Feature reconstruction from incomplete tomographic data without detour

• URL: http://arxiv.org/abs/2202.10724v1
• Date: Tue, 22 Feb 2022 08:37:14 GMT
• Title: Feature reconstruction from incomplete tomographic data without detour
• Authors: Simon G\"oppel, J\"urgen Frikel, Markus Haltmeier
• Abstract summary: We introduce a novel framework for the robust reconstruction of convolutional image features directly from CT data. Within our framework we use non-linear (variational) regularization methods that can be adapted to a variety of feature reconstruction tasks.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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