title: Automated Analysis of (123)I-beta-CIT SPECT Images with Statistical Probabilistic Anatomical Mapping.
abstract: BACKGROUND
  Population-based statistical probabilistic anatomical maps have been used to generate probabilistic volumes of interest for analyzing perfusion and metabolic brain imaging. We investigated the feasibility of automated analysis for dopamine transporter images using this technique and evaluated striatal binding potentials in Parkinson's disease and Wilson's disease.
  
  
  METHODS
  We analyzed 2β-Carbomethoxy-3β-(4-(123)I-iodophenyl)tropane ((123)I-beta-CIT) SPECT images acquired from 26 people with Parkinson's disease (M:F = 11:15, mean age = 49 ± 12 years), 9 people with Wilson's disease (M: F = 6:3, mean age = 26 ± 11 years) and 17 normal controls (M:F = 5:12, mean age = 39 ± 16 years). A SPECT template was created using striatal statistical probabilistic map images. All images were spatially normalized onto the template, and probability-weighted regional counts in striatal structures were estimated. The binding potential was calculated using the ratio of specific and nonspecific binding activities at equilibrium. Voxel-based comparisons between groups were also performed using statistical parametric mapping.
  
  
  RESULTS
  Qualitative assessment showed that spatial normalizations of the SPECT images were successful for all images. The striatal binding potentials of participants with Parkinson's disease and Wilson's disease were significantly lower than those of normal controls. Statistical parametric mapping analysis found statistically significant differences only in striatal regions in both disease groups compared to controls.
  
  
  CONCLUSIONS
  We successfully evaluated the regional (123)I-beta-CIT distribution using the SPECT template and probabilistic map data automatically. This procedure allows an objective and quantitative comparison of the binding potential, which in this case showed a significantly decreased binding potential in the striata of patients with Parkinson's disease or Wilson's disease.
authors.affiliation: Department of Nuclear Medicine, Seoul National University College of Medicine, 101 Daehang-ro, Jongro-gu, Seoul, 110-744 Korea ; Department of Nuclear Medicine, Korea University Anam Hospital, Seoul, Korea.
authors.forename: Jae Seon
authors.initials: JS
authors.lastname: Eo
authors.affiliation: Department of Nuclear Medicine, Seoul National University Bundang Hospital, Gyeonggi-do, Korea.
authors.forename: Ho-Young
authors.initials: HY
authors.lastname: Lee
authors.affiliation: Department of Nuclear Medicine, Seoul National University College of Medicine, 101 Daehang-ro, Jongro-gu, Seoul, 110-744 Korea.
authors.forename: Jae Sung
authors.initials: JS
authors.lastname: Lee
authors.affiliation: Department of Nuclear Medicine, Seoul National University College of Medicine, 101 Daehang-ro, Jongro-gu, Seoul, 110-744 Korea ; Department of Nuclear Medicine, Boramae Medical Center, Seoul, Korea.
authors.forename: Yu Kyung
authors.initials: YK
authors.lastname: Kim
authors.affiliation: Department of Neurology, Seoul National University College of Medicine, Seoul, Korea.
authors.forename: Bum-Seok
authors.initials: BS
authors.lastname: Jeon
authors.affiliation: Department of Nuclear Medicine, Seoul National University College of Medicine, 101 Daehang-ro, Jongro-gu, Seoul, 110-744 Korea ; Department of Molecular Medicine and Biopharmaceutical Sciences, Seoul National University, Seoul, Korea.
authors.forename: Dong Soo
authors.initials: DS
authors.lastname: Lee
keywords: 123I-beta-CIT; Automated analysis; Parkinson’s disease; Statistical probabilistic anatomical mapping; Wilson’s disease