Design and Implementation of an Automated Partial Volume Correction in PET: Application to Dopamine Receptor Quantification in the Normal Human Striatum

Abstract

The considerable effort and potential lack of reproducibility of human-driven PET quantification and partial volume correction (PVC) can be alleviated by use of atlas-based automatic analysis. The present study examined the application of a new algorithm designed to automatically define 3-dimensional regions of interest (ROIs) and their effect on dopamine receptor quantification in the normal human brain striatum, both without and with PVC. Methods: A total of 90 healthy volunteers (age range, 18-46 y) received a single injection of 11 C-raclopride, and automatic segmentation of concomitant structural MR images was performed using a maximum-probability atlas in combination with a trained neural network. For each identified tissue segment considered homogeneous for the tracer (or volumes of interest [VOIs]), an a priori criterion based on minimum axial recovery coefficient (RC zmin 5 50%, 75%, and 90%) was used to constrain the extent of each ROI. Results: With ROIs essentially overlapping the entire VOI volume (obtained with RC zmin 5 50%), the binding potential (BP ND ) of 11 C-raclopride was found to be around 2.2 for caudate and 2.9 for putamen, an underestimation by 35% and 28%, respectively, according to PVC values. At increased RC zmin , BP ND estimates of 11 C-raclopride were increased by 12% and 21% for caudate and 8% and 15% for putamen when the associated ROIs decreased to around 65% and 43% of total tissue volume (VOI) for caudate and 67% and 31% for putamen. After PVC, we observed relative increases in BP ND variance of 12% for caudate and 20% for putamen, whereas estimated BP ND values all increased to 3.4 for caudate and 4.0 for putamen, regardless of ROI size. Dopamine receptor concentrations appeared less heterogeneous in the normal human striatum after PVC than they did without PVC: the 25%-30% difference in BP ND estimates observed between caudate and putamen remained significant after PVC but was reduced to slightly less than 20%. Furthermore, the results were comparable with those obtained with a manual method currently in use in our laboratory. Conclusion: The new algorithm allows for traditional PET data extraction and PVC in an entirely automatic fashion, thus avoiding labor-intensive analyses and potential intra-or interobserver variability. This study also offers the first, to our knowledge, large-scale application of PVC to dopamine D 2 /D 3 receptor imaging with 11 C-raclopride in humans.

Partial Volume Correction using Structural–Functional Synergistic Resolution Recovery: Comparison with Geometric Transfer Matrix Method

“…10,17 The algorithm assumes that the observed activity concentration t j within a region D j can be simply expressed as follows:…”

Implementation of Filter for the Removal of Partial Volume Effect

“…지금까지 부분용적효과를 제거하기 위해 Geometric transfer matrix (GTM) method [12,13] , The fitting method [14] , Reconstruction -based method, Image -base method, Mask -base method 등의 여러 가지 방법들이 사용되어져 왔다 [15] . 이러한 보정 방법들은 사용하기에 간단하면 정확도가 낮고, 정확도가 높으면 임상환경에 서 사용하기에 어려운 특성이 있다 [16] .…”

Statistical parametric maps of 18F-FDG PET and 3-D autoradiography in the rat brain: a cross-validation study

“…This fact can be observed visually over our PET images, in which it is almost impossible to differentiate inner structures. Although pixel-based partial volume correction could restore activity in small structures, and several algorithms have been developed to correct this effect in human brain imaging [33][34][35], they need to be optimized for their application in rodents.…”