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In PET image reconstruction, it would be useful to obtain the entire posterior probability distribution of the image, because it allows for both estimating image intensity and assessing the uncertainty of the estimation, thus leading to more reliable interpretation. We propose a new entirely probabilistic model: the prior is a distribution over possible smooth regions (distance-driven Chinese restaurant process), and the posterior distribution is estimated using a Gibbs Markov chain Monte Carlo sampler. Data from other modalities (here one or several MR images) are introduced into the model as additional observed data, providing side information about likely smooth regions in the image. The reconstructed image is the posterior mean, and the uncertainty is presented as an image of the size of 95% posterior intervals. The reconstruction was compared with the maximum-likelihood expectation–maximization and OSEM algorithms, with and without post-smoothing, and with a penalized ML or MAP method that also uses additional images from other modalities. Qualitative and quantitative tests were performed on realistic simulated data with statistical replicates and on several clinical examinations presenting pathologies. The proposed method presents appealing properties in terms of obtained bias, variance, spatial regularization, and use of multimodal data, and produces, in addition, potentially valuable uncertainty information. [ABSTRACT FROM AUTHOR]

Iterative reconstructions in positron emission tomography (PET) need a model relating the recorded data to the object/patient being imaged, called the system matrix (SM). The more realistic this model, the better the spatial resolution in the reconstructed images. However, a serious concern when using a SM that accurately models the resolution properties of the PET system is the undesirable edge artefact, visible through oscillations near sharp discontinuities in the reconstructed images. This artefact is a natural consequence of solving an ill-conditioned inverse problem, where the recorded data are band-limited. In this paper, we focus on practical aspects when considering image-based point-spread function (PSF) reconstructions. To remove the edge artefact, we propose to use a particular case of the method of sieves (Grenander 1981 Abstract Inference New York: Wiley), which simply consists in performing a standard PSF reconstruction, followed by a post-smoothing using the PSF as the convolution kernel. Using analytical simulations, we investigate the impact of different reconstruction and PSF modelling parameters on the edge artefact and its suppression, in the case of noise-free data and an exactly known PSF. Using Monte-Carlo simulations, we assess the proposed method of sieves with respect to the choice of the geometric projector and the PSF model used in the reconstruction. When the PSF model is accurately known, we show that the proposed method of sieves succeeds in completely suppressing the edge artefact, though after a number of iterations higher than typically used in practice. When applying the method to realistic data (i.e. unknown true SM and noisy data), we show that the choice of the geometric projector and the PSF model does not impact the results in terms of noise and contrast recovery, as long as the PSF has a width close to the true PSF one. Equivalent results were obtained using either blobs or voxels in the same conditions (i.e. the blob's density function being the same as the voxel-based PSF). From a practical point-of-view, the method can be used to perform fast reconstructions based on very simple models (compared to sinogram-based PSF modelling), producing artefact-free images with a better compromise between noise and spatial resolution than images reconstructed without or with under-estimated PSF. Besides, the method inherently limits the spatial resolution in the reconstructed images to the intrinsic one of the PET system [ABSTRACT FROM AUTHOR]

Several developments were made in the GATE simulation platform to allow accurate modeling of the count rate performances of PET scanners over a wide range of activity concentrations. A background noise module, a dead time and limited bandwidth modeling for the coincidences, and a delayed coincidence builder were added in the code. The results obtained for the modeling of the ECAT HRRT and Focus 220 scanners with the newly developed modules are discussed. They show that GATE can be used to accurately simulate the single event, prompt coincidence and delayed coincidence rates, from very low activity levels in the field of view up to levels that saturate the acquisition system. The new developments were committed into the public release of GATE, making them available for the whole community, thanks to the open source license under which GATE is published (LGPL). [ABSTRACT FROM AUTHOR]

Monte Carlo-based PET simulators are powerful tools for accurately generating projections of tracer distributions for given scanner specifications and attenuating media distributions. High activity-related phenomena, such as the randoms contribution as well as block and system deadtimes constitute a large source of artefact and must therefore be integrated within the simulation model along with the -y-ray interaction within the tissue or the scanner material. We present here the features of a Monte Carlo simulator, dedicated to full ring tomographs, which is able to generate scattered, unscattered, and randoms distributions from voxelized phantom descriptions, and which accounts for the data losses due to system deadtime. Simulations of the count rate performance of the ECAT Exact HR+ operating in 2-D and 3-D modes were found to be in good agreement with experimental measurements obtained for a wide range of activity levels and distributions. [ABSTRACT FROM AUTHOR]

Monte Carlo simulation is an essential tool to assist in the design of new medical imaging devices for emission tomography. On one hand, dedicated Monte Carlo codes have been developed for PET and SPECT. However, they suffer from a variety of drawbacks and limitations in terms of validation, accuracy, and/or support. On the other hand, accurate and versatile simulation codes such as Geant3, EGS4, MCNP, and recently Geant4 have been written for high energy physics. They all include well-validated physics models, geometry modeling tools and efficient visualization utilities. Nevertheless these packages are quite complex and necessitate a steep learning curve. GATE, the Geant4 Application for Tomographic Emission, encapsulates the Geant4 libraries in order to achieve a modular, versatile, scripted simulation toolkit adapted to the field of nuclear medicine. In particular, GATE allows the users to describe time-dependent phenomena such as detector movements or source decay kinetics, thus allowing to simulate time curves under realistic acquisition conditions. At present, it is being further developed and validated within the OpenGATE Collaboration. We give a succinct overview of GATE and four examples of its validation against real data obtained with PET and SPECT cameras. [Copyright &y& Elsevier]