Longitudinal evaluation using FP-CIT in patients with parkinsonism

Imaging Procedure and Clinical Studies of [18F]FP-CIT PET

N-3-[18F]fluoropropyl-2β-carbomethoxy-3β-4-iodophenyl nortropane ([18F]FP-CIT) is a radiopharmaceutical for dopamine transporter (DAT) imaging using positron emission tomography (PET) to detect dopaminergic neuronal degeneration in patients with parkinsonian syndrome. [18F]FP-CIT was granted approval by the Ministry of Food and Drug Safety in 2008 as the inaugural radiopharmaceutical for PET imaging, and it has found extensive utilization across numerous institutions in Korea. This review article presents an imaging procedure for [18F]FP-CIT PET to aid nuclear medicine physicians in clinical practice and systematically reviews the clinical studies associated with [18F]FP-CIT PET.

Assisted Diagnosis of Parkinsonism Based on the Striatal Morphology

Parkinsonism is a clinical syndrome characterized by the progressive loss of striatal dopamine. Its diagnosis is usually corroborated by neuroimaging data such as DaTSCAN neuroimages that allow visualizing the possible dopamine deficiency. During the last decade, a number of computer systems have been proposed to automatically analyze DaTSCAN neuroimages, eliminating the subjectivity inherent to the visual examination of the data. In this work, we propose a computer system based on machine learning to separate Parkinsonian patients and control subjects using the size and shape of the striatal region, modeled from DaTSCAN data. First, an algorithm based on adaptative thresholding is used to parcel the striatum. This region is then divided into two according to the brain hemisphere division and characterized with 152 measures, extracted from the volume and its three possible 2-dimensional projections. Afterwards, the Bhattacharyya distance is used to discard the least discriminative measures and, finally, the neuroimage category is estimated by means of a Support Vector Machine classifier. This method was evaluated using a dataset with 189 DaTSCAN neuroimages, obtaining an accuracy rate over 94%. This rate outperforms those obtained by previous approaches that use the intensity of each striatal voxel as a feature.