Varian Medical Systems International Ag

Siemens Healthineers International Ag

Emmi Ruokokoski

Jarkko Yriana Peltola

Esa Kuusela

Janne Iimari Nord

Santtu Ollila

Lauri Halko

Christopher Boylan

Ramin Baghaie

Ville Pietila

Yves Archambault

Ville Pietilä

Lauri Jaakonpoiki Halko

Generally speaking, pursuant to these various embodiments a memory stores prescribed treatment instructions for a particular patient radiation therapy. Those treatment instructions specify clinical metrics for goals pertaining to at least one treatment volume for the particular patient. Either directly or inferentially those clinical metrics are prioritized. A control circuit automatically converts those treatment instructions into resultant radiation treatment plan optimization objectives where the automatic conversion can compatibly comprise a non-convex optimization objective. The control circuit then automatically iteratively optimizes the radiation treatment plan for the particular patient radiation therapy as a function, at least in part, of the aforementioned resultant optimization objectives to thereby produce an optimized radiation treatment plan for the particular patient that a radiation treatment platform then utilizes to administer therapeutic radiation to the particular patient.

Automatic generation of radiation treatment plan optimization objectives

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

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

A cost function is constructed so as to guide an optimization process to achieve similar coverage for all targets simultaneously in a concurrent radiation treatment of multiple targets, so that a single scaling factor may be used in a plan normalization to achieve the desired coverage for all the targets. The cost function includes a component that favors a solution that attains similar target coverages for all targets, as well as a component that favors a solution that approaches the desired target coverage value for each individual target. The cost function includes a max term relating to deficiencies of actual target coverages with respect to a desired target coverage, or alternatively a soft-max term relating to deviations of actual target coverages with respect to an average target coverage, as well as to deficiencies of actual target coverages with respect to a desired target coverage.

Methods to optimize coverage for multiple targets simultaneously for radiation treatments

In radiation therapy, treatment objectives applicable to different identified structures in a patient's body (e.g., a target structure and an organ at risk (OAR)) may conflict due to overlap between the structures. Automated systems and methods can detect and resolve such conflicts. For example, a set of modified regions that do not overlap with each other can be defined, and a modified treatment objective for each modified region can be determined based on the original treatment objectives. The modified regions and modified treatment objectives can be used in treatment planning processes.

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Helsinki, Finland

Emmi Ruokokoski