# US 10,369,381 B2 · System and method for robust intensity-modulated proton therapy planning > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,369,381 B2 - Document ID: US10369381B2 - Status: Granted - Kind: B2 - Application number: 14/843,384 - Filed: 2015-09-02 - Published: 2016-03-03 - Granted: 2019-08-06 - Earliest claimed: 2014-09-02 - Assignees: Mayo Foundation for Medical Education and Research - Applicants: Mayo Foundation for Medical Education and Research - Inventors: Wei Liu - Web page: https://patentgrep.com/patent/US10369381B2 ## CPC - A61N 5/1039: {using functional images, e.g. PET or MRI} - A61B 6/032: {Transmission computed tomography [CT]} - A61B 6/5217: {extracting a diagnostic or physiological parameter from medical diagnostic data} - A61B 34/10: Computer-aided planning, simulation or modelling of surgical operations - A61N 5/1031: {using a specific method of dose optimization} - G16H 50/30: for calculating health indices; for individual health risk assessment - A61B 5/7207: {of noise induced by motion artifacts} - A61B 5/748: {Selection of a region of interest, e.g. using a graphics tablet} - A61B 6/037: {Emission tomography} - A61B 6/542: {involving control of exposure} - A61N 2005/1087: {Ions; Protons} - 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) - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract The present invention provides a system and method for generating intensity modulated proton therapy plans. The method includes receiving a representation of a subject that includes information related to target and non-target volumes of interest, and computing, using the representation and a proton beam configuration that describes a number of beamlets and their respective arrangement, a dose distribution for each one of a plurality of uncertainty scenarios. The method also includes computing a robustness index for respective locations in the target volumes of interest using the dose distributions, and optimizing an objective function to determine a set of intensity weights for each beamlet in the proton beam configuration, wherein the objective function is based at least in part on deviations of the robustness indices from a prescribed robustness index. The method further includes generating a treatment plan using the determined intensity weights. ## Claims ### Claim 1 A computer-implemented method for generating an intensity modulated proton therapy plan, the steps of the method comprising: (a) providing a representation of a subject that includes information related to target and non-target volumes of interest to a treatment planning system; (b) providing to the treatment planning system, a proton beam configuration that describes a number of beamlets and their respective arrangement; (c) computing with the treatment planning system, a dose distribution for each one of a plurality of uncertainty scenarios, wherein the dose distribution is computed based on the provided representation of the subject and the provided proton beam configuration; (d) computing with the treatment planning system, a robustness index for respective locations in the target volumes of interest using the computed dose distributions; (e) optimizing an objective function with the treatment planning system to determine a set of intensity weights for each beamlet in the proton beam configuration, wherein the objective function is based at least in part on deviations of each computed robustness index from a prescribed robustness index; and (f) generating a treatment plan with the treatment planning system using the intensity weights determined by optimizing the objective function. ### Claim 2 (depends on 1) The method of claim 1, wherein the representation includes at least one image of the subject acquired using one of x-ray computed tomography (“CT”), magnetic resonance imaging (“MRI”), positron emission tomography (“PET”), or proton CT. ### Claim 3 (depends on 1) The method of claim 1, wherein the plurality of uncertainty scenarios include a combination of a series of setup uncertainty scenarios, a series of proton range uncertainty scenarios, and a series of motion uncertainty scenarios. ### Claim 4 (depends on 1) The method of claim 1, wherein the robustness index for each location is defined according to: RMSD i = ∑ m = 1 9 ⁢ ( 〈 D i m 〉 - D i _ ) 2 9 where is an accumulated dose for location, i, and for an uncertainty scenario, m, and wherein D i _ = ∑ m = 1 9 ⁢ 〈 D i m 〉 9 . ### Claim 5 (depends on 4) The method of claim 4, wherein the objective function is defined as: F 4 ⁢ D ⁡ ( ω j ) = ∑ i ∈ CTV ⁢ p CTV ⁡ ( 〈 D i 0 〉 - D 0 , CTV ) 2 + ∑ i ∈ OARs ⁢ p OARs ⁢ H ⁡ ( 〈 D i 0 〉 - D 0 , OARs ) ⁢ ( 〈 D i 0 〉 - D 0 , OARs ) 2 + ∑ i ∈ CTV ⁢ p CTV , Robust ⁢ H ⁡ ( RMSD i - RMSD 0 ) ⁢ ( RMSD i - RMSD 0 ) 2 where pCTV, pOARs, and pCTV,Robust are penalty weights, D0,CTV, D0,OARs, are prescribed doses for respective tissues, RMSD0 is the prescribed robustness index, RMSDi is the robustness index value for each location i in the target volumes of interest, and H(Di−D0) is a Heavyside function with value of unity if an argument is greater than zero and value of zero if the argument is less than or equal to zero. ### Claim 6 (depends on 4) The method of claim 4, further comprising generating a report that indicates a robustness of the treatment plan generated in step (f). ### Claim 7 (depends on 6) The method of claim 6, wherein generating the report includes generating a plurality of root-mean-square volume histograms (“RVHs”) using the robustness index for respective locations in the target volumes of interest computed at step (d) and displaying the RVHs in the generated report. ### Claim 8 (depends on 7) The method of claim 7, wherein generating the report includes computing an area under the curve (“AUC”) metric based on the plurality of RVHs and displaying the AUC metric in the generated report. ### Claim 9 A treatment planning system for use in intensity-modulated proton therapy, comprising: a memory for storing thereon a representation of a subject and a proton beam configuration, wherein the representation of the subject includes information related to target and non-target volumes of interest and the proton beam configuration describes a number of beamlets and their respective arrangement; a processor for receiving the representation of the subject and the proton beam configuration from the memory and for generating a treatment plan based on the representation of the subject and the proton beam configuration; a display for displaying the treatment plan; and wherein the processor generates the treatment plan by: computing a dose distribution for each one of a plurality of uncertainty scenarios, wherein each dose distribution is computed based on the representation of the subject and the proton beam configuration; computing a robustness index for respective locations in the target volumes of interest using the computed dose distributions; optimizing an objective function to determine a set of intensity weights for each beamlet in the proton beam configuration, wherein the objective function is based at least in part on deviations of each computed robustness index from a prescribed robustness index; and generating the treatment plan using the intensity weights determined by optimizing the objective function. ### Claim 10 (depends on 9) The treatment planning system of claim 9, wherein the representation of the subject includes at least one image of the subject acquired using one of x-ray computed tomography (“CT”), magnetic resonance imaging (“MRI”), positron emission tomography (“PET”), or proton CT. ### Claim 11 (depends on 10) The treatment planning system of claim 10, wherein the plurality of uncertainty scenarios include a combination of a series of setup uncertainty scenarios, a series of proton range uncertainty scenarios, and a series of motion uncertainty scenarios. ### Claim 12 (depends on 10) The treatment planning system of claim 10, wherein the robustness index for each location is defined according to: RMSD i = ∑ m = 1 9 ⁢ ( 〈 D i m 〉 - D i _ ) 2 ⁢ 9 where is an accumulated dose for location, i, and for an uncertainty scenario, m, and wherein D i _ = ∑ m = 1 9 ⁢ 〈 D i m 〉 9 . ### Claim 13 (depends on 12) The treatment planning system of claim 12, wherein the objective function is defined as: F 4 ⁢ D ⁡ ( ω j ) = ∑ i ∈ CTV ⁢ p CTV ⁡ ( 〈 D i 0 〉 - D 0 , CTV ) 2 + ∑ i ∈ OARs ⁢ p OARs ⁢ H ⁡ ( 〈 D i 0 〉 - D 0 , OARs ) ⁢ ( 〈 D i 0 〉 - D 0 , OARs ) 2 + ∑ i ∈ CTV ⁢ p CTV , Robust ⁢ H ⁡ ( RMSD i - RMSD 0 ) ⁢ ( RMSD i - RMSD 0 ) 2 where pCTV, pOARs, and pCTV,Robust are penalty weights, D0,CTV, D0,OARs, are prescribed doses for respective tissues, RMSD0 is the prescribed robustness index, RMSDi is the robustness index value for each location i in the target volumes of interest, and H(Di−D0) is a Heavyside function with value of unity if an argument is greater than zero and value of zero if the argument is less than or equal to zero. ### Claim 14 (depends on 12) The treatment planning system of claim 12, wherein the processor further generates at least one metric that indicates a robustness of the treatment plan. ### Claim 15 (depends on 14) The treatment planning system of claim 14, wherein the at least one metric includes a plurality of root-mean-square volume histograms (“RVHs”) computed using the robustness index for respective locations in the target volumes of interest. ### Claim 16 (depends on 15) The treatment planning system of claim 15, wherein the at least one metric further includes an area under the curve (“AUC”) metric computed based on the plurality of RVHs.