System and method for novel chance-constrained optimization in intensity-modulated proton therapy planning to account for range and patient setup uncertainties

Patent metadata

Field	Value
Publication number	US-10328282-B2
Application number	US-201615209699-A
Country	US
Kind code	B2
Filing date	Jul 13, 2016
Priority date	Jul 13, 2015
Publication date	Jun 25, 2019
Grant date	Jun 25, 2019

Abstract

A system and methods are provided for generating intensity modulated proton therapy plans robust to various kinds of delivery uncertainties with the capability for treatment planners to control the balance between plan quality and robustness. The system obtains a representation of a subject and a proton beam configuration that describes a number of beamlets and their respective arrangement. The system computes an objective function, in the treatment planning system, first part of which is about dose deviations from the prescribed dose in the target volumes, second part of which is about dose deviations from the dose constraint in the non-target volumes, and computing dose volume constraints for targets using a probability to control the dose distribution in the target volumes to be between a lower threshold and an upper threshold. Based on this information, the system obtains a robust chance-constrained treatment planning model with a user-adjustable tolerance level.

First claim

We claim:

1. A method for generating an intensity modulated proton therapy plan, the method comprising:
(a) obtaining, by a treatment planning system, a representation of a subject that includes information related to target and non-target volumes of interest;
(b) obtaining, by the treatment planning system, a proton beam configuration that describes a number of beamlets and their respective arrangement;
(c) obtaining, by the treatment planning system, a target dose distribution model in the target volumes, prescribed doses for the target volumes, and dose constraints in the non-target volumes;
(d) computing an objective function, using the treatment planning system, the objective function having a first part with dose deviations from the prescribed doses in the target volumes, a second part with dose deviations from the dose constraints in the non-target volumes, and computing dose volume constraints for targets using a probability controlling a dose distribution in the target volumes between a lower threshold and an upper threshold;
(e) optimizing the 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 calculated using the first part and second part and the dose volume constraints; and
(f) generating a treatment plan, executable by a proton treatment system, with the treatment planning system using the set of intensity weights determined by optimizing the objective function.

2. The method of claim 1 , further comprising: receiving, using a user interface of the treatment planning system, a tolerance level ε and setting a threshold level to be 1−ε, wherein the threshold level is used to define a chance constraint ensuring that the dose distributions in the target volumes are satisfied with the probability less than the threshold level.

3. The method of claim 2 , wherein the objective function minimizes a target function subject to the chance constraint on the probability, as follows:

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where ω 0 stands for a nominal scenario, CTV t denotes a set of all voxels in a clinical target volume (CTV) of a tumor t in a set of tumors T, V n denotes a set of all voxels of in a volume of an organ at risk (OAR) n in a set of OAR O, D t 0 is a prescribed dose of the tumor t, D n 0 is a dose constraint of OAR n, wherein a dose received by voxel i of tumor t is denoted by D ti , a dose received by voxel i of OAR n is denoted by D ni , α t denotes a penalty weight for the tumor t, and α n denotes a penalty weight for the OAR n.

4. The method of claim 3 , wherein the objective function minimizes the target function subject to a constraint of dose distribution in all OAR that is defined by:
f n ( d,ω 0 )≤ U n ∀n∈O,

where f n (d,ω 0 ) is a value f n (d) under an uncertainty scenario ω 0 ∈Ω.

5. The method of claim 1 , further comprising:
obtaining one or more model parameters for the dose distribution model based on at least one image acquired using one of x-ray computed tomography (“CT”), magnetic resonance imaging (“MRI”), positron emission tomography (“PET”), or proton CT.

6. The method of claim 1 , wherein the method further comprises computing the objective function under a plurality of uncertainty scenarios comprising a combination of a series of setup uncertainty scenarios, a series of proton range uncertainty scenarios, and a series of motio

Assignees

• An Yu
• Liang Jianming
• Liu Wei
• Mayo Found Medical Education & Res
• Univ Arizona State

Inventors

• An Yu
• Liang Jianming
• Liu Wei

Classifications

• A61N5/1084 Primary

  for delivering multiple intersecting beams at the same time, e.g. gamma knives · CPC title

• A61N2005/1087

  Ions; Protons · CPC title

• A61N5/1039 Primary

  using functional images, e.g. PET or MRI · CPC title

• A61N5/1031

  using a specific method of dose optimization · CPC title

• A61N5/1043

  Scanning the radiation beam, e.g. spot scanning or raster scanning · CPC title