A hybrid statistical sampling and iterative regularization method in sparse-view computed tomography

TL;DR

This paper introduces a hybrid approach combining statistical sampling and iterative regularization to improve sparse-view CT image reconstruction, reducing artifacts and preserving details while decreasing sampling time.

Contribution

It presents a novel hybrid model that integrates statistical sampling with iterative regularization, enhancing image quality in sparse-view CT reconstruction.

Findings

Effective artifact reduction demonstrated on multiple datasets

Improved image detail preservation compared to traditional methods

Validated on phantom, X-ray, and clinical lung CT images

Abstract

Sparse-view computed tomography (CT) is an effective method to reduce the radiation exposure in medical imaging. To reduce the severe streaking artifacts that occur in reconstructed images due to violation of the Nyquist/Shannon sampling criterion, regularization is widely used to minimize the cost function. However, the iterative methods may lead to the accumulation and propagation of errors, which adversely affects the restoration of image details and textures. In this paper, we propose a hybrid model that integrates statistical sampling with iterative regularization to simultaneously shorten the sampling time and enhance the reconstruction quality. The proposed method is validated using three datasets: the Shepp-Logan phantom, the actual walnut X-ray projections provided by the Finnish Inverse Problems Society, and the clinical lung CT images.