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%.
Dual-energy computed tomography (DECT) has been widely used to reconstruct basis components. In previous studies, ou DECT algorithm has shown high accuracy in stopping power ratio (SPR) estimation of fixed objects for proton radiotherapy planning. However, patient movement between sequential data acquisitions may lead to severe motion artifacts in the component images. In order to reduce or eliminate the motion artifacts in clinical applications, we combine a deformable registration method with an accurate joint statistical iterative reconstruction algorithm, dual-energy alternating minimization (DEAM). Image registration is a process of geometrically aligning two or more images. We implement a multi-modality symmetric deformable registration method based on Advanced Normalization Tools (ANTs) to automatically align the scans we acquire for the same patient. The precalculated registration mapping and its inverse are then embedded into each iteration of the DEAM algorithm. The performance of warped DEAM is quantitatively assessed. Theoretically, the performance of warped DEAM on moved patients should be comparable to the performance of the original DEAM algorithm on fixed objects. The warped DEAM algorithm reduces motion artifacts while preserving the accuracy of the iterative joint statistical CT reconstruction algorithm, which enables us to reconstruct accurate results from sequentially scanned dual-energy patient data.