Stanford researchers have developed a technology for the automated separation of arteries and veins in single-phase brain CT angiography (CTA) using graph neural networks, enabling precise collateral scoring and improved stroke prognosis.
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
Stanford researchers have developed a next-generation computational algorithm for diagnostic of pulmonary hypertension (PH) that provides an estimate of the tricuspid regurgitation (TR) velocity (Vmax) with increased accuracy and confidence.
Stanford scientists have invented an implicit an Neural Representation learning methodology with Prior embedding (NeRP) to reconstruct a computational medical image from sparsely sampled measurements using only a prior image of the subject.
Ultrasound technology is a safe, high-resolution, and cost-efficient tool for imaging. Other modalities, such as MRI or CT, may require the use of anesthesia. This makes it difficult to image pediatric patients and patients sensitive to anesthesia.
Near-infrared (NIR) imaging is a valuable research tool that produces quality images with high spatial and temporal resolution through millimeter tissue depths.
Stanford researchers at the Airan Lab have developed a new deep learning approach to dramatically reduce the amount of ultrasound data required to produce high quality power Doppler images for functional ultrasound (fUS).
The CheXbert labeler accurately detects the presence or absence of 14 common medical conditions in radiology reports, converting unstructured radiology text into a structured format.
Stanford researchers have developed CheXpert which can reduce noise and identify several pathologies on x-rays with very high accuracy via machine learning. CheXpert can read photos of x-rays from a mobile phone and is robust to noise.
Stanford researchers at the Liao and Xing Labs have developed and tested a machine learning algorithm for augmented detection of bladder cancer. Machine learning has the potential to enhance medical decision making in cancer detection and image analysis.
Stanford researchers at the Xing Lab have developed a novel technique to enable retrospective tuning of soft tissue contrast in MRI (i.e. adjusting the contrast after the image acquisition) using a deep learning-based strategy.
Stanford researchers at the Xing Lab have developed a dosimetric features driven- machine learning model for dose volume histograms (DVHs) and dose prediction for volumetric modulated arc therapy (VMAT) planning.