Elekta Ab (publ)

Elekta Instrument Ab

Elekta, Inc.

Elekta Ab

Jens Sjolund

Xiao Han

Niklas Gunnarsson

Dominik Fay

John Henry Dahlberg

Carl Axel Håkan Nordström

The present disclosure relates to the field of radiation therapy and methods, software and systems for treatment planning.

Treatment planning

Dose-volume criteria may be equivalently expressed in terms of quantiles. This re-formulation of a dose-volume criteria allows incorporation of dose-volume criteria into a full optimization problem. Radiotherapy treatment techniques are described that may apply optimization-based formulation of quantiles to express dose-volume criterion as an inequality involving an optimization problem, which may improve DVH modeling, improve computational speed and accuracy of radiation treatment planning, and improve the delivery accuracy and efficacy of radiation doses to a patient undergoing radiotherapy treatment.

Dose volume histogram optimization based on quantile regression

Techniques for generating a radiotherapy treatment plan parameter are provided. The techniques include receiving radiotherapy treatment plan information; processing the radiotherapy treatment plan information to estimate one or more radiotherapy treatment plan parameters based on a process that depends on the output of a subprocess that estimates a derivative of a dose calculation; and generating a radiotherapy treatment plan using the estimated one or more radiotherapy treatment plan parameters.

Radiotherapy treatment plans using differentiable dose functions

The present invention relates to the field of radiation therapy and methods, software and systems for adaptive radiotherapy. There is provided a method for adaptive radiotherapy treatment of a patient comprising receiving a sequence of measurement data of the treatment, the measurements being captured at different time points, mapping measurement data to a representation of a treatment geometry at the time points, using the representation in a dynamical model describing how variables in the representation evolve over time, based on the dynamical model, estimating positions over time of the treatment geometry or selected parts of the treatment geometry, determining a treatment action for the patient at a defined time point using the estimation from the dynamical model and executing the treatment action at the defined time point.

Methods for adaptive radiotherapy

Techniques for generating a radiotherapy treatment plan are provided. The techniques include receiving a radiotherapy optimization problem, the radiotherapy problem comprising a plurality of parameters; processing the radiotherapy optimization problem to instantiate a first set of candidate parameters; converting the first set of candidate parameters into an adapted representation; defining an adapted radiotherapy optimization problem as a function of the adapted representation such that a given solution to the adapted optimization problem estimates a solution to the radiotherapy optimization problem; processing the adapted radiotherapy optimization problem to estimate a function of the solution to the adapted radiotherapy optimization problem; and processing the estimated function of the solution to the adapted optimization problem to generate a deliverable radiotherapy treatment plan.

Parameter search in radiotherapy treatment plan optimization

Techniques for producing segmentation with privacy are provided. The techniques include receiving a medical image; processing the medical image with a student machine learning model to estimate radiotherapy plan parameters, the student machine learning model being trained to establish a relationship between a plurality of public training medical images and corresponding radiotherapy plan parameters, the radiotherapy plan parameters of the plurality of public training medical images being generated by aggregating a plurality of radiotherapy plan parameter estimates produced by: processing the plurality of public training medical images with a plurality of teacher machine learning models to generate sets of radiotherapy plan parameter estimates; and reducing respective dimensions of the sets of radiotherapy plan parameter estimates or medical images, the radiotherapy plan parameters of the plurality of public training medical images being perturbed in accordance with privacy criteria; and generating a radiotherapy treatment plan based on the estimated radiotherapy plan parameters.

Radiotherapy plan parameters with privacy guarantees

Embodiments of the present disclosure are directed to treatment planning systems for a radiotherapy apparatus. In one implementation, a treatment planning system may include a computational processor configured to apply a set of instructions to an input data set. The input data set may include a three-dimensional dose distribution for delivery by the radiotherapy apparatus to a volume to be irradiated, a three-dimensional volume image characterizing tissue types within the volume to be irradiated, and a set of apparatus parameters which characterize the radiotherapy apparatus. The set of instructions may include a computational process configured to output a treatment plan for delivery of the dose distribution by the radiotherapy apparatus, by optimising a function representing a time-dependent response of the tissue types to an applied radiation.

Radiotherapy planning systems

The present disclosure relates to systems, methods, and computer-readable storage media for radiotherapy. Embodiments of the present disclosure may receive a plurality of training data and determine one or more predictive models based on the training data. The one or more predictive models may be determined based on at least one of a conditional probability density associated with a selected output characteristic given one or more selected input variables or a joint probability density. Embodiments of the present disclosure may also receive patient specific testing data. In addition, embodiments of the present disclosure may predict a probability density associated with a characteristic output based on the one or more predictive models and the patient specific testing data. Moreover, embodiments of the present disclosure may generate a new treatment plan based on the prediction and may use the new treatment plan to validate a previous treatment plan.

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Stockholm, Sweden

Jens Sjolund