Quantitative Analysis of DAT SPECT Images by 3D Striatum Recontruction

Objective:Brain dopaminergic neurotransmission is currently evaluated by SPECT withdopamine transporter (DAT) ligands, with 123I FP-CIT (DaTSCAN - GE Healthcare)being the most utilized. Visual assessment of the DAT SPECT images, whichdepict the quantity of radioactivity absorbed by the striatum, is often usedand considered sufficient in many diagnostic situations of Parkinson Disease.Most of all the existing processing methods of DAT SPECT perform qualitative orsemi-quantitative analysis and allow medical doctors to easily distinguishParkinson's syndrome from essential tremor, but do not permit an exactassessment of the disease progression and the drugs' effects because of thehigh intra-observer and inter-observer variability due to the manualintervention both for the selection of the slices where the absorbedradioactivity is more visible and for the positioning of the ROIs (region ofinterest) in the selected slices. Therefore, the objective of this work is topropose a system that performs a quantitative analysis making the DAT-SPECTexamination fully reproducible. Method: We propose an effective 3D Striatumreconstruction method combined with pre- and post-filtering for the automaticstriatum extraction from SPECT images for performing quantitative and reliablemeasurements. In detail, the algorithm initially performs a pre-filtering phasein order to remove noise due to the acquisition step and then it automaticallyextracts all the slices where the striatum is present and finally reconstructsthe striatum volume. The 3D reconstruction algorithm builds a 3D modelrepresenting the surface of interest, starting from a set of SPECT slices. Thealgorithm builds an approximated 3D model and iteratively modifies it by aoptimization method. During each iteration, the position of every single vertexof the model is changed according to a composition of external and internalforces of the images. Finally, in order to increase the system's accuracy acorrection module for partial volume effects (PVEs) is developed. Conclusions:The proposed algorithm automatically performs the 3D segmentation of striatum.We have already collected a set of 200 patients together with the clinicaldiagnosis. We plan to estimate the error in the final assessment due toqualitative methods over the considered set of patients and to analyze thereproducibility of the automated method.