Artificial intelligence modeling for radiation therapy dose distribution analysis

Patent metadata

Field	Value
Publication number	US-12053646-B2
Application number	US-202318329129-A
Country	US
Kind code	B2
Filing date	Jun 5, 2023
Priority date	Mar 22, 2021
Publication date	Aug 6, 2024
Grant date	Aug 6, 2024

Abstract

Disclosed herein are methods and systems to optimize a radiation therapy treatment plan using dose distribution values predicted via a trained artificial intelligence model. A server trains the AI model using a training dataset comprising data associated with a plurality of previously implemented radiation therapy treatments on a plurality of previous patients and dose distributions associated with one or more organs of each previous patient. The server then executes the trained AI model to predict dose distribution for a patient. The server then displays a heat map illustrating the predicted values, transmits the predicted values to a plan optimizer to generate an optimized treatment plan for the patient, and/or transmits an alert when a treatment plan generated by a plan optimizer deviates from rules and thresholds indicated within the patient's plan objectives.

First claim

What we claim is:

1. A method comprising:
calculating, by a processor, a dose distribution value for an anatomical region of a patient; and
displaying, by the processor, a heat map having a set of segments where each segment corresponds to a first coordinate and a second coordinate of the anatomical region of the patient,
wherein a visual attribute of each segment corresponds to the calculated dose distribution value, and
wherein at least one segment corresponding to a first region exceeding a first threshold or a second region below a second threshold is visually distinct from other segments within the heat map.

2. The method of claim 1 , wherein the dose distribution value is calculated using an artificial intelligence model, wherein the artificial intelligence model is trained using a training dataset comprising data associated with a plurality of previously implemented radiation therapy treatments on a plurality of previous patients and dose distributions associated with one or more organs of each previous patient.

3. The method of claim 2 , wherein the artificial intelligence model uses a treatment plan associated with the patient to identify the dose distribution value associated with the anatomical region of the patient.

4. The method of claim 1 , wherein at least one of the first threshold or the second threshold is retrieved from a plan objective associated with the patient.

5. The method of claim 1 , further comprising:
displaying, by the processor, an input element configured to receive an acceptance or rejection of at least one of the first region or the second region.

6. The method of claim 1 , further comprising:
transmitting, by the processor, data associated with at least one of first region or the second region to a plan optimizer application.

7. The method of claim 1 , wherein the visual attribute of each segment corresponds to a color, a shading, or a visual pattern.

8. A computer system comprising:
a server comprising at least one processor and a non-transitory computer-readable medium containing instructions that when executed by the at least one processor causes the processor to perform operations comprising:
calculate a dose distribution value for an anatomical region of a patient; and
display a heat map having a set of segments where each segment corresponds to a first coordinate and a second coordinate of the anatomical region of the patient, wherein a visual attribute of each segment corresponds to the calculated dose distribution value, and wherein at least one segment corresponding to a first region exceeding a first threshold or a second region below a second threshold is visually distinct from other segments within the heat map.

9. The computer system of claim 8 , wherein the dose distribution value is calculated using an artificial intelligence model, wherein the artificial intelligence model is trained using a training dataset comprising data associated with a plurality of previously implemented radiation therapy treatments on a plurality of previous patients and dose distributions associated with one or more organs of each previous patient.

10. The computer system of claim 9 , wherein the artificial intelligence model uses a treatment plan associated with the patient to identify the dose distribution value associated with the anatomical region of the patient.

11. The computer system of claim 8 , wherein at least one of the first threshold or the second threshold is retrieved from a plan objective associated with the patient.

12. The computer system of claim 8 , wherein the instructions further cause the processor to:
display an input element configured to receive an acceptance or rejection of at least one of the first region or the second region.

13. The computer system of claim 8 , wherein the instructions further cause the processor to:
transmit data associated with at least one of first region or the second region to a plan optimizer application.

14. The computer system of claim 8 , wherein the visual attribute of each segment corresponds to a color, a shading, or a visual pattern.

15. A non-transitory machine-readable storage medium having computer-executable instructions stored thereon that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
calculate a dose distribution value for an anatomical region of a patient; and
display a heat map having a set of segments where each segment corresponds to a first coordinate and a second coordinate of the anatomical region of the patient, wherein a visual attribute of each segment corresponds to the calculated dose distribution value, and wherein at least one segment corresponding to a first region exceeding a first threshold or a second region below a second threshold is visually distinct from other segments within the heat map.

16. The non-transitory machine-readable storage medium of claim 15 , wherein the dose distribution value is calculated using an artificial intelligence model, wherein the artificial intelligence model is trained using a training dataset comprising data associated with a plurality of previously implemented radiation therapy treatments on a plurality of previous patients and dose distributions associated with one or more organs of each previous patient.

17. The non-transitory machine-readable storage medium of claim 16 , wherein the artificial intelligence model uses a treatment plan associated with the patient to identify the dose distribution value associated with the anatomical region of the patient.

18. The non-transitory machine-readable storage medium of claim 15 , wherein at least one of the first threshold or the second threshold is retrieved from a plan objective associated with the patient.

19. The non-transitory machine-readable storage medium of claim 15 , wherein the instructions are further configured to:
display an input element configured to receive an acceptance or rejection of at least one of the first region or the second region.

20. The non-transitory machine-readable storage medium of claim 15 , wherein the instructions are further configured to:
transmit data associated with at least one of first region or the second region to a plan optimizer application.

Assignees

• Siemens Healthineers Int Ag

Inventors

• Peltola Jarkko
• Rusanen Marko
• Pietila Ville

Classifications

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N3/092

  Reinforcement learning · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06N3/0895

  Weakly supervised learning, e.g. semi-supervised or self-supervised learning · CPC title

• G06N3/082

  modifying the architecture, e.g. adding, deleting or silencing nodes or connections · CPC title