Researchers from UC San Diego and Oregon Health Science Univeristy developed a microelectrode grid for continuous interoperative neuromonitoring. The microelectrode grid includes a flexible substrate with low impedance electrochemical interface materials on conducting metal pads. The metal pads are connectable to stimulation/acquisition electronics through metal lead interconnects forming stimulation and recording channels and eventually to bonding pads. A flap within the substrate is movable away from the remainder of the substrate while at least some of the metal pads on the remainder of the substrate can remain in contact with an organ when the flap is moved away from the remainder of the substrate.

This technology revolves around Optical Coherence Tomography (OCT), a noninvasive imaging method that provides detailed cross-sectional images of tissue microstructure and blood flow. OCT utilizes either time domain (TDOCT) or Fourier domain (FDOCT) approaches, with FDOCT offering superior sensitivity and speed. Doppler OCT combines Doppler principles with OCT to visualize tissue structure and blood flow concurrently. Additionally, polarization-sensitive OCT detects tissue birefringence. Advanced methods aim to enhance the speed and sensitivity of Doppler OCT, crucial for various clinical applications such as ocular diseases and cancer diagnosis. Swept source FDOCT systems further improve imaging capabilities by increasing range and sensitivity. Overall, this technology represents significant advancements in biomedical imaging, offering insights into both structural and functional aspects of tissue physiology.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a serious threat to human health without effective treatment. There is an urgent need for both real-time tracking and precise treatment of the SARS-CoV-2 infected cells to mitigate and ultimately prevent viral transmission. However, selective and responsive triggering and tracking of the therapeutic processin infected cells remains challenging.

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Researchers at the University of California, Davis have developed an invention that utilizes an integrated Raman scanning head and machine vision for high throughput chemical analysis of liquid biopsy samples.

A comprehensive method for automated multi-organ segmentation based on deep fully convolutional networks and adversarial training, achieving superior results compared to existing techniques.

This patent application describes methods for non-invasive, label-free imaging of the cellular immune response in human skin using a nonlinear optical imaging system.

The disclosed methods offer a robust approach to accurately quantify skin optical properties across different skin tones, facilitating improved diagnosis, monitoring, and treatment in dermatology.

An innovative technology that uses a device to move any imaging device precisely through a path in 3D space, enabling the generation of high-resolution 3D models.

Diagnosing retinal disease, which affects over 200 million people worldwide, requires expensive and complicated analysis of the structure and function of retinal tissue. Recently, UCI developed a training algorithm which, for the first time, is able to assess tissue health from images collected using more common and less expensive optics.

Researchers at the University of California, Davis have developed a method of using artificial intelligence for assessing the effectiveness or efficacy of drugs that is cheaper, faster, and more accurate than commonly used assay analyses.

Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications, especially in disease diagnosis and image-guided surgery. HSI acquires a three-dimensional dataset called hypercube, with two spatial dimensions and one spectral dimension. Spatially resolved spectral imaging obtained by HSI provides diagnostic information about the tissue physiology, morphology, and composition. Researchers from UC San Diego developed a new method using a pair of femtosecond mid-infrared and visible excitation pulses to distinguish chromophores, including molecules and quantum dots, that possess nearly identical emission spectra using multiplexed conditions in a three-dimensional space. 

There are 118 known elements. Nearly all of them have NMR active isotopes and at least 39 different nuclei have been shown to have biological relevance. Despite this, most of today’s MRI is based on only one nucleus – 1H. To work towards making use of all potential nuclei, here, UC Berkeley researchers have created a coil enabling the excitation of arbitrary nuclei in human-scale MRI with a single coil. To excite arbitrary nuclei, they developed a completely new type of RF coil, the Any-nuclei Distributed Active Programmable Transmit Coil (ADAPT Coil), that can operate at any relevant frequency. This coil eliminates the need of the expensive traditional RF amplifier by directly converting DC power into RF magnetic fields with frequencies chosen by digital control signals sent to the switches. Semiconductor switch imperfections are overcome by breaking the coil into several segments. The ADAPT Coil presents a scalable and efficient method of exciting arbitrary nuclei in human-scale MRI. This coil concept provides further opportunities for scaling, programmability, lowering coil costs, lowering dead-time, reducing multinuclear MRI workflow complexity, and enabling the study of dozens of biologically relevant nuclei.  

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Electrical properties (EP), such as permittivity and conductivity, dictate the interactions between electromagnetic waves and biological tissue. EP are biomarkers for pathology characterization, such as cancer. Imaging of EP helps monitor the health of the tissue and can provide important information in therapeutic procedures. Magnetic resonance (MR)-based electrical properties tomography (MR-EPT) uses MR measurements, such as the magnetic transmit field B1+, to reconstruct EP. These reconstructions rely on the calculations of spatial derivatives of the measured B1+. However, the numerical approximation of derivatives leads to noise amplifications introducing errors and artifacts in the reconstructions. Recently, a supervised learning-based method (DL-EPT) has been introduced to reconstruct robust EP maps from noisy measurements. Still, the pattern-matching nature of this method does not allow it to generalize for new samples since the network’s training is done on a limited number of simulated data pairs, which makes it unrealistic in clinical applications. Thus, there is a need for a robust and realistic method for EP map construction.

Researchers at UC Irvine have developed methods to facilitate the delivery of a high dose, low energy electron beam or X-ray in a compact manner.

Common nuclear imaging techniques include computed tomography (CT), single photon emission CT (SPECT), and positron emission tomography (PET). PET differs from other nuclear imaging techniques in that it can visualize both functional and biological activities, including detection of metabolism within human tissues. PET is especially good for imaging patients with cancer, or brain or heart conditions. At low energies, when positrons collide with electrons near the radionuclide decay, Gamma rays (annihilation photons) are created. Gammas originating from the same electron-positron annihilation are generated exclusively in an entangled Bell state. Gammas which do not share an annihilation origin event, such as randoms, are not entangled. Additionally, a gamma which undergoes an internal scatter becomes decoherent (unentangled) from its pair, such as the gammas found in the scattered coincidence pairs. Scattered and random events degrade the image quality. Recently, quantum-based techniques utilizing entanglement of annihilation photons has been recognized as one approach to address scatter and random and to optimize the signal to noise (SNR) ratio.

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High-frequency photoacoustic tomography (> 20 MHz) is becoming increasingly important in biomedical applications. However, it requires data acquisition (DAQ) to have commensurately high sampling rate, which imposes challenges to hardwires and increases the cost of building a PA imaging system. For example, the sampling rate should be higher than 80 MHz to cover 100% bandwidth of a 26-MHz transducer (Nuquist limit). A commercial PA imaging system such as Vevo LAZR X (Fujifilm VISUALSONICS Inc. ON, Canada) with 80-MHz sampling rate can cost more than 990,000$ in the United States.Many PA groups use clinical ultrasound DAQs, which are low cost but also have a low sampling rate, e.g., the iu22 system’s sampling rate is 32 MHz.

Hyperspectral imaging, the practice of capturing detailed spectral (color) information from the output of an optical instrument such as a microscope or telescope, is useful in biological and astronomical research and in manufacturing. In addition to being bulky and expensive, existing hyperspectral imagers typically require scanning across a specimen, limiting temporal resolution and preventing dynamic objects from being effectively imaged. Snapshot methods which eliminate scanning are limited by a tradeoff between spatial and spectral resolution.In order to address these problems, researchers at UC Berkeley have developed a hyperspectral imager which can be attached to the output of any benchtop microscope. The imager is compact (about 6-inches), and can achieve a higher spatial resolution than traditional snapshot imagers. Additionally, this imager needs only one exposure to collect measurements for an arbitrary number of spectral filters, giving it unprecedented spectral resolution.

Novel system for imaging of specimens in high refractive index solutions on the Zeiss Z.1 fluorescence light sheet microscope. System will allow for deep imaging of large and intact biological samples (i.e. mouse brain) for enhanced optical resolution and faster imaging speed.

Researchers at the University of California, Davis have developed a customizable polysaccharide that can be added to nanoparticles to reduce their rejection by the human immune system.

Positron emission tomography (PET) scanners map the metabolic or biochemical function of tissues by detecting the gamma radiation released by the decay of radioactive tracers ingested by a patient. This technology is particularly useful for mapping tumors because one can devise tracers which tumor cells uptake preferentially. Current gamma radiation detectors are expensive and inefficient, requiring large integration times and radionuclide doses for meaningful image quality. Additionally, the spatial resolution of the resulting map is limited by detector latency, which for traditional technology is 200-500 picoseconds.To address these problems, researchers at UC Berkeley have developed a novel gamma radiation detector with much greater time resolution (potentially down to 10 picoseconds), and higher efficiency (nearly all gamma rays successfully detected). Additionally, these detectors use well-established nanotechnology manufacturing methods and can be produced an order of magnitude more cheaply than existing detectors. The high efficiency of these detectors allows amounts of radioactive tracer used to be decreased by an order of magnitude and spatial resolution to be increased by an order of magnitude when compared to traditional methods.

Measurement of electrical activity in nervous tissue has many applications in medicine, but the implantation of a large number of sensors is traditionally very risky and costly. Devices must be large due to their necessary complexity and power requirements, driving up the risk further and discouraging adoption. To address these problems, researchers at UC Berkeley have developed devices and methods to allow small, very simple and power-efficient sensors to transmit information by backscatter feedback. That is, a much more complex and powerful external interrogator sends an electromagnetic or ultrasound signal, which is modulated by the sensor nodes and reflected back to the interrogator. Machine learning algorithms are then able to map the reflected signals to nervous activity. The asymmetric nature of this process allows most of the complexity to be offloaded to the external interrogator, which is not subject to the same constraints as implanted devices. This allows for larger networks of nodes which can generate higher resolution data at lower risks and costs than existing devices.

Researchers at the University of California, Davis have developed an approach to improve drug delivery to tumors and metastases in the brain. Their multi-barrier tackling delivery strategy has worked to efficiently impact brain tumor management while also achieving increased survival times in anti-cancer efficacy.