OBJECTIVE: 6-[(18)F]Fluoro-L: -DOPA (FDOPA) is a radiopharmaceutical valuable for assessing the presynaptic dopaminergic function when used with positron emission tomography (PET). More specifically, the striatal-to-occipital ratio (SOR) of FDOPA uptake images has been extensively used as a quantitative parameter in these PET studies. Our aim was to develop an easy, automated method capable of performing objective analysis of SOR in FDOPA PET images of Parkinson's disease (PD) patients. METHODS: Brain images from FDOPA PET studies of 21 patients with PD and 6 healthy subjects were included in our automated striatal analyses. Images of each individual were spatially normalized into an FDOPA template. Subsequently, the image slice with the highest level of basal ganglia activity was chosen among the series of normalized images. Also, the immediate preceding and following slices of the chosen image were then selected. Finally, the summation of these three images was used to quantify and calculate the SOR values. The results obtained by automated analysis were compared with manual analysis by a trained and experienced image processing technologist. RESULTS: The SOR values obtained from the automated analysis had a good agreement and high correlation with manual analysis. The differences in caudate, putamen, and striatum were -0.023, -0.029, and -0.025, respectively; correlation coefficients 0.961, 0.957, and 0.972, respectively. CONCLUSIONS: We have successfully developed a method for automated striatal uptake analysis of FDOPA PET images. There was no significant difference between the SOR values obtained from this method and using manual analysis. Yet it is an unbiased time-saving and cost-effective program and easy to implement on a personal computer.
OBJECTIVE:6-[(18)F]Fluoro-L: -DOPA (FDOPA) is a radiopharmaceutical valuable for assessing the presynaptic dopaminergic function when used with positron emission tomography (PET). More specifically, the striatal-to-occipital ratio (SOR) of FDOPA uptake images has been extensively used as a quantitative parameter in these PET studies. Our aim was to develop an easy, automated method capable of performing objective analysis of SOR in FDOPA PET images of Parkinson's disease (PD) patients. METHODS: Brain images from FDOPA PET studies of 21 patients with PD and 6 healthy subjects were included in our automated striatal analyses. Images of each individual were spatially normalized into an FDOPA template. Subsequently, the image slice with the highest level of basal ganglia activity was chosen among the series of normalized images. Also, the immediate preceding and following slices of the chosen image were then selected. Finally, the summation of these three images was used to quantify and calculate the SOR values. The results obtained by automated analysis were compared with manual analysis by a trained and experienced image processing technologist. RESULTS: The SOR values obtained from the automated analysis had a good agreement and high correlation with manual analysis. The differences in caudate, putamen, and striatum were -0.023, -0.029, and -0.025, respectively; correlation coefficients 0.961, 0.957, and 0.972, respectively. CONCLUSIONS: We have successfully developed a method for automated striatal uptake analysis of FDOPA PET images. There was no significant difference between the SOR values obtained from this method and using manual analysis. Yet it is an unbiased time-saving and cost-effective program and easy to implement on a personal computer.
Authors: Arnoldo Piccardo; Roberto Cappuccio; Gianluca Bottoni; Diego Cecchin; Luca Mazzella; Alessio Cirone; Sergio Righi; Martina Ugolini; Pietro Bianchi; Pietro Bertolaccini; Elena Lorenzini; Michela Massollo; Antonio Castaldi; Francesco Fiz; Laura Strada; Angelina Cistaro; Massimo Del Sette Journal: Eur Radiol Date: 2021-03-08 Impact factor: 5.315
Authors: Sinn-Rithy Toch; Sylvain Poussier; Emilien Micard; Marc Bertaux; Axel Van Der Gucht; Elodie Chevalier; Pierre-Yves Marie; Eric Guedj; Antoine Verger Journal: Mol Imaging Biol Date: 2019-06 Impact factor: 3.488
Authors: Felix P Kuhn; Geoffrey I Warnock; Cyrill Burger; Katharina Ledermann; Chantal Martin-Soelch; Alfred Buck Journal: EJNMMI Res Date: 2014-01-22 Impact factor: 3.138
Authors: James Bland; Abolfazl Mehranian; Martin A Belzunce; Sam Ellis; Casper da Costa-Luis; Colm J McGinnity; Alexander Hammers; Andrew J Reader Journal: Med Phys Date: 2019-10-04 Impact factor: 4.071