Claude Comtat

GATE: a simulation toolkit for PET and SPECT.

Monte Carlo simulation is an essential tool in emission tomography that can assist in the design of new medical imaging devices, the optimization of acquisition protocols and the development or assessment of image reconstruction algorithms and correction techniques. GATE, the Geant4 Application for Tomographic Emission, encapsulates the Geant4 libraries to achieve a modular, versatile, scripted simulation toolkit adapted to the field of nuclear medicine. In particular, GATE allows the description of time-dependent phenomena such as source or detector movement, and source decay kinetics. This feature makes it possible to simulate time curves under realistic acquisition conditions and to test dynamic reconstruction algorithms. This paper gives a detailed description of the design and development of GATE by the OpenGATE collaboration, whose continuing objective is to improve, document and validate GATE by simulating commercially available imaging systems for PET and SPECT. Large effort is also invested in the ability and the flexibility to model novel detection systems or systems still under design. A public release of GATE licensed under the GNU Lesser General Public License can be downloaded at http:/www-lphe.epfl.ch/GATE/. Two benchmarks developed for PET and SPECT to test the installation of GATE and to serve as a tutorial for the users are presented. Extensive validation of the GATE simulation platform has been started, comparing simulations and measurements on commercially available acquisition systems. References to those results are listed. The future prospects towards the gridification of GATE and its extension to other domains such as dosimetry are also discussed.

Impact of Image-Space Resolution Modeling for Studies with the High-Resolution Research Tomograph

Brain PET in small structures is challenged by low resolution inducing bias in the activity measurements. Improved spatial resolution may be obtained by using dedicated tomographs and more comprehensive modeling of the acquisition system during reconstruction. In this study, we assess the impact of resolution modeling (RM) during reconstruction on image quality and on the estimates of biologic parameters in a clinical study performed on a high-resolution research tomograph. Methods: An accelerated list-mode ordinary Poisson ordered-subset expectation maximization (OP-OSEM) algorithm, including sinogram-based corrections and an experimental stationary model of resolution, has been designed. Experimental phantom studies are used to assess contrast and noise characteristics of the reconstructed images. The binding potential of a selective tracer of the dopamine transporter is also assessed in anatomic volumes of interest in a 5-patient study. Results: In the phantom experiment, a slower convergence and a higher contrast recovery are observed for RM-OP-OSEM than for OP-OSEM for the same level of statistical noise. RM-OP-OSEM yields contrast recovery levels that could not be reached without RM as well as better visual recovery of the smallest spheres and better delineation of the structures in the reconstructed images. Statistical noise has lower variance at the voxel level with RM than without at matched resolution. In a uniform activity region, RM induces higher positive and lower negative correlations with neighboring voxels, leading to lower spatial variance. Clinical images reconstructed with RM demonstrate better delineation of cortical and subcortical structures in both time-averaged and parametric images. The binding potential in the striatum is also increased, a result similar to the one observed in the phantom study. Conclusion: In high-resolution PET, RM during reconstruction improves quantitative accuracy by reducing the partial-volume effects.

Comparison of 3-D reconstruction with 3D-OSEM and with FORE+OSEM for PET

The combination of Fourier rebinning (FORE) and the ordered subsets expectation-maximization (OSEM), a fast statistical algorithm, appears as a promising alternative to the fully three-dimensional (3-D) iterative approach for clinical positron emission tomography (PET) data. Here, the authors evaluated the properties of FORE+OSEM and compared it with fully 3-D OSEM using both simulations and data acquired by commercial scanners. The aim is to determine to what extent the speed advantage of FORE+OSEM is paid for by a possible degradation of image quality in the case of noisy clinical PET data. A forward- and back-projection pair based on a line integral model was used in two-dimensional OSEM and 3-D OSEM (3D-OSEM) instead of a system matrix. Different variants of both approaches have been studied with simulations in terms of contrast-noise tradeoff. Two variants-FORE+OSEM with attenuation weighting (AW) [FORE+OSEM(AW)] and 3D-OSEM with attenuation-normalization weighting (ANSP) and a shifted-Poisson (SP) model [3D-OSEM(ANSP)]-were compared with measured phantom data and patient data. Based on the results from both simulations and measured data, the authors conclude that: 1) both attenuation (-normalization) weighting and the SP model improve the image quality but slow down the convergence and 2) despite its approximate nature, FORE+OSEM does not show apparent image degradation compared with 3D-OSEM for data with a noise level typical of a whole-body FDG scan.

PET-SORTEO: a Monte Carlo-based Simulator with high count rate capabilities

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 /spl gamma/-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/sup +/ 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.

Joint estimation of dynamic PET images and temporal basis functions using fully 4D ML-EM

A fully 4D joint-estimation approach to reconstruction of temporal sequences of 3D positron emission tomography (PET) images is proposed. The method estimates both a set of temporal basis functions and the corresponding coefficient for each basis function at each spatial location within the image. The joint estimation is performed through a fully 4D version of the maximum likelihood expectation maximization (ML-EM) algorithm in conjunction with two different models of the mean of the Poisson measured data. The first model regards the coefficients of the temporal basis functions as the unknown parameters to be estimated and the second model regards the temporal basis functions themselves as the unknown parameters. The fully 4D methodology is compared to the conventional frame-by-frame independent reconstruction approach (3D ML-EM) for varying levels of both spatial and temporal post-reconstruction smoothing. It is found that using a set of temporally extensive basis functions (estimated from the data by 4D ML-EM) significantly reduces the spatial noise when compared to the independent method for a given level of image resolution. In addition to spatial image quality advantages, for smaller regions of interest (where statistical quality is often limited) the reconstructed time-activity curves show a lower level of bias and a lower level of noise compared to the independent reconstruction approach. Finally, the method is demonstrated on clinical 4D PET data.

Assessment of 11C-PE2I Binding to the Neuronal Dopamine Transporter in Humans with the High-Spatial-Resolution PET Scanner HRRT

The high-resolution research tomograph (HRRT), dedicated to brain imaging, may offer new perspectives for identifying small brain nuclei that remain neglected by the spatial resolution of conventional scanners. However, the use of HRRT for neuroimaging applications still needs to be fully assessed. The present study aimed at evaluating the HRRT for measurement of the dopamine transporter (DAT) binding to validate its quantification and explore the gain induced by the increased spatial resolution in comparison with conventional PET scanners. Methods: Fifteen and 11 healthy subjects were examined using the selective DAT radioligand 11C-PE2I with HRRT and HR+ scanners, respectively. Quantification of the DAT binding was assessed by the calculation of binding potential (BP) values using the simplified reference tissue model in anatomic regions of interest (ROIs) defined on the dorsal striatum and in a standardized ROI defined on the midbrain. Results: Quantification of 11C-PE2I binding to the DAT measured in the midbrain and striatum with both scanners at the same spatial resolution (smoothed HRRT images) exhibited similar BP values and intersubject variability, thus validating the quantification of DAT binding on the HRRT. For age-paired comparison, BP values of subjects examined with HRRT were significantly higher than those of the subjects examined with HR+. The increase ranged from 29% in the caudate and 35% in the putamen to 92% in the midbrain. The decline in DAT binding with age in the striatum was in good agreement between both scanners and literature, whereas no significant decrease in DAT binding with age was observed in the midbrain with either HRRT or HR+. Conclusion: HRRT allows quantitative measurements of neurotransmission processes in small brain nuclei and allows recovering higher values as compared with coarser spatial resolution PET scanners. High-spatial-resolution PET appears promising for a more accurate detection of neurobiologic modifications and also for the exploration of subtle modifications in small and complex brain structures largely affected by the partial-volume effect.

Striatal and extrastriatal dopamine transporter in cannabis and tobacco addiction: a high‐resolution PET study

The dopamine (DA) system is known to be involved in the reward and dependence mechanisms of addiction. However, modifications in dopaminergic neurotransmission associated with long‐term tobacco and cannabis use have been poorly documented in vivo. In order to assess striatal and extrastriatal dopamine transporter (DAT) availability in tobacco and cannabis addiction, three groups of male age‐matched subjects were compared: 11 healthy non‐smoker subjects, 14 tobacco‐dependent smokers (17.6 ± 5.3 cigarettes/day for 12.1 ± 8.5 years) and 13 cannabis and tobacco smokers (CTS) (4.8 ± 5.3 cannabis joints/day for 8.7 ± 3.9 years). DAT availability was examined in positron emission tomography (HRRT) with a high resolution research tomograph after injection of [11C]PE2I, a selective DAT radioligand. Region of interest and voxel‐by‐voxel approaches using a simplified reference tissue model were performed for the between‐group comparison of DAT availability. Measurements in the dorsal striatum from both analyses were concordant and showed a mean 20% lower DAT availability in drug users compared with controls. Whole‐brain analysis also revealed lower DAT availability in the ventral striatum, the midbrain, the middle cingulate and the thalamus (ranging from −15 to −30%). The DAT availability was slightly lower in all regions in CTS than in subjects who smoke tobacco only, but the difference does not reach a significant level. These results support the existence of a decrease in DAT availability associated with tobacco and cannabis addictions involving all dopaminergic brain circuits. These findings are consistent with the idea of a global decrease in cerebral DA activity in dependent subjects.

Image based resolution modeling for the HRRT OSEM reconstructions software

The implementation and the measurement of an approximate image based model of the ECAT HRRT PET scanner response function, designed for its regular OSEM reconstruction software, are presented. The system matrix used in the iterative reconstruction is factorized into two terms: first a matrix modeling the blurring effects in the image space, followed by the projection matrix. The methodology used to measure the elements of the image based blurring matrix is presented and applied to three HRRT scanners. A spatially invariant resolution model was chosen; the columns of the blurring matrix are then defined as shifted copies of a stationary blurring kernel. This kernel was modeled as the sum of two isotropic 3D Gaussian functions. The results of the resolution measurement varied between the three scanners: at the center of the field-of-view, the standard deviation varied between 0.85 and 1.00 mm for the first Gaussian and between 2.0 and 2.7 mm for the second Gaussian. The ratio between the second and the first Gaussians was 0.07.

Exact rebinning methods for three-dimensional PET

The high computational cost of data processing in volume PET imaging is still hindering the routine application of this successful technique, especially in the case of dynamic studies. This paper describes two new algorithms based on an exact rebinning equation, which can be applied to accelerate the processing of three-dimensional (3-D) PET data. The first algorithm, FOREPROJ, is a fast-forward projection algorithm that allows calculation of the 3-D attenuation correction factors (ACFs) directly from a two dimensional (2-D) transmission scan, without first reconstructing the attenuation map and then performing a 3-D forward projection. The use of FOREPROJ speeds up the estimation of the 3-D ACFs by more than a factor five. The second algorithm, FOREX, is a rebinning algorithm that is also more than five times faster, compared to the standard reprojection algorithm (3DRP) and does not suffer from the image distortions generated by the even faster approximate Fourier rebinning (FORE) method at large axial apertures. However, FOREX is probably not required by most existing scanners, as the axial apertures are not large enough to show improvements over FORE with clinical data. Both algorithms have been implemented and applied to data simulated for a scanner with a large axial aperture (30/spl deg/), and also to data acquired with the ECAT HR and the ECAT HR+ scanners. Results demonstrate the excellent accuracy achieved by these algorithms and the important speedup when the sinogram sizes are powers of two.

Fully 4D image reconstruction by estimation of an input function and spectral coefficients

A new dynamic image reconstruction method for PET is proposed. First, a set of exponential temporal basis functions is predefined, covering the entire range of kinetics (from static through to a delta function). Just as in spectral analysis, such a selection is designed to be able to handle all possible tissue responses for multi-compartmental tissue models. Second, an initial estimate of an input function is defined. The time-dependent PET radiotracer concentration is then modeled (through the system matrix in the reconstruction algorithm) as a superposition of the exponential temporal basis functions, convolved with the input function. The reconstruction method uses an expectation maximization (EM) algorithm to operate directly on the measured PET data in order to i. estimate the coefficients of the exponential functions, and ii. improve the estimate of the input function. The coefficients and the input function are estimated only as a means of regularizing the model of the time-dependent image: the final reconstruction is used with conventional post-reconstruction kinetic analysis, with a different input function if need be (as the estimated input function may not correspond to the true input function). Results from tests on simulated data reveal a simultaneous benefit of noise reduction and improved kinetic parameter estimates when compared to conventional methodology. The method is also demonstrated on measured HRRT PET data for an FDG study.