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In this pilot study, we present a new engineering approach to reconstruct a patient-specific model of the fetal head near term. Indeed, 3D visualization of the fetus prominent skull could help the obstetricians in decision-making to overcome dystocia, a delivery complication which results in labour obstruction. The full reconstruction pipeline is based on the recording of a small set of tracked 2D ultrasound images around the transthalamic brain plane. The use of 2D ultrasound images tracked in 3D space would allow to superimpose the fetal head model to other reconstructed organs. The fetal head is large at late pregnancy stages which causes occlusions in the ultrasound images. Moreover, fetal motion may affect the consistency of ultrasound images, in particular if many frames are needed. Therefore, we propose to extrapolate the full fetal head surface from 10 focused frames only. The reconstruction performance was evaluated in simulation based on a MRI dataset of 7 patients at 34-36 weeks of pregnancy; our best method achieves 1.6 mm of average reconstruction error.

Diffusion MRI is a well established imaging modality providing a powerful way to non invasively probe the structure of the white matter. Despite the potential of the technique the intrinsic long scan times of these sequences have hampered their use in clinical practice. For this reason a wide variety of methods have been proposed to shorten acquisition times. [...] We here review a recent work where we propose to further exploit the versatility of compressed sensing and convex optimization with the aim to characterize the fiber orientation distribution sparsity more optimally. We re formulate the spherical deconvolution problem as a constrained l0 minimization.

This paper presents a new non parametric atlas registration framework, derived from the optical flow model and the active contour theory, applied to automatic subthalamic nucleus (STN) targeting in deep brain stimulation (DBS) surgery. In a previous work, we demonstrated that the STN position can be predicted based on the position of surrounding visible structures, namely the lateral and third ventricles. A STN targeting process can thus be obtained by registering these structures of interest between a brain atlas and the patient image. Here we aim to improve the results of the state of the art targeting methods and at the same time to reduce the computational time. Our simultaneous segmentation and registration model shows mean STN localization errors statistically similar to the most performing registration algorithms tested so far and to the targeting expert’s variability. Moreover, the computational time of our registration method is much lower, which is a worthwhile improvement from a clinical point of view.

In this paper, we propose a strategy for fusing clustering maps obtained with different remote sensing sources. Dempster- Shafer (DS) Theory is a powerful fusion method that allows to combine classifications from different sources and handles ignorance, imprecision and conflict between them. To do so, it attributes evidences (weights) to different hypothesis representing single or unions of classes. We introduce a fully unsupervised evidence assignment strategy exploiting the entropy among cluster memberships. Ambiguous pixels get stronger evidences for union of classes to better represent the uncertainty among them. On two multisource experiments, the proposed Entropy-based Dempster-Shafer (EDS) performs best along the different fusion methods with VHR images, when the single class accuracies from each source are complementary and one of the sources shows low overall accuracy.

We present a segmentation method for fetal brain tissues of T2w MR images, based on the well known Expectation Maximization Markov Random Field (EM- MRF) scheme. Our main contribution is an intensity model composed of 7 Gaussian distribution designed to deal with the large intensity variability of fetal brain tissues. The second main contribution is a 3-steps MRF model that introduces both local spatial and anatomical priors given by a cortical distance map. Preliminary results on 4 subjects are presented and evaluated in comparison to manual segmentations showing that our methodology can successfully be applied to such data, dealing with large intensity variability within brain tissues and partial volume (PV).

In this work we present a method for the image analysis of Magnetic Resonance Imaging (MRI) of fetuses. Our goal is to segment the brain surface from multiple volumes (axial, coronal and sagittal acquisitions) of a fetus. To this end we propose a two-step approach: first, a Finite Gaussian Mixture Model (FGMM) will segment the image into 3 classes: brain, non-brain and mixture voxels. Second, a Markov Random Field scheme will be applied to re-distribute mixture voxels into either brain or non-brain tissue. Our main contributions are an adapted energy computation and an extended neighborhood from multiple volumes in the MRF step. Preliminary results on four fetuses of different gestational ages will be shown.

Interindividual functional and structural brain variability is a major problem in group studies, in which very focal activations are expected. Architectonic studies have shown that the human primary auditory area, which is located with a great constancy on Heschl's gyrus, is surrounded by several nonprimary auditory areas with surface areas of 40-310 mm(2). The small size of the latter makes them only partially accessible to fMRI group studies, because of imprecision in realignment when using currently available registration procedures. We describe here a new method for sulcal realignment using a non-rigid local landmark-based registration and show its application to the registration of fMRI acquisitions on the supratemporal plane. After an affine global voxel-based registration, which transforms all brains into the same standard space, we propose a non-rigid local landmark-based registration method based on thin-plate splines for matching the two sulci delimiting Heschl's gyrus of a given brain to the corresponding sulci of a reference brain. We show here that, in comparison with global affine and non-rigid approaches, our method leads in group studies to i) a much more precise alignment of Heschl's gyrus; and ii) a putatively optimal superposition of functionally corresponding areas on and around Heschl's gyrus. (C) 2008 Elsevier Inc. All rights reserved.