Statistical Evaluation of Single-Photon Emission Computed Tomography Image Using Smoothed Bootstrap Method

Abstract

Many of the neurodegenerative diseases associated with a decrease in regional cerebral blood flow (rCBF) are untreatable, and the appropriate therapeutic strategy is to slow the progression of the disease. Therefore, it is important that a definitive diagnosis is made as soon as possible when such diseases are suspected. Diagnostic imaging methods, such as positron emission tomography (PET) and single-photon emission computed tomography (SPECT), play an important role in such a definitive diagnosis. Since several problems arise when evaluating these images visually, a procedure to evaluate them objectively is necessary, and studies of image analyses using statistical evaluations have been suggested. However, the assumed data distribution in a statistical procedure may occasionally be inappropriate. Therefore, to evaluate the decrease of rCBF, it is important to use a statistical procedure without assumptions about the data distribution. In this study, we propose a new procedure that uses nonparametric or smoothed bootstrap methods to calculate a standardized distribution of the Z-score without assumptions about the data distribution. To test whether the judgment of the proposed procedure is equivalent to that of an evaluation based on the Z-score with a fixed threshold, the procedure was applied to a sample data set whose size was large enough to be appropriate for the assumption of the Z-score. As a result, the evaluations of the proposed procedure were equivalent to that of an evaluation based on the Z-score.