Abstract
Statistical image reconstruction methods provide improved image quality in low-dose X-ray CT. However, the long computation time of iterative algorithms limits their clinical use. Ordered subsets algorithms based on separable quadratic surrogates (OS-SQS) are attractive as they are simple and amenable for massive parallelization in modern computing architecture, but require many iterations to converge. Here, we further accelerate OS algorithms by using momentum techniques. We use real patient CT scan to illustrate that the proposed algorithms converge rapidly compared to previous OS algorithms.
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