Diagnosis support by multivariate analysis of SPECT images in regional cerebral blood flow

Abstract

Currently, the nuclear medicine image diagnostic methods such as single photon emission computed tomography (SPECT) and positron emission tomography (PET) are used for the diagnosis of the brain disease into which regional cerebral blood flow changes such as Alzheimer's disease, Parkinson's disease and so on. In the brain disease, the early diagnosis is extremely important for good prognosis, and one of the effective means is SPECT inspection. Along with the development of the technology, statistical image analysis methods such as Statistical Parametric Mapping (SPM) and easy Z-score Imaging System (eZIS) have been developed. These techniques enable us to find the abnormal blood flow sites in the images. However, these techniques don't provide the estimations of the diseases. Then, we tried to develop the new technique for estimations of a disease. We examined whether a more objective diagnosis is possible in brain disorders by applying the multivariate analysis to cerebral blood flow SPECT data.