Homodyne image reconstruction is combined with an iterative decomposition of water and fat from MR signals obtained from a partial k-space signal acquisition in order to maximize the resolution of calculated water and fat images.
This invention provides a novel strategy for depolymerizing polyesters and polycarbonates with alcohols through the use of nucleophilic N-heterocyclic carbenes as catalysts.
Disclosed is x-ray cone beam scan data reconstruction of an imaged object with a reconstruction algorithm using shift invariant filtering and backprojection with the maximum tomographic capability of a circular scan larger than p plus cone angle, when CB data is not truncated
Researchers in Prof. Karl Deisseroth's laboratory have developed specific, inducible animal models for depression that use targeted optogenetic strategies to precisely dissect the neuronal circuits underlying the condition.
Researchers in Prof. Karl Deisseroth's laboratory have developed a system to enhance optogenetic pumps using one tool to address current limitations in both inhibition and excitation.
Researchers in Prof. Robert Malenka's laboratory have developed a light-activated animal system that could be used to identify compounds that treat certain psychiatric disorders.
Stanford researchers have developed and tested a new method of stably and strongly doping CNTs and graphene using MoOx as a nontoxic, inexpensive, vacuum or solution deposited alternative to strong liquid acids.
Stanford Researchers from the Department of Pediatrics have created a family-based, group behavioral weight control program for overweight and obese children (ages 8-12) and adolescents (ages 13-15) and their parent/guardian support.
Stanford researchers have discovered a way of regulating pressure-driven flow in fluidic passages by utilizing phase change materials to seal fluidic passages.
Stanford researchers have developed an electrically addressable liquid dispenser. This patented technology stores and dispenses scent in hand-held devices.
Stanford researchers have discovered an algorithm that significantly increases the performance of poorly performing brain machine interfaces (BMIs). This novel algorithm has two major innovations.
A team of Stanford engineers have developed a low-cost, easy to fabricate membrane electrode assembly (MEA) that is nano-patterned to increase electrode reaction surface area in solid oxide fuel cells (SOFCs).