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Computed Tomography (CT) is one of the most used imaging modality in clinics. However, the associated high radiation is a major concern, and then the tradeoff between clinically accepted CT images and radiation dose is desired. In recent years, inspired by deep learning techniques, various deep learning-based methods have been developed for low-dose CT imaging, and they have potential to improve image quality of low dose CT. Meanwhile, most of these methods are trained on the CT datasets with the specific imaging geometry, and they could fail to successfully reconstruct the CT images from the other imaging geometries simultaneously, which might lead to poor generalization ability. In this work, to address this issue, we propose a dual-domain modulation for high-performance multi-geometry low-dose CT image reconstruction (DM-MG) method. Specific, the proposed DM-MG consists of two sub-networks, i.e., multilayer perceptron controlling the multiple imaging geometries, and inverse Radon transform approximation sub-network reconstructing the CT images from the different geometries. Moreover, the inverse Radon transform approximation sub-network contains sinogram-domain filtering module, back-projection module, and image-domain filtering module, which maps the Radon transformation to the network. Finally, the proposed DM-MG can reconstruct the CT images from the different imaging geometries and dose levels simultaneously. In this work, the CT data from 2016 NIHAAPM-Mayo Clinic Low Dose CT Grand Challenge are used to validate and evaluate the reconstruction performance of the proposed DM-MG. Experimental results demonstrate that the presented DM-MG method can produce high-quality CT images from multiple geometries simultaneously and outperform the competing method in the quantitative assessments and visual inspection show that the dual-domain modulated model we propose provides better reconstruction of low-dose CT images under different imaging geometries compared to other modulated models.
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