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• W2955646516 endingPage "155017" @default.
• W2955646516 startingPage "155017" @default.
• W2955646516 abstract "Mumford-Shah (MS) functional has emerged as a regularization technique in x-ray computed tomography (CT) recently. However, for high-resolution CT applications, the huge size of both projection data and image leads to an implementation difficulty. In this work, we propose an approach to implement and accelerate MS regularization on a multi-GPU platform to resolve the issue of data size and rich onboard memory and computing units. We have established a novel partition scheme of the 3D volume under reconstruction and corresponding multithread parallel acceleration strategy to fully utilize the computing resource of multi-GPUs. Our implementation is highly modularized and can be easily scaled with the configuration of GPUs. Experiment results with simulation data as well as real data demonstrate a superior reconstruction quality in contrast to the total variation regularization approach, especially for the ultra-low-dose case. Moreover, this is the first time that MS regularization is used for 3D reconstruction of huge images up to 30723 voxels within 12 min." @default.
• W2955646516 created "2019-07-12" @default.
• W2955646516 creator A5003467063 @default.
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• W2955646516 date "2019-08-07" @default.
• W2955646516 modified "2023-10-18" @default.
• W2955646516 title "Image reconstruction by Mumford–Shah regularization for low-dose CT with multi-GPU acceleration" @default.
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• W2955646516 doi "https://doi.org/10.1088/1361-6560/ab2c85" @default.
• W2955646516 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31239414" @default.
• W2955646516 hasPublicationYear "2019" @default.
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