Varian Medical Systems International Ag

Varian Medical Systems, Inc.

Siemens Healthineers International Ag

Varian Medical Systems Particle Therapy Gmbh & Co Kg

Sylvie Spiessens

Janne Iimari Nord

Reto W Filiberti

Michael Matthew Folkerts

Joakim Pyyry

Adam Scott Harrington

Dominique Gasser

Roland Meier

Joseph Schumm

Kari Jyrkkälä

Sean Herries

Eric Abel

Isabel Huth

Pierre Lansonneur

Miriam Krieger

Anssi Kesti-Helia

Anri Maarita Friman

Tina Pfeiler

Francesca Attanasi

Santtu T T Ollila

Anne Razavi

Tor-Erik Malen

Helen Phillips

Santtu TT Ollila

Tori-Erik Malen

A control circuit optimizes an energy treatment plan (such as, but not limited to, a Flash energy treatment plan) to therapeutically treat a given patient's treatment volume with energy to provide an optimized energy treatment plan. The control circuit determines at least one delivered dose rate as regards applying the energy pursuant to the optimized energy treatment plan and then determines a quality assurance dose rate metric that corresponds to that at least one delivered dose rate. The control circuit then informs a user regarding information that corresponds to the quality assurance dose rate metric.

Energy treatment plan quality assurance dose rate metric apparatus and method

Dose analysis radiotherapy systems and methods for determine delivered radiotherapy dose, dose rate, irradiation time and position information and planned radiotherapy dose, dose rate, irradiation time and position information. The dose analysis systems and methods further compare the as delivered dose, dose rate, irradiation time and position information to the planned dose, dose rate, irradiation time and position information to generate a graphical representation of one or more of the delivered dose, dose rate, irradiation time and position information versus planned dose, dose rate, irradiation time and position information.

Radiotherapy dose analysis methods and systems

Deep learning approaches automatically segment at least some breast tissue images while non-deep learning approaches automatically segment organs-at-risk. Both three-dimensional CT imaging information and two-dimensional orthogonal topogram imaging information can be used to determine virtual-skin volume. The foregoing imaging information can also serve to automatically determine a body outline for at least a portion of the patient. That body outline, along with the virtual-skin volume and registration information can serve as inputs to automatically calculate radiation treatment platform trajectories, collision detection information, and virtual dry run information of treatment delivery per the optimized radiation treatment plan.

Automatically-planned radiation-based treatment

Collision free regions are predetermined for one or more class solutions. Each class solution has defined limits for allowed field geometry variations. Collision free regions in planning can be defined as a set of allowed isocenter positions relative to a fixation device. The collision free regions may be visualized by a user to plan for field geometry and isocenter position tradeoffs. Collision free regions in delivery can be defined as a set of allowed couch support coordinates. The treatment fields in a radiation treatment plan can be checked against the collision free regions in delivery to determine whether they will pose any collision risks.

Radiation treatment planning and delivery using collision free regions

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Hulste-Harelbeke, Belgium

Sylvie Spiessens