Explore Stanford technologies available for licensing:

Stanford researchers have developed a robot-assisted mouse positioning system (RAMPS) that enables conformal, pluridirectional FLASH radiotherapy studies in small animals.

Stanford researchers have developed a metal water jet nozzle that enables laser-generated MeV ion beams. When paired with a laser system, the technology could enable more compact, cost-effective medical isotope production and other ion-beam applications.

Stanford researchers have developed an advanced method for accurately tracking and accumulating radiation dose in magnetic resonance-guided radiotherapy.

Stanford researchers in Prof. Corinne Beinat's lab have developed a small molecule radiotheranostic for targeted radionuclide therapy of cancers overexpressing system xc-, such as high-grade glioma and non-small cell lung cancer (NSCLC).

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.

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.

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.