AirNet-SNL: End-to-End Training of Iterative Reconstruction and Deep Neural Network Regularization for Sparse-Data XPCI CT
| Autor: | Edward S. Jimenez, Collin J. C. Epstein, Dennis J. Lee, Derek West, Ryan N. Goodner, John Mulcahy-Stanislawczyk |
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| Rok vydání: | 2021 |
| Předmět: |
Artificial neural network
Computer science business.industry Deep learning ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Streak Phase-contrast imaging Iterative reconstruction Regularization (mathematics) Convolutional neural network Computer vision Artificial intelligence business Sparse matrix |
| Zdroj: | OSA Imaging and Applied Optics Congress 2021 (3D, COSI, DH, ISA, pcAOP). |
| DOI: | 10.1364/dh.2021.df4f.2 |
| Popis: | We present a deep learning image reconstruction method called AirNet-SNL for sparse view computed tomography. It combines iterative reconstruction and convolutional neural networks with end-to-end training. Our model reduces streak artifacts from filtered back-projection with limited data, and it trains on randomly generated shapes. This work shows promise to generalize learning image reconstruction. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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