4D PET iterative deconvolution with spatiotemporal regularization for quantitative dynamic PET imaging

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dc.contributor.authorReilhac, Aen_AU
dc.contributor.authorCharil, Aen_AU
dc.contributor.authorWimberley, CAen_AU
dc.contributor.authorAngelis, GIen_AU
dc.contributor.authorHamze, Hen_AU
dc.contributor.authorCallaghan, PDen_AU
dc.contributor.authorGarcia, MPen_AU
dc.contributor.authorBoisson, Fen_AU
dc.contributor.authorRyder, Wen_AU
dc.contributor.authorMeikle, SRen_AU
dc.contributor.authorGrégoire, MCen_AU
dc.date.accessioned2018-09-16T23:01:09Zen_AU
dc.date.available2018-09-16T23:01:09Zen_AU
dc.date.issued2015-09-01en_AU
dc.date.statistics2018-09-16en_AU
dc.description.abstractQuantitative 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.en_AU
dc.identifier.citationReilhac, 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.2015.06.029en_AU
dc.identifier.govdoc8870en_AU
dc.identifier.issn1053-8119en_AU
dc.identifier.journaltitleNeuroimageen_AU
dc.identifier.pagination484-493en_AU
dc.identifier.urihttps://doi.org/10.1016/j.neuroimage.2015.06.029en_AU
dc.identifier.urihttp://apo.ansto.gov.au/dspace/handle/10238/8998en_AU
dc.identifier.volume118en_AU
dc.language.isoenen_AU
dc.publisherElsevieren_AU
dc.subjectPositron computed tomographyen_AU
dc.subjectImagesen_AU
dc.subjectIterative methodsen_AU
dc.subjectSimulationen_AU
dc.subjectImagesen_AU
dc.subjectMonte Carlo Methoden_AU
dc.subjectBiological variabilityen_AU
dc.title4D PET iterative deconvolution with spatiotemporal regularization for quantitative dynamic PET imagingen_AU
dc.typeJournal Articleen_AU
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