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%.
Conventional X-ray computed tomography (CT) systems obtain single- or dual-energy measurements, from which dual-energy CT has emerged as the superior way to recognize materials. Recently photon counting detectors have facilitated multi-spectral CT which captures spectral information by counting photon arrivals at different energy windows. However, the narrow energy bins result in a lower signal-to-noise ratio in each bin, particularly in the lower energy bins. This effect is significant and challenging when high-attenuation materials such as metal are present in the area to be imaged. In this paper, we propose a novel technique to estimate material properties with multi-spectral CT in the presence of high-attenuation materials. Our approach combines basis decomposition concepts using multiple-spectral bin information, as well as individual energy bin reconstructions. We show that this approach is robust in the presence of metal and outperforms alternative techniques for material estimation with multi-spectral CT as well with the state-of-art dual-energy CT.