Sangwook Kim, Jimin Lee, Jungye Kim, Bitbyeol Kim, Chang Heon Choi, Seongmoon Jung
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Conversion of single-energy computed tomography to parametric maps of dual-energy computed tomography using convolutional neural network.
We propose a deep learning (DL) multi-task learning framework using convolutional neural network (CNN) for a direct conversion of single-energy CT (SECT) to three different parametric maps of dual-energy CT (DECT): Virtual-monochromatic image (VMI), effective atomic number (EAN), and relative electron density (RED).
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