Motion correction of multi-frame PET data in neuroreceptor mapping: Simulation based validation

• Published in: NeuroImage (Orlando, Fla.) Vol. 47; no. 4; pp. 1496 - 1505
• Main Authors: Costes, Nicolas, Dagher, Alain, Larcher, Kevin, Evans, Alan C., Collins, D. Louis, Reilhac, Anthonin
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.10.2009; Elsevier Limited
• Subjects: Accuracy; Algorithms; Binding potential; Brain; Brain - diagnostic imaging; Brain - metabolism; Computer Simulation; Gene Expression Profiling - methods; Humans; Image Enhancement - methods; Inter frame registration; Medical imaging; Methods; Models, Neurological; Motion; Patient motion correction; PET; Positron-Emission Tomography - methods; Raclopride - pharmacokinetics; Radiopharmaceuticals - pharmacokinetics; Receptors, Dopamine D2 - metabolism; Reproducibility of Results; Scanners; Sensitivity and Specificity; Sensory Receptor Cells - metabolism; Simulation; Subtraction Technique; Binding potential; Brain; Patient motion correction; Simulation; Inter frame registration; PET
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2009.05.052

Patient motion during positron emission tomography scanning can affect the accuracy of the data analysis in two ways: 1) movement occurring during emission data acquisition alters the time activity curves (TACs), measured at a voxel or region of interest (ROI), and hence introduces errors in the parameter estimates derived from kinetic modeling; 2) emission–transmission mismatches introduce errors during attenuation and scatter correction, and hence in the radioactivity distribution estimates for each time frame of the scan. With the aim of designing an algorithm-based frame realignment method, we first conducted investigations that aimed at optimizing the parameters of a coregistration method, such as the choice of the target volume and the similarity criterion. Based on these results we designed a novel frame realignment strategy in a multi-step algorithm using uncorrected reconstructed images, cross-correlation similarity criteria for the determination of inter-frame motion parameters and emission-transmission mismatch for each frame. Features and validation results are reported here based on a multi-subject simulated [11C]raclopride dynamic PET scan database incorporating intra-frame movements of various magnitudes and with various times of occurrence. Performances of the proposed algorithm were evaluated at regional and voxel-based level for binding potential parametric images.