsupervised deep generative regularization approach for low-dose CT reconstruction that does not require training data, leveraging CNNs as priors to improve image quality.

Contribution

The proposed method uniquely uses randomly initialized CNNs as priors for reconstruction without any training, outperforming traditional and learning-based methods.

Findings

Outperforms FBP, SART, and TV-regularized SART in quality.

Effective on both phantom and human CT images.

Works with different loss functions and view configurations.

Abstract

Low-dose CT imaging requires reconstruction from noisy indirect measurements which can be defined as an ill-posed linear inverse problem. In addition to conventional FBP method in CT imaging, recent compressed sensing based methods exploit handcrafted priors which are mostly simplistic and hard to determine. More recently, deep learning (DL) based methods have become popular in medical imaging field. In CT imaging, DL based methods try to learn a function that maps low-dose images to normal-dose images. Although the results of these methods are promising, their success mostly depends on the availability of high-quality massive datasets. In this study, we proposed a method that does not require any training data or a learning process. Our method exploits such an approach that deep convolutional neural networks (CNNs) generate patterns easier than the noise, therefore randomly initialized generative neural networks can be suitable priors to be used in regularizing the reconstruction. In the experiments, the proposed method is implemented with different loss function variants. Both analytical CT phantoms and human CT images are used with different views. Conventional FBP method, a popular iterative method (SART), and TV regularized SART are used in the comparisons. We demonstrated that our method with different loss function variants outperforms the other methods both qualitatively and quantitatively.