Professor Bahman Anvari and colleagues from the University of California, Riverside and the University of Maryland have developed nanoparticle systems with greater fluorescence emission when compared to known dyes. These nanoparticles incorporate dual near infrared fluorescence (NIR) and magnetic resonance (MR) dyes for improved fluorescence.  The nanoparticles encapsulate brominated carbocyanine dyes with MR qualities and ICG with NIR properties. This technology is advantageous because these nanoparticles containing these dyes exhibit greater fluorescence emission when compared to the individual dyes.  This presents a promising dual-mode platform with high optical sensitivity and clinical diagnostic and research applications.  

Researchers at the University of California, Davis have developed a large-scale, energy-efficient, high-throughput, and compact tensorized optical neural network (TONN) exploiting the tensor-train decomposition architecture on an integrated III–V-on-silicon metal–oxide–semiconductor capacitor (MOSCAP) platform.

Researchers at the University of California, Davis have developed a technology involving statistically reconstructing positronium (or positron) lifetime imaging (PLI) for use with a positron emission tomography (PET) scanner, to produce images having resolutions better than can be obtained with existing time-of-flight (TOF) systems.

Researchers at the University of California, Davis, have developed a novel imaging system that improves the diagnostic accuracy of PET imaging. The system combines machine learning and computed tomography (CT) imaging to reduce noise and enhance resolution. This novel technique can integrate with commercial PET imaging systems, improving diagnostic accuracy and facilitating superior treatment of various diseases.

Researchers at the University of California, Davis have developed a solid-state device that uses Cherenkov Radiation to emit light at a tunable wavelength in the THz to IR range.

Researchers at the University of California, Davis, have developed a headset (e.g., virtual reality headset) in which two imaging modalities, optical coherence tomography (OCT) and scanning laser ophthalmoscopy (SLO), are incorporated with automated eye tracking and optical adjustment capabilities providing a fully automated imaging system in which patients are unaware that images of the retina are being acquired. Imaging takes place while the patient watches a soothing or entertaining video.

Researchers at the University of California, Davis have developed an optical lens module that uses a metalens or a metalens array having a controllable angular field-of-view.

Researchers at the University of California, Davis, have developed a positron emission tomography (PET) medical imaging system that allows for higher 3D position resolution, eliminates radiation background, and holds a similar production cost to existing technologies.

Researchers at the University of California, Davis have developed a novel cell segmentation technology for accurate analysis of non-spherical cells and that offers a comprehensive, high-throughput approach for analyzing the transcriptomic and metabolomic data to study complex biological processes at the single-cell level.

An innovative microscope integrating HSI, FLIM, and SHG for advanced optical metabolic imaging.

A revolutionary laser-based micro-biopsy tool designed for minimally invasive, precise tissue sampling and real-time histological analysis.

      While robots have proven effective in enhancing the precision and time efficiency of MRI-guided interventions across various medical applications, safety remains a formidable challenge for robots operating within MRI environments. As the robots assume full control of medical procedures, the reliability of their operation becomes paramount. Precise control over robot forces is particularly crucial to ensure safe interaction within the MRI environment. Furthermore, the confined space in the MRI bore complicates the safe operation of human-robot interaction, presenting challenges to maneuverability. However, there exists a notable scarcity of force-controlled robot actuators specifically tailored for MRI applications.       To overcome these challenges, UC Berkeley researchers have developed a novel MRI-compatible rotary series elastic actuator module utilizing velocity-sourced ultrasonic motors for force-controlled robots operating within MRI scanners. Unlike previous MRI-compatible SEA designs, the module incorporates a transmission force sensing series elastic actuator structure, while remaining compact in size. The actuator is cylindrical in shape with a length shorter than its diameter and integrates seamlessly with a disk-shaped motor. A precision torque controller enhances the robustness of the invention’s torque control even in the presence of varying external impedance; the torque control performance has been experimentally validated in both 3 Tesla MRI and non-MRI environments, achieving a settling time of 0.1 seconds and a steady-state error within 2% of its maximum output torque. It exhibits consistent performance across low and high external impedance scenarios, compared to conventional controllers for velocity-sourced SEAs that struggle with steady-state performance under low external impedance conditions.

Researchers at the University of California, Davis have developed a semiautomated solution for identifying differences in tissue architectures or cell types as well as visualizing and analyzing cell densities and cell-cell associations in a tissue sample.

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.

Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:8.0pt; mso-para-margin-left:0in; line-height:107%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri",sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi; mso-font-kerning:1.0pt; mso-ligatures:standardcontextual;} Researchers from UC San Diego developed a patent-pending convolutional neural network (CNN)-based deformable registration algorithm to reduce computation time for analysis of medical images such as CT and MRI. These fast, fully-automated CNN-based lung deformable registration algorithms can facilitate translation of measurements into clinical practice, potentially improving the diagnosis and severity assessment of small airway diseases.

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.

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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.

Researchers from UC San Diego's Neuroelectronic Lab (https://neuroelectronics.ucsd.edu/) demonstrate that they can predict neural activity at deeper layers of the brain by only recording potentials from brain surface. This was achieved by performing multimodal experiments with an ultra-high density transparent graphene electrode technology and developing neural network methods to learn nonlinear dynamic between different modalities. They used cross modality inference to predict the activity at deep layers from surface. Prediction of neural activity at depth have the potential to open up new possibilities for developing minimally invasive neural prosthetics or targeted treatments for various neurological disorders.

Researchers from UC San Diego have developed a patent-pending wearable device for concurrently assessing afferent and efferent visual functions. The invention details novel mobile brain-computer interfacing methods and systems for concurrently assessing afferent and efferent visual functions.