Varian Medical Systems International Ag

Siemens Healthineers International Ag

Varian Medical Systems, Inc.

Jarkko Yriana Peltola

Janne Iimari Nord

Esa Kuusela

Mikko Vainio

Juha Kauppinen

Santtu Ollila

Maria Cordero Marcos

Marko Rusanen

Christopher Boylan

Joona Hartman

Emmi Ruokokoski

Lauri Halko

Joakim Pyyry

Tuomas Tallinen

Viljo Petäjä

Ville Pietila

Corey Zankowski

Hannu Laaksonen

John Van Heteren

Martin Sabel

Stephen Thompson

Pauli Suhonen

Shahab Basiri

Heini Hyvönen

Ronan MacLaverty

Anri Maarita Friman

Santtu Tuomo Tapani Ollila

Janne Ilmari Nord

Elena Czeizler

Lasse Heikki Toimela

Steven W Prince

Mikko Oskari Hakala

Ramin Baghaie

Perttu Niemelä

Viljo Petaja

Jan Schreier

Yves Archambault

Steven Prince

Lasse Toimela

Risto Ahonen

Santtu Tt Ollila

Tao Sun

Mikko Laitinen

Ville Pietilä

Laura Korhonen

Marko T Rusanen

Maria Isabel Cordero Marcos

Santtu TT Ollila

Lauri Jaakonpoiki Halko

Cordero Marcos Maria

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

Embodiments described herein provide for radiotherapy treatment plan analysis and guidance using voice commands and human language processing. A processor monitors a computer model generating a radiation therapy treatment plan comprising a projected dose-volume distribution for a structure of a patient or an attribute of the radiation therapy treatment plan. The processor receives a query indicating the structure of the patient. The processor generates a query term based on the received query. The processor retrieves data associated with the query term by querying data generated based on monitoring the computer model. The processor transmits the retrieved data to computer device.

Plan analysis and guidance using voice commands and human language processing

In a radiation treatment plan that includes a plurality of treatment fields of multiple treatment modalities, such as IMRT modality and dynamic treatment path modality (e.g., VMAT and conformal arc therapy), an optimized spatial point sequence may be determined that optimizes the total treatment time, which includes both the beam-on time (i.e., during the delivery of radiation dose) and the beam-off time (i.e., during transitions between consecutive treatment fields). The result is a time-ordered field trajectory that intermixes and interleaves different treatment fields. In one embodiment, a dynamic treatment path may be cut into a plurality of sections, and one or more IMRT fields may be inserted between the plurality of sections.

Generating time-efficient treatment field trajectories for external-beam radiation treatments

A method of generating a treatment plan for treating a patient with radiotherapy, the method includes obtaining a plurality of sample plans, which are generated by use of a knowledge base comprising historical treatment plans and patient data. The method also includes performing a multi-criteria optimization based on the plurality of sample plans to construct a Pareto frontier, where the plurality of sample plans are evaluated with at least two objectives measuring qualities of the plurality of sample plans such that treatment plans on the constructed Pareto frontier are Pareto optimal with respect to the objectives. The method further includes identifying a treatment plan by use of the constructed Pareto frontier.

Knowledge based multi-criteria optimization for radiotherapy treatment planning

In the context of a multi-criteria optimization workspace, a control circuit provides a user opportunity to modify radiation treatment plan optimization objective values, wherein the optimization objectives include at least one of a radiation treatment plan complexity optimization objective and a radiation treatment delivery time optimization objective. These teachings then provide for the control circuit receiving input from the user comprising a change to at least one of these optimization objective values. By one approach the control circuit first accesses a prioritized list of clinical goals and automatically generates optimization objectives as a function of the prioritized list of clinical goals. The control circuit then generates a seed optimized radiation treatment plan as a function of the automatically generated optimization objectives and subsequently generates a collection of different radiation treatment plans by varying the automatically generated optimization objectives to thereby characterize a trade-off exploration space for the multi-criteria optimization workspace.

Method and apparatus to facilitate generating a deliverable therapeutic radiation treatment plan

A control circuit accesses a memory having stored therein a plurality of hierarchically-diversified radiation treatment planning templates. These templates include at least a first radiation treatment planning template that specifies radiation treatment planning information at a first hierarchical level. These templates also include at least a second radiation treatment planning template that specifies radiation treatment planning information at a second hierarchical level, wherein the second hierarchical level is more granular than the first hierarchical level. By one approach, the control circuit may access a plurality of differing ones of the second radiation treatment planning templates wherein each such template specifies radiation treatment planning information at the second hierarchical level. The control circuit is configured to optimize a radiation treatment plan for a given patient using a given radiation treatment facility as a function of at least the first and the second radiation treatment planning template.

Method and apparatus to optimize a radiation treatment plan using hierarchically-diversified planning templates

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

Disclosed herein are methods and systems to optimize a radiation therapy treatment plan using dose distribution values predicted via a trained artificial intelligence model. A server trains the AI model using a training dataset comprising data associated with a plurality of previously implemented radiation therapy treatments on a plurality of previous patients and dose distributions associated with one or more organs of each previous patient. The server then executes the trained AI model to predict dose distribution for a patient. The server then displays a heat map illustrating the predicted values, transmits the predicted values to a plan optimizer to generate an optimized treatment plan for the patient, and/or transmits an alert when a treatment plan generated by a plan optimizer deviates from rules and thresholds indicated within the patient's plan objectives.

Artificial intelligence modeling for radiation therapy dose distribution analysis

After accessing optimization information for a particular patient and for a particular radiation treatment platform, a control circuit generates an optimized radiation treatment plan by processing the optimization information using direct-aperture-optimization that includes fluence-based sub-optimization. By one approach, the control circuit includes the fluence-based sub-optimization in at least some, but not necessarily all, iterations of the direct-aperture-optimization. By one approach, the control circuit is configured to include only a few iterations of the fluence-based sub-optimization when including the fluence-based sub-optimization in at least some, but not necessarily all, iterations of the direct-aperture-optimization.

Method and apparatus to facilitate generating an optimized radiation treatment plan using direct-aperture optimization that includes fluence-based sub-optimization

A memory has a fluence map that corresponds to a particular patient stored therein. This memory also has at least one deep learning model stored therein trained to deduce a leaf sequence for a multi-leaf collimator from a fluence map. A control circuit operably coupled to that memory iteratively optimizes a radiation treatment plan to administer therapeutic radiation to that patient by, at least in part, generating a leaf sequence as a function of the at least one deep learning model and the fluence map that corresponds to the patient.

Method and apparatus to facilitate generating a leaf sequence for a multi-leaf collimator

A control circuit generates an optimized radiation treatment plan with respect to an adjustable collimation device and then automatically generates at least one quality assurance accuracy value corresponding to the optimized radiation treatment plan. By one approach, the aforementioned plan comprises a plurality of treatment fields. In such a case, automatically generating at least one quality assurance accuracy value can comprise, at least in part, automatically generating at least one quality assurance accuracy value for each of at least a substantial number (or all) of those treatment fields. By one approach, the aforementioned quality assurance accuracy value comprises a dimensionless metric. This dimensionless metric may represent, for example, dosimetric accuracy corresponding to the optimized radiation treatment plan.

Method and apparatus for radiation treatment planning

A multi-layer multi-leaf collimation system includes at least a two layers of collimation leaves. The first multi-leaf collimator layer is configured to primarily perform a first function to affect a radiation beam traveling from a radiation source to a target and a second multi-leaf collimator layer is configured to primarily perform a second function, different from the first function, to affect the radiation beam for the administration of a treatment plan.

Method and apparatus for using a multi-layer multi-leaf collimation system

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Tuusula, Finland

Jarkko Yriana Peltola