Optimal transport features for morphometric population analysis.

Brain pathologies often manifest as partial or complete loss of tissue. The goal of many neuroimaging studies is to capture the location and amount of tissue changes with respect to a clinical variable of interest, such as disease progression. Morphometric analysis approaches capture local differences in the distribution of tissue or other quantities of interest in relation to a clinical variable.

NousNav: A low-cost neuronavigation system for deployment in lower-resource settings.

Purpose: NousNav is a complete low-cost neuronavigation system that aims to democratize access to higher-quality healthcare in lower-resource settings. NousNav's goal is to provide a model for local actors to be able to reproduce, build and operate a fully functional neuronavigation system at an affordable cost.

Methods: NousNav is entirely open source and relies on low-cost off-the-shelf components, which makes it easy to reproduce and deploy in any region.

Exploratory Population Analysis with Unbalanced Optimal Transport.

The plethora of data from neuroimaging studies provide a rich opportunity to discover effects and generate hypotheses through exploratory data analysis. Brain pathologies often manifest in changes in shape along with deterioration and alteration of brain matter, i.e.

Image-Based Methods for Phase Estimation, Gating, and Temporal Superresolution of Cardiac Ultrasound.

Objective: Ultrasound is an effective tool for rapid noninvasive assessment of cardiac structure and function. Determining the cardiorespiratory phases of each frame in the ultrasound video and capturing the cardiac function at a much higher temporal resolution are essential in many applications. Fulfilling these requirements is particularly challenging in preclinical studies involving small animals with high cardiorespiratory rates, requiring cumbersome and expensive specialized hardware.

Automatic Estimation of the Optic Nerve Sheath Diameter from Ultrasound Images.

We present an algorithm to automatically estimate the diameter of the optic nerve sheath from ocular ultrasound images. The optic nerve sheath diameter provides a proxy for measuring intracranial pressure, a life threating condition frequently associated with head trauma. Early treatment of elevated intracranial pressures greatly improves outcomes and drastically reduces the mortality rate.

Morse-Smale Regression.

This paper introduces a novel partition-based regression approach that incorporates topological information. Partition-based regression typically introduce a quality-of-fit-driven decomposition of the domain. The emphasis in this work is on a topologically meaningful segmentation.

Visualization of uncertainty without a mean.

As dataset size and complexity steadily increase, uncertainty is becoming an important data aspect. So, today's visualizations need to incorporate indications of uncertainty. However, characterizing uncertainty for visualization isn't always straightforward.

Fast shape-based nearest-neighbor search for brain MRIs using hierarchical feature matching.

This paper presents a fast method for quantifying shape differences/similarities between pairs of magnetic resonance (MR) brain images. Most shape comparisons in the literature require some kind of deformable registration or identification of exact correspondences. The proposed approach relies on an optimal matching of a large collection of features, using a very fast, hierarchical method from the literature, called spatial pyramid matching (SPM).

Visual exploration of high dimensional scalar functions.

An important goal of scientific data analysis is to understand the behavior of a system or process based on a sample of the system. In many instances it is possible to observe both input parameters and system outputs, and characterize the system as a high-dimensional function. Such data sets arise, for instance, in large numerical simulations, as energy landscapes in optimization problems, or in the analysis of image data relating to biological or medical parameters.

Manifold modeling for brain population analysis.

This paper describes a method for building efficient representations of large sets of brain images. Our hypothesis is that the space spanned by a set of brain images can be captured, to a close approximation, by a low-dimensional, nonlinear manifold. This paper presents a method to learn such a low-dimensional manifold from a given data set.

A variational image-based approach to the correction of susceptibility artifacts in the alignment of diffusion weighted and structural MRI.

This paper presents a method for correcting the geometric and greyscale distortions in diffusion-weighted MRI that result from inhomogeneities in the static magnetic field. These inhomogeneities may due to imperfections in the magnet or to spatial variations in the magnetic susceptibility of the object being imaged--so called susceptibility artifacts. Echo-planar imaging (EPI), used in virtually all diffusion weighted acquisition protocols, assumes a homogeneous static field, which generally does not hold for head MRI.

On the manifold structure of the space of brain images.

This paper investigates an approach to model the space of brain images through a low-dimensional manifold. A data driven method to learn a manifold from a collections of brain images is proposed. We hypothesize that the space spanned by a set of brain images can be captured, to some approximation, by a low-dimensional manifold, i.