Feature reconstruction from incomplete tomographic data without detour
This addresses the issue of artefacts and missing features in medical imaging due to incomplete data, which complicates tasks like segmentation, though it appears incremental as it builds on existing regularization methods.
The paper tackles the problem of reconstructing image features directly from incomplete x-ray CT data, such as from dose reduction in medical imaging, and shows that their approach reliably reconstructs feature maps like edges from angularly undersampled data.
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.