Paper Info
Title
Surface-based partial-volume correction for high-resolution PET.
Abstract
Tissue radioactivity concentrations, measured with positron emission tomography (PET) are subject to partial volume effects (PVE) due to the limited spatial resolution of the scanner. Last generation high-resolution PET cameras with a full width at half maximum (FWHM) of 2–4mm are less prone to PVEs than previous generations. Corrections for PVEs are still necessary, especially when studying small brain stem nuclei or small variations in cortical neuroreceptor concentrations which may be related to cytoarchitectonic differences. Although several partial-volume correction (PVC) algorithms exist, these are frequently based on a priori assumptions about tracer distribution or only yield corrected values of regional activity concentrations without providing PVE corrected images. We developed a new iterative deconvolution algorithm (idSURF) for PVC of PET images that aims to overcome these limitations by using two innovative techniques: 1) the incorporation of anatomic information from a cortical gray matter surface representation, extracted from magnetic resonance imaging (MRI) and 2) the use of anatomically constrained filtering to attenuate noise. PVE corrected images were generated with idSURF implemented into a non-interactive processing pipeline. idSURF was validated using simulated and clinical PET data sets and compared to a frequently used standard PVC method (Geometric Transfer Matrix: GTM). The results on simulated data sets show that idSURF consistently recovers accurate radiotracer concentrations within 1–5% of true values. Both radiotracer concentrations and non-displaceable binding potential (BPnd) values derived from clinical PET data sets with idSURF were highly correlated with those obtained with the standard PVC method (R2=0.99, error=0%–3.2%). These results suggest that idSURF is a valid and potentially clinically useful PVC method for automatic processing of large numbers of PET data sets.
Year
DOI
Venue
2014
10.1016/j.neuroimage.2014.08.037
NeuroImage
Keywords
Field
DocType
Positron emission tomography,Partial-volume correction,Cortical thickness,Magnetic resonance imagery
Biomedical engineering,Data set,Cognitive psychology,Deconvolution,Positron emission tomography,Artificial intelligence,Computer vision,Psychology,Filter (signal processing),Scanner,Partial volume,Image resolution,Binding potential
Journal
Volume
ISSN
Citations 
102
1053-8119
2
PageRank 
References 
Authors
0.38
11
4
Name
Order
Citations
PageRank
Thomas Funck120.72
Caroline Paquette220.38
Alan Evans379942.82
Alexander Thiel420.38