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Title: 4D PET iterative deconvolution with spatiotemporal regularization for quantitative dynamic PET imaging
Authors: Reilhac, A
Charil, A
Wimberley, C
Angelis, GI
Hamze, H
Callaghan, PD
Garcia, MP
Boisson, F
Ryder, W
Meikle, SR
Gregoire, MC
Keywords: Positron computed tomography
Iterative methods
Monte Carlo method
Biological variability
Issue Date: 1-Sep-2015
Publisher: Elsevier
Citation: Reilhac, A., Charil, A., Wimberley, C., Angelis, G., Hamze, H., Callaghan, P., Garcia, M. P., Boisson, F., Ryder, W., Meikle, S. R., & Gregoire, M. C. (2015). 4D PET iterative deconvolution with spatiotemporal regularization for quantitative dynamic PET imaging. Neuroimage, 118, 484-493. doi:10.1016/j.neuroimage.2015.06.029
Abstract: Quantitative measurements in dynamic PET imaging are usually limited by the poor counting statistics particularly in short dynamic frames and by the low spatial resolution of the detection system, resulting in partial volume effects (PVEs). In this work, we present a fast and easy to implement method for the restoration of dynamic PET images that have suffered from both PVE and noise degradation. It is based on a weighted least squares iterative deconvolution approach of the dynamic PET image with spatial and temporal regularization. Using simulated dynamic [11C] Raclopride PET data with controlled biological variations in the striata between scans, we showed that the restoration method provides images which exhibit less noise and better contrast between emitting structures than the original images. In addition, the method is able to recover the true time activity curve in the striata region with an error below 3% while it was underestimated by more than 20% without correction. As a result, the method improves the accuracy and reduces the variability of the kinetic parameter estimates calculated from the corrected images. More importantly it increases the accuracy (from less than 66% to more than 95%) of measured biological variations as well as their statistical detectivity. © 2015 Elsevier Inc.
Gov't Doc #: 8870
ISSN: 1053-8119
Appears in Collections:Journal Articles

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