Material Decomposition in Spectral CT Using Deep Learning: A Sim2Real Transfer Approach

Material Decomposition in Spectral CT Using Deep Learning: A Sim2Real Transfer Approach

The state-of-the art for solving the nonlinear material decomposition problem in spectral computed tomography is based on variational methods, but these are computationally slow and critically depend on the particular choice of the regularization functional. Convolutional neural networks have been p...

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Bibliographic Details
Main Authors: Juan F. P. J. Abascal, Nicolas Ducros, Valeriya Pronina, Simon Rit, Pierre-Antoine Rodesch, Thomas Broussaud, Suzanne Bussod, Philippe C. Douek, Andreas Hauptmann, Simon Arridge, Francoise Peyrin
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Spectral CT
inverse problem
deep learning
transfer learning
Online Access:https://ieeexplore.ieee.org/document/9343830/

Internet

https://ieeexplore.ieee.org/document/9343830/

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