Feature reconstruction from incomplete tomographic data without detour
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.
Cite
@article{arxiv.2202.10724,
title = {Feature reconstruction from incomplete tomographic data without detour},
author = {Simon Göppel and Jürgen Frikel and Markus Haltmeier},
journal= {arXiv preprint arXiv:2202.10724},
year = {2022}
}