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WE‐C‐303A‐03: Pharmacokinetic Analysis of Hypoxia 18‐Fluoromisonidazole Dynamic PET Imaging in Head and Neck Cancer
Author(s) -
Wang W,
Lee N,
Georgi J,
Narayanan M,
Humm J
Publication year - 2009
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.3182481
Subject(s) - nuclear medicine , tumor hypoxia , medicine , head and neck cancer , radiation therapy , concordance , perfusion , hypoxia (environmental) , positron emission tomography , image registration , radiology , chemistry , artificial intelligence , computer science , image (mathematics) , oxygen , organic chemistry
Purpose: This paper uses pharmacokinetic analysis of 18‐Fluoromisonidazole (FMISO) dynamic PET imaging to investigate if there is any correlation between tumor hypoxia ( K i ), tumor‐to‐blood ratio ( T/B ) in late‐time image, local blood perfusion ( k 1 ), and local vasculature fraction (β) for head‐and‐neck cancer patients. Methods and Materials: Newly diagnosed patients with head‐and‐neck cancer prior to chemotherapy or radiotherapy underwent dynamic FMISO‐PET scan. The data was acquired in 3 consecutive PET/CT dynamic scan segments, with start acquisition time [0, 1, 2, 3, 4, 5, 10, 15, 20, 25, 90, 95, 180, 185] minutes, consisting of 5 frames in 1‐minute frames, following by 5‐minutes frames. The dynamic PET images were first registered with each other and then analyzed using Philips Resarch's Voxulus pharmacokinetic software. The ( K i , k 1 , β) kinetic parameter images were derived for each patient. Results: Nine head‐and‐neck cancer patients' data were analyzed. Representative images of FDG‐PET (showing the tumor), CT (showing the anatomy), late‐time FMISO‐PET (showing T/B ), and ( K i , k 1 , β) kinetic parameter images were shown consisting of a patient example with good concordance of tumor hypoxia and high T/B , one with concordance of no tumor hypoxia and low T/B , and one with ambiguity between tumor hypoxia and T/B . Scatter diagrams were plotted between each pair of T/B, K i , k 1 , β and corresponding correlation coefficient computed. Conclusions: There is strong positive correlation between ROI's T/B and hypoxia index K i . However, due to the statistical photon counting noise in PET imaging, and the amplification of noise in kinetic analysis, the direct correlation between individual pixel's T/B and hypoxia is not obvious. For a tumor ROI, there is slight negative correlation between k 1 and K i , moderate positive correlation between β and K i , but no correlation between β and K i . This work is supported in part by NIH PO1 CA115675.