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The global inhomogeneity (GI) index is a electrical impedance tomography (EIT) parameter that quantifies the tidal volume distribution within the lung. In this work the global inhomogeneity index was computed for twenty subjects in order to evaluate his potential use in the detection and follow up of chronic obstructive pulmonary disease (COPD) patients. EIT data of 17 subjects were acquired: 14 patients...
The three dimensional (3D) innervation zone (IZ) imaging approach (3DIZI) has been developed in our group to localize the IZ of a particular motor unit (MU) from its motor unit action potentials decomposed from high-density surface electromyography (EMG) recordings. In this study, the developed 3DIZI approach was combined with electrical stimulation to investigate global distributions of IZs in muscles...
EEG source imaging is able to reconstruct sources in the brain from scalp measurements with high temporal resolution. Due to the limited number of sensors, it is very challenging to locate the source accurately with high spatial resolution. Recently, several total variation (TV) based methods have been proposed to explore sparsity of the source spatial gradients, which is based on the assumption that...
The cardiac conduction system (CCS) is responsible for the initiation and propagation of action potentials through the heart ensuring efficient pumping of blood. Understanding the anatomy of the CCS and its relationship with other major cardiac components is important to help understand arrhythmias and how certain procedures may increase the incidence of arrhythmias developing. We sectioned a whole...
In this paper, we present a framework to reconstruct spatially localized sources from Magnetoencephalography (MEG)/Electroencephalography (EEG) using spatiotemporal constraint. The source dynamics are represented by a Multivariate Autoregressive (MAR) model whose matrix elements are constrained by the anatomical connectivity obtained from diffusion Magnetic Resonance Imaging (dMRI). The framework...
Stochastic models of nano-biomachines have been studied by 3-D reconstruction from cryo electron microscopy images in recent years. The image data is the projection of many heterogeneous instances of the object under study (e.g., a virus). Initial reconstruction algorithms require different instances of the object, while still heterogeneous, to have the same symmetry. This paper presents a maximum...
Most current Brain-Computer Interfaces (BCIs) achieve high information transfer rates using spelling paradigms based on stimulus-evoked potentials. Despite the success of this interfaces, this mode of communication can be cumbersome and unnatural. Direct synthesis of speech from neural activity represents a more natural mode of communication that would enable users to convey verbal messages in real-time...
Magnetic Resonance Fingerprinting (MRF) is a relatively new approach that provides quantitative MRI using randomized acquisition. Extraction of physical quantitative tissue values is preformed off-line, based on acquisition with varying parameters and a dictionary generated according to the Bloch equations. MRF uses hundreds of radio frequency (RF) excitation pulses for acquisition, and therefore...
This paper deals with the development of a fast and smart acquisition technique of Optical Coherence Tomography (OCT) data that has the capability to reconstruct missing data of OCT image. The main objective is to reduce the acquisition time (i.e., increase the frame rate) of an OCT-scan system by choosing a trajectory that covers entirely the image but that does not take all the measurements. The...
Electron micrography (EM) is an important method for determining the three-dimensional (3D) structure of macromolecular complexes and biological specimens. But there are several limitations such as poor signal-to-noise, limitation on range of tilt angles and sub-region subject to electron exposure, unintentional movements of the specimen, with EM systems that make the reconstruction procedure a severely...
The development of novel neurotechnologies for treating refractory neuropsychiatry disorders depends on understanding and manipulating the dynamics of neural circuits across large-scale brain networks. The mesolimbic pathway plays an essential role in reward processing and mood regulation and disorders of this pathway underlie many neuropsychiatric disorders. Here, we present the design of a customized...
Limited data and low-dose constraints are common problems in a variety of tomographic reconstruction paradigms, leading to noisy and incomplete data. Over the past few years, sinogram denoising has become an essential preprocessing step for low-dose Computed Tomographic (CT) reconstructions. We propose a novel sinogram denoising algorithm inspired by signal processing on graphs. Graph-based methods...
Few-view CT reconstruction attracts wide attention in the purpose of radiation dose reduction. For analytical algorithm filtered back projection (FBP), the reconstructed image would have extremely severe artifacts. Prior information could be used in few-view CT reconstruction to improve image quality, and similar prior images of the same person could provide much more reliable information. However,...
Microglia are immune cells of the central nervous system. Good knowledge about their morphology leads us to a better understanding of their functionality. In this article we propose an automated method to trace microglia in microscopy images. Our approach is powered by two main algorithms: multilevel thresholding (MT) and minimum spanning tree (MST). MT quantizes intensities of image pixels to several...
Echo planar imaging (EPI)-based magnetic resonance imaging (MRI) data are often corrupted by Nyquist ghost artifacts resulting from odd-even shifts of the EPI read-outs. Algorithms that corrects for the Nyquist ghost artifacts rely on calibration scans that are collected prior to the data acquisition. However, a more complex pattern of ghosting artifacts arises when diffusion-weighted data are acquired...
A computer-aided detection (CADe) algorithm for clustered microcalcifications (MCs) in reconstructed digital breast tomosynthesis (DBT) images is suggested. The MC-like objects were enhanced by a Hessian-based 3D calcification response function, and a signal-to-noise ratio (SNR) enhanced image was also generated to screen the MC clustering seed objects. A connected component segmentation method was...
Cerenkov luminescence tomography (CLT) is a powerful imaging technique that allows dynamically and three-dimensionally resolving the metabolic process of radiopharmaceuticals. It uses optical method to detect radiopharmaceuticals with low cost and high sensitivity. However, because of the strong absorption and scatter of biological tissues, the reconstruction of CLT is always converted to an ill-posed...
In this paper we investigate the utility of several low-rank models for recovery of Magnetic Resonance Imaging (MRI) data from limited sampling in the k — t space for dynamic imaging. In particular, for 3D temporal (2D space + time) MRI data we employ several tensor factorization techniques and assess the degree of dimensionality reduction, or compressibility, that can be obtained. This algebraic...
Today's artificial neural networks use computational models and algorithms inspired by the knowledge of the brain in the '90s. Powerful as they are, artificial networks are impressive but their domain specificity and reliance on vast numbers of labeled examples are obvious limitations. About a decade ago, spiking neural networks (SNNs) emerged as a new formalism that takes advantage of the spike timing...
Digitally reconstructed radiographs (DRRs) play a significant role in modern clinical radiation therapy. They are used to verify patient alignments during image guided therapies with 2D-3D image registration. The generation of DRRs can be implemented intuitively in O(N3) relying on direct volume rendering (DVR) methods, such as ray marching. This complexity imposes certain limitations on the rendering...
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