Related Experiment Video
Updated: Aug 26, 2026
Whole-body PET/MRI of Pediatric Patients: The Details That Matter
Published on: December 19, 2017
Application of MRI-based partial-volume correction to the analysis of PET images of mu-opioid receptors using
Badreddine Bencherif1, Martin J Stumpf, Jonathan M Links
1Department of Radiology, The Johns Hopkins Medical Institutions, Baltimore, Maryland, USA.
Unlabelled:
The accurate quantification of brain radioactivity concentration is limited by the spatial resolution of the PET scanner for structures smaller than 2-3 times the resolution. In the presence of enlarged cerebrospinal fluid spaces or regions of cortical neuronal loss, a significant underestimation of gray-matter radioactivity concentration due to the resulting partial-volume averaging can potentially occur. To recover the true radioactivity concentration from PET data, algorithms that use the high-resolution anatomic information provided by MRI have been developed. Their effect on PET quantification has been assessed using regions of interest and non-operator-dependent voxel-based analyses such as statistical parametric mapping (SPM), although the mechanisms that lead to an improvement in PET quantification after partial-volume correction (PVC), compared with no PVC, have not been addressed.
Methods:
We studied the influence of our previously described MRI-based PVC algorithm on SPM analysis of age effects on mu-opioid receptor (mu -OR) binding using (11)C-carfentanil PET in 14 healthy subjects (age range, 29-74 y).
Results:
Mu-OR binding increased with age at a rate of about 0.9% per year in the left temporal cortex after PVC, consistent with the results obtained from human autoradiographic studies. Without PVC, no significant relationship with age was observed. PVC decreased mainly the residual variability of voxel mu-OR binding values around the age regression line.
Conclusion:
MRI-based PVC improves the sensitivity and accuracy of voxel-based statistical analysis of PET data.
Insights
Partial-volume correction (PVC) using MRI data significantly improves the accuracy of positron emission tomography (PET) scans. This technique enhances the detection of age-related changes in brain mu-opioid receptor binding, which are otherwise undetectable.
Area of Science:
- Neuroimaging
- Radiochemistry
- Biostatistics
Background:
- Positron emission tomography (PET) quantification is limited by scanner resolution, causing underestimation in small brain structures due to partial-volume averaging (PVA).
- Magnetic resonance imaging (MRI)-based partial-volume correction (PVC) algorithms aim to recover true radioactivity concentrations from PET data.
- The precise mechanisms by which PVC improves PET quantification, particularly in voxel-based analyses like statistical parametric mapping (SPM), require further elucidation.
Purpose of the Study:
- To investigate the impact of an MRI-based PVC algorithm on SPM analysis of age-related changes in brain mu-opioid receptor (μ-OR) binding.
- To assess the sensitivity and accuracy improvements in PET data analysis after applying PVC.
Main Methods:
- Utilized an established MRI-based PVC algorithm.
- Applied the PVC algorithm to (11)C-carfentanil PET data from 14 healthy subjects (age range 29-74 years).
- Analyzed age effects on μ-OR binding using SPM with and without PVC.
Main Results:
- After PVC, a significant age-related increase in μ-OR binding (approximately 0.9% per year) was observed in the left temporal cortex, consistent with autoradiographic findings.
- Without PVC, no significant age-related relationship in μ-OR binding was detected.
- PVC significantly reduced the variability of voxel-wise μ-OR binding values around the age regression line.
Conclusions:
- MRI-based PVC enhances the sensitivity and accuracy of voxel-based statistical analyses of PET data.
- PVC is crucial for detecting subtle age-related changes in neuroreceptor binding that may be obscured by partial-volume effects.
- The study demonstrates the utility of PVC in improving the statistical power of neuroimaging studies analyzing age-related brain changes.
