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 process that utilizes artificial intelligence-based image mapping to improve the image of frozen tissue sections and reduce artifacts and distortion of those specimens.

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.

UCLA researchers in the Department of Electrical and Computer Engineering have developed an algorithm for hallucination-free resolution enhanced brain MRI images with better quality and superior computational time compared to the current state of the art.

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 Department of Electrical Engineering have developed an on-chip UV holographic imaging microscope that offers a low-cost, portable, and robust technique to image and distinguish protein crystals from salt crystals.

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 have developed a novel scanning and analysis method for breathing motion-correlated CT that can provide breathing motion-artifacts free images for subsequent use in biomechanical modeling for COPD diagnosis and radiation therapy treatment planning.

Heart disease is a major leading cause of morbidity and mortality in the U.S. largely due to coronary artery atherosclerotic disease (CAD), which affects millions and costs billions annually. The concept of plaque vulnerability, based on likelihood of fibroatheroma rupture, has prompted many pursuits to identify high risk lesions, costing $150 million per year. However, identifying vulnerable plaques based on structure, via coronary angiograms or CT/MRI scans, has not translated to improved clinical outcome. Thus, the failure to identify and predict plaques at high risk of rupture, which may lead to myocardial infarction, heart failure and/or sudden cardiac death, is likely because structure may not optimally discern plaque vulnerability. Molecular imaging, in contrast, offers an innovative approach for discriminating the vulnerable plaque in that it not only visualizes structure, but also interrogates underlying molecular function. Based on the current methods to detect plaques, there is a need for a better method for measuring plaque rupture vulnerability.

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.

Researchers led by Tzung Hsiai from the David Geffen School of Medicine at UCLA have developed a way to visualize moving objects using virtual reality.

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.

UCLA researchers in the Department of Radiology have developed a method for automated calibration of phantom images.

UCLA researchers have developed an algorithm that enables construction of 3D images from tomographic data through iterative methods with the incorporation of mathematical constraints. This methodology is an improvement over conventional techniques as it allows for radiation dose reduction and improved resolution.

UCLA researchers in the Department of Radiological Sciences and Department of Radiation Oncology have developed a real-time tomosynthesis design that can produce sufficient contrast to guide radiation therapy of small lung tumors.

UCLA researchers in the Department of Radiology have developed a novel method that addresses a common issue of MRI imaging misinterpretation due to the high field effects of B1+ inhomogeneity.

Researchers led by Xiao Hu from the Department of Surgery at UCLA have created a novel and convenient way to compress and query medical images from a PACS system.

UCLA researchers in the Department of Radiological Sciences have developed a deep-learning-based computerized algorithm for classification of prostate cancer using multi-parametric-MRI images.