Browsing by Author "Charil, A"

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    4D PET iterative deconvolution with spatiotemporal regularization for quantitative dynamic PET imaging
    (Elsevier, 2015-09-01) Reilhac, A; Charil, A; Wimberley, CA; Angelis, GI; Hamze, H; Callaghan, PD; Garcia, MP; Boisson, F; Ryder, W; Meikle, SR; Grégoire, MC
    Quantitative measurements in dynamic PET imaging are usually limited by the poor counting statistics particularly in short dynamic frames and by the low spatial resolution of the detection system, resulting in partial volume effects (PVEs). In this work, we present a fast and easy to implement method for the restoration of dynamic PET images that have suffered from both PVE and noise degradation. It is based on a weighted least squares iterative deconvolution approach of the dynamic PET image with spatial and temporal regularization. Using simulated dynamic [11C] Raclopride PET data with controlled biological variations in the striata between scans, we showed that the restoration method provides images which exhibit less noise and better contrast between emitting structures than the original images. In addition, the method is able to recover the true time activity curve in the striata region with an error below 3% while it was underestimated by more than 20% without correction. As a result, the method improves the accuracy and reduces the variability of the kinetic parameter estimates calculated from the corrected images. More importantly it increases the accuracy (from less than 66% to more than 95%) of measured biological variations as well as their statistical detectivity. © 2015 Elsevier Inc.
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    Hierarchical multivariate covariance analysis of metabolic connectivity
    (SAGE Publications, 2014-10-08) Carbonell, F; Charil, A; Zijdenbos, AP; Evans, AC; Bedell, BJ
    Conventional brain connectivity analysis is typically based on the assessment of interregional correlations. Given that correlation coefficients are derived from both covariance and variance, group differences in covariance may be obscured by differences in the variance terms. To facilitate a comprehensive assessment of connectivity, we propose a unified statistical framework that interrogates the individual terms of the correlation coefficient. We have evaluated the utility of this method for metabolic connectivity analysis using [18F]2-fluoro-2-deoxyglucose (FDG) positron emission tomography (PET) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. As an illustrative example of the utility of this approach, we examined metabolic connectivity in angular gyrus and precuneus seed regions of mild cognitive impairment (MCI) subjects with low and high β-amyloid burdens. This new multivariate method allowed us to identify alterations in the metabolic connectome, which would not have been detected using classic seed-based correlation analysis. Ultimately, this novel approach should be extensible to brain network analysis and broadly applicable to other imaging modalities, such as functional magnetic resonance imaging (MRI).© 2014,SAGE Publications
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    In vivo PET imaging with [18F]FDG to explain improved glucose uptake in an apolipoprotein A-I treated mouse model of diabetes
    (Springer Nature, 2016-05-18) Cochran, BJ; Ryder, WJ; Parmar, A; Tang, S; Reilhac, A; Arthur, A; Charil, A; Hamze, H; Barter, PJ; Kritharides, L; Meikle, SR; Grégoire, MC; Rye, KA
    Type 2 diabetes is characterised by decreased HDL levels, as well as the level of apolipoprotein A-I (apoA-I), the main apolipoprotein of HDLs. Pharmacological elevation of HDL and apoA-I levels is associated with improved glycaemic control in patients with type 2 diabetes. This is partly due to improved glucose uptake in skeletal muscle.© 2016 Springer Nature
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    Optimal target region for subject classification on the basis of amyloid PET images
    (Society of Nuclear Medicine and Molecular Imaging, 2015-09-01) Carbonell, F; Zijdenbos, AP; Charil, A; Grand’Maison, MG; Bedell, BJ
    Classification of subjects on the basis of amyloid PET scans is increasingly being used in research studies and clinical practice. Although qualitative, visual assessment is currently the gold standard approach, automated classification techniques are inherently more reproducible and efficient. The objective of this work was to develop a statistical approach for the automated classification of subjects with different levels of cognitive impairment into a group with low amyloid levels (AβL) and a group with high amyloid levels (AβH) through the use of amyloid PET data from the Alzheimer Disease Neuroimaging Initiative study. Methods: In our framework, an iterative, voxelwise, regularized discriminant analysis is combined with a receiver operating characteristic approach that optimizes the selection of a region of interest (ROI) and a cutoff value for the automated classification of subjects into the AβL and AβH groups. The robustness, spatial stability, and generalization of the resulting target ROIs were evaluated by use of the standardized uptake value ratio for 18F-florbetapir PET images from subjects who served as healthy controls, subjects who had mild cognitive impairment, and subjects who had Alzheimer disease and were participating in the Alzheimer Disease Neuroimaging Initiative study. Results: We determined that several iterations of the discriminant analysis improved the classification of subjects into the AβL and AβH groups. We found that an ROI consisting of the posterior cingulate cortex/precuneus and the medial frontal cortex yielded optimal group separation and showed good stability across different reference regions and cognitive cohorts. A key step in this process was the automated determination of the cutoff value for group separation, which was dependent on the reference region used for the standardized uptake value ratio calculation and which was shown to have a relatively narrow range across subject groups. Conclusion: We developed a data-driven approach for the determination of an optimal target ROI and an associated cutoff value for the separation of subjects into the AβL and AβH groups. Future work should include the application of this process to other datasets to facilitate the determination of the translatability of the optimal ROI obtained in this study to other populations. Ideally, the accuracy of our target ROI and cutoff value could be further validated with PET-autopsy data from large-scale studies. It is anticipated that this approach will be extremely useful for the enrichment of study populations in clinical trials involving putative disease-modifying therapeutic agents for Alzheimer disease. © 2015, by the Society of Nuclear Medicine and Molecular Imaging, Inc.
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    OSSI-PET: open-access database of simulated [11C]raclopride scans for the inveon preclinical PET scanner: application to the optimization of reconstruction methods for dynamic studies
    (IEEE, 2016-07) Garcia, MP; Charil, A; Callaghan, PD; Wimberley, CA; Busso, F; Grégoire, MC; Bardiès, M; Reilhac, A
    A wide range of medical imaging applications benefits from the availability of realistic ground truth data. In the case of positron emission tomography (PET), ground truth data is crucial to validate processing algorithms and assessing their performances. The design of such ground truth data often relies on Monte-Carlo simulation techniques. Since the creation of a large dataset is not trivial both in terms of computing time and realism, we propose the OSSI-PET database containing 350 simulated [ 11 C]Raclopride dynamic scans for rats, created specifically for the Inveon pre-clinical PET scanner. The originality of this database lies on the availability of several groups of scans with controlled biological variations in the striata. Besides, each group consists of a large number of realizations (i.e., noise replicates). We present the construction methodology of this database using rat pharmacokinetic and anatomical models. A first application using the OSSI-PET database is presented. Several commonly used reconstruction techniques were compared in terms of image quality, accuracy and variability of the activity estimates and of the computed kinetic parameters. The results showed that OP-OSEM3D iterative reconstruction method outperformed the other tested methods. Analytical methods such as FBP2D and 3DRP also produced satisfactory results. However, FORE followed by OSEM2D reconstructions should be avoided. Beyond the illustration of the potential of the database, this application will help scientists to understand the different sources of noise and bias that can occur at the different steps in the processing and will be very useful for choosing appropriate reconstruction methods and parameters. © 2016 IEEE
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    Selective, high-contrast detection of syngeneic glioblastoma in vivo
    (Springer Nature, 2020-06-19) Banati, RB; Wilcox, P; Xu, R; Yin, G; Si, E; Son, ET; Shimizu, M; Holsinger, RMD; Parmar, A; Zahra, D; Arthur, A; Middleton, RJ; Liu, GJ; Charil, A; Graeber, MB
    Glioblastoma is a highly malignant, largely therapy-resistant brain tumour. Deep infiltration of brain tissue by neoplastic cells represents the key problem of diffuse glioma. Much current research focuses on the molecular makeup of the visible tumour mass rather than the cellular interactions in the surrounding brain tissue infiltrated by the invasive glioma cells that cause the tumour’s ultimately lethal outcome. Diagnostic neuroimaging that enables the direct in vivo observation of the tumour infiltration zone and the local host tissue responses at a preclinical stage are important for the development of more effective glioma treatments. Here, we report an animal model that allows high-contrast imaging of wild-type glioma cells by positron emission tomography (PET) using [18 F]PBR111, a selective radioligand for the mitochondrial 18 kDa Translocator Protein (TSPO), in the Tspo−/− mouse strain (C57BL/6-Tspotm1GuMu(GuwiyangWurra)). The high selectivity of [18 F]PBR111 for the TSPO combined with the exclusive expression of TSPO in glioma cells infiltrating into null-background host tissue free of any TSPO expression, makes it possible, for the first time, to unequivocally and with uniquely high biological contrast identify peri-tumoral glioma cell invasion at preclinical stages in vivo. Comparison of the in vivo imaging signal from wild-type glioma cells in a null background with the signal in a wild-type host tissue, where the tumour induces the expected TSPO expression in the host’s glial cells, illustrates the substantial extent of the peritumoral host response to the growing tumour. The syngeneic tumour (TSPO+/+) in null background (TSPO−/−) model is thus well suited to study the interaction of the tumour front with the peri-tumoral tissue, and the experimental evaluation of new therapeutic approaches targeting the invasive behaviour of glioblastoma. © 2020, The Author(s).
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    Simultaneous scanning of two mice in a small-animal PET scanner: a simulation-based assessment of the signal degradation
    (IOP science publishing, 2016-01-21) Reilhac, A; Boisson, F; Wimberley, CA; Parmar, A; Zahra, D; Hamze, H; Davis, E; Arthur, A; Bouillot, C; Charil, A; Grégoire, MC
    In PET imaging, research groups have recently proposed different experimental set ups allowing multiple animals to be simultaneously imaged in a scanner in order to reduce the costs and increase the throughput. In those studies, the technical feasibility was demonstrated and the signal degradation caused by additional mice in the FOV characterized, however, the impact of the signal degradation on the outcome of a PET study has not yet been studied. Here we thoroughly investigated, using Monte Carlo simulated [18F]FDG and [11C]Raclopride PET studies, different experimental designs for whole-body and brain acquisitions of two mice and assessed the actual impact on the detection of biological variations as compared to a single-mouse setting. First, we extended the validation of the PET-SORTEO Monte Carlo simulation platform for the simultaneous simulation of two animals. Then, we designed [18F]FDG and [11C]Raclopride input mouse models for the simulation of realistic whole-body and brain PET studies. Simulated studies allowed us to accurately estimate the differences in detection between single- and dual-mode acquisition settings that are purely the result of having two animals in the FOV. Validation results showed that PET-SORTEO accurately reproduced the spatial resolution and noise degradations that were observed with actual dual phantom experiments. The simulated [18F]FDG whole-body study showed that the resolution loss due to the off-center positioning of the mice was the biggest contributing factor in signal degradation at the pixel level and a minimal inter-animal distance as well as the use of reconstruction methods with resolution modeling should be preferred. Dual mode acquisition did not have a major impact on ROI-based analysis except in situations where uptake values in organs from the same subject were compared. The simulated [11C]Raclopride study however showed that dual-mice imaging strongly reduced the sensitivity to variations when mice were positioned side-by-side while no sensitivity reduction was observed when they were facing each other. This is the first study showing the impact of different experimental designs for whole-body and brain acquisitions of two mice on the quality of the results using Monte Carlo simulated [18F]FDG and [11C]Raclopride PET studies. © 2016 Institute of Physics and Engineering in Medicine
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    β-Amyloid is associated with aberrant metabolic connectivity in subjects with mild cognitive impairment
    (SAGE Journals, 2014-04-16) Carbonell, F; Charil, A; Zijdenbos, AP; Evans, AC; Bedell, BJ
    Positron emission tomography (PET) studies using [18F]2-fluoro-2-deoxyglucose (FDG) have identified a well-defined pattern of glucose hypometabolism in Alzheimer's disease (AD). The assessment of the metabolic relationship among brain regions has the potential to provide unique information regarding the disease process. Previous studies of metabolic correlation patterns have demonstrated alterations in AD subjects relative to age-matched, healthy control subjects. The objective of this study was to examine the associations between β-amyloid, apolipoprotein E ɛ4 (APOE ɛ4) genotype, and metabolic correlations patterns in subjects diagnosed with mild cognitive impairment (MCI). Mild cognitive impairment subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study were categorized into β-amyloid-low and β-amyloid-high groups, based on quantitative analysis of [18F]florbetapir PET scans, and APOE ɛ4 non-carriers and carriers based on genotyping. We generated voxel-wise metabolic correlation strength maps across the entire cerebral cortex for each group, and, subsequently, performed a seed-based analysis. We found that the APOE ɛ4 genotype was closely related to regional glucose hypometabolism, while elevated, fibrillar β-amyloid burden was associated with specific derangements of the metabolic correlation patterns. © 2014 International Society for Cerebral Blood Flow & Metabolism, Inc. This work is licensed under a Creative Commons Attribution 3.0