Researchers at Stanford have developed a new therapeutic to promote survival of retinal ganglion cells (RGCs) and optic nerve regeneration after traumatic injury or optic neuropathies.
Stanford researchers have developed a new machine learning method for extracting gait parameters, such as cadence, step length, peak knee flexion, and Gait Deviation Index (GDI), from a single video.
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 at the Dahl Lab have developed a method to reduce artifacts in ultrasound image reconstruction using a trained convolutional neural network (CNN).
Stanford researchers have developed software that offers a solution to presenting tasks in a clinical magnetic resonance imaging facility to evoke specific responses within the human brain.
Dr. Shreyas Vasanawala and collaborators have developed a nonrigid motion correction technique that will allow for motion-free Magnetic Resonance (MR) images to be obtained even during lengthy scans. Motion is a major source of image artifacts for MR studies.
Stanford researchers have developed a versatile computational approach for easily visualizing and analyzing multidimensional molecular data, such as flow cytometry data.