Stanford researchers have developed a method which can simultaneously observe two positron emitting isotopes using two distinct molecular probes and a modified PET scanner. This system enables the simultaneous observation of two different molecular processes.
Engineers in Prof. Shanhui Fan's laboratory have developed an efficient, scalable, in-situ method to train, configure and tune complex photonic circuits for artificial intelligence and machine learning.
Stanford researchers at the Vuckovic Lab have created a computational nanophotonic design library for gradient-based optimization called the Stanford Photonic INverse design Software (Spins).
Stanford researchers have prototyped a system to enhance the sensitivity of triple coincidences for multi-isotope PET by adding an extra detector dedicated for the detection of the third prompt gamma in coincidence with the annihilation photons.
A Stanford researcher has developed two advanced approaches for the positron sensitive high-energy photon sensor technology for Positron Emission Tomography (PET).
Current techniques for reconstructing images in positron emission tomography (PET) cannot correctly use events in which at least one photon of a pair has scattered in tissue (also known as scatter coincidence events).
Stanford researchers have patented a novel concept for a position sensitive high-energy photon sensor device for high resolution radiation imaging that can enhance capabilities of Positron Emission Tomography (PET).
Stanford researchers successfully manufactured high quality optical components using commercially available 3D printing. The 3D printed optics were easy to fabricate and inexpensive.
Stanford researchers have patented a silicon germanium (SiGe) electroabsorption modulator that can operate well in excess of 10 Gbps and is entirely compatible with Silicon (Si) complementary metal-oxide semiconductor (CMOS) integrated circuit fabrication.
Researchers in Prof. Shanhui Fan's laboratory have invented a thermal extraction device that is designed to enhance power emission from thermal radiators up to 10x compared to conventional structures.
Stanford researchers have patented a fabrication process for monolithic integration of different epitaxial materials on the same substrate for improved coupling of optoelectronic devices.
Stanford inventors have developed a deep learning framework that is able to label individual points from 3D Point Clouds that are acquired by various sensors (RGBD sensors, LIDAR sensors, etc.). This framework obtains a point-level fine-grained labeling of 3D Scenes.
Stanford researchers patented a method to design, computationally optimize and fabricate efficient optical devices using semiconducting and dielectric nanostructures.