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Multi-Scale hierarchical generation of PET parametric maps: application and testing on a [11C]DPN study
G Rizzo1, F E Turkheimer, S Keihaninejad
1Department of Information Engineering, University of Padova, Padova, Italy.
Neuroimage
|September 20, 2011
Summary
This study introduces a fast, hierarchical method for generating parametric maps, accurately quantifying opioid receptor distribution (VT) using [11C]diprenorphine (DPN) PET imaging. The approach ensures reliable results regardless of prior segmentation method.
Area of Science:
- Neuroimaging
- Pharmacokinetics
- Quantitative analysis
Background:
- Parametric mapping in PET imaging is crucial for quantifying tracer distribution and receptor densities.
- Accurate estimation of kinetic parameters like the volume of distribution (VT) is essential for understanding neuroreceptor systems.
- Existing methods can be computationally intensive and sensitive to noise, especially with tracers exhibiting slow tissue equilibration.
Purpose of the Study:
- To develop and validate a general, multi-stage hierarchical approach for generating accurate parametric maps in PET imaging.
- To assess the method's performance using challenging [11C]diprenorphine (DPN) data, known for slow tissue equilibration.
- To evaluate the impact of different prior definition strategies (anatomical atlas vs. unsupervised clustering) on quantitative results.
Main Methods:
- A hierarchical scheme was implemented, cascading kinetic information from whole-brain analysis to anatomical systems and then to the voxel level.
- Voxel-wise priors were generated using either anatomical atlas segmentation or unsupervised clustering.
- Maximum a posteriori (MAP) estimation was employed to transmit kinetic properties to voxels within each class.
Main Results:
- The method accurately estimated parametric maps of the volume of distribution (VT), reflecting known opioid receptor distributions.
- Excellent agreement and strong test-retest reliability were observed between voxel MAP and region-of-interest (ROI) results when using anatomical priors.
- Voxel-level estimates remained consistent regardless of whether priors were derived from an anatomical atlas or unsupervised clustering.
Conclusions:
- The proposed hierarchical method provides a fast (15 min/subject) and accurate approach for quantifying VT in [11C]DPN PET studies.
- The method generates high-quality parametric VT images.
- The choice of prior definition (anatomical or clustering-based) does not compromise the accuracy or reliability of the kinetic parameter estimates.
