Siemens Healthineers International Ag

Varian Medical Systems International Ag

Varian Medical Systems, Inc.

Tuomas Tallinen

Jarkko Yriana Peltola

Janne Iimari Nord

Perttu Niemelä

Mikko Vainio

Daniel Valenzuela

Esa Kuusela

Joona Hartman

Christopher Boylan

Marko Rusanen

Martin Sabel

Stephen Thompson

Heini Hyvönen

Laura Korhonen

Systems and methods for radiation treatment planning can include a computing system determining an estimate of a radiation dose distribution within an anatomical region of a patient, and determining a cost matrix representing an objective function, using the estimate of the radiation dose distribution. The computing system can project the cost matrix on each of a plurality of fluence planes. Each of the plurality of fluence planes can be associated with a corresponding gantry-couch orientation of a plurality of gantry-couch orientations of a medical linear accelerator. The computing system can determine, using projections of the cost matrix on each of the plurality of fluence planes, a sequence of gantry-couch orientations among the plurality of gantry-couch orientations representing a treatment path.

Trajectory optimization using dose estimation and conflict detection

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

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

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 search space used for multicriteria optimization in radiation treatment planning can be expanded using extrapolation. For instance, given an initial set of base plans, one or more virtual plans can be generated by assigning a weight to each base plan such that at least one of the weights is less than zero and/or such that the sum of the weights is not normalized to 1. The dose distribution for the virtual plan is computed as the weighted sum of the dose distributions of the base plans. Virtual plans criteria can be used together with the initial set of base plans to define an expanded search space within which interpolation can be performed to identify an optimal treatment plan.

Expansion of search space for multicriteria optimization in radiation treatment planning

Computer-implemented methods and systems can be used to facilitate generation of VMAT treatment plans for providing radiation therapy to patients. Starting from a seed plan, a library of alternative plans, each with an associated outcome for a set of treatment objectives, can be generated via a set of parallel optimization operations performed on the seed plan. A graphical user interface is provided, via which the user can navigate within a treatment space defined by the alternative plans to identify a satisfactory outcome. Based on the satisfactory outcome and a candidate plan selected from the alternative plans, a deliverable VMAT treatment plan can be generated using another optimization operation.

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Espoo, Finland

Tuomas Tallinen