A novel technology for real-time, non-invasive monitoring and adaptive control of electron radiotherapy treatments using acoustic signals.

X-radiation (X-ray) imaging is one of the most common imaging techniques in medicine. Presently, thin-film transistor flat panel detectors are the gold standard for X-ray detection; however, these detectors average across the absorbed X-ray spectrum and thus suffer from poor material decomposition and lesion differentiation. Modern efforts to address this focus on three methods of energy differentiation: dual-shot, photon counting, and dual-layer detectors. Dual-shot detection utilizes a single detector to image a patient with two shots of X-rays at low and high energies. While this has been shown to effectively differentiate between soft and hard tissues, (e.g., chest radiography) this results in a higher dose level to the patient and motion artifacts from slight movement between images. Photon counting detectors offer an alternative to multiple shots, providing high spatial resolution, low dose, and multiple energy binning with photon weighting. However, these detectors also require more complex circuit design for fast readout, have limited material options with great enough yield and detective quantum efficiency at low to mid energy ranges, and are limited in detective area. Dual-layer detectors that stack two detector layers to each process low and high energy X-rays remove motion artifacts by utilizing a single shot of polyenergetic X-rays. These most commonly employ two indirect detectors separated by a Cu filtering layer, which photon-starves the second higher energy detector. Unfortunately, this also requires a higher X-ray intensity, resulting in a higher dose level to the patient.

In PET imaging, patient motion, such as respiratory and cardiac motion, are a major source of blurring and motion artifacts. Researchers at the University of California, Davis have developed a technology designed to enhance PET imaging resolution without the need for external devices by effectively mitigating these artifacts

A novel method using Diffusion Tensor Imaging (DTI) combined with Statistical Parametric Mapping (SPM) as an effective diagnostic tool for Traumatic Brain Injury.

A breakthrough imaging technique that provides high-resolution visualization of optically transparent materials at a low cost.

A novel method for predicting the effective lens position (ELP) in cataract surgery through pre-operative measurements of natural lens curvatures.

An innovative technology that uses dual energy CT to measure blood flow in organs, offering a non-invasive, accurate assessment of diseases like INOCA.

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.

In magnetic resonance imaging (MRI) scanners, the detection of nuclear magnetic resonance (NMR) signals is achieved using radiofrequency, or RF, coils. RF coils are often equivalently called “resonance coils” due to their circuitry being engineered for resonance at a single frequency being received, for low-noise voltage gain and performance. However, such coils are therefore limited to a small bandwidth around the center frequency, restricting MRI systems from imaging more than one type of nucleus at a time (typically just hydrogen-1, or H1), at one magnetic field strength.To overcome the inherent restriction without sacrificing performance, UC Berkeley researchers have developed an MRI coil that can perform low-noise voltage gain at arbitrary relevant frequencies. These frequencies can be programmably chosen and can include magnetic resonance signals from any of various nuclei (e.g., 1H, 13C, 23Na, 31P, etc.), at any magnetic field strength (e.g., 50 mT, 1.5T, 3T, etc.). The multi-frequency resonance can be performed in a single system. The invention has further advantages in terms of resilience due to its decoupled response relative to other coils and system elements.

      Image quality and sample structure information from an optical microscope is in large part determined by optical aberrations. Optical aberrations originating from the microscope optics themselves or the sample can degrade the imaging performance of the system. Given the difficulty to find and correct all sources of aberration, a collection of methods termed adaptive optics is used to measure and correct optical aberrations in other ways, to recover imaging performance. However, state-of-the-art adaptive optics systems typically comprise complex hardware and software integration, which has impeded their wide adoption in microscopy. UC Berkeley researchers recently demonstrated how self-supervised machine learning (ML)-based adaptive optics can accurately estimate optical aberrations from a single 3D fluorescence image stack, without requiring external datasets for training. While demonstrated for widefield fluorescence microscopy, many optical microscopy modalities present unique challenges.       In the present technology, UC Berkeley researchers have developed a novel self-supervised ML-based adaptive optics system for two-photon fluorescence microscopy, which should also be extensible to confocal and other modalities. The system can effectively image tissues and samples for cell biology applications. Importantly, the method can address common errors in optical conjugation/alignment in commercial microscopy systems that have yet to be systematically addressed. It can also integrate advanced computational techniques to recover sample structure.

      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.

UCB researchers have developed a novel class of bright, fluorescent rhodamine dyes with a novel structural modification resulting in a deep red shift relative to the parent carborhodamine dye, with the new dye absorbing and emitting near-infrared light in the same region as the commercially successful silicon rhodamine dyes. Biological imaging with near-infrared light is advantageous for numerous biological and surgical applications.  Furthermore, bis-trifluoromethyl carborhodamines offer improved properties desirable for biological imaging applications due to their unique physical and electronic properties. 

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.