Raysearch Laboratories Ab

Raysearch Laboratories Ab (publ)

Martin Janson

Erik Traneus

Albin Fredriksson

Erik Engwall

Lars Glimelius

Rasmus Bokrantz

Kjell Eriksson

Oscar Stål

Agnes Angerud

Tore Ersmark

It is provided a method for generating a plurality of potential treatment plans, each treatment plan specifying a distribution of radiation to a target volume, the method being performed by a treatment planning system and comprising the steps of: generating a plurality of potential treatment plans (), for performing multi-criteria optimization (MCO), based on a set of optimization functions. At least one optimization function depends on the distribution of the product of radiation dose and linear energy transfer, LET, in a specified region of the patient, yielding a plurality of treatment plans.

Generating a plurality of potential treatment plans for multi-criteria optimization

In cases where material properties of a structure within or outside a patient receiving radiotherapy treatment, robust planning can be used, based on different scenarios with different material property values using a material override function present in the treatment planning system. A computer-based method of generating a radiotherapy treatment plan for a patient, based on an image of the patient and a desired dose. In cases where material properties of a structure within or outside a patient receiving radiotherapy treatment, robust planning can be used, based on different scenarios with different material property values using a material override function present in the treatment planning system. Robust planning is performed based on the at least two scenarios to provide robust evaluation data for each of the at least two scenarios and/or a robust optimized treatment plan with respect to all values in the set of material override values.

Robust treatment planning or plan evaluation

A method of optimizing a radiotherapy treatment plan for delivering charged particles to a patient by pencil beam scanning, involves optimizing the treatment plan using an optimization problem that is designed to allow spots to differ in at least one of shape and orientation, and optionally also in size. This enables the optimization spots so as to cover the target in the best possible way and with a sharp penumbra along the outer edges of the target. The invention also relates to a computer program product and a computer system for use in such planning and a treatment delivery system for delivering such a plan.

Method of generating a radiotherapy treatment plan, computer program and computer system for generating a radiotherapy treatment plan, and radiotherapy delivery system

It is provided a method for determining a distribution of spots for use with ion beam therapy for providing the spots in a target volume, wherein each spot represents a collection of ions of a specific energy level and of a specific size at a specific lateral location. The method is performed in a treatment planning system and comprises the steps of: dividing the target volume in a plurality of target sections based on a user configuration comprising at least one spot size strategy defining a maximum spot size at the location of a Bragg peak; assigning a spot size strategy to each one of the target sections based on the location of the respective target section; and determining, within each target section, spots in accordance with its spot size strategy.

Determining a distribution of spots of varying sizes for ion beam therapy based on user configuration

A method of optimizing a radiation treatment plan of ion treatment, in which the optimization procedure is interrupted, some but not all low-weight spots are discarded and the optimization procedure is resumed with a reduced set of spots. The weight of one or more remaining spots may be increased before resuming the optimization procedure, for example by adding the spot weight of one or more of the discarded spots to one or more of the remaining spots.

System and method for radiotherapy treatment planning

A system for determining a treatment plan in active ion beam treatment, to minimize unwanted dose, while maintaining or improving target dose coverage, whereby a beam is split into at least two sub-beams and where each sub-beam has a range shifter of different settings.

System and method for determining a treatment plan for active ion beam treatment

It is provided a method for generating a plurality of treatment plans for radiation therapy, each treatment plan specifying weights for a plurality of geometrically defined fluence elements. Each weight defines an amount of radiation fluence, to thereby provide radiation dose to a target volume. The method is performed in a treatment planning system and comprises the steps of: generating a first set of treatment plans; determining a subset of the fluence elements, based on the first set of treatment plans; and generating a second set of at least two treatment plans, wherein the treatment plans only contain weights for the subset of fluence elements.

Generating a plurality of treatment plans for radiation therapy

It is provided a method for determining ripple filter settings for an ion therapy beam being capable of providing ions of different energy levels to a target volume. The method is performed in a treatment planning system and comprises the steps of: determining at least one beam direction to use to cover a target volume; and assigning a ripple filter setting to each one of a plurality of sub-beams of each one of the at least one beam direction such that each sub-beam is assigned a different ripple filter setting, wherein each ripple filter setting results in a different effect on a Bragg-peak width in a direction along the ion therapy beam, and each energy level is assigned to one of the plurality of sub-beams. The step of assigning a ripple filter setting comprises optimising based on different filter settings for different sub-beams for each beam direction.

Assigning ripple filter settings to a plurality of sub-beams

It is provided a method for obtaining an energy spectrum of a focused ion beam when a Bragg peak chamber is used to measure an integrated depth dose, IDD. The method comprises the steps of: simulating doses of a set of nominally mono energetic focused ion beams; determining a lateral extension of a Bragg peak chamber to evaluate; calculating a set of theoretic component IDD curves, CIDDs, by laterally integrating the dose of the simulated set of the nominally mono energetic focused ion beams, over the lateral extension of the Bragg peak chamber; storing calculated CIDDs; obtaining a measured IDD of a focused ion beam with a nominal energy using the Bragg peak chamber; and performing a fit of a linear combination of CIDDs to the measured IDD, to determine an energy spectrum for the focused ion beam with the nominal beam energy.

Obtaining an energy spectrum of a focused ion beam

According to a first aspect, it is presented a method for determining a treatment plan comprising a distribution of spots for use with ion beam therapy for providing the spots in a target volume. The method comprises the steps of: selecting energy layers to be used in the treatment plan; determining a number of spot sizes to use; generating, for each energy layer, one copy of the energy layer for each spot size to use and populating each copy with spots of the spot size for that copy; optimizing spots of all copies of all energy layers, by repeatedly varying a weight of at least a subset of the spots and calculating an effect on a performance measurement, wherein the performance measurement is calculated by combining a plurality of evaluation criteria, comprising a first criterion related to total treatment time and a second criterion related to a desired dose distribution.

Determining a distribution of spots of varying sizes for ion beam therapy using optimization

An optimization method for ion based radiotherapy includes inverse planning based on optimization variables related to the particle energy, a range modulator or ridge filter, a block and/or a range compensator. This enables automatic optimization of complex cases.

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Enskededalen, Sweden

Martin Janson