Comparison of Sinogram-based Iterative Reconstruction with Compressed Sensing Techniques in X-ray CT

TL;DR

This paper compares sinogram-based iterative reconstruction with compressed sensing techniques in sparse view X-ray CT, showing it can achieve better accuracy without parameter tuning and with comparable computation times.

Contribution

It demonstrates that sinogram-based iterative reconstruction can outperform some regularization methods in sparse view CT without needing parameter tuning.

Findings

Sinogram-based iterative reconstruction can yield higher accuracy in sparse view CT.

The method operates with no parameters to tune.

Reconstruction times are comparable to existing techniques.

Abstract

Performing X-ray computed tomography (CT) examinations with less radiation has recently received increasing interest: in medical imaging this means less (potentially harmful) radiation for the patient; in non-destructive testing of materials/objects such as testing jet engines, the redution of the number of projection angles (which for large objects is in general high) leads to a substantial decreasing of the experiment time. In the experiment, less radiation is usually achieved by either (1) reducing the radiation dose used at each projection angle or (2) using sparse view X-ray CT, which means significantly less projection angles are used during the examination. In this work, we study the performance of the recently proposed sinogram-based iterative reconstruction algorithm in sparse view X-ray CT and show that it provides, in some cases, reconstruction accuracy better than that obtained by some of the Total Variation regularization techniques. The provided accuracy is obtained with computation times comparable to other techniques. An important feature of the sinogram-based iterative reconstruction algorithm is that it has no parameters to be set.