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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.

Diagnostic Methodology for Essential Tremor

Essential Tremor is a disease of the nervous system, that can severely impact daily life for sufferers. Diagnosis is currently made solely on clinical grounds, and no direct tremor measure from the brain has been developed to objectively measure tremor severity. The current methodologies use devices placed on the hands to measure the tremor magnitude. A pressing need is for an accurate diagnostic tool, as the symptoms of tremor can be similar to other conditions. UC San Diego researchers have recently developed a new diagnostic methodology, and current data suggest 95% accuracy.

Improved Cryosectioned Tissue Imaging Using Artificial Intelligence-Based Image Mapping

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.

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.

A Method For Enhancement Of Medical Images

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.

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.


Hyperspectral imaging is a technique combining imaging and spectroscopy resulting in images with extraordinary precision and detail. Current approaches to capture hyperspectral images are costly and time-consuming. The proposed technique makes use of inexpensive filters and reduces the number of required exposures, thereby improving the efficiency and practicability of obtaining hyperspectral images.

Breathing Motion Artifact Reduction In CT

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.

New Method for the Detection of Vulnerable Plaques in Coronary Artery Atherosclerotic Disease (CAD)

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.

Intravascular Ultrasound-guided Electrochemical Impedance Spectroscopy (IVUS-EIS) to Assess Lipid-Laden Plaques

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.

Virtual Reality Visualization Of Dynamic Images Using Deformable Image Segmentation

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.

Stereo Image Acquisition By Lens Translation

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

A New Human-Monitor Interface For Interpreting Clinical Images

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.

Incorporation of Mathematical Constraints in Methods for Dose Reduction and Image Enhancement in Tomography

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.

Real-Time Tomosynthesis For Radiation Therapy Guidance

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.

A Method For Accurate Parametric Mapping Based On Characterization Of A Reference Tissue Or Region

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.

Dicom/Pacs Compression Techniques

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.

Deep-Learning-Based Computerized Prostate Cancer Classification Using A Hierarchical Classification Framework

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.

Equally Sloped (Pseudopolar) Tomography With Applications To Biological And Medical Imaging

UCLA researchers in the Department of Physics and Astronomy and the California NanoSystems Institute have developed a new tomographic imagine technique providing higher spatial resolution at a lower radiation dose.

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