Abstract
Processing of positron emission tomography (PET) data typically involves manual work, causing inter-operator variance. Here we introduce the Magia toolbox that enables processing of brain PET data with minimal user intervention. We investigated the accuracy of Magia with four tracers: [11C]carfentanil, [11C]raclopride, [11C]MADAM, and [11C]PiB. We used data from 30 control subjects for each tracer. Five operators manually delineated reference regions for each subject. The data were processed using Magia using the manually and automatically generated reference regions. We first assessed inter-operator variance resulting from the manual delineation of reference regions. We then compared the differences between the manually and automatically produced reference regions and the subsequently obtained binding potentials and standardized-uptake-value-ratios. The results show that manually produced reference regions can be remarkably different from each other, leading to substantial differences also in outcome measures. While the Magia-derived reference regions were anatomically different from the manual ones, Magia produced outcome measures highly consistent with the average of the manually obtained estimates. For [11C]carfentanil and [11C]PiB there was no bias, while for [11C]raclopride and [11C]MADAM Magia produced 3–5% higher binding potentials. Based on these results and considering the high inter-operator variance of the manual method, we conclude that Magia can be reliably used to process brain PET data.
Highlights
Modelling of the radioactivity images produced by positron emission tomography (PET) scanners into biologically meaningful quantities, such as binding potential, is a complex multi-stage process involving data retrieval, preprocessing, drawing reference regions, kinetic modelling, and post-processing of parametric images
Compared to functional magnetic resonance images (fMRI) preprocessing, preprocessing of PET data is relatively straightforward because confounding temporal signals are rarely regressed out of the data, and the preprocessing only consists of spatial processes, such as frame-realignment and coregistration
The reference region volumes were most similar between the operators for [11C]carfentanil (ICC = 69 %)
Summary
Modelling of the radioactivity images produced by PET scanners into biologically meaningful quantities, such as binding potential, is a complex multi-stage process involving data retrieval, preprocessing, drawing reference regions, kinetic modelling, and post-processing of parametric images. The process is challenging to automatize mainly because of manual work related to input generation, prohibiting large-scale standardized analysis of brain PET data. To resolve this problem, we introduce the Magia pipeline that enables processing of brain PET data with minimal user intervention. Several standardized and highly automatized preprocessing pipelines are publicly available for processing functional magnetic resonance images (fMRI) 7. Such standardized methods are not, currently widely used for analysis of positron emission tomography (PET) data. The reference region should be defined separately for each individual before spatial normalization
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