Dual Network Architecture for Few-view CT -- Trained on ImageNet Data and Transferred for Medical Imaging
This addresses the challenge of minimizing cancer risk from ionizing radiation in medical imaging, though it is incremental as it builds on existing data-driven approaches.
The paper tackles the problem of reducing radiation dose in CT scans by developing a dual network architecture (DNA) for few-view CT image reconstruction, achieving competitive performance with state-of-the-art methods while using significantly less memory.
X-ray computed tomography (CT) reconstructs cross-sectional images from projection data. However, ionizing X-ray radiation associated with CT scanning might induce cancer and genetic damage. Therefore, the reduction of radiation dose has attracted major attention. Few-view CT image reconstruction is an important topic to reduce the radiation dose. Recently, data-driven algorithms have shown great potential to solve the few-view CT problem. In this paper, we develop a dual network architecture (DNA) for reconstructing images directly from sinograms. In the proposed DNA method, a point-based fully-connected layer learns the backprojection process requesting significantly less memory than the prior arts do. Proposed method uses O(C*N*N_c) parameters where N and N_c denote the dimension of reconstructed images and number of projections respectively. C is an adjustable parameter that can be set as low as 1. Our experimental results demonstrate that DNA produces a competitive performance over the other state-of-the-art methods. Interestingly, natural images can be used to pre-train DNA to avoid overfitting when the amount of real patient images is limited.