# US 10,441,812 B2 · System and method for determining a radiation treatment plan and a radiation therapy machine > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,441,812 B2 - Document ID: US10441812B2 - Status: Granted - Kind: B2 - Application number: 15/367,872 - Filed: 2016-12-02 - Published: 2017-06-08 - Granted: 2019-10-15 - Earliest claimed: 2015-12-03 - Assignees: RaySearch Laboratories AB - Applicants: RaySearch Laboratories AB - Inventors: Rasmus Bokrantz; Björn Hårdemark - Web page: https://patentgrep.com/patent/US10441812B2 ## CPC - A61N 5/1038: {taking into account previously administered plans applied to the same patient, i.e. adaptive radiotherapy} - A61N 5/103: {Treatment planning systems} - A61N 5/1036: {Leaf sequencing algorithms} - A61N 5/1045: {using a multi-leaf collimator, e.g. for intensity modulated radiation therapy or IMRT} - A61N 5/1047: {with movement of the radiation head during application of radiation, e.g. for intensity modulated arc therapy or IMAT} - A61N 5/1071: {for verifying the dose delivered by the treatment plan} - A61N: ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY (measurement of bioelectric currents A61B; surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body A61B18/00; anaesthetic apparatus in general A61M; incandescent lamps H01K; infrared radiators for heating H05B) ## Abstract An output radiation treatment plan for at least one target in a treatment volume is determined. Each target is associated with a prescribed radiation dose. An updated treatment plan causes the prescribed radiation dose to be delivered to the target when implemented by a radiation therapy machine. The updated treatment plan requires an updated delivery time to complete and is calculated by: [i] receiving a numerical value designating an upper bound on modulation efficiency of the updated treatment plan, [ii] receiving a current treatment plan, [iii] calculating a current delivery time for the current treatment plan, [iv] calculating an un-modulated delivery time for the current treatment plan, and [v] calculating the updated treatment plan by executing an optimization process while satisfying the upper bound on the modulation efficiency. Steps [ii] to [v] are traversed a predetermined number of times. Thereafter, the output radiation treatment plan is generated. ## Claims ### Claim 1 A system for generating an output radiation treatment plan for a treatment volume associated with at least one target, each of the at least one target being associated with a respective prescribed radiation dose, the system comprising: a data processor configured to: calculate an updated treatment plan which when implemented by a radiation therapy machine is configured to cause the respective prescribed radiation dose to be delivered to the at least one target, the updated treatment plan requiring an updated delivery time to complete, wherein the data processor is configured to: [i] receive a numerical value designating an upper bound on modulation efficiency of the updated treatment plan, [ii] receive a current treatment plan, [iii] calculate a current delivery time for the current treatment plan, [iv] calculate an un-modulated delivery time for the current treatment plan, [v] calculate the updated treatment plan by executing an optimization process while satisfying the upper bound on the modulation efficiency, and [vi] generate the output radiation treatment plan based on the updated treatment plan. ### Claim 2 (depends on 1) The system according to claim 1, wherein the un-modulated delivery time is calculated based on an un-modulated delivery time per field of the current treatment plan, each of which is a time required to deliver an un-modulated field with an exposed fluence region equal to an exposed fluence region for the field of the current treatment plan, a fluence inside the exposed fluence region of the un-modulated field having a constant value being scaled such that an average fluence is equal to an average fluence inside the exposed fluence region for the field of the current treatment plan, the exposed fluence region being a sub-region of a beam plane to be exposed by an opening between a set of collimator leaves in a multi-leaf collimator of the radiation therapy machine during delivery of the field. ### Claim 3 (depends on 2) The system according to claim 2, wherein the upper bound on the modulation efficiency represents a quotient between the updated delivery time and the un-modulated delivery time, and the upper bound on the modulation efficiency is satisfied if the modulation efficiency is less than or equal to the numerical value. ### Claim 4 (depends on 2) The system according to claim 2, wherein the data processor is configured to traverse steps [ii] to [vi] repeatedly a particular number of times where: in an initial traversal, the current treatment plan represents an initial treatment plan for the treatment volume, and in each traversal subsequent to the initial traversal, the current treatment plan is the updated treatment plan obtained in a preceding traversal. ### Claim 5 (depends on 4) The system according to claim 4, wherein the initial treatment plan represents one of: delivering uniform fluence profiles scaled such that an average radiation dose in the at least one target equals a highest prescription level of the respective prescribed radiation doses, delivering no radiation to the treatment volume, or delivering radiation to the treatment volume according to a previously generated output radiation treatment plan for the treatment volume. ### Claim 6 (depends on 2) The system according to claim 2, wherein the data processor is configured to generate the output radiation treatment plan by completing at least one of the optimization phases: a fluence-map optimization phase, wherein fluence per bixel are variables in the optimization process, each bixel being a surface element of a discretization of the exposed fluence region on a rectilinear grid in which the rows are aligned with a direction of travel for the collimator leaves in the multi-leaf collimator; a leaf-sequencing optimization phase, wherein leaf event times are variables in the optimization process, each of said leaf event times defining a respective point in time when each leaf arrives at and leaves a bixel edge; and a direct-machine-parameter optimization phase, wherein leaf positions, dose rate and delivery time per control point are variables in the optimization process; and the data processor is configured to apply steps [i] to [v] during execution of at least one of said at least one optimization phases. ### Claim 7 (depends on 6) The system according to claim 6, wherein: in the fluence-map optimization phase, the data processor is configured to calculate a set of field delivery times, each delivery time per field being a maximum delivery time per row of bixels, the delivery time per row of bixels being calculated as a sum of the delivery time for each bixel in the row, the delivery time per bixel being calculated as a sum of a leaf traversal time of the bixel and a positive difference between the irradiation time of the bixel and an irradiation time of a preceding bixel with respect to the direction of travel for the collimator leaves, the irradiation time for a bixel being defined as a fluence in the bixel divided by a dose rate of the radiation therapy machine, and the leaf traversal time of the bixel being defined as a width of the bixel along the direction of travel divided by a maximum leaf speed of the radiation therapy machine; in the leaf-sequencing optimization phase, the data processor is configured to determine a field delivery time calculated as a duration from a first leaf event until a last leaf event; and in the direct-machine-parameter optimization phase, the data processor is configured to calculate field delivery times as a sum of control point delivery times for a set of control points that belong to the field, the set of control points being the variables in the optimization process. ### Claim 8 (depends on 7) The system according to claim 7, wherein the data processor is configured to mitigate the upper bound on the modulation efficiency of the updated treatment plan by introducing a penalty term in the objective function that is to be minimized by the optimization, the penalty term measuring a deviation from satisfying the upper bound on the modulation efficiency of the updated treatment plan. ### Claim 9 (depends on 1) The system according to claim 1, wherein the data processor is configured to execute the optimization process under at least one of: a first process wherein the upper bound on the modulation efficiency is implemented with respect to a delivery time calculated as a sum of the delivery times for all fields and an un-modulated delivery time calculated as the sum of the un-modulated delivery times for all fields; and a second process wherein the upper bound on the modulation efficiency is implemented with respect to delivery time calculated as a sum of the delivery times for a subset of the fields and an un-modulated delivery time calculated as the sum of the un-modulated delivery times for the same subset of the fields. ### Claim 10 (depends on 1) The system according to claim 1, wherein the upper bound on the modulation efficiency comprises a constraint on a set of optimization variables in addition to a number of constraints associated with causing the prescribed radiation dose to be delivered to the at least one target while aiming at not delivering more than a threshold amount of radiation to at least one organ-at-risk. ### Claim 11 A method of generating an output radiation treatment plan for a treatment volume associated with at least one target, each of the at least one target being associated with a respective prescribed radiation dose, the method comprising: calculating an updated treatment plan which when implemented by a radiation therapy machine is configured to cause the respective prescribed radiation dose to be delivered to the at least one target, the updated treatment plan requiring an updated delivery time to complete, [i] receiving a numerical value designating an upper bound on modulation efficiency of the updated treatment plan, [ii] receiving a current treatment plan, [iii] calculating a current delivery time for the current treatment plan, [iv] calculating an un-modulated delivery time for the current treatment plan, [v] calculating the updated treatment plan by executing an optimization process while satisfying the upper bound on the modulation efficiency, and [vi] generating the output radiation treatment plan based on the updated treatment plan. ### Claim 12 (depends on 11) The method according to claim 11, wherein the un-modulated delivery time is calculated based on an un-modulated delivery time per field of the current treatment plan, each of which is a time required to deliver an un-modulated field with an exposed fluence region equal to an exposed fluence region for the field of the current treatment plan, a fluence inside the exposed fluence region of the un-modulated field having a constant value being scaled such that an average fluence is equal to an average fluence inside the exposed fluence region for the field of the current treatment plan, the exposed fluence region being a sub-region of a beam plane to be exposed by an opening between a set of collimator leaves in a multi-leaf collimator of the radiation therapy machine during delivery of the field. ### Claim 13 (depends on 12) The method according to claim 12, wherein the upper bound on the modulation efficiency represents a quotient between the updated delivery time and the un-modulated delivery time, and the upper bound on the modulation efficiency is satisfied if the modulation efficiency is less than or equal to the numerical value. ### Claim 14 (depends on 12) The method according to claim 12, wherein steps [ii] to [vi] are traversed repeatedly a particular number of times, where: in an initial traversal, the current treatment plan represents an initial treatment plan for the treatment volume, and in each traversal subsequent to the initial traversal, the current treatment plan is the updated treatment plan obtained in a preceding traversal. ### Claim 15 (depends on 14) The method according to claim 14, wherein the initial treatment plan represents one of: delivering uniform fluence profiles scaled such that an average radiation dose in the at least one target equals a highest prescription level of the respective prescribed radiation doses, delivering no radiation to the treatment volume, or delivering radiation to the treatment volume according to a previously generated output radiation treatment plan for the treatment volume. ### Claim 16 (depends on 12) The method according to claim 12, comprising generating the output radiation treatment plan by completing at least one of the optimization phases: a fluence-map optimization phase, wherein fluence per bixel are variables in the optimization process, each bixel being a surface element of a discretization of the exposed fluence region on a rectilinear grid in which the rows are aligned with the direction of travel for the collimator leaves in the multi-leaf collimator; a leaf-sequencing optimization phase, wherein leaf event times are variables in the optimization process, each of said leaf event times defining a respective point in time when each leaf arrives at and leaves a bixel edge; and a direct-machine-parameter optimization phase, wherein leaf positions, dose rate and delivery time per control point are variables in the optimization process; and the data processor is configured to apply steps [i] to [v] during execution of at least one of said at least one optimization phases. ### Claim 17 (depends on 16) The method according to claim 16, wherein: in the fluence-map optimization phase, the method comprises calculating a set of field delivery times, each delivery time per field being a maximum delivery time per row of bixels, the delivery time per row of bixels being calculated as a sum of the delivery time for each bixel in the row, the delivery time per bixel being calculated as a sum of a leaf traversal time of the bixel and a positive difference between the irradiation time of the bixel and an irradiation time of a preceding bixel with respect to the direction of travel for the collimator leaves, the irradiation time for a bixel being defined as a fluence in the bixel divided by a dose rate of the radiation therapy machine, and the leaf traversal time of the bixel being defined as a width of the bixel along the direction of travel divided by a maximum leaf speed of the radiation therapy machine; in the leaf-sequencing optimization phase, the method comprises determining a field delivery time calculated as a duration from a first leaf event until a last leaf event; and in the direct-machine-parameter optimization phase, the method comprises calculating field delivery times as a sum of control point delivery times for a set of control points that belong to the field, the set of control points being the variables in the optimization process. ### Claim 18 (depends on 17) The method according to claim 17, comprising mitigating the upper bound on the modulation efficiency of the updated treatment plan by introducing a penalty term in the objective function that is to be minimized by the optimization, the penalty term measuring a deviation from satisfying the upper bound on the modulation efficiency of the updated treatment plan. ### Claim 19 (depends on 11) The method according to claim 11, comprising executing the optimization process under at least one of: a first process wherein the upper bound on the modulation efficiency is implemented with respect to a delivery time calculated as a sum of the delivery times for all fields and an un-modulated delivery time calculated as the sum of the un-modulated delivery times for all fields; and a second process wherein the upper bound on the modulation efficiency is implemented with respect to delivery time calculated as a sum of the delivery times for a subset of the fields and an un-modulated delivery time calculated as the sum of the un-modulated delivery times for the same subset of the fields. ### Claim 20 (depends on 11) The method according to claim 11, wherein the upper bound on the modulation efficiency comprises a constraint on a set of optimization variables in addition to a number of constraints associated with causing the prescribed radiation dose to be delivered to the at least one target while aiming at not delivering more than a threshold amount of radiation to at least one organ-at-risk. ### Claim 21 (depends on 11) A radiation therapy machine configured to: receive an output radiation treatment plan determined by the method according to claim 11, and carry out therapy in respect of a treatment volume by controlling a radiation source in accordance with the output radiation treatment plan. ### Claim 22 (depends on 11) A computer program product loadable into a memory of at least one computer, comprising software for performing the steps of the method according to claim 11 when executed on the at least one computer. ### Claim 23 (depends on 11) A computer readable medium having a program recorded thereon, where the program is to make at least one computer perform the steps of claim 11. ### Claim 24 (depends on 11) A radiation therapy method comprising: receiving an output radiation treatment plan determined by the method according to claim 11, and carrying out therapy in respect of a treatment volume by controlling a radiation source in accordance with the output radiation treatment plan.