Deep-neural-network based sinogram synthesis for sparse-view CT image reconstruction

TL;DR

This paper introduces a deep neural network approach for synthesizing sinograms in sparse-view CT, significantly improving image reconstruction quality over traditional interpolation and iterative methods.

Contribution

The study presents a novel deep learning-based sinogram synthesis method that outperforms existing interpolation and iterative reconstruction techniques in sparse-view CT.

Findings

Deep neural network-based sinogram synthesis outperforms traditional methods.

The proposed method reduces image artifacts and improves reconstruction quality.

Faster and more accurate image reconstruction in sparse-view CT.

Abstract

Recently, a number of approaches to low-dose computed tomography (CT) have been developed and deployed in commercialized CT scanners. Tube current reduction is perhaps the most actively explored technology with advanced image reconstruction algorithms. Sparse data sampling is another viable option to the low-dose CT, and sparse-view CT has been particularly of interest among the researchers in CT community. Since analytic image reconstruction algorithms would lead to severe image artifacts, various iterative algorithms have been developed for reconstructing images from sparsely view-sampled projection data. However, iterative algorithms take much longer computation time than the analytic algorithms, and images are usually prone to different types of image artifacts that heavily depend on the reconstruction parameters. Interpolation methods have also been utilized to fill the missing data in the sinogram of sparse-view CT thus providing synthetically full data for analytic image reconstruction. In this work, we introduce a deep-neural-network-enabled sinogram synthesis method for sparse-view CT, and show its outperformance to the existing interpolation methods and also to the iterative image reconstruction approach.