PET-SORTEO: A Monte Carlo-Based Simulator With High Count Rate Capabilities

Reilhac, Anthonin; Lartizien, Carole; Costes, Nicolas; Sans, S.; Comtat, Claude; Gunn, Roger N.; Evans, Alan C.

doi:10.1109/tns.2003.823011

Cited by 98 publications

(77 citation statements)

References 25 publications

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“…were performed using a model of the mMR in PET SORTEO [26]. A 30 minute scan representing an acquisition at 40-70 minutes post-injection was simulated.…”

Section: Monte Carlo Pib Simulationmentioning

confidence: 99%

PETPVC: a toolbox for performing partial volume correction techniques in positron emission tomography

et al. 2016

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Positron emission tomography (PET) images are degraded by a phenomenon known as the partial volume effect (PVE). Approaches have been developed to reduce PVEs, typically through the utilisation of structural information provided by other imaging modalities such as MRI or CT.These methods, known as partial volume correction (PVC) techniques, reduce PVEs by compensating for the effects of the scanner resolution, thereby improving the quantitative accuracy. The PETPVC toolbox described in this paper comprises a suite of methods, both classic and more recent approaches, for the purposes of applying PVC to PET data.Eight core PVC techniques are available. These core methods can be combined to create a total of twenty-two different PVC techniques. Simulated brain PET data are used to demonstrate the utility of toolbox in idealised conditions, the effects of applying PVC with mismatched pointspread function (PSF) estimates and the potential of novel hybrid PVC methods to improve the quantification of lesions.All anatomy-based PVC techniques achieve complete recovery of the PET signal in cortical grey matter (GM) when performed in idealised conditions. Applying deconvolution-based approaches results in incomplete recovery due to premature termination of the iterative process. PVC techniques are sensitive to PSF mismatch, causing a bias of up to 16.7% in GM recovery when over-estimating the PSF by 3mm. The recovery of both GM and a simulated lesion was improved by combining two PVC techniques together.The PETPVC toolbox has been written in C++, supports Windows, Mac and Linux operating systems, is open-source and publicly available.3

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“…were performed using a model of the mMR in PET SORTEO [26]. A 30 minute scan representing an acquisition at 40-70 minutes post-injection was simulated.…”

Section: Monte Carlo Pib Simulationmentioning

confidence: 99%

PETPVC: a toolbox for performing partial volume correction techniques in positron emission tomography

et al. 2016

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“…We then truly hope to deliver these realistic models to the community for evaluation of cardiac image processing algorithms, especially 4D cardiac segmentation methods Error! Reference source not found., [11].…”

Section: Discussionmentioning

confidence: 99%

A realistic anthropomorphic dynamic heart phantom

Haddad

Clarysse

Orkisz

et al. 2005

Computers in Cardiology, 2005

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“…The voxel size used to generate the phantom was 1mm× 1mm× 1mm. 30 3min scans of the NCAT phantom were simulated using a Monte Carlo simulator (PET-SORTEO [10]) which models among others the spatially variant point spread function (PSF) of the ECAT Exact HR+ scanner. Attenuation and scatter were also modeled.…”

Section: Simulated Lesionsmentioning

confidence: 99%

Fuzzy Multi-class Statistical Modeling for Efficient Total Lesion Metabolic Activity Estimation from Realistic PET Images

George

Vunckx

Casteele

et al. 2012

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

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Abstract. 18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET) has become the de facto standard for current clinical therapy follow up evaluations. In pursuit of robust biomarkers for predicting early therapy response, an efficient marker quantification procedure is certainly a necessity. Among various PET derived markers, the clinical investigations indicated that the total lesion metabolic activity (TLA) of a tumor lesion has a good prognostic value in several longitudinal studies. We utilize a fuzzy multi-class modeling using a stochastic expectation maximization (SEM) algorithm to fit a finite mixture model (FMM) to the PET image. We then propose a direct estimation formula for TLA and SUVmean from this multi-class statistical model. In order to evaluate our proposition, a realistic liver lesion is simulated and reconstructed. All results were evaluated with reference to the ground truth knowledge. Our experimental study conveys that the proposed method is robust enough to handle background heterogeneities in realistic scenarios.

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PET-SORTEO: A Monte Carlo-Based Simulator With High Count Rate Capabilities

Cited by 98 publications

References 25 publications

PETPVC: a toolbox for performing partial volume correction techniques in positron emission tomography

PETPVC: a toolbox for performing partial volume correction techniques in positron emission tomography

A realistic anthropomorphic dynamic heart phantom

Fuzzy Multi-class Statistical Modeling for Efficient Total Lesion Metabolic Activity Estimation from Realistic PET Images

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