Simulation-based partial volume correction for dopaminergic PET imaging: Impact of segmentation accuracy
Simulation-based partial volume correction for dopaminergic PET imaging: Impact of segmentation accuracy
AimPartial volume correction (PVC) is an essential step for quantitative positron emission tomography (PET). In the present study, PVELab, a freely available software, is evaluated for PVC in 18F-FDOPA brain-PET, with a special focus on the accuracy degradation introduced by various MR-based segment...
Saved in:
Personal Name(s): | Rong, Ye |
---|---|
Vernaleken, Ingo / Winz, Oliver H. / Goedicke, Andreas / Mottaghy, Felix M. (Corresponding author) / Rota Kops, Elena | |
Contributing Institute: |
Physik der Medizinischen Bildgebung; INM-4 JARA-BRAIN; JARA-BRAIN |
Published in: | Zeitschrift für Medizinische Physik Zeitschrift für medizinische Physik, 25 25 (2015 2015) 3 3, S. 230-242 230-242 |
Imprint: |
Amsterdam
Elsevier, Urban & Fischer71512
2015
2015-09-01 2015-09-01 |
DOI: |
10.1016/j.zemedi.2014.08.001 |
Document Type: |
Journal Article |
Research Program: |
Neuroimaging |
Publikationsportal JuSER |
AimPartial volume correction (PVC) is an essential step for quantitative positron emission tomography (PET). In the present study, PVELab, a freely available software, is evaluated for PVC in 18F-FDOPA brain-PET, with a special focus on the accuracy degradation introduced by various MR-based segmentation approaches.MethodsFour PVC algorithms (M-PVC; MG-PVC; mMG-PVC; and R-PVC) were analyzed on simulated 18F-FDOPA brain-PET images. MR image segmentation was carried out using FSL (FMRIB Software Library) and SPM (Statistical Parametric Mapping) packages, including additional adaptation for subcortical regions (SPML). Different PVC and segmentation combinations were compared with respect to deviations in regional activity values and time-activity curves (TACs) of the occipital cortex (OCC), caudate nucleus (CN), and putamen (PUT). Additionally, the PVC impact on the determination of the influx constant (Ki) was assessed.ResultsMain differences between tissue-maps returned by three segmentation algorithms were found in the subcortical region, especially at PUT. Average misclassification errors in combination with volume reduction was found to be lowest for SPML (PUT < 30%) and highest for FSL (PUT > 70%). Accurate recovery of activity data at OCC is achieved by M-PVC (apparent recovery coefficient varies between 0.99 and 1.10). The other three evaluated PVC algorithms have demonstrated to be more suitable for subcortical regions with MG-PVC and mMG-PVC being less prone to the largest tissue misclassification error simulated in this study. Except for M-PVC, quantification accuracy of Ki for CN and PUT was clearly improved by PVC.ConclusionsThe regional activity value of PUT was appreciably overcorrected by most of the PVC approaches employing FSL or SPM segmentation, revealing the importance of accurate MR image segmentation for the presented PVC framework. The selection of a PVC approach should be adapted to the anatomical structure of interest. Caution is recommended in subsequent interpretation of Ki values. The possible different change of activity concentrations due to PVC in both target and reference regions tends to alter the corresponding TACs, introducing bias to Ki determination. The accuracy of quantitative analysis was improved by PVC but at the expense of precision reduction, indicating the potential impropriety of applying the presented framework for group comparison studies. |