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Journal of Medical Imaging

Assessment of patient selection criteria for quantitative imaging with respiratory-gated positron emission tomography
Author(s): Stephen R. Bowen; Larry A. Pierce; Adam M. Alessio; Chi Liu; Scott D. Wollenweber; Charles W. Stearns; Paul E. Kinahan
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Paper Abstract

The objective of this investigation was to propose techniques for determining which patients are likely to benefit from quantitative respiratory-gated imaging by correlating respiratory patterns to changes in positron emission tomography (PET) metrics. Twenty-six lung and liver cancer patients underwent PET/computed tomography exams with recorded chest/abdominal displacements. Static and adaptive amplitude-gated [18F]fluoro-D-glucose (FDG) PET images were generated from list-mode acquisitions. Patients were grouped by respiratory pattern, lesion location, or degree of lesion attachment to anatomical structures. Respiratory pattern metrics were calculated during time intervals corresponding to PET field of views over lesions of interest. FDG PET images were quantified by lesion maximum standardized uptake value (SUVmax). Relative changes in SUVmax between static and gated PET images were tested for association to respiratory pattern metrics. Lower lung lesions and liver lesions had significantly higher changes in SUVmax than upper lung lesions (14 versus 3%, p<0.0001). Correlation was highest (0.42±0.10, r2=0.59, p<0.003) between changes in SUVmax and nonstandard respiratory pattern metrics. Lesion location had a significant impact on changes in PET quantification due to respiratory gating. Respiratory pattern metrics were correlated to changes in SUVmax, though sample size limited statistical power. Validation in larger cohorts may enable selection of patients prior to acquisition who would benefit from respiratory-gated PET imaging.

Paper Details

Date Published: 24 September 2014
PDF: 8 pages
J. Med. Img. 1(2) 026001 doi: 10.1117/1.JMI.1.2.026001
Published in: Journal of Medical Imaging Volume 1, Issue 2
Show Author Affiliations
Stephen R. Bowen, Univ. of Washington Medical Ctr. (United States)
Larry A. Pierce, Univ. of Washington Medical Ctr. (United States)
Adam M. Alessio, Univ. of Washington Medical Ctr. (United States)
Chi Liu, Yale School of Medicine (United States)
Scott D. Wollenweber, GE Healthcare (United States)
Charles W. Stearns, GE Healthcare (United States)
Paul E. Kinahan, Univ. of Washington Medical Ctr. (United States)


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