J. Abascal et al., "Material Decomposition Problem in Spectral CT: A Transfer Deep Learning Approach," 2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops), Iowa City, IA, USA, 2020, pp. 1-4, doi: 10.1109/ISBIWorkshops50223.2020.9153440
Material decomposition problem in spectral CT : a transfer deep learning approach
|Author:||Abascal, J1; Ducros, N1; Pronina, V1;|
1Univ Lyon, INSA-Lyon, CNRS, Inserm, CREATIS UMR 5220, U1206, Lyon, France
2Research Unit of Mathematical Sciences, University of Oulu, Oulu, Finland
3Department of Computer Science, University College London, London, United Kingdom
|Online Access:||PDF Full Text (PDF, 1.8 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020100578109
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2020-10-05
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.
|Pages:||1 - 4|
2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops)
IEEE International Symposium on Biomedical Imaging Workshops
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
113 Computer and information sciences
217 Medical engineering
This project has received funding from the European Union’s Horizon2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement N◦701915. It was also performed within the framework of the LabEx PRIMES (ANR-11-LABX-0063) of University de Lyon and under the support of the ANR project SALTO (ANR-17-CE19-0011-01) . The project has also received funding from the European Union’s Horizon 2020 research and innovation programme under the grant agreement N 668142.This work was partly funded by France Life Imaging (grant ANR-11-INBS-0006) from the French Investissements d’Avenir.
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