Data-driven algorithm for myelin water imaging: Probing subvoxel compartmentation based on identification of spatially global tissue features.

Purpose: Multicomponent analysis of MRI T relaxation time (mcT ) is commonly used for estimating myelin content by separating the signal at each voxel into its underlying distribution of T values. This voxel-based approach is challenging due to the large ambiguity in the multi-T space and the low SNR of MRI signals. Herein, we present a data-driven mcT analysis, which utilizes the statistical strength of identifying spatially global mcT motifs in white matter segments before deconvolving the local signal at each voxel.

Integrating Domain Knowledge Into Deep Networks for Lung Ultrasound With Applications to COVID-19.

Lung ultrasound (LUS) is a cheap, safe and non-invasive imaging modality that can be performed at patient bed-side. However, to date LUS is not widely adopted due to lack of trained personnel required for interpreting the acquired LUS frames. In this work we propose a framework for training deep artificial neural networks for interpreting LUS, which may promote broader use of LUS.

Improved Bluetooth Low Energy Sensor Detection for Indoor Localization Services.

Advancements in protocols, computing paradigms, and electronics have enabled the development of wireless sensor networks (WSNs) with high potential for various location-based applications in different fields. One of the most important topics in WSNs is the localization in environments with sensor nodes being scattered randomly over a region. Localization techniques are often challenged by localization latency, efficient energy consumption, accuracy, environmental factors, and others.

On Detection of Faint Edges in Noisy Images.

A fundamental question for edge detection in noisy images is how faint can an edge be and still be detected. In this paper we offer a formalism to study this question and subsequently introduce computationally efficient multiscale edge detection algorithms designed to detect faint edges in noisy images. In our formalism we view edge detection as a search in a discrete, though potentially large, set of feasible curves.

WIS-NeuroMath enables versatile high throughput analyses of neuronal processes.

Automated analyses of neuronal morphology are important for quantifying connectivity and circuitry in vivo, as well as in high content imaging of primary neuron cultures. The currently available tools for quantification of neuronal morphology either are highly expensive commercial packages or cannot provide automated image quantifications at single cell resolution. Here, we describe a new software package called WIS-NeuroMath, which fills this gap and provides solutions for automated measurement of neuronal processes in both in vivo and in vitro preparations.