CT Field of View Extension Using Combined Channels Extension and Deep Learning Methods

Éric Fournié, Matthias Baer-Beck, Karl Stierstorfer

Keywords: Extended field of view, EFoV, CT, sinogram, U-Net, artifact reduction

TL;DR: EFoV CT images can be reconstructed by backprojecting extended sinograms and cleaning up the artifacts with a neural network.

Abstract:

This paper proposes a method to extend the field of view of computed tomography images. In a first step, the field of view is extended by extrapolating linearly the outer channels in the sinogram space. The modified sinogram is then used to reconstruct extended field of view (EFoV) images containing artifacts due to the channels extension. In a second step, those artifacts are reduced by a deep learning network in image space.
The proposed method has been evaluated on a collection of clinical scans. The resulting volumes have been checked for consistency and plausibility and compared to an existing state of the art EFoV method.

Code Of Conduct: I have read and accept the code of conduct.

Remove If Rejected: Remove submission from public view if paper is rejected.