Abstract: The imaging workup in acute stroke can be simplified by reconstructing the non-contrast CT (NCCT) from CT perfusion (CTP) images, resulting in reduced workup time and radiation dose. This work presents a stacked bidirectional convolutional LSTM (C-LSTM) network to predict 3D volumes from 4D spatiotemporal data. Several parameterizations of the C-LSTM network were trained on a set of 17 CTP-NCCT pairs to learn to reconstruct NCCT from CTP and were subsequently quantitatively evaluated on a separate cohort of 16 cases. The results show that C-LSTM network clearly outperforms basic reconstruction methods and provides a promising general deep learning approach for handling high-dimensional spatiotemporal medical data.
Keywords: Deep learning, convolutional LSTM, CNN, LSTM, reconstruction, regression, non-contrast CT, NCCT, CT perfusion, CTP, CT, Stroke
Author Affiliation: Radboud University Medical Center, Nijmegen, The Netherlands