Combining reconstruction and edge detection in computed tomography

TL;DR

This paper introduces two innovative methods that integrate image reconstruction with edge detection in CT scans, enhancing image quality and edge detection, especially in undersampled data scenarios.

Contribution

It presents a novel extension of filtered backprojection and a regularization-based approach for improved edge detection in CT imaging.

Findings

The regularization method effectively compensates for undersampled CT data.

Both methods improve edge detection accuracy in reconstructed images.

The extended backprojection enhances reconstruction quality.

Abstract

We present two methods that combine image reconstruction and edge detection in computed tomography (CT) scans. Our first method is as an extension of the prominent filtered backprojection algorithm. In our second method we employ $\ell^{1}$-regularization for stable calculation of the gradient. As opposed to the first method, we show that this approach is able to compensate for undersampled CT data.