Varian Medical Systems International Ag

Siemens Healthineers International Ag

Varian Medical Systems, Inc.

Varian Medical Systems Particle Therapy Gmbh & Co Kg

Lauri Halko

Esa Kuusela

Jarkko Yriana Peltola

Janne Iimari Nord

Juha Kauppinen

Elena Czeizler

Shahab Basiri

Mikko Oskari Hakala

Marco Lessard

Emmi Ruokokoski

Edit Siket-Szasz

Eric Abel

Christel Smith

Hannu Laaksonen

Perttu Niemela

Michael Matthew Folkerts

Mikko Vainio

Jessica Perez

Christopher Boylan

Matti Ropo

Pierre Lansonneur

Viljo Petaja

María Cordero Marcos

Michiko Rossi

Adam Scott Harrington

Ville Pietila

Simon Busold

Risto Ahonen

Tao Sun

Ville Pietilä

An apparatus for developing an intensity-modulated radiation therapy treatment plan includes a memory that stores machine instructions and a processor that executes the machine instructions to receive a clinical goal associated with the treatment plan as a user input. The processor further executes the machine instructions to determine a plan objective based on the clinical goal, generate a cost function comprising a term based on the plan objective, and assign an initial value to a parameter associated with the term. The processor also executes the machine instructions to identify a microstate that results in a reduced value associated with the cost function, evaluate a fulfillment level associated with the clinical goal, and adjust the value of the parameter to improve the fulfillment level.

Clinical goal treatment planning and optimization

Methods and systems are provided which relate to the planning and delivery of radiation treatments by modalities which involve moving a radiation source along a trajectory relative to a subject while delivering radiation to the subject. An artificial intelligence (AI) agent trained using reinforcement learning (and/or some other suitable form of machine learning) is used to control the radiation delivery parameters in effort to achieve desired delivery of radiation therapy. In some embodiments, the AI agent selects suitable control steps (e.g. radiation delivery parameters for particular time steps), while accounting for patient motions, difference(s) in patient anatomical geometry and/or the like.

Methods and apparatus for controlling treatment delivery using reinforcement learning

A method used for planning radiation treatment accessing information that includes calculated doses and calculated dose rates for sub-volumes in a treatment target, and also accessing information that includes values of a measure of the sub-volumes as a function of the calculated doses and the calculated dose rates. A graphical user interface includes a rendering that is based on the calculated doses, the calculated doses rates, and the values of the measure.

Correlation of dose and dose rate information to volume for radiation treatment planning

A prescribing user can designate and prioritize clinical goals that can, in turn, serve as the basis for optimization objectives to guide the development of a radiation treatment plan. The prescribing user can then alter that prioritization of one or more clinical goals and view information regarding changes to fluence-based dose distributions that occur in response to those changes to relative prioritization amongst the clinical goals to thereby understand dosing tradeoffs that correspond to prioritization amongst the clinical goals.

Apparatus and method for forming prescribed radiation therapy treatment instructions

New techniques are described herein for providing a user-friendly interface for adjusting dose distribution values during optimization of a radiation application plan. In an embodiment, a graphical user interface is provided that provides an image of a target area for a radiation application, and a graphical overlay of a dose distribution disposed over the target area that visually represents the optimized dose distribution according to the input dose parameters. In one or more embodiments, the dose distribution may be automatically calculated from input parameters supplied by a user through the graphical user interface prior to optimization. A user (such as a clinician, radiation oncologist, or radiation therapy operator, etc.) is able to modify the visualization of the dose distribution volume during optimization via a user input device in conjunction with the graphical user interface and have the modification adjusted in real-time.

Interactive and intuitive method to shape 3D dose distribution during optimization of IMRT plans

Cost functions and cost function gradients for use in radiation treatment planning can be computed based on an approximation of an 'isodose' surface. Where a clinical goal is expressed by reference to a threshold isodose surface, a corresponding cost function component can be defined directly by reference to that isodose surface, and a corresponding contribution to the cost function gradient can be approximated by identifying voxels that are intersected by the threshold isodose surface and approximating the gradient of the dose distribution within each such voxel.

Using isodose surfaces for optimizing dose distribution in radiation treatment planning

A treatment planning apparatus include: a quality metric generator configured to electronically generate a first quality metric based on input received via a user interface; an optimizer configured to determine treatment parameters for a treatment plan that is executable by a processing unit of a treatment machine to operate the treatment machine, wherein the optimizer is configured to determine the treatment parameters for the treatment plan based on the first quality metric; and a controller configured to influence how the optimizer performs optimization, wherein the controller is configured to provide a first continuous function for the first quality metric.

Systems and methods for treatment planning using continuous quality metrics

In a method of interactive manipulation of the dose distribution of a radiation treatment plan, after an initial candidate treatment plan has been obtained, a set of clinical goals are transferred into a set of constraints. Each constraint may be expressed in terms of a threshold value for a respective quality index of the dose distribution. The dose distribution can then be modified interactively by modifying the threshold values for the set of constraints. Re-optimization may be performed based on the modified threshold values. A user may assign relative priorities among the set of constraints. When a certain constraint is modified, a re-optimized treatment plan may not violate those constraints that have priorities that are higher than that of the modified constraint, but may violate those constraints that have priorities that are lower than that of the modified constraint.

Interactive dose manipulation using prioritized constraints

Example methods and systems are provided to perform radiation dose estimation for a target object. In one example, radiation dose estimation may include obtaining projection image data acquired using an imaging system, generating partially reconstructed volume image data based on the projection image data, and generating a partition of the projection image data. The partially reconstructed volume image data may be associated with a first region of a total radiation field. The partition may be associated with a second region of the total radiation field that is located before the first region from a direction of a radiation source. Radiation dose data may be estimated for the target object based on the partially reconstructed volume image data associated with the first region and the partition associated with the second region.

Methods and systems for radiation dose estimation

One example method for generating a dose estimation model for radiotherapy treatment planning may include obtaining training data that includes multiple treatment plans associated with respective multiple past patients. The method may also include processing the training data to determine, from each of the multiple treatment plans, first data that includes one or more features associated with a particular past patient, second data associated with treatment planning trade-off selected for the particular past patient and third data associated with radiation dose for delivery to the particular past patient. The method may further include generating the dose estimation model by training, based on the first data, second data and third data from the multiple treatment plans, the dose estimation model to estimate a relationship that transforms the first data and second data to the third data.

Methods and systems for generating dose estimation models for radiotherapy treatment planning

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Helsinki, Finland

Lauri Halko