Shape analysis of 123I-N-ω-fluoropropyl-2-β-carbomethoxy-3β-(4-iodophenyl) nortropane single-photon emission computed tomography images in the assessment of patients with parkinsonian syndromes

PURPOSE: The purpose of this study was to show the viability and performance of a shape-based pattern recognition technique applied to I-N-omega-fluoropropyl-2-beta-carbomethoxy-3beta-(4-iodophenyl) nortropane single-photon emission computed tomography (FP-CIT SPECT) in patients with parkinsonism. METHODS: A fully automated pattern recognition tool, based on the shape of FP-CIT SPECT images, was written using Java. Its performance was evaluated and compared with QuantiSPECT, a region-of-interest-based quantitation tool, and observer performance using receiver operating characteristic analysis and kappa statistics. The techniques were compared using a sample of patients and controls recruited from a prospective community-based study of first presentation of parkinsonian symptoms with longitudinal follow up (median 3 years). RESULTS: The shape-based technique as well as the conventional semiquantitative approach was performed by experienced observers. The technique had a high level of automation, thereby avoiding observer/operator variability. CONCLUSION: A pattern recognition approach is a viable alternative to traditional methods of analysis in FP-CIT SPECT and has additional advantages.