# Randomized Iterative Reconstruction for Sparse View X-ray Computed Tomography

## TL;DR

This paper introduces a randomized iterative reconstruction method that enhances analytical algorithms for sparse view X-ray CT, reducing required projection angles by up to 35% while maintaining image quality.

## Contribution

It presents a novel combination of analytical and randomized iterative reconstruction algorithms to improve efficiency in sparse view X-ray CT imaging.

## Key findings

- Achieves up to 35% reduction in projection angles needed.
- Maintains comparable reconstruction quality to traditional methods.
- Does not significantly increase computational time.

## Abstract

With the availability of more powerful computers, iterative reconstruction algorithms are the subject of an ongoing work in the design of more efficient reconstruction algorithms for X-ray computed tomography. In this work, we show how two analytical reconstruction algorithms can be improved by correcting the corresponding reconstructions using a randomized iterative reconstruction algorithm. The combined analytical reconstruction followed by randomized iterative reconstruction can also be viewed as a reconstruction algorithm which, in the experiments we have conducted, uses up to $35\%$ less projection angles as compared to the analytical reconstruction algorithms and produces the same results in terms of quality of reconstruction, without increasing the execution time significantly.