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Physics guided machine learning for multi-material decomposition of tissues from dual-energy CT scans of simulated breast mod...

Publication Type
Conference Paper
Journal Name
Electronic Imaging
Publication Date
Conference Name
Electronic Imaging Symposium 2023 - High Performance Computing for Imaging 2023 (HPCI)
Conference Location
San Francisco, California, United States of America
Conference Sponsor
Society for Imaging Sciences and Technology
Conference Date

We introduce a physics guided data-driven method for image-based multi-material decomposition for dual-energy computed tomography (CT) scans. The method is demonstrated for CT scans of virtual human phantoms containing more than two types of tissues. The method is a physics-driven supervised learning technique. We take advantage of the mass attenuation coefficient of dense materials compared to that of muscle tissues to perform a preliminary extraction of the dense material from the images using unsupervised methods. We then perform supervised deep learning on the images processed by the extracted dense material to obtain the final multi-material tissue map. The method is demonstrated on simulated breast models with calcifications as the dense material placed amongst the muscle tissues. The physics-guided machine learning method accurately decomposes the various tissues from input images, achieving a normalized root-mean-squared error of 2.75%.