Partial Volume Correction Strategies in PET
Section snippets
Recovery coefficient method
The first attempts to compensate for PVE using recovery coefficients were made by Hoffman and colleagues [5], who computed a set of recovery coefficients based on known size, shape, and location (within the PET scanner) of the objects being imaged. The same group later extended this approach to predict the recovery coefficients for different brain structures that were approximated using a series of non-overlapping spheres [6]. At this time, the PVE was tackled in the context of hot objects
Reconstruction-based partial volume correction methods
PVC commonly is presented in the context of compensating for the spill-in/spill-out effects present in reconstruction images, resulting from the limited resolution capabilities of the imaging system. One therefore would think that, for a structure of a given size, the higher the resolution of a modality, the less the PVE will be. Another way of thinking about this problem, however, is to note that improving the resolution of reconstructions performed on a given scanner would, in effect, be a
Clinical and research applications of partial volume correction
Quantitative PET measurements of physiologic and biologic processes in vivo are influenced by various physical degrading factors including partial volume averaging among neighboring tissues with differing tracer concentrations resulting from the limited spatial resolution of state-of-the-art PET scanners. PVC is important for describing the true functional contribution of PET in providing clinicians and scientists with relevant functional information in various pathologies. This information
Potential pitfalls
Despite the remarkable advances and achievements to date, PVC still is limited by the inaccuracies of the various procedures involved in the implementation of sophisticated methods, particularly those relying on an adjunct structural modality image (CT or MR imaging). These limitations, which include the spatial realignment of functional and anatomic images, segmentation of the high-resolution anatomic image, and tissue inhomogeneities, are discussed briefly.
Summary
It is gratifying to see the progress that PVC has made in PET. Recent developments have been enormous, particularly in the last decade. The focus has been on improving accuracy, precision, and computational speed through efficient implementation in conjunction with decreasing the amount of operator interaction. PVC of PET data is well established in research environments, but its use in clinical settings is still limited to institutions with advanced technical support. As the challenges
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This work was supported by Grant No. SNSF 3100A0-116547 from the Swiss National Foundation, and by Grants RO1AA12839 (NIAAA), K24DA00412-01A1 (NIDA), NS38927 (NIH), and P01HD24448-10 (NIH).