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Material Decomposition Problem in Spectral CT: A Transfer Deep Learning Approach

Abascal, J; Ducros, N; Pronina, V; Bussod, S; Hauptmannz, A; Arridge, S; Douek, P; (2020) Material Decomposition Problem in Spectral CT: A Transfer Deep Learning Approach. In: Proceedings of the 2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops). IEEE: Iowa City, IA, USA. Green open access

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Abstract

Current model-based variational methods used for solving the nonlinear material decomposition problem in spectral computed tomography rely on prior knowledge of the scanner energy response, but this is generally unknown or spatially varying. We propose a twostep deep transfer learning approach that can learn the energy response of the scanner and its variation across the detector pixels. First, we pretrain U-Net on a large data set assuming ideal data, and, second, we fine-tune the pretrained model using few data corresponding to a non-ideal scenario. We assess it on numerical thorax phantoms that comprise soft tissue, bone and kidneys marked with gadolinium, which are built from the kits19 dataset. We find that the proposed method solves the material decomposition problem without prior knowledge of the scanner energy response. We compare our approach to a regularized Gauss-Newton method and obtain a superior image quality.

Type: Proceedings paper
Title: Material Decomposition Problem in Spectral CT: A Transfer Deep Learning Approach
Event: 2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops)
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/ISBIWorkshops50223.2020.9153440
Publisher version: http://dx.doi.org/10.1109/ISBIWorkshops50223.2020....
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Spectral CT, inverse problem, deep learning, transfer learning
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10111851
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