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Real-Time Motion Prediction for Dynamic MRI

UCLA researchers in the Department of Bioengineering and Department of Radiological Sciences have developed a novel motion prediction algorithm using MRI-based motion tracking to provide accurate and real-time motion information for dynamic MRI and MRI-guided interventions.

Integrative Leakage Correction For Contrast Agent Extravasation In Dynamic Susceptibility Contrast (DSC) - MRI

UCLA researchers in the Department of Radiological Sciences have developed a new technique for more accurately estimating relative cerebral blood volume (rCBV) from dynamic susceptibility contrast (DSC) perfusion MRI by improved modeling and correction of contrast agent leakage.

Automated Activity Classification Of Video From Body Worn Cameras

UCLA researchers in the Department of Mathematics have developed an approach to classify different ego-motion categories from body-worn video.

Air Quality Monitoring Using Mobile Microscopy And Machine Learning

UCLA researchers have developed a novel method to monitor air quality using mobile microscopy and machine learning.

Multi-Echo Spin-, Asymmetric Spin-, And Gradient Echo Echoplanar Imaging (Message-EPI) MRI

UCLA researchers in the Department of Radiological Sciences have developed a new MRI pulse sequence optimized for brain imaging.

Cloud based platform for display and analysis of image time series

Current microscopy systems commonly used in biomedical research labs and companies generate large amounts of large data, known as image stacks. There is currently no easy, streamlined way to store, organize and analyze these datasets on a cloud. Researchers at UCI have developed a software consisting of a cloud-based data management and analysis platform that make visualization and analysis of large image stacks simpler and faster.

Convex Optimized Diffusion Encoding (CODE) For Motion Compensated Diffusion Weighted Magnetic Resonance Imaging With Shortened Echo Times

UCLA researchers in the Department of Radiological Sciences have developed a novel method for diffusion weighted MRI that minimizes echo times and/or incorporates bulk motion compensation through application of a convex optimized diffusion encoding (CODE).

Anisotropic Elastoplasticity For Codimensional Frictional Contact

UCLA researchers in the Department of Mathematics and Department of Computer Science have developed a novel hybrid Lagrangian/Eulerian approach to simulate frictional contact in thin codimensional elastic objects, such as cloth, hair, and knit. It allows a more smooth and vivid animation of those objects with faster speed and higher robustness.

Reducing Computational Complexity of Training Algorithms for Artificial Neural Networks

Researchers at UCLA have developed a novel mathematical theorem to revolutionize the training of large-scale artificial neural networks (ANN).

Geometrical Characterization of Surfaces from Noisy 3D Fluorescence Microscopy Data

A fully automated algorithm to determine the location and curvatures of an object’s surface from 3D fluorescence images.

Deep Learning Microscopy

UCLA researchers in the Department of Electrical Engineering have developed a novel microscopy analysis that improves resolution, field-of-view and depth-of-field in optical microscopy images.

Computational Out-Of-Focus Imaging Increases The Space-Bandwidth Product In Lens-Based Coherent Microscopy

UCLA researchers in the Department of Electrical Engineering have developed a wide-field and high-resolution coherent imaging method that uses a stack of out-of-focus images to provide much better utilization of the space-bandwidth product (SBP) of an objective-lens.

Sparsity-Based Multi-Height Phase Recovery In Holographic Microscopy

UCLA researchers in the Department of Electrical Engineering have developed a sparsity-based phase reconstruction technique implemented in wavelet domain to achieve more than 3-fold reduction in the number of holographic measurements for coherent imaging of densely connected samples with minimal impact on the reconstructed image quality.

A General Noise Suppression Scheme With A Reference Beam In Optical Heterodyne Spectroscopy

A methodology to suppress additive and convolved noise in optical heterodyne signals

Automated Reconstruction Of The Cardiac Chambers From MRI

This is a fast, fully automated method to accurately model a patient’s left heart ventricle via machine learning algorithms.

Developing Physics-Based High-Resolution Head And Neck Biomechanical Models

UCLA researchers in the Department of Radiation Oncology at the David Geffen School of Medicine have developed a new computational method to model head and neck movements during medical imaging/treatment procedures.

Assessment Of Wound Status And Tissue Viability Via Analysis Of Spatially Resolved Thz Reflectometry Maps

UCLA researchers in the Department of Bioengineering have developed an algorithm to assess the burn wound severity and predict its future outcomes using Terahertz imaging.

Pixel Super-Resolution Using Wavelength Scanning

UCLA researchers have developed a novel way to significantly improve the resolution of an undersampled or pixelated image.

Robust Visual-Inertial Sensor Fusion For Navigation, Localization, Mapping, And 3D Reconstruction

UCLA researchers in the Computer Science Department have invented a novel model for a visual-inertial system (VINS) for navigation, localization, mapping, and 3D reconstruction applications.

Dsp-Sift: Domain-Size Pooling For Image Descriptors For Image Matching And Other Applications

UCLA researchers in the Computer Science Department have invented a novel modification to the scale-invariant feature transform (SIFT) algorithm that shows significant improvement for computer vision applications.

A Method of Computational Image Analysis for Predicting Tissue Infarction After Acute Ischemic Stroke

UCLA researchers in the Departments of Radiological Sciences and Neurology have designed an algorithm to predict tissue infarctions using pre-therapy magnetic resonance (MR) perfusion-weighted images (pre-PWIs) acquired from patients with acute ischemic stroke. The predictions generated by the algorithm provide information that may assist in physicians’ treatment decisions.

Software for auto-generation of text reports from radiology studies

Imaging machines used for radiology studies often export data (such as vascular velocities, bone densitometry, radiation dose, etc.) as characters stored in image format. Radiologists are expected to interpret this data and also store it in their text-based reports of the studies. This is usually accomplished by dictating the data into the text report or copying it by typing it. However, these methods are error-prone and time-intensive.

Atom Probe Tomography Method and Algorithm

Most cluster analysis parameters in atom probe tomography (APT) are selected ad hoc. This can often lead to data misinterpretation and misleading results by instrument technicians and researchers. Moreover, arbitrary cluster parameters can have suboptimal consequences on data quality and integrity, leading to inefficiencies for downstream data users. To address these problems, researchers at the University of California, Berkeley, have developed a framework and specific cluster analysis methods to efficiently extract knowledge from better APT data. By using parameter selection protocols with theoretical explanations, this technology allows for a more optimized and robust multivariate statistical analysis technique from the start, thus improving the quality of analysis and outcomes for both upstream and downstream data users.

Software for Differential Dynamic Microscopy (DDMCalc)

A MATLAB code for performing differential dynamic microscopy (DDM).

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