Positive correlations between tumor uptake on FDG PET and energy expenditure of patients with esophageal cancer

Mitamura, Atsushi; Kaneta, Tomohiro; Miyata, Goh; Takanami, Kentaro; Hiraide, Tomomichi; Fukuda, Hiroshi; Takahashi, Syouki; Satoh, Susumu

doi:10.1007/s12149-010-0456-9

Cited by 10 publications

(2 citation statements)

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“…Another group of 16 male rats, 9 weeks-11 weeks old, weighing 300 g ± 20 g, was also used, of which 8 underwent left MCAO and the other 8 were healthy controls. Intraluminal occlusion of the MCA was accomplished using a modification of the Longa technique [7,12]. All the rats were deprived of food for 12 h-15 h before 18 F-FDG injection, but had free access to drinking water [13].…”

Section: Animals and Data Acquisitionmentioning

confidence: 99%

“…However, in group analysis, the FDG-PET image intensity is not only determined by cellular activity, but is also affected by several other controllable factors, such as the injected dose of 18 F-FDG, the total scan time, and the PET imaging count rate, as well as the uncontrollable factor of basal metabolic ratio in each subject [7,8]. Therefore, despite the influence of brain diseases, the basic level of image intensity varies in each individual, and this should be eliminated prior to statistical analysis.…”

Section: Introductionmentioning

confidence: 99%

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An Automatic Method for Generating an Unbiased Intensity Normalizing Factor in Positron Emission Tomography Image Analysis After Stroke

et al. 2018

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Positron emission tomography (PET) imaging of functional metabolism has been widely used to investigate functional recovery and to evaluate therapeutic efficacy after stroke. The voxel intensity of a PET image is the most important indicator of cellular activity, but is affected by other factors such as the basal metabolic ratio of each subject. In order to locate dysfunctional regions accurately, intensity normalization by a scale factor is a prerequisite in the data analysis, for which the global mean value is most widely used. However, this is unsuitable for stroke studies. Alternatively, a specified scale factor calculated from a reference region is also used, comprising neither hyper- nor hypo-metabolic voxels. But there is no such recognized reference region for stroke studies. Therefore, we proposed a totally data-driven automatic method for unbiased scale factor generation. This factor was generated iteratively until the residual deviation of two adjacent scale factors was reduced by < 5%. Moreover, both simulated and real stroke data were used for evaluation, and these suggested that our proposed unbiased scale factor has better sensitivity and accuracy for stroke studies.

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Section: Animals and Data Acquisitionmentioning

confidence: 99%