Unsupervised Fiber Bundles Registration using Weighted Measures Geometric Demons

International audienceBrain image registration aims at reducing anatomical variability across subjects to create a common space for group analysis. Multi-modal approaches intend to minimize cortex shape variations along with internal structures, such as fiber bundles. A di ficulty is that it requires a prior identi fication of these structures, which remains a challenging task in the absence of a complete reference atlas. We propose an extension of the log-Geometric Demons for jointly registering images and fi ber bundles without the need of point or ber correspondences. By representing fi ber bundles as Weighted Measures we can register subjects with di fferent numbers of fiber bundles. The ef ficacy of our algorithm is demonstrated by registering simultaneously T1 images and between 37 and 88 ber bundles depending on each of the ten subject used. We compare results with a multi-modal T1 + Fractional Anisotropy (FA) and a tensor-based registration algorithms and obtain superior performance with our approach

Improved Parkinson’s disease classification from diffusion MRI data by Fisher vector descriptors

Due to the complex clinical picture of Parkinson’s disease (PD), the reliable diagnosis of patients is still challenging. A promising approach is the structural characterization of brain areas affected in PD by diffusion magnetic resonance imaging (dMRI). Standard classification methods depend on an accurate non-linear alignment of all images to a common reference template, and are challenged by the resulting huge dimensionality of the extracted feature space. Here, we propose a novel diagnosis pipeline based on the Fisher vector algorithm. This technique allows for a precise encoding into a high-level descriptor of standard diffusion measures like the fractional anisotropy and the mean diffusivity, extracted from the regions of interest (ROIs) typically involved in PD. The obtained low dimensional, fixed-length descriptors are independent of the image alignment and boost the linear separability of the problem in the description space, leading to more efficient and accurate diagnosis. In a test cohort of 50 PD patients and 50 controls, the implemented methodology outperforms previous methods when using a logistic linear regressor for classification of each ROI independently, which are subsequently combined into a single classification decision

Joint T1 and Brain Fiber Log-Demons Registration Using Currents to Model Geometry

International audienceWe present an extension of the diffeomorphic Geometric Demons algorithm which combines the iconic registration with geometric constraints. Our algorithm works in the log-domain space, so that one can efficiently compute the deformation field of the geometry. We represent the shape of objects of interest in the space of currents which is sensitive to both location and geometric structure of objects. Currents provides a distance between geometric structures that can be defined without specifying explicit point-to-point correspondences. We demonstrate this framework by registering simultaneously T1 images and 65 fiber bundles consistently extracted in 12 subjects and compare it against non-linear T1, tensor, and multi-modal T1+ Fractional Anisotropy (FA) registration algorithms. Results show the superiority of the Log-domain Geometric Demons over their purely iconic counterparts

Symmetric Log-Domain Diffeomorphic Registration: A Demons-based Approach

pmid 18979814International audienceModern morphometric studies use non-linear image registration to compare anatomies and perform group analysis. Recently, log-Euclidean approaches have contributed to promote the use of such computational anatomy tools by permitting simple computations of statistics on a rather large class of invertible spatial transformations. In this work, we propose a non-linear registration algorithm perfectly fit for log-Euclidean statistics on diffeomorphisms. Our algorithm works completely in the log-domain, i.e. it uses a stationary velocity field. This implies that we guarantee the invertibility of the deformation and have access to the true inverse transformation. This also means that our output can be directly used for log-Euclidean statistics without relying on the heavy computation of the log of the spatial transformation. As it is often desirable, our algorithm is symmetric with respect to the order of the input images. Furthermore, we use an alternate optimization approach related to Thirion's demons algorithm to provide a fast non-linear registration algorithm. First results show that our algorithm outperforms both the demons algorithm and the recently proposed diffeomorphic demons algorithm in terms of accuracy of the transformation while remaining computationally efficient

Particle-guided image registration

We present a novel image registration method based on B-spline free-form deformation that simultaneously optimizes particle correspondence and image similarity metrics. Different from previous B-spline based registration methods optimized w.r.t. the control points, the deformation in our method is estimated from a set of dense unstructured pair of points, which we refer as corresponding particles. As intensity values are matched on the corresponding location, the registration performance is iteratively improved. Moreover, the use of corresponding particles naturally extends our method to a group-wise registration by computing a mean of particles. Motivated by a surface-based group-wise particle correspondence method, we developed a novel system that takes such particles to the image domain, while keeping the spirit of the method similar. The core algorithm both minimizes an entropy based group-wise correspondence metric as well as maximizes the space sampling of the particles. We demonstrate the results of our method in an application of rodent brain structure segmentation and show that our method provides better accuracy in two structures compared to other registration methods. ?? 2013 Springer-Verlag

Predictive Modeling of Anatomy with Genetic and Clinical Data

We present a semi-parametric generative model for predicting anatomy of a patient in subsequent scans following a single baseline image. Such predictive modeling promises to facilitate novel analyses in both voxel-level studies and longitudinal biomarker evaluation. We capture anatomical change through a combination of population-wide regression and a non-parametric model of the subject’s health based on individual genetic and clinical indicators. In contrast to classical correlation and longitudinal analysis, we focus on predicting new observations from a single subject observation. We demonstrate prediction of follow-up anatomical scans in the ADNI cohort, and illustrate a novel analysis approach that compares a patient’s scans to the predicted subject-specific healthy anatomical trajectory. Keywords: Population Trend, Baseline Image, Kernel Machine, Good Linear Unbiased Predictor, Segmentation LabelNational Institute for Biomedical Imaging and Bioengineering (U.S.) (Grant 1K25EB013649-01)BrightFocus Foundation (AHAF-A2012333)Neuroimaging Analysis Center (U.S.) (P41EB015902)National Institutes of Health (U.S.) (DA022759)Wistron Corporatio