Medical image registration using Edgeworth-based approximation of Mutual Information

International audienceWe propose a new similarity measure for iconic medical image registration, an Edgeworth-based third order approximation of Mutual Information (MI) and named 3-EMI. Contrary to classical Edgeworth-based MI approximations, such as those proposed for inde- pendent component analysis, the 3-EMI measure is able to deal with potentially correlated variables. The performance of 3-EMI is then evaluated and compared with the Gaussian and B-Spline kernel-based estimates of MI, and the validation is leaded in three steps. First, we compare the intrinsic behavior of the measures as a function of the number of samples and the variance of an additive Gaussian noise. Then, they are evaluated in the context of multimodal rigid registration, using the RIRE data. We finally validate the use of our measure in the context of thoracic monomodal non-rigid registration, using the database proposed during the MICCAI EMPIRE10 challenge. The results show the wide range of clinical applications for which our measure can perform, including non-rigid registration which remains a challenging problem. They also demonstrate that 3-EMI outperforms classical estimates of MI for a low number of samples or a strong additive Gaussian noise. More generally, our measure gives competitive registration results, with a much lower numerical complexity compared to classical estimators such as the reference B-Spline kernel estimator, which makes 3-EMI a good candidate for fast and accurate registration tasks

Object-based 3D binary reconstruction from sparse projections in cone beam CT: Comparison of three projection operators

International audienceWe present herein a level set approach to the X-ray tomography problem with sparse projection data and study the impact of the projection operator on the binary reconstruction accuracy and computation time. The comparison is carried out on three projectors: the Separable Footprint (Trapeze-Trapeze, SF-TT) [3], a classical Raydriven (RD) and a Simplified version of the Distance-Driven (SDD) projector respectively. The performance, are evaluated for each operator, on a binary 3D Shepp-Logan phantom by varying the number of projections from 5 to 13, and considering noise free and noisy cone beam projection data

Turning Tangent Empirical Mode Decomposition: A Framework for Mono- and Multivariate Signals.

International audienceA novel Empirical Mode Decomposition (EMD) algorithm, called 2T-EMD, for both mono- and multivariate signals is proposed in this paper. It differs from the other approaches by its computational lightness and its algorithmic simplicity. The method is essentially based on a redefinition of the signal mean envelope, computed thanks to new characteristic points, which offers the possibility to decompose multivariate signals without any projection. The scope of application of the novel algorithm is specified, and a comparison of the 2T-EMD technique with classical methods is performed on various simulated mono- and multivariate signals. The monovariate behaviour of the proposed method on noisy signals is then validated by decomposing a fractional Gaussian noise and an application to real life EEG data is finally presented

Reconstruction 3d Des Artères Coronaires En Imagerie Rotationnelle Rx

National audienceNous présentons une méthode de reconstruction 3D des artères coronaires à partir de 4 projections acquises en imagerie rotationnelle R-X. L'approche retenue considère un problème d'optimisation d'une fonction "objectif", en se basant sur un estimateur Bayésien (MAP : Maximum à postériori) et un modèle de distribution des données de projection de type Poisson. Le problème étant sous déterminé, nous introduisons un a priori afin d'améliorer la convergence de l'algorithme. Trois fonctions de régularisation sont ainsi considérées de type normes L0, L1 et L2 respectivement. Les algorithmes ont été testés sur des séquences de projections simulées à partir de séquences dynamiques d'arbres coronaires 3D extraits à partir d'examens acquis sur un scanner hélicoïdal multibarettes

Direct Experimental Observation of in situ Dehydrogenation of an Amine-Borane System Using Gas Electron Diffraction

In situ dehydrogenation of azetidine-BH3, which is a candidate for hydrogen storage, was observed with the parent and dehydrogenated analogue subjected to rigorous structural and thermochemical investigations. The structural analyses utilized gas electron diffraction supported by high-level quantum calculations, while the pathway for the unimolecular hydrogen release reaction in the absence and presence of BH3 as a bifunctional catalyst was predicted at the CBS-QB3 level. The catalyzed dehydrogenation pathway has a barrier lower than the predicted B-N bond dissociation energy, hence favoring the dehydrogenation process over the dissociation of the complex. The predicted enthalpy of dehydrogenation at the CCSD(T)/CBS level indicates that mild reaction conditions would be required for hydrogen release and that the compound is closer to thermoneutral than linear amine boranes. The entropy and free energy change for the dehydrogenation process show that the reaction is exergonic, energetically feasible, and will proceed spontaneously toward hydrogen release, all of which are important factors for hydrogen storage

Election turnout statistics in many countries: similarities, differences, and a diffusive field model for decision-making

We study in details the turnout rate statistics for 77 elections in 11 different countries. We show that the empirical results established in a previous paper for French elections appear to hold much more generally. We find in particular that the spatial correlation of turnout rates decay logarithmically with distance in all cases. This result is quantitatively reproduced by a decision model that assumes that each voter makes his mind as a result of three influence terms: one totally idiosyncratic component, one city-specific term with short-ranged fluctuations in space, and one long-ranged correlated field which propagates diffusively in space. A detailed analysis reveals several interesting features: for example, different countries have different degrees of local heterogeneities and seem to be characterized by a different propensity for individuals to conform to the cultural norm. We furthermore find clear signs of herding (i.e. strongly correlated decisions at the individual level) in some countries, but not in others.Comment: 15 pages, 9 figures, 7 table

Segmentation d'images scanner X du foie par Max-Flow/Min-Cut

Dans le contexte d'une application médicale spécifique, le traitement mini-invasif des cancers primitifs du foie par ultrasons à haute intensités, notre étude porte sur une méthode de segmentation 3D semi-automatique rapide qui permet d'isoler le foie et les différents réseaux vasculaires hépatiques à partir de volumes acquis en scanner X. Cette méthode est caractérisée par une description du volume sous la forme d'un graphe où les poids des liens entre noeuds décrivent soit des degrés de similarité entre voxels de la même classe (approche région), soit des degrés de discontinuité d'un voxel par rapport à un voisin (approche contours). Ces différents poids sont définis après une première phase d'apprentissage interactive. L'algorithme de Max-Flow/Min-Cut est ensuite utilisé pour partitionner le volume en deux sous-ensembles représentatifs des classes

Sparse reconstruction from a limited projection number of the coronary artery tree in X-ray rotational imaging

International audienceThis paper deals with the 3D reconstruction of sparse data in X-ray rotational imaging. Due to the cardiac motion, the number of available projections for this reconstruction is equal to four, which leads to a strongly undersampled reconstruction problem. We address thus this illness problem through a regularized iterative method. The whole algorithm is divided into two steps. Firstly, a minimal path segmentation step extracts artery tree boundaries. Secondly, a MAP reconstruction comparing L0-norm and L1-norm priors is applied on this extracted coronary tree. The reconstruction optimization process relies on a separable paraboloidal (SPS) algorithm. Some preliminary results are provided on simulated rotational angiograms

A deep learning model to generate synthetic CT for prostate MR-only radiotherapy dose planning: a multicenter study

IntroductionFor radiotherapy based solely on magnetic resonance imaging (MRI), generating synthetic computed tomography scans (sCT) from MRI is essential for dose calculation. The use of deep learning (DL) methods to generate sCT from MRI has shown encouraging results if the MRI images used for training the deep learning network and the MRI images for sCT generation come from the same MRI device. The objective of this study was to create and evaluate a generic DL model capable of generating sCTs from various MRI devices for prostate radiotherapyMaterials and methodsIn total, 90 patients from three centers (30 CT-MR prostate pairs/center) underwent treatment using volumetric modulated arc therapy for prostate cancer (PCa) (60 Gy in 20 fractions). T2 MRI images were acquired in addition to computed tomography (CT) images for treatment planning. The DL model was a 2D supervised conditional generative adversarial network (Pix2Pix). Patient images underwent preprocessing steps, including nonrigid registration. Seven different supervised models were trained, incorporating patients from one, two, or three centers. Each model was trained on 24 CT-MR prostate pairs. A generic model was trained using patients from all three centers. To compare sCT and CT, the mean absolute error in Hounsfield units was calculated for the entire pelvis, prostate, bladder, rectum, and bones. For dose analysis, mean dose differences of D99% for CTV, V95% for PTV, Dmax for rectum and bladder, and 3D gamma analysis (local, 1%/1 mm) were calculated from CT and sCT. Furthermore, Wilcoxon tests were performed to compare the image and dose results obtained with the generic model to those with the other trained models.ResultsConsidering the image results for the entire pelvis, when the data used for the test comes from the same center as the data used for training, the results were not significantly different from the generic model. Absolute dose differences were less than 1 Gy for the CTV D99% for every trained model and center. The gamma analysis results showed nonsignificant differences between the generic and monocentric models.ConclusionThe accuracy of sCT, in terms of image and dose, is equivalent to whether MRI images are generated using the generic model or the monocentric model. The generic model, using only eight MRI-CT pairs per center, offers robust sCT generation, facilitating PCa MRI-only radiotherapy for routine clinical use