Varian Medical Systems, Inc.

Varian Medical Systems International Ag

Siemens Healthineers International Ag

Friedrich-alexander-universitaet Erlangen-nuernberg

Varian Medical Systems Particle Therapy Gmbh & Co Kg

Arian Medical Systems, Inc.

Corey Zankowski

Janne Iimari Nord

Esa Kuusela

Joona Hartman

Eric Abel

Michelle Marie Svatos

Josh Star-Lack

Christel Smith

Jarkko Yriana Peltola

Maria Cordero Marcos

Deepak Khuntia

Charles Adelsheim

Wolfgang Kaissl

Camille Noel

Joakim Pyyry

Sasa Mutic

Jan Timmer

Ramin Baghaie

Daren Sawkey

Burton Lang

James Edward Clayton

Petr Jordan

Gary Fredric Virshup

Stanley Mansfield

Timo K Koponen

Anthony Magliari

Michael C Green

Patrik Kunz

Michael Matthew Folkerts

Lasse Heikki Toimela

Reynald Vanderstraeten

Jessica Perez

David H Humber

Marc Kachelriess

Jan H Timmer

Markus Oelhafen

Timo Berkus

Sami P Siljamäki

Marcus Brehm

Karla Knott

Parin Dalal

Oskar Radermecker

Simant Dube

Ioan Antoniu Popescu

Bill Hansen

Timo Koponen

Maria Isabel Cordero Marcos

Jan Hein Timmer

A control circuit accesses information regarding a plurality of pre-existing vetted radiation treatment plans for a variety of patients and uses that information to train at least one model (such as a dose volume histogram estimation model). The control circuit then uses that model to develop estimates for a radiation treatment plan for a particular patient. The control circuit can then use those estimates to develop a candidate radiation treatment plan.

Apparatus comprising a memory, a control circuit, a user interface, and a radiation treatment platform to facilitate an administration of a knowledge-based radiation treatment plan

Embodiments described herein provide for training an artificial intelligence model to become a preference-aware model. The artificial intelligence model preferences as the artificial intelligence model trains. Reinforcement learning is used to train experts in the artificial intelligence model such that each expert is trained to converge to a unique preference. The architecture of the artificial intelligence model is highly flexible. Upon executing a trained model, users can select automatically images according to various preferences based on medical professional preferences, geographic preferences, patient anatomy, and institutional guidelines.

Artificial intelligence enabled preference learning

In various embodiments, a radiation therapy method can include loading a planning image of a target in a human. In addition, the position of the target can be monitored. A computation can be made of an occurrence of substantial alignment between the position of the target and the target of the planning image. Furthermore, after the computing, a beam of radiation is triggered to deliver a dosage to the target in a short period of time (e.g., less than a second).

Triggered treatment systems and methods

An apparatus includes a first multileaf collimator comprising a plurality of pairs of beam-blocking leaves each comprising an end portion. The end portions of beam-blocking leaves of two adjacent pairs are configured to collectively form an aperture when the two adjacent pairs of beam-blocking leaves are closed. The aperture may be sized and shaped to allow a radiation beam to pass through for radiosurgery.

Multileaf collimator cone enabling stereotactic radiosurgery

A system for estimating a dose from a radiation therapy plan includes a memory that stores machine-readable instructions and a processor communicatively coupled to the memory, the processor operable to execute the instructions to subdivide a representation of a volume of interest into voxels. The processor also determines distances between a planned radiation field origin and each respective voxel. The processor further computes geometry-based expected (GED) metrics based on the distances, a plan parameter, and a field strength parameter. The processor sums the metrics to yield an estimated dose received by the volume of interest from the planned radiation field.

Knowledge-based spatial dose metrics and methods to generate beam orientations in radiotherapy

Example methods and systems for quality-aware continuous learning for radiotherapy treatment planning are provided. One example method may comprise: obtaining an artificial intelligence (AI) engine that is trained to perform a radiotherapy treatment planning task. The method may also comprise: based on input data associated with a patient, performing the radiotherapy treatment planning task using the AI engine to generate output data associated with the patient; and obtaining modified output data that includes one or more modifications made by a treatment planner to the output data. The method may further comprise: performing quality evaluation based on (a) first quality indicator data associated with the modified output data, and/or (b) second quality indicator data associated with the treatment planner. In response to a decision to accept, a modified AI engine may be generated by re-training the AI engine based on the modified output data.

Methods and systems for quality-aware continuous learning for radiotherapy treatment planning

A system for treating a patient during radiation therapy includes range compensators. Each of the range compensators shapes a distribution of a dose delivered to the patient by a beam emitted from a nozzle of a radiation treatment system. A positioning component holds the range compensator in place relative to the patient such that the range compensator lies on a path of the beam.

Range compensators for radiation therapy

Apparatus to facilitate the administration of a knowledge-based radiation treatment plan

A system for treating a patient during radiation therapy is disclosed. The system includes a shell, a plurality of material inserts disposed in the shell, where each material insert of the plurality of material inserts respectively shapes a distribution of a dose delivered to the patient by a respective beam of a plurality of beams emitted from a nozzle of a radiation treatment system, and a scaffold component disposed in the shell that holds the plurality material inserts in place relative to the patient such that each material insert lies on a path of at least one of the beams.

Material inserts for radiation therapy

A method for implementing a radiation therapy knowledge exchange starts with searching a database of cases studies and selecting a case study. The selected case study is downloaded. The downloaded case study is applied to a medical case, wherein the downloaded case is applied using deformable image registration to deform reference images of the downloaded case to medical images of the medical case. After application of the downloaded case study, the medical case is uploaded to the network, wherein uploading the medical case allows at least the submitting clinician to download, review, and edit at least a portion of the medical case to create a reviewed medical case. Finally, the reviewed medical case is downloaded and applied to the medical case to create a final medical case.

Radiation therapy knowledge exchange

A method for medical image segmentation. The method includes accessing and updating a knowledge-base in accordance with embodiments of the present invention. The techniques include: receiving a medical image and computing a sparse landmark signature based on the medical image content. Next, a knowledge-base is searched for representative matches to form a base set, wherein the base set comprises a plurality of reference image sets. A portion of the plurality of reference image sets of the base set is deformed to generate mappings from the base set to the medical image set. Finally a weighted average segmentation for each structure of interest of the medical image set is determined.

Knowledge-based automatic image segmentation

Example methods and systems for radiotherapy treatment planning are provided. One example method may comprise obtaining image data associated with a patient; and processing the image data to generate a treatment plan for the patient using an inferential chain that includes multiple AI engines that are trained separately to perform respective multiple treatment planning steps. A first treatment planning step may be performed using a first AI engine to generate first output data based on at least one of: (i) the image data, and (ii) first input data generated based on the image data. A second treatment planning step may be performed using a second AI engine to generate the treatment plan based on at least one of: (i) the first output data, and (ii) second input data generated based on the first output data.

Radiotherapy treatment planning using artificial intelligence (AI) engines

Growing community of inventors

San Jose, CA, United States of America

Corey Zankowski