Paper
25 March 2016 Solving outside-axial-field-of-view scatter correction problem in PET via digital experimentation
Andriy Andreyev, Yang-Ming Zhu, Jinghan Ye, Xiyun Song, Zhiqiang Hu
Author Affiliations +
Abstract
Unaccounted scatter impact from unknown outside-axial-field-of-view (outside-AFOV) activity in PET is an important degrading factor for image quality and quantitation. Resource consuming and unpopular way to account for the outside- AFOV activity is to perform an additional PET/CT scan of adjacent regions. In this work we investigate a solution to the outside-AFOV scatter problem without performing a PET/CT scan of the adjacent regions. The main motivation for the proposed method is that the measured random corrected prompt (RCP) sinogram in the background region surrounding the measured object contains only scattered events, originating from both inside- and outside-AFOV activity. In this method, the scatter correction simulation searches through many randomly-chosen outside-AFOV activity estimates along with known inside-AFOV activity, generating a plethora of scatter distribution sinograms. This digital experimentation iterates until a decent match is found between a simulated scatter sinogram (that include supposed outside-AFOV activity) and the measured RCP sinogram in the background region. The combined scatter impact from inside- and outside-AFOV activity can then be used for scatter correction during final image reconstruction phase. Preliminary results using measured phantom data indicate successful phantom length estimate with the method, and, therefore, accurate outside-AFOV scatter estimate.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Andriy Andreyev, Yang-Ming Zhu, Jinghan Ye, Xiyun Song, and Zhiqiang Hu "Solving outside-axial-field-of-view scatter correction problem in PET via digital experimentation", Proc. SPIE 9783, Medical Imaging 2016: Physics of Medical Imaging, 97831N (25 March 2016); https://doi.org/10.1117/12.2217332
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KEYWORDS
Positron emission tomography

Data acquisition

Image quality

Computer simulations

Contamination

Medical imaging

Monte Carlo methods

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