Dual energy X-ray Computed Tomography (DECT) enables to automatically
decompose materials in clinical images without the manual segmentation using
the dependency of the X-ray linear attenuation with energy. In this work we
propose a deep learning procedure called End-to-End Material Decomposition
(E2E-DEcomp) for quantitative material decomposition which directly convert the
CT projection data into material images. The algorithm is based on
incorporating the knowledge of the spectral model DECT system into the deep
learning training loss and combining a data-learned prior in the material image
domain. Furthermore, the training does not require any energy-based images in
the dataset but rather only sinogram and material images. We show the
effectiveness of the proposed direct E2E-DEcomp method on the AAPM spectral CT
dataset (Sidky and Pan, 2023) compared with state of the art supervised deep
learning networks.

该研究论文提出了一种名为 End-to-End Material Decomposition（E2E-DEcomp）的深度学习方法，可将 CT 投影数据直接转换为材料图像，无需基于能量的图像进行训练。该方法在 AAPM 光谱 CT 数据集上展示了其与现有监督深度学习网络相比的有效性。