Can texture indices derived from PET images differentiate tissue types?

Congrès sous l’égide de la Société Française de Génie Biologique et Médical (SFGBM).National audienceAim: Texture indices (TI) are of growing interest for tumor characterization. Yet, whether FDG-PET images can evidence tissue-specific pattern has received little attention so far. We studied the ability of enhanced TI to determine tissue types. Materials and Methods: Forty-eight patients with non-small cell lung cancer underwent FDG-PET before treatment. Seven enhanced TI were calculated using a new resampling method. Standardized Uptake Value (SUV) and metabolic volume (MV) were also systematically computed. The ability of each index to distinguish between tumor and liver tissue and between two subtypes of cancer was investigated using ROC analyses. Results: All enhanced TI could differentiate tumor from liver tissue with an Area Under the ROC Curve (AUC) higher than 0.692. Homogeneity and Low Gray-Level Emphasis could differentiate the adenocarcinomas (n=28) and squamous cell carcinomas (n=12) with AUC better than that of SUVmax and MV (Delong’s test). Liver tissue had a more homogeneous texture than tumor tissue and adenocarcinomas exhibited a more homogeneous texture than squamous cell carcinomas. Conclusion: Enhanced TI vary as a function of the tissue type and cancer subtype, and might be used as a new tool for tumor characterization

Conception, reconstruction et évaluation d'une géométrie de collimation multi-focale en tomographie d'émission monophotonique préclinique

La tomographie d'émission monophotonique (TEMP) dédiée au petit animal est une technique d'imagerie nucléaire qui joue un rôle important en imagerie moléculaire. Les systèmes TEMP, à l'aide de collimateurs pinholes ou multi-pinholes, peuvent atteindre des résolutions spatiales submillimétriques et une haute sensibilité pour un petit champ de vue, ce qui est particulièrement attractif pour imager des souris. Une géométrie de collimation originale a été proposée, dans le cadre d'un projet, appelé SIGAHRS, piloté par la société Biospace. Ce collimateur présente des longueurs focales qui varient spatialement dans le plan transaxial et qui sont fixes dans le plan axial. Une haute résolution spatiale est recherchée au centre du champ de vue, avec un grand champ de vue et une haute sensibilité. Grâce aux simulations Monte Carlo, dont nous pouvons maîtriser tous les paramètres, nous avons étudié cette collimation originale que nous avons positionnée par rapport à un collimateur parallèle et un collimateur monofocal convergent. Afin de générer des données efficacement, nous avons développé un module multi-CPU/GPU qui utilise une technique de lancer de rayons dans le collimateur et qui nous a permis de gagner un facteur ~ 60 en temps de calcul, tout en conservant ~ 90 % du signal, pour l'isotope ^mTc (émettant à 140,5 keV), comparé à une simulation Monte Carlo classique. Cependant, cette approche néglige la pénétration septale et la diffusion dans le collimateur. Les données simulées ont ensuite été reconstruites avec l'algorithme OSEM. Nous avons développé quatre méthodes de projection (une projection simple (S-RT), une projection avec volume d'intersection (S-RT-IV), une projection avec calcul de l'angle solide (S-RT-SA) et une projection tenant compte de la profondeur d'interaction (S-RT-SA-D)). Nous avons aussi modélisé une PSF dans l'espace image, anisotrope et non-stationnaire, en nous inspirant de la littérature existante. Nous avons étudié le conditionnement de la matrice système pour chaque projecteur et collimateur, et nous avons comparé les images reconstruites pour chacun des collimateurs et pour chacun des projecteurs. Nous avons montré que le collimateur original proposé est le système le moins bien conditionné. Nous avons aussi montré que la modélisation de la PSF dans l'image ainsi que de la profondeur d'intéraction améliorent la qualité des images reconstruites ainsi que le recouvrement de contraste. Cependant, ces méthodes introduisent des artefacts de bord. Comparé aux systèmes existants, nous montrons que ce nouveau collimateur a un grand champ de vue (~ 70 mm dans le plan transaxial), avec une résolution de 1,0 mm dans le meilleur des cas, mais qu'il a une sensibilité relativement faible (1,32x10 %).Small animal single photon emission computed tomography (SPECT) is a nuclear medicine imaging technique that plays an important role in molecular imaging. SPECT systems using pinhole or multi-pinhole collimator can achieve submillimetric spatial resolution and high sensitivity in a small field of view, which is particularly appropriate for imaging mice. In our work, we studied a new collimator dedicated to small animal SPECT, in the context of a project called SIGAHRS, led by the Biospace company. In this collimator, focal lengths vary spatially in the transaxial plane and are fixed in the axial plane. This design aims at achieving high spatial resolution in the center of the field of view, with a large field of view and high sensitivity. Using Monte Carlo simulations, where all parameters can be controlled, we studied this new collimator geometry and compared it to a parallel collimator and a cone-beam collimator. To speed up the simulations, we developed a multi-CPU/GPU module that uses a technique of ray tracing. Using this approach, the acceleration factor was ~ 60 and we restored ~ 90 % of the signal for ^mTc (140.5 keV emission), compared to a classical Monte Carlo simulation. The 10 % difference is due to the fact that the multi-CPU/GPU module neglects the septal penetration and scatter in the collimator. We demonstrated that the data acquired with the new collimator could be reconstructed without artifact using an OSEM algorithm. We developed four forward projectors (simple projector (S-RT), projector accounting for the surface of the detecting pixel (S-RT-IV), projection modeling the solid angle (S-RT-SA) of the projection tube, and projector modeling the depth of interaction (S-RT-SA-D)). We also modeled the point spread function of the collimator in the image domain, using an anisotropic non-stationary function. To characterize the reconstruction, we studied the conditioning number of the system matrix for each projector and each collimator. We showed that the new collimator was more ill-conditioned than a parallel collimator or a cone-beam collimator. We showed that the image based PSF and the modeling of the depth of interaction improved the quality of the images, but edge artefacts were introduced when modeling the PSF in the image domain. Compared to existing systems, we showed that this new collimator has a large field of view (~ 70 mm in the transaxial plane) with a resolution of 1.0 mm in the best case but suffers from a relatively low sensitivity (1.32x10 %).PARIS11-SCD-Bib. électronique (914719901) / SudocSudocFranceF

Contribution of tissue textural pattern and conventional index to glioma staging in FDopa-PET/CT

National audienceAim: We studied whether the characterization of tumor texture in FDopa-PET/CT could assist in the identification of tumor grades in both primitive and recurrent gliomas. Materials and Methods: Eighty one patients with gliomas were studied, including 52 newly diagnosed tumors and 29 recurrent tumors. For each tumor, the SUVpeak and metabolic volume (MV) were measured, as well as 32 textural indices (TI). The ability of SUVpeak, MV and TI was investigated by using each index alone first (with ROC analyses), and then by using couples consisting of one TI with SUVpeak in a binomial model (with ROC analyses and a reclassification method). The pathological examination was assumed to provide the gold standard grade. Results: Neither SUVpeak nor MV could discriminate low-grade tumors (LG) from high-grade tumors (HG) in newly-diagnosed tumors, while SUVpeak alone could discriminate LG from HG in recurrent tumors (p=0.02). Combining a TI with SUVpeak led to a significant LG / HG discrimination for newly-diagnosed tumors (p = 0.01). Among all TI, entropy led to the best reclassification performance. Conclusion: The co-analysis of FDopa-PET/CT SUVpeak and well-selected TI (such as entropy) made it possible to improve the classification of newly-diagnosed gliomas

Caractérisation des tumeurs et de leur évolution en TEP/TDM au F-FDG pour le suivi thérapeutique

La Tomographie par Emission de Positons (TEP) au Fluoro-déoxyglucose marqué au Fluor 18 ( F-FDG), analogue du glucose, permet d'obtenir une image de la consommation de glucose dans l'organisme. La plupart des foyers tumoraux présentant une consommation excessive de glucose, son utilisation en oncologie permet d'améliorer la prise en charge des patients en diminuant le temps nécessaire pour évaluer l'efficacité des traitements tels que la chimiothérapie et la radiothérapie. Mon projet de recherche visait à proposer et améliorer des méthodes de quantification en TEP au F-FDG afin de caractériser au mieux l'évolution métabolique des volumes tumoraux.De nombreux facteurs biaisent la quantification en TEP. Parmi eux, l'Effet de Volume Partiel (EVP) reste difficile à corriger, notamment à cause de la faible résolution spatiale des images TEP. Afin de déterminer l'impact de la correction de l EVP sur l évaluation des réponses des tumeurs, une étude sur données simulées par Monte Carlo a tout d abord été effectuée. Cette étude a été complétée par l analyse de données TEP/TDM (Tomodensitométrie) acquises chez 40 patients atteints de cancers colorectaux métastatiques (CCM), traités par chimiothérapie à l'Institut Jules Bordet (Bruxelles). L analyse de 101 tumeurs a montré que les critères tels que le SUV, n incluant pas de correction de l'EVP, et qui reflètent alors le volume tumoral et son activité, prédisaient mieux l évolution tumorale que les critères corrigés de l EVP. Compte tenus des résultats prometteurs récents de méthodes de caractérisation de l hétérogénéité de la fixation du FDG dans les tumeurs, un second volet de notre travail a consisté à étudier l intérêt de la prise en compte de la texture dans le cadre du suivi thérapeutique. L application de l analyse de texture aux cas de CCM étudiés précédemment n a pas permis de démontrer une valeur ajoutée des indices de texture par rapport aux index quantitatifs couramment employés en clinique. Nous avons montré que cette conclusion s expliquait en partie par la non-robustesse des indices de texture vis-à-vis des paramètres impliqués dans leur mesure. Nous avons enfin cherché à évaluer une méthode d Analyse Factorielle de Séquences d Images Médicales (AFSIM), appliquée au contexte du suivi thérapeutique, pour caractériser l évolution tumorale tout au long du traitement. Cette étude a porté sur 9 séries de 4 à 6 examens TEP/TDM de patients traités par radiothérapie au Centre Hospitalier Universitaire Henri Becquerel de Rouen. Outre l information visuelle globale apportée par cette méthode, l analyse quantitative des résultats obtenus a permis de caractériser l hétérogénéité de la réponse vue par l AFSIM. L échec des index classiques, provenant entre autres de leur incapacité à distinguer les processus inflammatoires de l activité métabolique tumorale, a permis de monter la valeur ajoutée de l AFSIM par rapport aux index tels que le SUV maximal ou moyen.Positron Emission Tomography (PET) using F-FluoroDeoxyGlucose ( F-FDG), a radiolabelled analogue of the glucose, is used to get an image of the glucose consumption in the body. As most tumor masses show a high glucose consumption, PET is widely used in oncology for diagnosis and patient monitoring. In the context of patient monitoring, the motivation is to decrease the time interval needed to assess treatment (radiotherapy or chemotherapy efficieny) compared to therapeutic follow-up based only on anatomic imaging only (Computed Tomography or Magnetic Resonance Imaging). My research project aimed at proposing and improving quantitative methods in FDG-PET to better characterize tumor evolution.In PET, many factors affect the accuracy of parameters estimated from the images. Among them, Partial Volume Effect (PVE) remains difficult to correct, mainly due to the low spatial resolution of PET images. To determine the impact of PVE on treatment response evaluation, a preliminary study was performed using Monte Carlo simulated PET scans. An additional study was conducted based on the analysis of the PET/CT (Computed Tomography) data of 40 Metastatic Colorectal Cancer (MCC) patients treated with chemotherapy at the Jules Bordet Institute (Brussels, Belgium). The analysis of the 101 tumors showed that criteria such as the Standardized Uptake Value (SUV), which does not include PVE correction, were better predictors of tumors evolutions than PVE corrected criteria. This is because without PVE correction, SUV includes information on both metabolic volume and metabolic activity, which are two relevant pieces of information to characterize the tumor. A second part of our work was to study the potential of tumor texture analysis in patient monitoring, following promising results reported in the literature. Texture analysis was applied to the MMC patients data previously mentioned but did allow to a better segregation of tumors responses as compared to indices currently used in the clinics. We found that this was partly due to the lack of robustness of the textures indices.Finally, we evaluated a Factor Analysis in Medical Images Series (FAMIS) method to characterize tumor evolution during treatment. This study focused on 9 series of 4 to 6 PET/CT scans acquired all along the radiotherapy/radio-chemotherapy of patients treated at the Centre Hospitalier Universitaire Henri Becquerel (Rouen, France). In addition to the rich visual information brought by this method, a quantitative analysis of the results made it possible to characterize response heterogeneity as seen using FAMIS. In particular, FAMIS clearly demonstrated the occurrence of inflammatory processes. In addition, due to the low metabolic activity of the tumors at the end of the treatment, many conventional indices could not describe the tumor changes, while FAMIS gave a full assessment of the tumor change over time.PARIS11-SCD-Bib. électronique (914719901) / SudocSudocFranceF

4DGVF-based filtering of vector-valued images

International audienceIn this paper, we propose a new method for vector-valued image restoration in order to reduce noise while simultaneously sharpening vector edges. Our approach is a coupled anisotropic diffusion and shock filtering scheme that exploits a new robust 4DGVF vector field tailored for vector-valued images. The proposed scheme sharpens edges in directions diffused from the entire spatio-spectral information available with a more precise and a more stable sharpening effect along the iterative processing. We validate our method on color images as well as on realistic simulations of dynamic PET images

Approche 4DGVF pour la restauration d'images multi-composantes

National audienceDans cet article, nous proposons une nouvelle méthode pour la restauration d'images multi-composantes afin de diminuer le bruit tout en rehaussant simultanément les contours. Notre approche repose sur un filtrage couplant diffusion anisotrope et filtre de choc qui exploite un nouveau champ de decteurs 4DGVF robuste adapté aux images multi-composantes. Le schéma proposé permet de rehausser les contours dans des directions calculées à partir de l'intégralité de l'information spatio-spectrale disponible et d'obtenir un rehaussement plus précis et plus stable au cours du traitement itératif. Nous validons notre méthode sur des images couleurs ainsi que des simulations réalistes d'images TEP dynamiques du cerveau

4-D Gradient Vector Flow : segmentation par surface active pour images multi-composantes

National audienceDans cet article, nous généralisons le flux de vecteurs gradients à la segmentation par surface active d'images 3-D à valeurs vectorielles. Nous basons notre méthode sur la définition d'un tenseur de structure multi-composantes pondéré exploitant l'intégralité de l'information de l'image pour réduire la sensibilité au bruit et améliorer la précision du modèle. Appliquée à la segmentation de volumes biologiques en imagerie par tomographie d'émission de positrons (TEP) dynamique, nous validons notre méthode sur des simulations Monte Carlo réalistes d'images TEP de fantômes numériques

4DGVF segmentation of vector-valued images

International audienceIn this paper, we extend the gradient vector flow field to the vector-valued case for robust variational segmentation of 4D images with active surfaces. Instead of only exploiting scalar edge strength in order to identify vector edges, we propagate both directions and amplitudes of vector gradients computed from the analysis of a structure tensor of the vector-valued image. To reduce contributions from noise in the calculation of the structure tensor, image channels are weighted according to a blind estimator of contrast that take profit of the deformable models framework. The proposed 4DGVF vector field is validated on synthetic image datasets and applied to biological volume delineation in dynamic PET imaging

Potentials and caveats of AI in Hybrid Imaging

State-of-the-art patient management frequently mandates the investigation of both anatomy and physiology of the patients. Hybrid imaging modalities such as the PET/MRI, PET/CT and SPECT/CT have the ability to provide both structural and functional information of the investigated tissues in a single examination. With the introduction of such advanced hardware fusion, new problems arise such as the exceedingly large amount of multi-modality data that requires novel approaches of how to extract a maximum of clinical information from large sets of multi-dimensional imaging data. Artificial intelligence (AI) has emerged as one of the leading technologies that has shown promise in facilitating highly integrative analysis of multi-parametric data. Specifically, the usefulness of AI algorithms in the medical imaging field has been heavily investigated in the realms of (1) image acquisition and reconstruction, (2) post-processing and (3) data mining and modelling. Here, we aim to provide an overview of the challenges encountered in hybrid imaging and discuss how AI algorithms can facilitate potential solutions. In addition, we highlight the pitfalls and challenges in using advanced AI algorithms in the context of hybrid imaging and provide suggestions for building robust AI solutions that enable reproducible and transparent research

Fully 3D Monte Carlo image reconstruction in SPECT using functional regions

Image reconstruction in Single Photon Emission Computed Tomography (SPECT) is affected by physical effects such as photon attenuation, Compton scatter and detector response. These effects can be compensated for by modeling the corresponding spread of photons in 3D within the system matrix used for tomographic reconstruction. The fully 3D Monte Carlo (F3DMC) reconstruction technique consists in calculating this system matrix using Monte Carlo simulations. The inverse problem of tomographic reconstruction is then solved using conventional iterative algorithms such as maximum likelihood expectation maximization (MLEM). Although F3DMC has already shown promising results, its use is currently limited by two major issues: huge size of the fully 3D system matrix and long computation time required for calculating a robust and accurate system matrix. To address these two issues, we propose to calculate the F3DMC system matrix using a spatial sampling matching the functional regions to be reconstructed. In this approach, different regions of interest can be reconstructed with different spatial sampling. For instance, a single value is reconstructed for a functional region assumed to contain uniform activity. To assess the value of this approach, Monte Carlo simulations have been performed using GATE. Results suggest that F3DMC reconstruction using functional regions improves quantitative accuracy compared to the F3DMC reconstruction method proposed so far. In addition, it considerably reduces disk space requirement and duration of the simulations needed to estimate the system matrix. The concept of functional regions might therefore make F3DMC reconstruction practically feasible.Comment: 6 pages, 3 figures, 3rd International Conference on maging Technologies in Biomedical Sciences : ITBS2005, Milos Island, Greece, 25-28 september 2005, submitted to NIM