Estimating dual-energy CT imaging from single-energy CT data with material decomposition convolutional neural network
Abstract
Dual-energy computed tomography (DECT) is of great significance for clinical practice due to its huge potential to provide material-specific information. However, DECT scanners are usually more expensive than standard single-energy CT (SECT) scanners and thus are less accessible to undeveloped regions. In this paper, we show that the energy-domain correlation and anatomical consistency between standard DECT images can be harnessed by a deep...
Paper Details
Title
Estimating dual-energy CT imaging from single-energy CT data with material decomposition convolutional neural network
Published Date
May 1, 2021
Journal
Volume
70
Pages
102001 - 102001
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