Quantitative and Qualitative Improvement of Low-Count 68GaCitrate and 90YMicrospheres PET Image Reconstructions Using Block Sequential Regularized Expectation Maximization Algorithm

There are several important positron emission tomography (PET) imaging scenarios that require imaging with very low photon statistics, for which both quantitative accuracy and visual quality should not be neglected. For example, PET imaging with the low photon statistics is closely related to active...

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Published in:Molecular imaging and biology 2020-02, Vol.22 (1), p.208
Main Authors: Seo, Youngho, Khalighi, Mohammad Mehdi, Wangerin, Kristen A, Deller, Timothy W, Wang, Yung-Hua, Jivan, Salma, Kohi, Maureen P, Aggarwal, Rahul, Flavell, Robert R, Behr, Spencer C, Evans, Michael J
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Language:English
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Summary:There are several important positron emission tomography (PET) imaging scenarios that require imaging with very low photon statistics, for which both quantitative accuracy and visual quality should not be neglected. For example, PET imaging with the low photon statistics is closely related to active efforts to significantly reduce radiation exposure from radiopharmaceuticals. We investigated two examples of low-count PET imaging: (a) imaging [90Y]microsphere radioembolization that suffers the very small positron emission fraction of Y-90's decay processes, and (b) cancer imaging with [68Ga]citrate with uptake time of 3-4 half-lives, necessary for visualizing tumors. In particular, we investigated a type of penalized likelihood reconstruction algorithm, block sequential regularized expectation maximization (BSREM), for improving both image quality and quantitative accuracy of these low-count PET imaging cases.PURPOSEThere are several important positron emission tomography (PET) imaging scenarios that require imaging with very low photon statistics, for which both quantitative accuracy and visual quality should not be neglected. For example, PET imaging with the low photon statistics is closely related to active efforts to significantly reduce radiation exposure from radiopharmaceuticals. We investigated two examples of low-count PET imaging: (a) imaging [90Y]microsphere radioembolization that suffers the very small positron emission fraction of Y-90's decay processes, and (b) cancer imaging with [68Ga]citrate with uptake time of 3-4 half-lives, necessary for visualizing tumors. In particular, we investigated a type of penalized likelihood reconstruction algorithm, block sequential regularized expectation maximization (BSREM), for improving both image quality and quantitative accuracy of these low-count PET imaging cases.The NEMA/IEC Body phantom filled with aqueous solution of Y-90 or Ga-68 was scanned to mimic the low-count scenarios of corresponding patient data acquisitions on a time-of-flight (TOF) PET/magnetic resonance imaging system. Contrast recovery, background variation, and signal-to-noise ratio were evaluated in different sets of count densities using both conventional TOF ordered subset expectation (TOF-OSEM) and TOF-BSREM algorithms. The regularization parameter, beta, in BSREM that controls the tradeoff between image noise and resolution was evaluated to find a value for improved confidence in image interpretation. Visual quality assessment o
ISSN:1860-2002
1860-2002
DOI:10.1007/s11307-019-01347-0