Longitudinal Prediction of Infant Diffusion MRI Data via Graph Convolutional Adversarial Networks
| Autor: | Dinggang Shen, Geng Chen, Pew Thian Yap, Weili Lin, Jaeil Kim, Yoonmi Hong |
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| Rok vydání: | 2019 |
| Předmět: |
Computer science
Residual Convolutional neural network Article 030218 nuclear medicine & medical imaging 03 medical and health sciences Adversarial system Child Development 0302 clinical medicine Longitudinal prediction Image Interpretation Computer-Assisted Humans Electrical and Electronic Engineering Approximation theory Radiological and Ultrasound Technology business.industry Infant Newborn Brain Pattern recognition Missing data Computer Science Applications Diffusion Magnetic Resonance Imaging Graph (abstract data type) Neural Networks Computer Artificial intelligence business Software Diffusion MRI |
| Zdroj: | IEEE Trans Med Imaging |
| ISSN: | 1558-254X 0278-0062 |
| Popis: | Missing data is a common problem in longitudinal studies due to subject dropouts and failed scans. We present a graph-based convolutional neural network to predict missing diffusion MRI data. In particular, we consider the relationships between sampling points in the spatial domain and the diffusion wave-vector domain to construct a graph. We then use a graph convolutional network to learn the non-linear mapping from available data to missing data. Our method harnesses a multi-scale residual architecture with adversarial learning for prediction with greater accuracy and perceptual quality. Experimental results show that our method is accurate and robust in the longitudinal prediction of infant brain diffusion MRI data. |
| Databáze: | OpenAIRE |
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