Motion estimation in sequences with transparencies is an important problem in robotics and medical imaging applications. In this work we propose two procedures to improve the transparent optical flow ...computation. We build from a variational approach for estimating multi-valued velocity fields in transparent sequences. That method estimates multi-valued velocity fields which are not necessarily piecewise constant on a layer –each layer can evolve according to a non-parametric optical flow. First we introduce a robust statistical spatial interaction weight which allows to segment the multi-motion field. As result, our method is capable to recover the object’s shape and the velocity field for each object with high accuracy. Second, we develop a procedure to separate the component layers of rigid objects from a transparent sequence. Such a separation is possible because of the high accuracy of the object’s shape recovered from our transparent optical flow computation. Our proposal is robust to the presence of several objects in the same sequence as well as different velocities for the same object along the sequence. We show how our approach outperforms existing methods and we illustrate its capabilities on challenging sequences.
Diffusion Weighted Magnetic Resonance Imaging is widely used to study the structure ofthe fiber pathways of white matter in the brain. However, the recovered axon orientationscan be prone to error ...because of the low signal to noise ratio. Spatial regularization canreduce the error, but it must be done carefully so that real spatial information is not removedand false orientations are not introduced. In this paper we investigate the advantagesof applying an anisotropic filter based on single and multiple axon bundle orientation kernels.To this end, we compute local diffusion kernels based on Diffusion Tensor and multiDiffusion Tensor models. We show the benefits of our approach to three different types ofDW-MRI data: synthetic, in vivo human, and acquired from a diffusion phantom.
In this work we develop a methodology to approximate the covariance matrix associated to the simulation of water diffusion inside the brain tissue. The computation is based on an implementation of ...the Discontinuous Galerkin method of the diffusion equation, in accord with the physical phenomenon. The implementation in in parallel using GPUs in the CUDA language. Numerical results are presented in 2D problems.
Diffusion weighted magnetic resonance imaging is widely used in the study of the structure of the fiber pathways in brain white matter. In this work we present a new method for denoising intra–voxel ...axon fiber tracks. In order to improve local (voxelwise) estimations, we use the general–purpose segmentation method called Entropy–Controlled Quadratic Markov Measure Field Models. Our proposal is capable of spatially–regularize multiple axon fiber orientations (intra-voxel orientations). In order to provide the best as possible local axon orientations to our spatial regularization procedure, we evaluate two optimization methods for fitting a Diffusion Basis Function model. We present qualitative results on real human Diffusion Weighted MRI data where the ground–truth is not available, and we quantitatively validate our results by synthetic experiments.
We study the impact of the brain tractography false positives in the brain connectivity graphs. The representative input database for the analysis is the set of tractograms from the participants on ...the ISMRM-2015 Tractography Challenge. We propose 2 novel metrics to rank the quality of a tractogram when it is compared with known ground truth. The results of this study indicate that the estimation of graph communities is robust to high levels of overestimation in the connectivity.
Monte-Carlo Diffusion Simulations (MCDS) have been used extensively as a ground truth tool for the validation of microstructure models for Diffusion-Weighted MRI. However, methodological pitfalls in ...the design of the biomimicking geometrical configurations and the simulation parameters can lead to approximation biases. Such pitfalls affect the reliability of the estimated signal, as well as its validity and reproducibility as ground truth data. In this work, we first present a set of experiments in order to study three critical pitfalls encountered in the design of MCDS in the literature, namely, the number of simulated particles and time steps, simplifications in the intra-axonal substrate representation, and the impact of the substrate's size on the signal stemming from the extra-axonal space. The results obtained show important changes in the simulated signals and the recovered microstructure features when changes in those parameters are introduced. Thereupon, driven by our findings from the first studies, we outline a general framework able to generate complex substrates. We show the framework's capability to overcome the aforementioned simplifications by generating a complex crossing substrate, which preserves the volume in the crossing area and achieves a high packing density. The results presented in this work,along with the simulator developed, pave the way towards more realistic and reproducible Monte-Carlo simulations for Diffusion-Weighted MRI.
Diffusion tensor magnetic resonance imaging is widely used to study the structure of the fiber pathways of brain white matter. However, the diffusion tensor cannot capture complex intra-voxel fiber ...architecture such as fiber crossings. Consequently, a number of methods have been proposed to recover intra-voxel fiber bundle orientations from high angular-resolution diffusion imaging scans, which are optimized to resolve fiber crossings. In this work we study how multi-tensor, spherical deconvolution, analytical QBall and diffusion basis function methods perform under clinical scanning conditions. Our experiments indicate that it is feasible to apply some of these methods in clinical data sets.
We present a new method for estimating and recovering the intra-voxel fiber paths, using Diffusion Weighted Magnetic Resonance Images (DW-MRI). The method recovers the intra-voxel information at ...voxels that contain fiber crossings or bifurcations by means of a combination of a known tensor basis functions (a "multi-tensor" field). In contrast with the state-of-the art methods, our formulation requires a small number of DWMR images and the solution schema is simple. Another advantage is that the solution to our formulation is numerically stable when more than two fiber orientations are present within a voxel. Additionally, we apply a spatial regularization to the multi-tensor field being estimated in order to denoise the data. The regularization uses a generic piece-wise smooth prior on the fiber orientation. Several examples are presented to demonstrate the performance of the proposed algorithm on synthetic and real DW-MRI data
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