ABSTRACT
The Patlak graphical analysis (PGAREF) for quantification of irreversible tracer binding with a reference tissue model was approximated by a dual time point imaging approach (DTPREF). The DTPREF was applied to 18 [18F]-FDOPA brain scans using the occipital cortex as reference region (DTPOCC) and compared to both PGAOCC and striatal-to-occipital ratios (SOR). Pearson correlation analysis and Bland-Altman plots showed an excellent correlation and good agreement between DTPOCC and PGAOCC, while correlations between SOR and PGAOCC were consistently lower. Linear discriminant analysis (LDA) demonstrated a similar performance for all methods in differentiating patients with Parkinson's disease (PD) from healthy controls (HC). Specifically for [18F]-FDOPA brain imaging, these findings validate DTPOCC as an approximation for PGAOCC, providing the same quantitative information while reducing the acquisition time to two short static scans. For PD patients, this approach can greatly improve patient comfort while reducing motion artifacts and increasing image quality. In general, DTPREF can improve the clinical applicability of tracers with irreversible binding characteristics when a reference tissue is available.
Subject(s)
Brain/diagnostic imaging , Dihydroxyphenylalanine/analogs & derivatives , Image Processing, Computer-Assisted/methods , Models, Theoretical , Parkinson Disease/diagnostic imaging , Positron-Emission Tomography/methods , Brain/metabolism , Case-Control Studies , Caudate Nucleus/diagnostic imaging , Caudate Nucleus/metabolism , Dihydroxyphenylalanine/blood , Dihydroxyphenylalanine/metabolism , Discriminant Analysis , Fluorine Radioisotopes , Humans , Linear Models , Parkinson Disease/metabolism , Putamen/diagnostic imaging , Putamen/metabolism , Reference Values , Time FactorsABSTRACT
With resting-state functional MRI (rs-fMRI) there are a variety of post-processing methods that can be used to quantify the human brain connectome. However, there is also a choice of which preprocessing steps will be used prior to calculating the functional connectivity of the brain. In this manuscript, we have tested seven different preprocessing schemes and assessed the reliability between and reproducibility within the various strategies by means of graph theoretical measures. Different preprocessing schemes were tested on a publicly available dataset, which includes rs-fMRI data of healthy controls. The brain was parcellated into 190 nodes and four graph theoretical (GT) measures were calculated; global efficiency (GEFF), characteristic path length (CPL), average clustering coefficient (ACC), and average local efficiency (ALE). Our findings indicate that results can significantly differ based on which preprocessing steps are selected. We also found dependence between motion and GT measurements in most preprocessing strategies. We conclude that by using censoring based on outliers within the functional time-series as a processing, results indicate an increase in reliability of GT measurements with a reduction of the dependency of head motion.
