Continuous positive airway pressure (CPAP) therapy is currently the first line non-surgical treatment when a patient is diagnosed with obstructive sleep apnea (OSA).
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 a system that assesses altered mental states in both human and animal subjects using neural biomarkers, allowing for repeatable cross-species studies of potential treatments for psychiatric and neurological disorders.
Stanford researchers have developed an advanced method for accurately tracking and accumulating radiation dose in magnetic resonance-guided radiotherapy.
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.
Stanford researchers have developed a machine learning-based application that standardizes patient radiology reporting in a local and secure manner and outperforms all other general large language models (LLMs).
Stanford scientists have developed PVSeg, a tool that automatically segments vascular and perivascular compartments in brain MRI data. This innovative tool can identify non-demented individuals at increased risk of developing dementia and accelerated brain atrophy.
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.
Stanford researchers at the Ferrara Lab have designed an ultra-fast standing device for breast ultrasound which is more comfortable than current designs and has higher resolution.
Stanford researchers have invented a unified AI architecture that integrates foundational models (FMs) with AI techniques for efficient analysis of fMRI data in psychiatric disorders.
Stanford scientists developed a novel strategy that uses resting-state functional connectivity magnetic resonance imaging (rs-fMRI) to determine whether a person will respond to treatment for depression.
Stanford inventors have created a novel, interactive, highly scalable computational approach for representing dynamic brain activity as a network for use in clinical settings.
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.