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 advanced method for accurately tracking and accumulating radiation dose in magnetic resonance-guided radiotherapy.
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 have identified an appropriate method and dosage for radiotherapy-based noninvasive lung volume reduction to treat severe emphysema.
As of 2020, radiation therapy has saved over 3.38 million cancer patients in the US. Radiation therapy treatment planning often involves a time-consuming and labor-intensive process where physicians must manually optimize the prescribed radiation dose.
Stanford researchers have developed a novel technique to control proton beams for radiation therapy to deliver a very high, full dose across a tumor in less than one second.
Stanford researchers have used deep learning to create a radiotherapy treatment plan verification algorithm. Patient specific dose verification is traditionally done by checking the dose in a patient-mimicking phantom or by using an independent dose calculation algorithm.
Researchers at Stanford University have developed a quick, robust, machine learning based method for linear accelerator (LINAC) commissioning and beam data modeling.
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.