Stanford researchers have developed a novel PET agent for diseases stemming from T cell origin. The probes help visualize the disease status as well as the progress of therapy.
Researchers at Stanford have developed a general software framework that reconstructs high-resolution spatial fields from sparse, irregular, or noisy measurements.
Researchers in Professor Justin Sonnenburg's laboratory have developed genetic tools for manipulating Bacteroides, a prominent genus of gut bacteria, for imaging, diagnostics, and therapeutic drug delivery.
Stanford researchers developed and patented a multiplexed immunohistochemistry method called multiplexed ion beam imaging (MIBI), which uses antibodies tagged with non-biological elemental isotopes (e.g. rare earth elements) and secondary ion mass spectroscopy.
Stanford researchers have developed an exceptionally fast, sensitive, and compact X-ray imaging system for distinguishing liquids and other materials in aviation security applications.
Stanford researchers have developed a machine learning-based application that standardizes patient radiology reporting in a local and secure manner and outperforms all other general large language models (LLMs).
Stanford researchers at the Steven Chu Lab have developed and patented a method and apparatus to optimize speckle suppression in ultrasound imaging, usable for diagnostic purposes. This method uses Fourier-transform limited pulses for spectral compounding.
Stanford inventors have created a novel, interactive, highly scalable computational approach for representing dynamic brain activity as a network for use in clinical settings.
Stanford researchers have designed and prototyped an inexpensive, compact and easy-to-use smartphone lens mount for the capture of high quality photographs and videos of the eye's front and back structures.
Researchers in the Molecular Imaging Instrumentation Laboratory at Stanford University have developed a PET (positron emission tomography) detector and front end readout assembly that can operate in a high field MRI (magnetic resonance imaging) system.
Stanford researchers have developed CheXpert which can reduce noise and identify several pathologies on x-rays with very high accuracy via machine learning. CheXpert can read photos of x-rays from a mobile phone and is robust to noise.
Stanford researchers at the Liao and Xing Labs have developed and tested a machine learning algorithm for augmented detection of bladder cancer. Machine learning has the potential to enhance medical decision making in cancer detection and image analysis.
Researchers at Stanford have developed reactive oxygen species (ROS) sensing nanoparticles (NP) that can amplify Raman fingerprint signals and detect ROS changes.