Stanford researchers have identified an appropriate method and dosage for radiotherapy-based noninvasive lung volume reduction to treat severe emphysema.
Stanford inventors have created an audio-visual system with a radiotransparent screen provides a means for communication and visual distractions during procedures such as radiation therapy and radiation imaging.
Stanford researchers developed a device that emits electromagnetic radiation that oscillates between at least first and second distinct polarization states.
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.
Stanford researchers have developed the first topical regenerative treatment for the oral cavity following chemo/radiation. Approximately 60,000 patients in the U.S. are annually diagnosed with head and neck cancer.
Radiation therapy is a common option in diseases like breast cancer, but can also cause significant damage to the skin. Permanent scarring and fibrosis can result, with both aesthetic and functional consequences for cancer patients.
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 have developed and validated a quality assurance (QA) phantom that will facilitate the translation of a frameless volumetric modulated arc therapy radiosurgery technique.
Stanford researchers have developed a simple and non-toxic method for more streamlined and precise electron beam radiotherapy using 3D printed electron field shaping devices.
Stanford researchers have developed a novel phantom which can integrate quality assurance (QA) procedures for radiofrequency tracking system, surface mapping system, Winston-Lutz test, the imaging system isocenter test and laser verification.
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