Active manipulation of light beams is required for a range of emerging optical technologies, including sensing, optical computing, virtual/augmented reality, dynamic holography, and computational imaging.
Researchers in Prof. Karl Deisseroth's laboratory have patented a revolutionary technique that can be utilized to map neural circuits in the whole brain.
Researchers at Stanford University have established a deep learning segmentation algorithm for non-contrast CT images to aid clinicians in decision making and improve the speed of symptom to treatment in acute ischemic stroke
Stanford scientists developed a novel strategy that uses resting-state functional connectivity magnetic resonance imaging (rs-fMRI) to determine whether a person will respond to treatment for depression.
Stanford researchers have developed a compact, scalable electronic readout that can multiplex 24 or more fast outputs of each 6x4 SiPM array to only 1 timing channel per detector layer unit.
Stanford inventors have created a novel, interactive, highly scalable computational approach for representing dynamic brain activity as a network for use in clinical settings.
Researchers at Stanford have developed a methodology for deep learning-based image reconstruction by incorporating the physics or geometry priors of the imaging system with deep neural networks.
Stanford researchers have developed a new controllable methodology for molecularly targeted ultrasound contrast agent production with pre-formed ligand-phospholipid bioconjugates.
Stanford researchers from the Khuri-Yakub group have designed an improved, high spatial resolution ultrasonic neuromodulation device that implements chip waveform instead of continuous wave PIRF.
Stanford researchers have developed a next-generation computational algorithm for diagnostic of pulmonary hypertension (PH) that provides an estimate of the tricuspid regurgitation (TR) velocity (Vmax) with increased accuracy and confidence.
Stanford scientists have invented an implicit an Neural Representation learning methodology with Prior embedding (NeRP) to reconstruct a computational medical image from sparsely sampled measurements using only a prior image of the subject.
Stanford researchers developed a programmable tuning circuit for dynamic, all-electronic tuning of the resonance frequency, sensitivity, and bandwidth of ultrasound transducers.
Using advances in flexible electronics, researchers at Stanford have developed a stretchable strain sensor for monitoring solid tumor size progression on or near the skin in real time.
Stanford inventors have developed a near infrared (NIR) tumor imaging platform that couples a novel rare earth cancer targeting agent and a handheld NIR-IIb fluorescence imager to enable tumor resection down to the few-cell level.