Stanford researchers have invented a unified AI architecture that integrates foundational models (FMs) with AI techniques for efficient analysis of fMRI data in psychiatric disorders.
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 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.
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.
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.
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.
Stanford researchers at the Xing Lab have developed a novel technique to enable retrospective tuning of soft tissue contrast in MRI (i.e. adjusting the contrast after the image acquisition) using a deep learning-based strategy.
Dr. Manish Saggar at Stanford University has developed a new method to visualize and quantify transitions in brain activity, which may be used as a diagnostic tool for mental illness.
Researchers at Stanford have developed an in vivo drug release monitoring method using magnetic particle imaging (MPI). In vivo drug release monitoring is beneficial to doctors as it provides information to guide drug dosing and helps reduce therapeutic side effects.
Stanford researchers at the Xing Lab have developed a novel method using deep neural networks called "Q2MRI" to simultaneously acquire qualitative MR image and quantitative MRI parametric maps without changing the clinical imaging protocol or elongating MRI scan tim
Prof. Alison Marsden and her colleagues have developed a computational framework that uses personalized anatomical information to identify patients that have a high risk for saphenous vein graft (SVG) failure after coronary artery bypass graft (CABG).