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High Resolution Laser Speckle Imaging of Blood Flow

Prof. Guillermo Aguilar and his colleagues from the University of California, Riverside have developed a new approach to laser speckle imaging, called Laser Speckle Optical Flow Imaging (LSOFI) to be used for autonomous blood vessel detection and as a qualitative tool for blood flow visualization. LSOFI works by capturing the speckle displacement caused by different physical behavior and use the data to create a mapped image. It has been shown that LSOFI has many advantages over LSCI methods both in temporal and spatial resolution. Namely, LSOFI can be used to produce higher resolution images compared with the LSCI method using less frames. Combining this technology with Graphics Processing Unit (GPU) computation increases the speed of LSOFI, so GPU enabled LSOFI shows potential to create a fast and fully functional quasi-real time blood flow imaging system.  Fig 1: Comparison of blood flow imaging techniques applied to the raw image. The shown results are for Laser Speckle Optical Flow Imaging (LSOFI) using the Farneback Optical Flow algorithm, traditional Laser Speckle Imaging (LSI), and Temporal Frame Averaging (sLASCA).  

Development of a Thermal Endoscope for ENT Clinical Diagnostics

There is a clinical need for improved visual inspection for ENT diagnosis and surgeries. Endoscopy is required to access locations of ENT conditions. However, the assessment and identification of ENT abnormalities and pathologies remain challenging due to the difficult-to- reach ENT locations and the complex nature of the related pathologies. An imaging technique that could provide additional information, high contrast, and quantitative data about the patient condition will be useful, especially to assist ENT clinicians in diagnosis and surgeries and to avoid the need to resort to more expensive imaging techniques (e.g., CT scans, ultrasound imaging,MRI).

Imaging Modalities and Methods for Enhanced, Label-free Histopathology During Surgery

Researchers at the University of California, Davis have developed new techniques capable of producing near real-time tissue analysis with quality and accuracy attributes comparable to traditional Haemotoxylin and Eosin (H&E) histopathology methods.

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.

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.  

New Bright Green Fluorescent Proteins

Fluorescent proteins (FP) have been widely used as research tools in both academia and pharma for many years.  Naturally occurring FP have been mutated to either be brighter, be monomers, and/or for easier folding and expression in cells.  The most common FP to date has been the green fluorescent protein (GFP) of the jelly fish Aequorea victoria which can be expressed in cells and fused with proteins of interest, and has proven to be an excellent tool to study protein localization, expression, signaling, etc. in real time via microscopy and other techniques. 

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.

Simple Imaging Tool for Oral Cancer Detection and Monitoring

UCI researchers have developed a miniature, flexible intra-oral probe with a camera that allows early detection of oral cancer lesions in difficult-to-see, high risk areas of the mouth and throat. The tool allows for a low cost, non-invasive procedure that can be easily adopted in non-specialist medical settings.

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.

Microscale Device and Method for Purification of Radiopharmaceuticals

UCLA researchers from the Departments of Molecular & Medical Pharmacology and Bioengineering have developed a novel method for the purification of radiopharmaceuticals for the on-demand production of positron emission tomography (PET) tracers.

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.

Method for Concentration and Formulation of Radiopharmaceuticals

Researchers at the UCLA Department of Medical and Molecular Pharmacology have developed a compact microfluidic device that is able to achieve rapid concentration and/or reformulation of PET tracers after HPLC purification.

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.

Strongly Interacting Magnetic Particle Imaging

Nuclear medicine is a diagnostic imaging method that works very well, but it is both expensive and gives off excess radiation. X-rays also are used for diagnostic imaging but have poor contrast. Magnetic Particle Imaging (MPI)is a promising new tracer modality with zero attenuation in tissue, near-ideal contrast and sensitivity, and an excellent safety profile, however, the spatial resolution of MPI is currently the modality’s only weak technical attribute. UC Berkeley and UF researchers have developed a novel, compact, and intuitive MPI scanner that resolves this issue.  The research demonstrated proof-of-concept studies for an MPI modality, referred to herein as strongly-interacting magnetic particle imaging (siMPI) that enables a super-resolution breakthrough. The siMPI provided more than a 6-fold improvement in every dimension of space spatial resolution and 37-fold increase in sensitivity. The MPI can be used for early-stage detection of cancer, gut bleeds, strokes, pulmonary embolism, and tracking immunotherapies and MPI can penetrate any tissue, including bone, lungs, and dense breast tissue.

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.

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.

Development of Novel Fluorescent Puromycin Derivatives

Puromycin is an aminonucleoside antibiotic produced by the bacterium Streptomyces alboniger. Its mode of action is to inhibit protein synthesis by disrupting peptide transfer on ribosomes, leading to premature chain termination during protein translation. Puromycin blocks protein synthesis in both eukaryotes and prokaryotes and is routinely used as a research tool in cell culture. The native Puromycin is also used assays such as mRNA display. As such, derivatives have been synthesized in which the amino acid of the 3' end of adenosine based antibiotics is altered to change the compound's antibiotic activity. Other compounds have been synthesized with differing amino acids and functionalities to examine the effect it has on bacterial viability. The majority do not show useful absorption or emission profiles. What is needed is a method to track the compounds in biological systems.

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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