Browsing by Author "Angelis, GI"

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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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    Attenuation correction for freely moving small animal brain PET studies based on a virtual scanner geometry
    (IOP Publishing, 2014-09-05) Angelis, GI; Kyme, AZ; Ryder, WJ; Fulton, RR; Meikle, SR
    Attenuation correction in positron emission tomography brain imaging of freely moving animals is a very challenging problem since the torso of the animal is often within the field of view and introduces a non negligible attenuating factor that can degrade the quantitative accuracy of the reconstructed images. In the context of unrestrained small animal imaging, estimation of the attenuation correction factors without the need for a transmission scan is highly desirable. An attractive approach that avoids the need for a transmission scan involves the generation of the hull of the animal’s head based on the reconstructed motion corrected emission images. However, this approach ignores the attenuation introduced by the animal’s torso. In this work, we propose a virtual scanner geometry which moves in synchrony with the animal’s head and discriminates between those events that traversed only the animal’s head (and therefore can be accurately compensated for attenuation) and those that might have also traversed the animal’s torso. For each recorded pose of the animal’s head a new virtual scanner geometry is defined and therefore a new system matrix must be calculated leading to a time-varying system matrix. This new approach was evaluated on phantom data acquired on the microPET Focus 220 scanner using a custom-made phantom and step-wise motion. Results showed that when the animal’s torso is within the FOV and not appropriately accounted for during attenuation correction it can lead to bias of up to 10% . Attenuation correction was more accurate when the virtual scanner was employed leading to improved quantitative estimates (bias < 2%), without the need to account for the attenuation introduced by the extraneous compartment. Although the proposed method requires increased computational resources, it can provide a reliable approach towards quantitatively accurate attenuation correction for freely moving animal studies. © 2014 Institute of Physics
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    Determining glucose metabolism kineticsuUsing 18F-FDG micro-PET/CT
    (MyJoVE Corporation., 2017-05-02) Cochran, BJ; Ryder, WJ; Parmar, A; Klaeser, K; Reilhac, A; Angelis, GI; Meikle, SR; Barter, PJ; Rye, KA
    This paper describes the use of 18F-FDG and micro-PET/CT imaging to determine in vivo glucose metabolism kinetics in mice (and is transferable to rats). Impaired uptake and metabolism of glucose in multiple organ systems due to insulin resistance is a hallmark of type 2 diabetes. The ability of this technique to extract an image-derived input function from the vena cava using an iterative deconvolution method eliminates the requirement of the collection of arterial blood samples. Fitting of tissue and vena cava time activity curves to a two-tissue, three compartment model permits the estimation of kinetic micro-parameters related to the 18F-FDG uptake from the plasma to the intracellular space, the rate of transport from intracellular space to plasma and the rate of 18F-FDG phosphorylation. This methodology allows for multiple measures of glucose uptake and metabolism kinetics in the context of longitudinal studies and also provides insights into the efficacy of therapeutic interventions. © 2022 MyJoVE Corporation
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    Impact of extraneous mispositioned events on motion-corrected brain SPECT images of freely moving animals
    (American Association of Physicists in Medicine, 2014-08-18) Angelis, GI; Ryder, WJ; Bashar, R; Fulton, RR; Meikle, SR
    Purpose: Single photon emission computed tomography (SPECT) brain imaging of freely moving small animals would allow a wide range of important neurological processes and behaviors to be studied, which are normally inhibited by anesthetic drugs or precluded due to the animal being restrained. While rigid body motion of the head can be tracked and accounted for in the reconstruction, activity in the torso may confound brain measurements, especially since motion of the torso is more complex (i.e., nonrigid) and not well correlated with that of the head. The authors investigated the impact of mispositioned events and attenuation due to the torso on the accuracy of motion corrected brain images of freely moving mice. Methods: Monte Carlo simulations of a realistic voxelized mouse phantom and a dual compartment phantom were performed. Each phantom comprised a target and an extraneous compartment which were able to move independently of each other. Motion correction was performed based on the known motion of the target compartment only. Two SPECT camera geometries were investigated: a rotating single head detector and a stationary full ring detector. The effects of motion, detector geometry, and energy of the emitted photons (hence, attenuation) on bias and noise in reconstructed brain regions were evaluated. Results: The authors observed two main sources of bias: (a) motion-related inconsistencies in the projection data and (b) the mismatch between attenuation and emission. Both effects are caused by the assumption that the orientation of the torso is difficult to track and model, and therefore cannot be conveniently corrected for. The motion induced bias in some regions was up to 12% when no attenuation effects were considered, while it reached 40% when also combined with attenuation related inconsistencies. The detector geometry (i.e., rotating vs full ring) has a big impact on the accuracy of the reconstructed images, with the full ring detector being more advantageous. Conclusions: Motion-induced inconsistencies in the projection data and attenuation/emission mismatch are the two main causes of bias in reconstructed brain images when there is complex motion. It appears that these two factors have a synergistic effect on the qualitative and quantitative accuracy of the reconstructed images. © 2014 American Association of Physicists in Medicine.
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    Optimisation of PET data processing for a single injection experiment with [11C]Raclopride using a simulations based approach
    (Society of Nuclear Medicine, 2014-11-05) Wimberley, CA; Angelis, GI; Boisson, F; Callaghan, PD; Fischer, K; Pichler, BJ; Meikle, SR; Grégoire, MC; Reilhac, A
    Objectives Positron emission tomography (PET) with [11C]Raclopride is an important tool for studying dopamine D2 receptor expression in vivo. [11C]Raclopride PET binding experiments conducted using the Partial Saturation Approach (PSA) (a simple, single injection experiment, Delforge 1995) allow the estimation of receptor density (Bavail) and the in vivo affinity 1/(KD). To achieve accurate and stable parameter estimates, and the ability to detect small changes in these parameters, the impact of the data processing chain should be investigated and optimised. Methods Two groups of PET scans were generated for a Partial Saturation Approach (PSA) experiment using Monte Carlo simulation software with a biological phenomenon inferred between the groups. The kinetic parameters Bavail and KD were estimated and the impact of spatial smoothing, temporal denoising and image resolution recovery on the statistical detectability of change in the estimates was investigated. Results Before optimisation, the inferred Bavail difference between the two groups was underestimated by 42% and detected in 66% of cases (at p<0.05), while a false decrease of KD by 13% was detected in more than 11% of cases. After optimisation, the calculated Bavail difference was underestimated by only 3.7% and detected in 89% of cases, while a false slight increase of KD by 3.7 % was detected in only 2% of cases. Conclusions The use of Monte Carlo generated PET scans allowed the optimisation of the data processing chain in order to reliably estimate and detect changes in the parameters Bavail and KD.
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    Simulation-based optimisation of the PET data processing for partial saturation approach protocols
    (Elsevier B.V., 2014-08-15) Wimberley, CA; Angelis, GI; Boisson, F; Callaghan, PD; Fischer, K; Pichler, BJ; Meikle, SR; Grégoire, MC; Reilhac, A
    Positron emission tomography (PET) with [11C]Raclopride is an important tool for studying dopamine D2 receptor expression in vivo. [11C]Raclopride PET binding experiments conducted using the Partial Saturation Approach (PSA) allow the estimation of receptor density (Bavail) and the in vivo affinity appKD. The PSA is a simple, single injection, single scan experimental protocol that does not require blood sampling, making it ideal for use in longitudinal studies. In this work, we generated a complete Monte Carlo simulated PET study involving two groups of scans, in between which a biological phenomenon was inferred (a 30% decrease of Bavail), and used it in order to design an optimal data processing chain for the parameter estimation from PSA data. The impact of spatial smoothing, noise removal and image resolution recovery technique on the statistical detection was investigated in depth. We found that image resolution recovery using iterative deconvolution of the image with the system point spread function associated with temporal data denoising greatly improves the accuracy and the statistical reliability of detecting the imposed phenomenon. Before optimisation, the inferred Bavail variation between the two groups was underestimated by 42% and detected in 66% of cases, while a false decrease of appKD by 13% was detected in more than 11% of cases. After optimisation, the calculated Bavail variation was underestimated by only 3.7% and detected in 89% of cases, while a false slight increase of appKD by 3.7% was detected in only 2% of cases. We found during this investigation that it was essential to adjust a factor that accounts for difference in magnitude between the non-displaceable ligand concentrations measured in the target and in the reference regions, for different data processing pathways as this ratio was affected by different image resolutions. © 2014 Elsevier B.V..