Stanford researchers have developed a novel PET agent for diseases stemming from T cell origin. The probes help visualize the disease status as well as the progress of therapy.
Researchers at Stanford have developed a general software framework that reconstructs high-resolution spatial fields from sparse, irregular, or noisy measurements.
Stanford researchers have developed an exceptionally fast, sensitive, and compact X-ray imaging system for distinguishing liquids and other materials in aviation security applications.
CT imaging and MR imaging are two essential and widely used techniques for diagnostics. Currently, the two imaging techniques are performed sequentially, which is time-consuming and prone to errors due to anatomical changes that occur between the scans.
Among the many medical imaging modalities, CT and MRI scans are utilized most often for imaging bone and soft tissue respectively. As such, physicians often require both images to fully diagnose patients and determine treatment plans.
Researchers at Stanford University have established a deep learning segmentation algorithm for non-contrast CT images to aid clinicians in decision making and improve the speed of symptom to treatment in acute ischemic stroke
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 at Stanford have developed reactive oxygen species (ROS) sensing nanoparticles (NP) that can amplify Raman fingerprint signals and detect ROS changes.
Stanford researchers have developed an exceptionally fast, sensitive, and compact X-ray imaging system for distinguishing liquids and other materials in aviation security applications.
Stanford researchers have developed a novel phantom which can integrate quality assurance (QA) procedures for radiofrequency tracking system, surface mapping system, Winston-Lutz test, the imaging system isocenter test and laser verification.
Stanford researchers have developed a novel and efficient method for generating real-time 3D volumetric computed tomography (CT) images with 2D single or few-view projections, instead of several hundreds of projections as required in existing CT imaging system.
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 method for a high-resolution photon imaging device with high fill factor (the ratio of the area of the active imaging elements vs. the dead area occupied by non-imaging elements).
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).