Image Reconstruction In Computed Tomography Using Variance-Reduced Stochastic Gradient Descent
2017 IEEE 14TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2017)(2017)
摘要
Iterative image reconstruction algorithms have the potential to reduce the radiation dose in computed tomography (CT), but they are computationally intensive and their performance usually depends on careful parameter tuning. In this paper, we propose an iterative CT reconstruction algorithm based on the new class of variance-reduced stochastic gradient descent (VR-SGD) algorithms. Our experiments show that the proposed algorithm has a very fast convergence, while also eliminating the need for step size tuning. This study shows that VR-SGD can be used to devise very efficient iterative CT reconstruction algorithms.
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关键词
computed tomography, iterative reconstruction, stochastic gradient descent, variance-reduced SGD
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