14
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Magia: Robust Automated Image Processing and Kinetic Modeling Toolbox for PET Neuroinformatics

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          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.

          Related collections

          Most cited references14

          • Record: found
          • Abstract: found
          • Article: not found

          Parametric imaging of ligand-receptor binding in PET using a simplified reference region model.

          A method is presented for the generation of parametric images of radioligand-receptor binding using PET. The method is based on a simplified reference region compartmental model, which requires no arterial blood sampling, and gives parametric images of both the binding potential of the radioligand and its local rate of delivery relative to the reference region. The technique presented for the estimation of parameters in the model employs a set of basis functions which enables the incorporation of parameter bounds. This basis function method (BFM) is compared with conventional nonlinear least squares estimation of parameters (NLM), using both simulated and real data. BFM is shown to be more stable than NLM at the voxel level and is computationally much faster. Application of the technique is illustrated for three radiotracers: [11C]raclopride (a marker of the D2 receptor), [11C]SCH 23390 (a marker of the D1 receptor) in human studies, and [11C]CFT (a marker of the dopamine transporter) in rats. The assumptions implicit in the model and its implementation using BFM are discussed. Copyright 1997 Academic Press.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found

            From Brain Maps to Cognitive Ontologies: Informatics and the Search for Mental Structure.

            A major goal of cognitive neuroscience is to delineate how brain systems give rise to mental function. Here we review the increasingly large role informatics-driven approaches are playing in such efforts. We begin by reviewing a number of challenges conventional neuroimaging approaches face in trying to delineate brain-cognition mappings--for example, the difficulty in establishing the specificity of postulated associations. Next, we demonstrate how these limitations can potentially be overcome using complementary approaches that emphasize large-scale analysis--including meta-analytic methods that synthesize hundreds or thousands of studies at a time; latent-variable approaches that seek to extract structure from data in a bottom-up manner; and predictive modeling approaches capable of quantitatively inferring mental states from patterns of brain activity. We highlight the underappreciated but critical role for formal cognitive ontologies in helping to clarify, refine, and test theories of brain and cognitive function. Finally, we conclude with a speculative discussion of what future informatics developments may hold for cognitive neuroscience.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Graphical analysis of PET data applied to reversible and irreversible tracers.

              Jean Logan (2000)
              The differential equations of compartmental analysis form the basis of the models describing the uptake of tracers used in imaging studies. Graphical analyses convert the model equations into linear plots, the slopes of which represent measures of tracer binding. The graphical methods are not dependent upon a particular model structure but the slopes can be related to combinations of the model parameters if a model structure is assumed. The input required is uptake data from a region of interest vs time and an input function that can either be plasma measurements or uptake data from a suitable reference region. Graphical methods can be applied to both reversible and irreversibly binding tracers. They provide considerable ease of computation compared to the optimization of individual model parameters in the solution of the differential equations generally used to describe the binding of tracers. Conditions under which the graphical techniques are applicable and some problems encountered in separating tracer delivery and binding are considered. Also the effect of noise can introduce a bias in the distribution volume which is the slope of the graphical analysis of reversible tracers. Smoothing techniques may minimize this problem and retain the model independence. In any case graphical techniques can provide insight into the binding kinetics of tracers in a visual way.
                Bookmark

                Author and article information

                Contributors
                Journal
                Front Neuroinform
                Front Neuroinform
                Front. Neuroinform.
                Frontiers in Neuroinformatics
                Frontiers Media S.A.
                1662-5196
                04 February 2020
                2020
                : 14
                : 3
                Affiliations
                [1] 1Turku PET Centre, University of Turku and Turku University Hospital , Turku, Finland
                [2] 2The Royal’s Institute of Mental Health Research, University of Ottawa , Ottawa, ON, Canada
                [3] 3Department of Radiology, University of Turku , Turku, Finland
                [4] 4Department of Psychiatry, Faculty of Medicine, University of Turku and Turku University Hospital , Turku, Finland
                [5] 5Division of Clinical Neurosciences, Turku University Hospital , Turku, Finland
                [6] 6Department of Psychology, University of Turku , Turku, Finland
                Author notes

                Edited by: Ludovico Minati, Tokyo Institute of Technology, Japan

                Reviewed by: Harumasa Takano, National Center of Neurology and Psychiatry, Japan; Frithjof Kruggel, University of California, Irvine, United States

                *Correspondence: Tomi Karjalainen tomi.karjalainen@ 123456utu.fi
                Article
                10.3389/fninf.2020.00003
                7012016
                32116627
                40f62584-4d35-4eae-add7-787732c84730
                Copyright © 2020 Karjalainen, Tuisku, Santavirta, Kantonen, Bucci, Tuominen, Hirvonen, Hietala, Rinne and Nummenmaa.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 18 November 2019
                : 15 January 2020
                Page count
                Figures: 6, Tables: 2, Equations: 0, References: 29, Pages: 13, Words: 7146
                Categories
                Neuroscience
                Original Research

                Neurosciences
                pet,neuroinformatics,modeling,reference region,carfentanil,raclopride,madam,pib
                Neurosciences
                pet, neuroinformatics, modeling, reference region, carfentanil, raclopride, madam, pib

                Comments

                Comment on this article