Feature reconstruction from incomplete tomographic data without detour

TL;DR

This paper introduces a novel variational regularization framework for directly reconstructing image features from incomplete CT data, bypassing full image reconstruction, and demonstrates its effectiveness in edge detection with limited data.

Contribution

The paper presents a new method for direct feature reconstruction from incomplete CT data using non-linear regularization, avoiding traditional image reconstruction.

Findings

Successfully reconstructs edge features from undersampled data

Robust feature maps despite limited data scenarios

Applicable to various feature reconstruction tasks

Abstract

In this paper, we consider the problem of feature reconstruction from incomplete x-ray CT data. Such problems occurs, e.g., as a result of dose reduction in the context medical imaging. Since image reconstruction from incomplete data is a severely ill-posed problem, the reconstructed images may suffer from characteristic artefacts or missing features, and significantly complicate subsequent image processing tasks (e.g., edge detection or segmentation). In this paper, we introduce a novel framework for the robust reconstruction of convolutional image features directly from CT data, without the need of computing a reconstruction firs. Within our framework we use non-linear (variational) regularization methods that can be adapted to a variety of feature reconstruction tasks and to several limited data situations . In our numerical experiments, we consider several instances of edge reconstructions from angularly undersampled data and show that our approach is able to reliably reconstruct feature maps in this case.