Computationally Efficient Cascaded Training for Deep Unrolled Network in CT Imaging
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Dose reduction in computed tomography (CT) has been of great research interest for decades with the endeavor to reduce the health risk related to radiation. Promising results have been achieved by the recent application of deep learning to image reconstruction algorithms. Unrolled neural networks have reached state-of-the-art performance by learning the image reconstruction algorithm end-to-end. However, it suffers from huge memory consumption and long training time, which made it hard to scale to 3D data with current hardware. In this paper, we proposed an unrolled neural network for image reconstruction which can be trained step-by-step instead of end-to-end. Multiple cascades of image domain network were trained sequentially and connected with iterations which enforced data fidelity. Local image patches could be utilized for the neural network training, which made it fully scalable to 3D CT data. The proposed method was validated with both simulated and real data and demonstrated competing performance against the end-to-end networks.
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