Learned Spectral Computed Tomography
- URL: http://arxiv.org/abs/2003.04138v1
- Date: Mon, 9 Mar 2020 13:39:12 GMT
- Title: Learned Spectral Computed Tomography
- Authors: Dimitris Kamilis, Mario Blatter, Nick Polydorides
- Abstract summary: We propose a Deep Learning imaging method for Spectral Photon-Counting Computed Tomography.
The method takes the form of a two-step learned primal-dual algorithm that is trained using case-specific data.
The proposed approach is characterised by fast reconstruction capability and high imaging performance, even in limited-data cases.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Spectral Photon-Counting Computed Tomography (SPCCT) is a promising
technology that has shown a number of advantages over conventional X-ray
Computed Tomography (CT) in the form of material separation, artefact removal
and enhanced image quality. However, due to the increased complexity and
non-linearity of the SPCCT governing equations, model-based reconstruction
algorithms typically require handcrafted regularisation terms and meticulous
tuning of hyperparameters making them impractical to calibrate in variable
conditions. Additionally, they typically incur high computational costs and in
cases of limited-angle data, their imaging capability deteriorates
significantly. Recently, Deep Learning has proven to provide state-of-the-art
reconstruction performance in medical imaging applications while circumventing
most of these challenges. Inspired by these advances, we propose a Deep
Learning imaging method for SPCCT that exploits the expressive power of Neural
Networks while also incorporating model knowledge. The method takes the form of
a two-step learned primal-dual algorithm that is trained using case-specific
data. The proposed approach is characterised by fast reconstruction capability
and high imaging performance, even in limited-data cases, while avoiding the
hand-tuning that is required by other optimisation approaches. We demonstrate
the performance of the method in terms of reconstructed images and quality
metrics via numerical examples inspired by the application of cardiovascular
imaging.
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