Stanford researchers at the Airan Lab have developed a new deep learning approach to dramatically reduce the amount of ultrasound data required to produce high quality power Doppler images for functional ultrasound (fUS).
Stanford researchers have developed machine learning algorithms to characterize and diagnose lung graft-versus-host disease (GVHD) subtypes from volumetric chest computed tomography (CT).
Researchers in the Stanford University Power Electronics Research Lab have designed an easy to implement, high-efficiency, high-frequency power amplifier with low voltage stress.
Stanford researchers are developing an improved prophylactic against pancreatitis caused by endoscopic retrograde cholangiopancreatography (ERCP), by targeting two key inflammatory pathways.
Tracking in vivo cell distribution, migration, and engraftment using conventional techniques including MRI, PET/CT and conventional optical imaging is often hindered by low resolution, radioactive risks, and limited tissue penetration depth.
Stanford researchers at the de la Zerda Lab have developed an innovative alignment methodology using Optical Coherence Tomography (OCT) in conjunction with histopathology to diagnose cancer or determine tumor margins.
Ear infections are a serious condition, especially in children, and represent a $4B market. Otitis media (OM) is when the middle ear becomes inflamed and affects 90% of children worldwide.
Volumetric contrast-enhanced ultrasound is a new approach to collect 3D imaging data of a contrast signal. So far, analysis of 3D contrast ultrasound has relied on averaging a set of voxels embedded in an ROI or a VOI.
The CheXbert labeler accurately detects the presence or absence of 14 common medical conditions in radiology reports, converting unstructured radiology text into a structured format.
Researchers in Prof. Karl Deisseroth's laboratory have combined optogenetics with functional magnetic resonance imaging (fMRI) to enable highly specific in vivo analysis of brain circuits.
Researchers in Prof. Karl Deisseroth's laboratory have developed a novel system for modulating brain activity with moderate intensity focused ultrasound. In this technique, ultrasound is used to increase the intrinsic firing rate of targeted neurons.
Stanford researchers have developed an algorithm using deep learning architectures to predict cardiac function (ejection fraction) and trace the endocardium of the left ventricle from ultrasound echocardiogram videos.
Researchers in the Fan group have developed a method for epitaxial growth of double heterojunction semiconductor diodes capable of suppressing parasitic non-radiative recombination effects.
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.