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 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.
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.
Researchers at Stanford University have developed a quick, robust, machine learning based method for linear accelerator (LINAC) commissioning and beam data modeling.
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.
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
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.
In the presence of intra-fraction organ motion, target localization uncertainty can hamper the advantage of using highly conformal dose techniques such as intensity modulated radiation therapy (IMRT).
Real-time internal target position estimation is of high interest in radiotherapy, particularly with the recent development of robotic, linear accelerator, DMLC and couch-based systems which can continuously align the radiation beam with the target.
Stanford researchers have discovered a novel scheme of treatment planning and delivery of radiation therapy, termed station parameter optimized radiation therapy, or SPORT.
Researcher in Prof. Lei Xing's laboratory have developed an improved method for Monitor Unit ("MU") calculations in Intensity Modulated Radiation Therapy (IMRT).
The purpose of this invention is to provide a simple and efficient method for Monitor Unit ("MU") calculation for intensity modulated radiation therapy (IMRT).