Search

The aim of this work was to obtain a set of parameters to be applied in [123I]FP-CIT SPECT reconstruction in order to minimize the error between standardized and true values of the specific uptake ratio (SUR) in dopaminergic neurotransmission SPECT studies. To this end, Monte Carlo simulation was used to generate a database of 1380 projection data-sets from 23 subjects, including normal cases and a variety of pathologies. Studies were reconstructed using filtered back projection (FBP) with attenuation correction and ordered subset expectation maximization (OSEM) with correction for different degradations (attenuation, scatter and PSF). Reconstruction parameters to be optimized were the cut-off frequency of a 2D Butterworth pre-filter in FBP, and the number of iterations and the full width at Half maximum of a 3D Gaussian post-filter in OSEM. Reconstructed images were quantified using regions of interest (ROIs) derived from Magnetic Resonance scans and from the Automated Anatomical Labeling map. Results were standardized by applying a simple linear regression line obtained from the entire patient dataset. Our findings show that we can obtain a set of optimal parameters for each reconstruction strategy. The accuracy of the standardized SUR increases when the reconstruction method includes more corrections. The use of generic ROIs instead of subject-specific ROIs adds significant inaccuracies. Thus, after reconstruction with OSEM and correction for all degradations, subject-specific ROIs led to errors between standardized and true SUR values in the range [−0.5, +0.5] in 87% and 92% of the cases for caudate and putamen, respectively. These percentages dropped to 75% and 88% when the generic ROIs were used. [ABSTRACT FROM AUTHOR]

The aim of this work was to evaluate the influence of anatomical variability between subjects and of the partial volume effect (PVE) on the standardized Specific Uptake Ratio (SUR) in [123I]FP-bib SPECT studies. To this end, magnetic resonance (MR) images of 23 subjects with differences in the striatal volume of up to 44% were segmented and used to generate a database of 138 Monte Carlo simulated SPECT studies. Data included normal uptakes and pathological cases. Studies were reconstructed by filtered back projection (FBP) and the ordered-subset expectation-maximization algorithm. Quantification was carried out by applying a reference method based on regions of interest (ROIs) derived from the MR images and ROIs derived from the Automated Anatomical Labelling map. Our results showed that, regardless of anatomical variability, the relationship between calculated and true SUR values for caudate and putamen could be described by a multiple linear model which took into account the spill-over phenomenon caused by PVE ( for caudate and ≥0.980 for putamen) and also by a simple linear model (R2 ≥ 0.952 for caudate and ≥0.973 for putamen). Calculated values were standardized by inverting both linear systems. Differences between standardized and true values showed that, although the multiple linear model was the best approach in terms of variability ( ≥ 11.79 for caudate and  ≤7.36 for putamen), standardization based on a simple linear model was also suitable ( ≥ 12.44 for caudate and  ≤12.57 for putamen). [ABSTRACT FROM AUTHOR]

The aim of this work was to obtain a set of parameters to be applied in [ 123 I]FP-CIT SPECT reconstruction in order to minimize the error between standardized and true values of the specific uptake ratio (SUR) in dopaminergic neurotransmission SPECT studies. To this end, Monte Carlo simulation was used to generate a database of 1380 projection data-sets from 23 subjects, including normal cases and a variety of pathologies. Studies were reconstructed using filtered back projection (FBP) with attenuation correction and ordered subset expectation maximization (OSEM) with correction for different degradations (attenuation, scatter and PSF). Reconstruction parameters to be optimized were the cut-off frequency of a 2D Butterworth pre-filter in FBP, and the number of iterations and the full width at Half maximum of a 3D Gaussian post-filter in OSEM. Reconstructed images were quantified using regions of interest (ROIs) derived from Magnetic Resonance scans and from the Automated Anatomical Labeling map. Results were standardized by applying a simple linear regression line obtained from the entire patient dataset. Our findings show that we can obtain a set of optimal parameters for each reconstruction strategy. The accuracy of the standardized SUR increases when the reconstruction method includes more corrections. The use of generic ROIs instead of subject-specific ROIs adds significant inaccuracies. Thus, after reconstruction with OSEM and correction for all degradations, subject-specific ROIs led to errors between standardized and true SUR values in the range [-0.5, +0.5] in 87% and 92% of the cases for caudate and putamen, respectively. These percentages dropped to 75% and 88% when the generic ROIs were used.