1,721,020 research outputs found
Multi-scale hierarchical approach for parametric mapping: assessment on multi-compartmental models.
No full textThis paper investigates a new hierarchical method to apply basis function to mono- and multi-compartmental models (Hierarchical-Basis Function Method, H-BFM) at a voxel level. This method identifies the parameters of the compartmental model in its nonlinearized version, integrating information derived at the region of interest (ROI) level by segmenting the cerebral volume based on anatomical definition or functional clustering. We present the results obtained by using a two tissue-four rate constant model with two different tracers ([11C]FLB457 and [carbonyl-11C]WAY100635), one of the most complex models used in receptor studies, especially at the voxel level. H-BFM is robust and its application on both [11C]FLB457 and [carbonyl-11C]WAY100635 allows accurate and precise parameter estimates, good quality parametric maps and a low percentage of voxels out of physiological bound (<8%). The computational time depends on the number of basis functions selected and can be compatible with clinical use (~6h for a single subject analysis).The novel method is a robust approach for PET quantification by using compartmental modeling at the voxel level. In particular, different from other proposed approaches, this method can also be used when the linearization of the model is not appropriate. We expect that applying it to clinical data will generate reliable parametric maps
SAKE: A new quantification tool for positron emission tomography studies.
No full textIn dynamic positron emission tomography (PET) studies, spectral analysis (SA) refers to a data-driven quantification method, based on a single-input single-output model for which the transfer function is described by a sum of exponential terms. SA allows to quantify numerosities, amplitudes and eigenvalues of the transfer function allowing, in this way, to separate kinetic components of the tissue tracer activity with minimal model assumptions. The SA model can be solved with a linear estimator alone or with numerical filters, resulting in different types of SA approaches. Once estimated the number, amplitudes and eigenvalues of the transfer function, one can distinguish the presence in the system of irreversible and/or reversible components as well as derive parameters of physiological significance. These characteristics make it an appealing alternative method to compartmental models which are widely used for the quantitative analysis of dynamic studies acquired with PET. However, despite its applicability to a large number of PET tracers, its implementation is not straightforward and its utilization in the nuclear medicine community has been limited especially by the lack of an user-friendly software application. In this paper we proposed SAKE, a computer program for the quantitative analysis of PET data through the main SA methods. SAKE offers a unified pipeline of analysis usable also by people with limited computer knowledge but with high interest in SA
Voxel-wise quantification of [11C]PBR28 brain data accounting for endothelial TSPO binding
No full textQuantification of [11C]PBR28 brain data at the voxel level modelling of the endothelial TSPO binding
No full textIn-vivo measurement of activated microglia in dementia
No full textBACKGROUND: Activated microglia have a key role in the brain's immune response to neuronal degeneration. The transition of microglia from the normal resting state to the activated state is associated with an increased expression of receptors known as peripheral benzodiazepine binding sites, which are abundant on cells of mononuclear phagocyte lineage. We used brain imaging to study expression of these sites in healthy individuals and patients with Alzheimer's disease.
METHODS: We studied 15 normal individuals (age 32-80 years), eight patients with Alzheimer's disease, and one patient with minimal cognitive impairment. Quantitative in-vivo measurements of glial activation were obtained with positron emission tomography (PET) and carbon-11-labelled (R)-PK11195, a specific ligand for the peripheral benzodiazepine binding site.
FINDINGS: In normal individuals, regional [11C](R)-PK11195 binding did not significantly change with age, except in the thalamus, where an age-dependent increase was found. By contrast, patients with Alzheimer's disease showed significantly increased regional [11C](R)-PK11195 binding in the entorhinal, temporoparietal, and cingulate cortex.
INTERPRETATION: In-vivo detection of increased [11C](R)-PK11195 binding in Alzheimer-type dementia, including mild and early forms, suggests that microglial activation is an early event in the pathogenesis of the disease
Multi-Scale hierarchical generation of PET parametric maps: Application and testing on a [(11)C]DPN study.
No full textWe propose a general approach to generate parametric maps. It consists in a multi-stage hierarchical scheme where, starting from the kinetic analysis of the whole brain, we then cascade the kinetic information to anatomical systems that are akin in terms of receptor densities, and then down to the voxel level. A-priori classes of voxels are generated either by anatomical atlas segmentation or by functional segmentation using unsupervised clustering. Kinetic properties are transmitted to the voxels in each class using maximum a posteriori (MAP) estimation method. We validate the novel method on a [(11)C]diprenorphine (DPN) test-retest data-set that represents a challenge to estimation given [(11)C]DPN's slow equilibration in tissue. The estimated parametric maps of volume of distribution (V(T)) reflect the opioid receptor distributions known from previous [(11)C]DPN studies. When priors are derived from the anatomical atlas, there is an excellent agreement and strong correlation among voxel MAP and ROI results and excellent test-retest reliability for all subjects but one. Voxel level results did not change when priors were defined through unsupervised clustering. This new method is fast (i.e. 15min per subject) and applied to [(11)C]DPN data achieves accurate quantification of V(T) as well as high quality V(T) images. Moreover, the way the priors are defined (i.e. using an anatomical atlas or unsupervised clustering) does not affect the estimates
Brain shaving: adaptive detection for brain PET data.
No full textThe intricacy of brain biology is such that the variation of imaging end-points in health and disease exhibits an unpredictable range of spatial distributions from the extremely localized to the very diffuse. This represents a challenge for the two standard approaches to analysis, the mass univariate and the multivariate that exhibit either strong specificity but not as good sensitivity (the former) or poor specificity and comparatively better sensitivity (the latter). In this work, we develop an analytical methodology for positron emission tomography that operates an extraction ('shaving') of coherent patterns of signal variation while maintaining control of the type I error. The methodology operates two rotations on the image data, one local using the wavelet transform and one global using the singular value decomposition. The control of specificity is obtained by using the gap statistic that selects, within each eigenvector, a subset of significantly coherent elements. Face-validity of the algorithm is demonstrated using a paradigmatic data-set with two radiotracers, [11C]-raclopride and [11C]-(R)- PK11195, measured on the same Huntington's disease patients, a disorder with a genetic based diagnosis. The algorithm is able to detect the two well-known separate but connected processes of dopamine neuronal loss (localized in the basal ganglia) and neuroinflammation (diffusive around the whole brain). These processes are at the two extremes of the distributional envelope, one being very sparse and the latter being perfectly Gaussian and they are not adequately detected by the univariate and the multivariate approache
Increased binding potential of [11C]PK11195 in alzheimer disease by new SRTM accounting for blood volume
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