Physics-informed Deep Learning for Dual-Energy Computed Tomography Image Processing
| Autor: | Sarah J. Moum, Michael H. Lev, Ramon Gonzalez, Can Ozan Tan, Florine C. Jolink, Rick H. J. Bergmans, Bart R. Thomson, Maarten G. Poirot, Rajiv Gupta |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Multidisciplinary
business.industry Deep learning Computational science lcsh:R Process (computing) Digital Enhanced Cordless Telecommunications lcsh:Medicine Pattern recognition Dual-Energy Computed Tomography Image processing Convolutional neural network Article 030218 nuclear medicine & medical imaging Image (mathematics) 03 medical and health sciences 0302 clinical medicine Decomposition (computer science) lcsh:Q Artificial intelligence business lcsh:Science Computed tomography 030217 neurology & neurosurgery |
| Zdroj: | Scientific Reports, Vol 9, Iss 1, Pp 1-9 (2019) Scientific Reports |
| ISSN: | 2045-2322 |
| Popis: | Dual-energy CT (DECT) was introduced to address the inability of conventional single-energy computed tomography (SECT) to distinguish materials with similar absorbances but different elemental compositions. However, material decomposition algorithms based purely on the physics of the underlying attenuation process have several limitations, leading to low signal-to-noise ratio (SNR) in the derived material-specific images. To overcome these, we trained a convolutional neural network (CNN) to develop a framework to reconstruct non-contrast SECT images from DECT scans. We show that the traditional physics-based decomposition algorithms do not bring to bear the full information content of the image data. A CNN that leverages the underlying physics of the DECT image generation process as well as the anatomic information gleaned via training with actual images can generate higher fidelity processed DECT images. |
| Databáze: | OpenAIRE |
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