Régine Trébossen

In vivo imaging of brain lesions with [11C]CLINME, a new PET radioligand of peripheral benzodiazepine receptors

The peripheral benzodiazepine receptor (PBR) is expressed by microglial cells in many neuropathologies involving neuroinflammation. PK11195, the reference compound for PBR, is used for positron emission tomography (PET) imaging but has a limited capacity to quantify PBR expression. Here we describe the new PBR ligand CLINME as an alternative to PK11195. In vitro and in vivo imaging properties of [11C]CLINME were studied in a rat model of local acute neuroinflammation, and compared with the reference compound [11C]PK11195, using autoradiography and PET imaging. Immunohistochemistry study was performed to validate the imaging data. [11C]CLINME exhibited a higher contrast between the PBR‐expressing lesion site and the intact side of the same rat brain than [11C]PK11195 (2.14 ± 0.09 vs. 1.62 ± 0.05 fold increase, respectively). The difference was due to a lower uptake for [11C]CLINME than for [11C]PK11195 in the non‐inflammatory part of the brain in which PBR was not expressed, while uptake levels in the lesion were similar for both tracers. Tracer localization correlated well with that of activated microglial cells, demonstrated by immunohistochemistry and PBR expression detected by autoradiography. Modeling using the simplified tissue reference model showed that R1 was similar for both ligands (R1 ∼ 1), with [11C]CLINME exhibiting a higher binding potential than [11C]PK11195 (1.07 ± 0.30 vs. 0.66 ± 0.15). The results show that [11C]CLINME performs better than [11C]PK11195 in this model. Further studies of this new compound should be carried out to better define its capacity to overcome the limitations of [11C]PK11195 for PBR PET imaging.

Minimally invasive input function for 2- 18F-fluoro-A-85380 brain PET studies

PurposeQuantitative neuroreceptor positron emission tomography (PET) studies often require arterial cannulation to measure input function. While population-based input function (PBIF) would be a less invasive alternative, it has only rarely been used in conjunction with neuroreceptor PET tracers. The aims of this study were (1) to validate the use of PBIF for 2-18F-fluoro-A-85380, a tracer for nicotinic receptors; (2) to compare the accuracy of measures obtained via PBIF to those obtained via blood-scaled image-derived input function (IDIF) from carotid arteries; and (3) to explore the possibility of using venous instead of arterial samples for both PBIF and IDIF.MethodsTen healthy volunteers underwent a dynamic 2-18F-fluoro-A-85380 brain PET scan with arterial and, in seven subjects, concurrent venous serial blood sampling. PBIF was obtained by averaging the normalized metabolite-corrected arterial input function and subsequently scaling each curve with individual blood samples. IDIF was obtained from the carotid arteries using a blood-scaling method. Estimated Logan distribution volume (VT) values were compared to the reference values obtained from arterial cannulation.ResultsFor all subjects, PBIF curves scaled with arterial samples were similar in shape and magnitude to the reference arterial input function. The Logan VT ratio was 1.00u2009±u20090.05; all subjects had an estimation error <10%. IDIF gave slightly less accurate results (VT ratio 1.03u2009±u20090.07; eight of ten subjects had an error <10%). PBIF scaled with venous samples yielded inaccurate results (VT ratio 1.13u2009±u20090.13; only three of seven subjects had an error <10%). Due to arteriovenous differences at early time points, IDIF could not be calculated using venous samples.ConclusionPBIF scaled with arterial samples accurately estimates Logan VT for 2-18F-fluoro-A-85380. Results obtained with PBIF were slightly better than those obtained with IDIF. Due to arteriovenous concentration differences, venous samples cannot be substituted for arterial samples.

A nonparametric bayesian approach for PET reconstruction

We introduce a PET reconstruction algorithm following a nonparametric Bayesian (NPB) approach. In contrast with expectation maximization (EM), the proposed technique does not rely on any space discretization. Namely, the activity distribution - normalized emission intensity of the spatial Poisson process - is considered as a spatial probability density and observations are the projections of random emissions whose distribution has to be estimated. This approach is nonparametric in the sense that the quantity of interest belongs to the set of probability measures on Rk (for reconstruction in k-dimensions) and it is Bayesian in the sense that we define a prior directly on this spatial measure and infer on the posterior distribution of the activity distribution. In this context, we propose to model the nonparametric probability density as an infinite mixture of multivariate normal distributions. As a prior for this mixture we consider a Dirichlet process mixture (DPM) with a normal-inverse wishart (NTW) model as base distribution of the Dirichlet process. As in EM-family reconstruction, we use a data augmentation scheme where the set of hidden variables are the emission locations in the continuous object space for each observed coincidence. Thanks to the data augmentation, we propose a Markov chain Monte Carlo (MCMC) algorithm (Gibbs sampler) which is able to generate draws from the posterior distribution of the spatial intensity. A difference with EM is that hidden variables involved in the Gibbs sampler correspond to generated emission locations while the number of emissions per pixel detected on a projection line is used for complete data in EM. Another key difference is that the estimated spatial intensity is a continuous function - such that there is no need to compute a projection matrix - while parameters in EM are given by the mean intensity per pixel. Finally, draws from the intensity posterior distribution allow the estimation of posterior functionals like the mean and variance or confidence intervals. The nonparametric behavior is characterized by an increase of DPM components (clusters) and consequently a resolution improvement with the number of recorded events. Results are presented for simulated data based on a 2D brain phantom and compared to ML-EM and Bayesian MAP-EM.

Temporal wavelet denoising of PET sinograms and images

The level of noise in PET dynamic studies makes it difficult to provide accurate and robust kinetic parameters from time activity curves, particularly at the voxel level. Several approaches have been followed to lower noise: denoising reconstructed images with spatial wavelets, adding a priori information during reconstruction about the signal without noise, including the temporal dimension during reconstruction. In this work, we propose to use a temporal wavelet denoising approach, based on the characteristics of PET time activity curves in sinograms (or reconstructed images). This approach has recently been proposed in image processing and relies on discriminating signal from noise by including relevant “a priori” information (on the statistical distribution of the wavelet coefficient for a whole sinogram or reconstructed image), as well as appropriate noise formation model in the time domain. This approach is tested in a 2D spatial + 1D time Monte Carlo simulation mimicking brain, and compared with a standard denoising approach : SUREShrink. Preliminary results indicate that better performances are obtained for sinogram denoising with the proposed approach compared with SUREShrink, and that the resulting sinograms can be reconstructed with a weighted least-squares (WLS) algorithm for all techniques. Denoising in the reconstructed images with this approach was also investigated.

Co-registration of glucose metabolism with positron emission tomography and vascularity with fluorescent diffuse optical tomography in mouse tumors

BackgroundBimodal molecular imaging with fluorescence diffuse optical tomography (fDOT) and positron emission tomography (PET) has the capacity to provide multiple molecular information of mouse tumors. The objective of the present study is to co-register fDOT and PET molecular images of tumors in mice automatically.MethodsThe coordinates of bimodal fiducial markers (FM) in regions of detection were automatically detected in planar optical images (x, y positions) in laser pattern optical surface images (z position) and in 3-D PET images. A transformation matrix was calculated from the coordinates of the FM in fDOT and in PET and applied in order to co-register images of mice bearing neuroendocrine tumors.ResultsThe method yielded accurate non-supervised co-registration of fDOT and PET images. The mean fiducial registration error was smaller than the respective voxel sizes for both modalities, allowing comparison of the distribution of contrast agents from both modalities in mice. Combined imaging depicting tumor metabolism with PET-[18u2009F]2-deoxy-2-fluoro-d-glucose and blood pool with fDOT demonstrated partial overlap of the two signals.ConclusionsThis automatic method for co-registration of fDOT with PET and other modalities is efficient, simple and rapid, opening up multiplexing capacities for experimental in vivo molecular imaging.

A bootstrap method for a totally non-invasive input function and pharmacokinetic parameters estimation in 18F-FDG PET images of the human brain

The pharmacokinetics extracted from PET images in the brain is relied to the physiological mechanisms that describe the processing of the tracer by the brain structures. These processes can be described through a multi-compartments model detailing the different states of the tracer and the transfer rate between these states. Numerous work have been proposed for the estimation of the input function (IF) from the internal carotids in the brain, but these methods are not able to extract efficiently the individual parameters of the model [1].

Monte Carlo PET camera modelling for proton range evaluation in proton therapy

Proton therapy is a conformal method for dose delivery in sensitive regions. Offline PET monitoring has been proposed as proton irradiation produces positron emitters such as ¹¹C and ¹⁵O [1].

Monte Carlo simulation of positron-emitting nuclei distributions in proton therapy

Proton therapy is mainly used to treat cancer where a conformal dose is essential. Methods for monitoring both the spatial distribution and the delivered dose have thus been proposed [1]. Proton irradiation on organic tissue produces positron emitters (mainly 11C and 15O). Proton therapy monitoring may thus be performed using Positron Emission Tomography (PET) acquisition. The proposed methods most often consist in comparing a Monte Carlo simulation of the positron emitters produced during irradiation with PET measurements. The aim of this work is the modelling of the whole process from the irradiation to the PET image production using GATE toolkit [2].

Spatiotemporal Decomposition in Object-Space along Reconstruction in Emission Tomography

Emission tomography has provided a new insight in brain mechanisms past years. Although reconstructions are nowadays mostly static, trend is going toward dynamic acquisitions and reconstructions. This opens a new range of investigations, for instance for drugs discovery. Indeed new drugs are studied through the dynamic ability of tissues to catch them. However, it is required to know radiotracer concentration of blood that irrigates tissues in order to draw conclusions on potentials of these drugs. This concentration is called input function and this paper presents a new method for measuring it in a non-invasive way. Our new method relies on simultaneous estimations of vessels kinetics and vessels spatial distribution. These estimations are performed during the reconstruction process and take into account the statistical nature of measured signals. Indeed, this method is based on the maximisation of the likelihood of counts in detectors. It takes advantages of a non-negative matrix factorisation which separate spatial and temporal components. Results are very promising, since it estimates arterial input function accurately although object emits just a limited amount of photons, especially within the first minutes.