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Dual-energy computed tomography (DECT) is a promising imaging modality. It has the potential to quantify different material densities and plays an important role in many clinical applications. To enable multiple material decomposition (MMD), the conventional analytical MMD algorithm assumes the presence of at most three materials in each image pixel, and each pixel is decomposed into a certain basis material triplet. However, the MMD algorithm requires strong prior knowledge of the mixture composition, and the decomposition performance is compromised around the boundaries of different compositions. In this work, we developed an analytical model based deep neural network MMD-Net to achieve multi-material decomposition in DECT. In particular, the type of the basis material triplet in each image pixel and the attenuation coefficients of each material are learned by dedicated convolution neural network modules, and the material-specific density maps are obtained from the analytical MMD algorithm. Physical experiments of a pig leg and a pork backbone specimen with inserted iodine concentrations were acquired to evaluate the performance of the MMD-Net. Results show that the proposed MMD-Net could provide high decomposition accuracy, and reduce the decomposition artifacts.

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For the U-Net based low dose CT (LDCT) imaging, there remains an interesting question: can the LDCT imaging neural network trained at one image resolution be transferred and applied directly onto another LDCT imaging application of different image resolution, provided that both the noise level and the structural content are similar? To answer this question, numerical simulations are performed with high-resolution (HR) and low-resolution (LR) LDCT images having comparable noise levels. Results demonstrated that the U-Net trained with LR CT images can be used to effectively reduce the noise on HR CT images, and vice versa. However, additional artifacts may be generated when transferring the same U-Net to a different LDCT imaging task with varied image spatial resolution due to the noise induced 2D features. For example, noticeable bright spots were generated at the edges of the FOV when the HR CT image is denoised by the LR CT image trained U-Net. In conclusion, this study suggests that it is necessary to retrain the U-Net for a dedicated LDCT imaging application.