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US-12616852-B2 - Methods and system related to radiotherapy treatment planning

US12616852B2US 12616852 B2US12616852 B2US 12616852B2US-12616852-B2

Abstract

The present disclosure relates to the use of machine learning for determining initial machine setting parameters for radiotherapy treatment planning. A machine-learning system is trained on data sets including a dose distribution and a set of machine parameter settings resulting from that dose distribution. The trained system can be used for determining machine parameter settings based on a desired dose distribution, which may be used as initial machine parameter settings for radiation treatment optimization.

Inventors

  • Rasmus HELANDER
  • Mats HOLMSTROM

Assignees

  • RAYSEARCH LABORATORIES AB (PUBL)

Dates

Publication Date
20260505
Application Date
20230207
Priority Date
20220405

Claims (12)

  1. 1 . A computer-based method, executed on a radiotherapy treatment planning system, comprising: inputting to a machine learning system a plurality of historical data sets, each data set including a dose distributions and a corresponding set of machine parameter settings used to deliver the dose distributions; training the machine learning system, based on the historical data sets, to generate machine parameter settings from a reference dose distribution, wherein the machine parameter settings correspond to initialization values used by a radiotherapy treatment plan optimization algorithm; generating, using the trained machine learning system, an initial set of machine parameter settings from a reference dose distribution associated with a patient, the initial set being directly usable as initialization values for the radiotherapy treatment plan optimization algorithm; automatically initializing, using the generated initial machine parameter settings, a radiotherapy treatment plan optimization algorithm that simulates radiation beam paths using the initialization values and adjusts machine parameters to satisfy patient-specific dosimetric objectives and constraints; and outputting the optimized radiotherapy treatment plan, including the adjusted machine parameter settings, in a format executable by a radiotherapy apparatus to deliver the prescribed dose distribution to the patient.
  2. 2 . A machine learning system which has been trained according to claim 1 , the machine learning system being arranged to take input data in the form of one or more desired reference dose distributions and output a set of machine parameter settings including at least one machine parameter setting that is suitable for producing the one or more desired reference dose distribution by the radiotherapy delivery apparatus.
  3. 3 . A computer-based method for determining machine parameter settings for a radiotherapy delivery apparatus, using a machine learning system according to claim 2 , the method comprising: inputting one or more reference dose distributions into the machine learning system; initializing parameters, by the machine learning system; and outputting, from the machine learning system, a set of machine parameter settings for the radiotherapy delivery apparatus.
  4. 4 . The method of claim 3 , wherein the set of machine parameter settings includes Multi Leaf Collimator leaf settings.
  5. 5 . The method of claim 3 , wherein the set of machine parameter settings includes Monitor Unit settings.
  6. 6 . The method of claim 3 , wherein the set of machine parameter settings includes one or more of spot placement, spot weights and beam energy.
  7. 7 . A method for computer-based radiotherapy treatment plan optimization method including, before performing the plan optimization, performing the method of determining machine parameter settings according to claim 3 and using the resulting at least one machine parameter setting as an initial setting for that machine parameter in the radiotherapy treatment plan optimization.
  8. 8 . The method of claim 7 , wherein the plan optimization is performed by optimizing an optimization problem.
  9. 9 . The method of claim 8 , wherein the plan optimization is performed by dose mimicking.
  10. 10 . A computer program product comprising computer-readable code means which, when run in a computer will cause the computer to perform the method of claim 1 .
  11. 11 . A computer program product comprising non-transitory storage means having stored thereon computer-readable code means which, when run in a computer will cause the computer to perform the method of claim 1 .
  12. 12 . A computer system comprising a processor and a program memory, the program memory having stored thereon a computer program product according to claim 10 .

Description

TECHNICAL FIELD The present invention relates to radiotherapy treatment plan optimization and in particular to parameter initialization for such optimization procedures. BACKGROUND In radiotherapy treatment plan optimization, an optimization problem is set up and the radiotherapy treatment plan is optimized to achieve a desired dose distribution in the patient, given a set of variables which typically include machine parameters of the radiotherapy treatment apparatus that is to be used. The initial values for these parameters may be set in different ways. For photon treatment, machine parameters typically include MLC leaf sequencing. These initial machine parameter values may be determined by solving a fluence map optimization problem and subsequently performing a conversion from the optimized fluence map dose to feasible machine parameters. This involves target projection, fluence map optimization, and conversion to machine parameters. This is a time-consuming process and the conversion step is generally a source of inaccuracy in the planning procedure, which leads to long optimization times since many iterations are needed. For ion therapy, such as proton therapy, machine parameters include spot placement, spot weights and beam energy. For pencil beam scanning, as an example, the initial value of these parameters can be set in a number of different ways and implementation varies across different clinics. One possible implementation includes computing target projections and subsequently using some mathematical formula to decide initial values for the spot weights. The present disclosure aims at making the treatment plan optimization procedure faster and enable better treatment plans resulting from the treatment plan optimization procedure. SUMMARY OF THE INVENTION The present disclosure relates to the use of machine learning for determining initial machine setting parameters for radiotherapy treatment planning. Accordingly, the disclosure relates to a computer-based method of training a machine learning system including inputting to the machine learning system a plurality of data sets, each data set including one or more dose distributions and a set of machine parameter settings including at least one machine parameter setting resulting from the one or more dose distributions in a planning procedure, to train the machine learning system to output a set of at least one machine parameter setting based on a reference dose distribution. The disclosure also relates to a machine learning system which has been trained according to the above. Said machine learning system is arranged to take input data in the form of one or more dose distribution and output at least one machine parameter setting that will is suitable for producing the dose distribution for a particular radiotherapy delivery apparatus. The disclosure also relates to a computer-based method using such a machine-learning system for determining machine parameter settings. The method includes the step of inputting one or more reference dose distributions into the machine learning system, performing parameter initialization by the machine learning system and outputting from the machine learning system a set of machine parameter settings including at least one machine parameter setting for a radiotherapy delivery apparatus. By basing the machine parameter settings on knowledge about suitable machine parameter settings for similar dose distributions, a better set of machine parameter settings can be obtained. Hence, according to the invention, more correct input data regarding machine parameters of the radiation therapy delivery apparatus can be obtained in an efficient way by means of machine-learning. This means that the plan optimization times can be shortened since the initial data will be more correct. The method is particularly useful for machine parameter initialization for use in machine learning based optimization, but it is also useful for any other type of optimization procedure. For photon treatment, the set of machine parameter settings may include one or more of MLC leaf settings, MU settings, start and stop angles, couch angles, and pitch. For Intensity Modulated Radiation Therapy (IMRT) applications, the set of machine parameter settings may include one or more of Segmental MLC (SMLC) or Dynamic MLC (DMLC). For ion treatment, such as proton treatment, the set of machine parameter settings may include, for example, one or more of spot placement, spot weights and beam energy. The disclosure also relates to a computer-based radiotherapy treatment plan optimization method including, before performing the plan optimization, performing the method of determining a set of machine parameter settings according to any of the embodiments outlined above, and using the resulting set of machine parameter settings as an initial setting for the corresponding machine parameters in the radiotherapy treatment plan optimization. The machine parameter setting, o