# Edge-masked CT image reconstruction from limited data

## TL;DR

This paper introduces an iterative CT image reconstruction algorithm that leverages edge masks derived from limited data to improve accuracy and speed, supported by theoretical and empirical evidence.

## Contribution

The proposed method combines image domain and statistical techniques, using edge masks to enhance reconstruction from limited data, adaptable to various models and transforms.

## Key findings

- High accuracy with limited data
- Fast convergence in iterative reconstruction
- Flexible framework for different models

## Abstract

This paper presents an iterative inversion algorithm for computed tomography image reconstruction that performs well in terms of accuracy and speed using limited data. The computational method combines an image domain technique and statistical reconstruction by using an initial filtered back projection reconstruction to create a binary edge mask, which is then used in an l2-regularized reconstruction. Both theoretical and empirical results are offered to support the algorithm. While in this paper a simple forward model is used and physical edges are used as the sparse feature, the proposed method is flexible and can accommodate any forward model and sparsifying transform.