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

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.

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.

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.  

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. 

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

UCLA researchers in the Department of Medicine have developed a novel intravascular ultrasound-guided electrochemical impedance spectroscopy (IVUS-EIS) system for the detection of oxLDL-laden plaques in arteries.

UCLA researchers in the Department of Mechanical and Aerospace Engineering have developed a novel single-objective lens stereo imaging setup for endoscopic applications.

UCLA researchers in the Department of Radiological Sciences have invented a novel interactive tool that can rapidly focus and zoom on a large number of images using eye tracking technology.