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Purpose: Positron emission tomography (PET) is a noninvasive molecular imaging tool with various clinical and preclinical applications. The polygonal structure of small-diameter PET scanners that are designed for specific purposes can lead to gaps between the detector modules and result in loss of PET data during measurement. In the current study, the authors applied the compressed sensing (CS)-based total variation (TV) minimization method to PET image reconstructions to reduce the artifacts caused by gaps in small-diameter PET systems.
Methods: The first step in each iteration estimates whether an image is consistent with the measured PET data using the existing common reconstruction algorithms (ART, OSEM, and RAMLA). The second step recovers sparsity in the gradient domain of the image by minimizing the TV of an estimated image. The authors evaluated the gap-compensable reconstruction algorithms with uniform disk and Shepp-Logan phantoms by simulating sinograms which contained Poisson random noise and a data loss due to detector gaps. In addition, these methods were applied to real high resolution research tomography (HRRT)-like sinograms of human brain and uniform phantom. A comparison with other methods for gap compensation prior to or during image reconstruction was also made. Quantitative evaluations were performed by computing the uniformity, root mean squared error, and difference between the reconstructed images of nongapped and gapped sinograms.
Results: The simulation results showed that the gap-compensable methods incorporating TV minimization could control gap artifacts, as well as Poisson random noise. In particular, OSEM-TV and RAMLA-TV showed distinct potential via the properties of convergence and robustness to different noise levels and gap angle.
Conclusions: A TV minimization strategy incorporated into commonly used PET reconstruction algorithms was useful for reducing the occurrence of artifacts due to gaps between detector modules in small-diameter PET scanners.
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| http://dx.doi.org/10.1118/1.3673775 | DOI Listing |
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