Siemens Healthineers International Ag

Varian Medical Systems International Ag

Christopher Boylan

Jarkko Yriana Peltola

Esa Kuusela

Benjamin M Haas

Thomas Coradi

Marco Lessard

Jonas Honegger

Tomasz Morgas

Tobias Gass

Stephen Thompson

Pauli Suhonen

Hannu Laaksonen

Marko Rusanen

Janne Iimari Nord

Juha Kauppinen

Maria Cordero Marcos

Shahab Basiri

Lauri Halko

Elena Czeizler

Mikko Oskari Hakala

Sami P Siljamaki

Tuomas Tallinen

Viljo Petaja

Emmi Ruokokoski

Jan Schreier

Tuomas Erik Torsti

Ville Pietila

Ari Harju

Heini Hyvönen

Ville Pietilä

Timo Ikonen

Petri Hiltunen

Petri Kokkonen

Antti Karjalainen

Marko T Rusanen

Tuomas E Torsti

Systems and methods for the automatic generation and optimization of radiation therapy treatment plans, and systems and methods for the automatic generation and optimization of an adapted plan in an adaptive radiation therapy workflow.

Systems and methods for automatic treatment planning and optimization

A radiation treatment plan three-dimensional dose prediction machine learning model is trained using a training corpus that includes a plurality of radiation treatment plans that are not specific to a particular patient and wherein the training corpus includes some, but not all, possible patient volumes of interest. Information regarding the patient (including information regarding at least one volume of interest for the patient that was not represented in the training corpus) is input to the radiation treatment plan three-dimensional dose prediction machine model. The latter generates predicted three-dimensional dose distributions that include a predicted three-dimensional dose distribution for the at least one volume of interest that was not represented in the training corpus.

Machine learning-based generation of 3D dose distributions for volumes not included in a training corpus

Patient information for a particular patient, radiation treatment information, and information regarding a particular radiation treatment platform that includes at least one multi-leaf collimator are each accessed. These teachings then provide for optimizing a radiation treatment plan to dose at least one treatment volume in the particular patient using the particular radiation treatment platform as a function of the patient information, the radiation treatment information, and the information regarding the particular radiation treatment platform, wherein the optimizing does not include optimizing movement of any leaves of the multi-leaf collimator.

Method and apparatus to optimize a radiation treatment plan

Systems and methods for implementing an adaptive therapy workflow that minimizes time needed to create a session patient model, select an appropriate plan for the treatment session, and treat the patient.

Streamlined, guided on-couch adaptive workflow

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 control circuit accesses historical information regarding previously optimized radiation treatment plans for different patients and processes that information to determine the relative importance of different clinical goals. The circuit then facilitates development of a particular plan for a particular patient as a function of the relative importance of the clinical goals. By one approach the control circuit can be configured as a radiation treatment plan recommendation resource that accesses a database of radiation treatment plan formulation content items including at least one of a radiation treatment plan template, an auto-planning algorithm, and an auto-segmentation algorithm. By one approach the control circuit can be configured to, when presenting automatically-generated radiation treatment plans to a user, also co-present an opportunity for the user to signal to a remote entity that none of the plans are acceptable and that the user will instead employ a user-generated plan for the particular patient.

Methods and apparatus pertaining to radiation treatment plans

A method of calculating radiation dose includes dosimetric projection of a collimator geometry. The method includes defining a three-dimensional (3D) geometry of a collimating device which defines an aperture configured to allow a radiation beam passing through, projecting the collimating device along the radiation beam into a two-dimensional (2D) geometry in a plane, calculating dosimetric opacity values of the collimating device at locations adjacent to the aperture based on the 3D geometry of the collimating device, and calculating transport of the radiation beam through the collimating device based on the 2D geometry projected in the plane and using the dosimetric opacity values of the collimating device at the locations adjacent to the aperture.

Dosimetric projection of collimator geometry

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Helsinki, Finland

Christopher Boylan