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Label-free Rapid Pathologic Tissue Diagnosis During Surgery

Researchers at the University of California, Davis have developed a method to rapidly identify regions of diseased tissue while a surgery is being conducted.

Detecting Cardiovascular Disease Using Noninvasive Imaging of the Eye

Cardiovascular disease is the leading cause of mortality and disability worldwide. It is also prevalent, affecting 9% of the population over 20 years of age. Patients with cardiovascular risk factors can reduce their risk of developing catastrophic cardiovascular events such as heart attack and stroke through lifestyle modification and medications. Unfortunately for many, the disease may go undiagnosed until the occurrence of serious events. Identifying biomarkers of subclinical ischemia can help identify patients with occult cardiovascular disease.

Blood Flow Velocimetry via Data Assimilation of Medical Imaging

Cardiovascular disease (CVD) is a tremendous burden on the population in terms of morbidity and mortality, as well as on the healthcare system in terms of cost. Various forms of CVD including atherosclerosis, valve and ventricular dysfunction, aneurysms, and thrombogenesis can be identified by measuring localized abnormalities in blood flow. Accordingly, the ability to noninvasively interrogate physiological flows enables identification and diagnosis of disease, monitoring of the effects of therapy, and research on the hemodynamic nature of CVD and its associated interventions. In the clinic, blood flow measurements are primarily made using phase contrast magnetic resonance imaging (PC-MRI) and ultrasonic color Doppler imaging. Certain limitations of these techniques for patients who have contraindications or suffer from arrhythmias, as well as the desire for volumetric flow information necessitate the development of a new modality for blood flow velocimetry.

Mechanisms and Devices Enabling Arbitrarily Shaped, Deep-Subwavelength, Acoustic Patterning

UCLA researchers in the Department of Mechanical and Aerospace Engineering have developed a Compliant Membrane Acoustic Patterning (CAMP) technology capable of patterning cells in an arbitrary pattern at a high resolution over a large area.

Ultrasound Based Volumetric Particle Tracking Method

The disclosure relates to method of processing three-dimensional images or volumetric datasets to determine a configuration of a medium or a rate of a change of the medium, wherein the method includes tracking changes of a field related to the medium to obtain a deformation or velocity field in three dimensions. In some cases, the field is a brightness field inherent to the medium or its motion. In other embodiments, the brightness field is from a tracking agent that includes floating particles detectable in the medium during flow of the medium.  

Shear Wave Based Elasticity Imaging Using 3D Segmentation For Ocular Disease Diagnosis

 Retinal diseases, such as age-related macular degeneration (AMD), are the leading cause of blindness in the elderly population. Since no known cures are currently present, it is crucial to diagnose the condition in its early stages so that disease progression is monitored. Systems and methods for detecting and mapping the mechanical elasticity of retinal layers in the posterior eye are disclosed herein. A system including confocal shear wave acoustic radiation force optical coherence elastography (SW-ARF-OCE) is provided, wherein an ultrasound transducer and an optical scan head are co-aligned to facilitate in-vivo study of the retina. In addition, an automatic segmentation algorithm is used to isolate tissue layers and analyze the shear wave propagation within the retinal tissue to estimate mechanical stress on the retina and detect early stages of retinal diseases based on the estimated mechanical stress. US patent application no.  20190335996 

A Fully‐automated Deep Learning System (software code) for the Detection, Prognosis, and Visualization of Pulmonary Disease.

The majority of state‐of‐the‐art lung segmentation algorithms in the literature do not simultaneously segment lungs, lung lobes, and airway in a single algorithm. Additionally, automated algorithms typically perform the segmentation task on a series of 2D slices, which can reduce segmentation accuracy of anatomical structures (i.e. lung lobes) that may require contextual information across all three spatial dimensions. Many existing algorithms also have not been validated on chest CTs across a wide variety of conditions to evaluate algorithm generalizability. Currently, quantification of respiratory measurements requires a radiologist, trained analyst, or technician to recognize, identify, and manually annotate anatomical landmarks such as the lung lobes or airway in the chest. A fully‐automated deep learning system may eliminate the need for manual analysis, thereby improving efficiency and expanding applicability to a large number of CTs.

Software-Automated Medical Imaging Software for Standardizing the Diagnosis of Sarcopenia

Sarcopenia  is defined as an age associated decline in or loss of lean skeletal muscle mass. The pathophysiology can be multifactorial and the change in body composition may be difficult to detect due to obesity, changes in fat mass, or edema. Changes in weight, limb or waist circumference are not reliable indicators of muscle mass changes. Sarcopenia may also cause reduced strength, functional decline and increased risk of falling. Sarcopenia is otherwise asymptomatic and is often unrecognized.  

Oldest-Old Mri Registration Template

MRI scans of patients/participants can be compared to template scans in order to identify differences or changes in brain anatomy. However, the templates that are used are typically of young brains, which lack the atrophy that naturally occurs in the aged brain. UCI researchers have developed a template for oldest old images (90+ age group) that takes into consideration the natural anatomical changes that can occur with aging.

Techniques for Improving Positron Emission Tomography Image Quality and Tracking Real-Time Biological Processes

Researchers at the University of California, Davis have developed methodologies that perform dynamic PET imaging and provide opportunities for tracing blood flow and other biological systems in real-time.

Software - Unified algorithm for data cleaning, source separation, and imaging of electroencephalographic signals: Recursive Sparse Bayesian Learning (RSBL)

Electroencephalographic source imaging (a.k.a. magnetic/electric or M/EEG source imaging, ESI, or brain electrical tomography) usually depends upon sophisticated signal processing algorithms for data cleaning, source separation and imaging. Typically, these problems are addressed separately using a variety of heuristics, making it difficult to systematize a methodology for extracting robust brain source images on a wide range of applications.

Noninvasive Method and Apparatus for Peripheral Assessment of Vascular Health

UCI researchers introduce a medical device which noninvasively and accurately monitors vascular health metrics such as endothelial function, arterial stiffness, and blood pressure.

A Method for Characterization of Device and Material and Communication at Thz Frequencies

UCLA researchers in the Department of Electrical and Computer Engineering have developed a novel method for real-time detection and characterization of pulsed THz waveforms that features differential detection of high sensitivity, and phase diversity to overcome the dispersion penalty for wideband operation.

Head-Mounted Display EEG Device

Diagnosis and detection of progression of neurological disorders remain challenging tasks. For example, a validated portable objective method for assessment of degenerative diseases would have numerous advantages compared to currently existing methods to assess functional loss in the disease. An objective EEG-based test would remove the subjectivity and decision-making involved when performing perimetry, potentially improving reliability of the test. A portable and objective test could be done quickly at home under unconstrained situations, decreasing the required number of office visits and the economic burden of the disease. In addition, a much larger number of tests could be obtained over time. This would greatly enhance the ability of separating true deterioration from measurement variability, potentially allowing more accurate and earlier detection of progression. In addition, more precise estimates of rates of progression could be obtained.

Novel Non-Immunogenic Positron Emission Tomography Gene Reporter

UCLA researchers in the Department of Pharmacology and Department of Microbiology, Immunology, & Molecular Genetics have developed a novel positron emission tomography reporter gene to preferentially trap radiolabeled deoxycytidine analogs.

Non-Immunogenic Positron Emission Tomography Gene Reporter Systems

UCLA researchers in the Department of Pharmacology and Department of Microbiology, Immunology, & Molecular Genetics have developed a novel dual gene positron emission tomography reporter system for the enhanced labeling of cells in vitro and in vivo.

Device and Method for Microscale Chemical Reactions

UCLA researchers in the Departments of Bioengineering and Molecular and Medical Pharmacology have developed a passive microfluidic reactor chip with a simplified design that is less costly than existing microfluidic chips.

pH-Weighted MRI Using Fast Amine Chemical Exchange Saturation Transfer (CEST) Imaging

UCLA researchers in the Department of Radiological Sciences and Department of Biomedical Physics have developed a novel magnetic resonance imaging (MRI) technique that utilizes amine chemical exchange saturation transfer (CEST) to capture pH-weighted images for measuring tissue acidity.

In Vivo Retinal Imaging via Improved Visible Light Optical Coherence Tomography (OCT)

Researchers at the University of California, Davis have developed a technique that integrates multiple technological innovations to use visible light OCT for improved retinal imaging.

Automated Selection of Myocardial Inversion Time with a Convolutional Neural Network

Magnetic resonance imaging (MRI) has been noted for its excellent soft tissue imaging capability with zero radiation dose. It has repeatedly been touted as the imaging modality of the future, but due to its complexity, long exam times and high cost, its growth has been severely limited. This especially has been the case for cardiac MRI, which only accounts for about I percent of all MRI exams in the United States. Delayed enhancement (DE) imaging is an essential component of cardiac MRI, widely used for the evaluation of myocardial scar and viability. The selection of an optimal inversion time (TI), known as the myocardial null point (TINP), to suppress the background myocardial signal is required to optimize image contrast in myocardial delayed enhancement (MDE) acquisitions. Incorrect selection of TINP can impair diagnostic quality. In certain diffuse myocardial diseases such as amyloidosis, it may be difficult to identify a single optimal null point. Further, it is known that TINP varies after intravenous contrast administration, and is therefore time-sensitive. In practice, selection of myocardial inversion time is generally performed through visual inspection and selection of TINP from an inversion recovery scout acquisition. This is dependent on the skill of a technologist or physician to select the optimal inversion time, which may not be readily available outside of specialized centers. However, such methods still rely on visual inspection of an image series by a trained human observer to select an optimal myocardial inversion time. A way to overcome these deficiencies is to embrace Deep learning approaches, including convolutional neural networks (CNNs),     which have the potential to automate selection of inversion time, and are the current state-of-the-art technology for image classification, segmentation, localization, and Spatial Temporal Ensemble Myocardium Inversion NETwork (STEMI-NET) prediction. However, these static CNN models have some drawbacks which could be overcome via the use of dynamic temporal activities for object recognition.

Preventative Trackable Anticoagulants for Atrial Fibrillation Treatment

Researchers at the University of California, Davis have developed a process to localize anticoagulation drugs for treatment of inflammation and atrial fibrillations.

Real-time 3D Image Processing Platform for Visualizing Blood Flow Dynamics

Researchers at UCI have developed an image processing platform capable of visualizing 3D blood flow dynamics of the heart in real-time. This technology aims to be a promising tool for looking at areas of the heart that were previously difficult to image and to better understand the dynamics in cardiac dysfunctions.

A Method For Digital Pathology Using Augmented Reality

UCLA researchers in the Departments of Electrical Engineering and Computer Engineering have developed a novel method for automated image analysis of digital pathology slides.

Use of a Radiation Detector that Combines Virtual Frisch Grid and Cerenkov Readouts

Researchers at the University of California, Davis have developed a radiation detector for high energy photons that employs a transparent semiconductor with a high index of refraction to combine benefits of Virtual Frisch Grid devices and the readout of Cerenkov light.

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