Purpose: To design a novel algorithm (BasGan) for automatic segmentation of striatal (123)I-FP-CIT SPECT.
Methods: The BasGan algorithm is based on a high-definition, three-dimensional (3D) striatal template, derived from Talairach's atlas. A blurred template, obtained by convolving the former with a 3D Gaussian kernel (FWHM = 10 mm), approximates striatal activity distribution. The algorithm performs translations and scale transformation on the bicommissural aligned image to set the striatal templates with standard size in an appropriate initial position. An optimization protocol automatically performs fine adjustments in the positioning of blurred templates to best match the radioactive counts, and locates an occipital ROI for background evaluation. Partial volume effect correction is included in the process of uptake computation of caudate, putamen and background. Experimental validation was carried out by means of six acquisitions of an anthropomorphic striatal phantom. The BasGan software was applied to a first set of patients with Parkinson's disease (PD) versus patients affected by essential tremor.
Results: A highly significant correlation was achieved between true binding potential and measured (123)I activity from the phantom. (123)I-FP-CIT uptake was significantly lower in all basal ganglia in the PD group versus controls with both BasGan and a conventional ROI method used for comparison, but particularly with the former. Correlations with the motor UPDRS score were far more significant with the BasGan.
Conclusion: The novel BasGan algorithm automatically performs the 3D segmentation of striata. Because co-registered MRI is not needed, it can be used by all nuclear medicine departments, since it is freely available on the Web.