Nihon Hoshasen Gijutsu Gakkai zasshi · 2015
[Evaluation of optimal threshold of Z score map for statistical brain image analysis].
Оценочное исследованиеСтатьяНазвание и аннотация: Перевод пока не подготовлен
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Sakai Takayuki, Onishi Hideo, Miyazoe Koji
Аннотация
PURPOSE: Aim of this study was to investigate optimal threshold of Z score when evaluating statistics image of Alzheimer's disease visually.
METHOD: We classified 53 clinical patients in control and target group, and evaluated the distribution of Z score calculated with statistical brain image analysis for magnetic resonance and perfusion single photon emission computed tomography (SPECT). The optimal Z score threshold was determined from statistical significance that compared previously mentioned groups.
RESULTS: Target group was able to classify significantly Z score at 1.25 from control group in wide region of parietal lobe with statistical brain image analysis for perfusion SPECT.
DISCUSSION: The optimal threshold is equal or less than 2.0, in the case of Z score variance is close to the standard normal distribution. In contrast, the threshold is over 2.0 in the case of Z score variance is more than 1.0, and then by using ordinary threshold 2.0, it cannot point out abnormality.
METHOD: We classified 53 clinical patients in control and target group, and evaluated the distribution of Z score calculated with statistical brain image analysis for magnetic resonance and perfusion single photon emission computed tomography (SPECT). The optimal Z score threshold was determined from statistical significance that compared previously mentioned groups.
RESULTS: Target group was able to classify significantly Z score at 1.25 from control group in wide region of parietal lobe with statistical brain image analysis for perfusion SPECT.
DISCUSSION: The optimal threshold is equal or less than 2.0, in the case of Z score variance is close to the standard normal distribution. In contrast, the threshold is over 2.0 in the case of Z score variance is more than 1.0, and then by using ordinary threshold 2.0, it cannot point out abnormality.