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.
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).
Stanford researchers have developed a fast, multi-dimensional MRI procedure which records and correlates at least five dimensions of anatomic, physiologic, and functional information applicable for cardiac imaging.
Stanford researchers have developed an algorithm to achieve uniform excitation and image uniformity in the presence of a non-uniform transmit field while limiting local power deposition or "hot spots" using multiple transmit channels and the methid of "parallel transmit" or p
Nonstationary image artifacts frequently arise in MRI from off-resonance and motion. Current methods to correct these nonstationary effects are computationally expensive. Stanford researchers have developed a new deep learning framework to improve image quality in minutes.
Stanford researchers have developed an improved imaging protocol for better visualization of the thalamus. This faster acquisition leads to a better delineation of structures due to the multiple dimensions of information.
Multi-channel coil receivers for magnetic resonance imaging (MRI) accelerate the scan for fast imaging. Acceleration is typically achieved by subsampling the data acquisition and leveraging the localized spatial profiles of each coil element to reconstruct the images.