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    Dictionary learning for data recovery in positron emission tomography

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    Author
    Valiollahzadeh, SeyyedMajid; Clark, John W. Jr.; Mawlawi, Osama
    Date
    2015
    Abstract
    Compressed sensing (CS) aims to recover images from fewer measurements than that governed by the Nyquist sampling theorem. Most CS methods use analytical predefined sparsifying domains such as total variation, wavelets, curvelets, and finite transforms to perform this task. In this study, we evaluated the use of dictionary learning (DL) as a sparsifying domain to reconstruct PET images from partially sampled data, and compared the results to the partially and fully sampled image (baseline).A CS model based on learning an adaptive dictionary over image patches was developed to recover missing observations in PET data acquisition. The recovery was done iteratively in two steps: a dictionary learning step and an image reconstruction step. Two experiments were performed to evaluate the proposed CS recovery algorithm: an IEC phantom study and five patient studies. In each case, 11% of the detectors of a GE PET/CT system were removed and the acquired sinogram data were recovered using the proposed DL algorithm. The recovered images (DL) as well as the partially sampled images (with detector gaps) for both experiments were then compared to the baseline. Comparisons were done by calculating RMSE, contrast recovery and SNR in ROIs drawn in the background, and spheres of the phantom as well as patient lesions.For the phantom experiment, the RMSE for the DL recovered images were 5.8% when compared with the baseline images while it was 17.5% for the partially sampled images. In the patients' studies, RMSE for the DL recovered images were 3.8%, while it was 11.3% for the partially sampled images. Our proposed CS with DL is a good approach to recover partially sampled PET data. This approach has implications toward reducing scanner cost while maintaining accurate PET image quantification.
    Citation
    Valiollahzadeh, SeyyedMajid, Clark, John W. Jr. and Mawlawi, Osama. "Dictionary learning for data recovery in positron emission tomography." Physics in Medicine and Biology, 60, no. 15 (2015) IOP Publishing: http://dx.doi.org/10.1088/0031-9155/60/15/5853.
    Published Version
    http://dx.doi.org/10.1088/0031-9155/60/15/5853
    Type
    Journal article
    Publisher
    IOP Publishing
    Citable link to this page
    https://hdl.handle.net/1911/94229
    Rights
    This is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by IOP Publishing.
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    • ECE Publications [1443]
    • Faculty Publications [4988]

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    Home | FAQ | Contact Us | Privacy Notice | Accessibility Statement
    Managed by the Digital Scholarship Services at Fondren Library, Rice University
    Physical Address: 6100 Main Street, Houston, Texas 77005
    Mailing Address: MS-44, P.O.BOX 1892, Houston, Texas 77251-1892
    Site Map