	US 12,327,628 B2
	Methods and systems for adaptive radiotherapy treatment planning using deep learning engines
Hannu Laaksonen, Espoo (FI); Janne Nord, Espoo (FI); and Sami Petri Perttu, Helsinki (FI)
Assigned to SIEMENS HEALTHINEERS INTERNATIONAL AG, (CH)
Filed by SIEMENS HEALTHINEERS INTERNATIONAL AG, Palo Alto, CA (US)
Filed on May 7, 2023, as Appl. No. 18/144,238.
Application 18/144,238 is a continuation of application No. 17/953,346, filed on Sep. 27, 2022, granted, now 11,682,485.
Application 17/953,346 is a continuation of application No. 16/145,673, filed on Sep. 28, 2018, granted, now 11,475,991.
Prior Publication US 2023/0274817 A1, Aug. 31, 2023
Int. Cl. G16H 30/40 (2018.01); A61N 5/10 (2006.01); G06N 3/08 (2023.01); G16B 40/00 (2019.01); G16H 20/40 (2018.01)

CPC G16H 30/40 (2018.01) [A61N 5/1031 (2013.01); A61N 5/1038 (2013.01); A61N 5/1039 (2013.01); G06N 3/08 (2013.01); G16B 40/00 (2019.02); G16H 20/40 (2018.01); A61N 2005/1041 (2013.01)]

18 Claims

1. A method for a computer system to perform adaptive radiotherapy treatment planning, wherein the method comprises:

obtaining treatment image data associated with a first imaging modality, wherein the treatment image data is acquired during a treatment phase of a patient;

obtaining planning image data associated with a second imaging modality, wherein the planning image data is acquired prior to the treatment phase to generate a treatment plan for the patient;

based on a determination that a difference between the treatment image data and the planning image data does not exceed a predetermined significance threshold;

generating transformed image data associated with the first imaging modality based on (a) the treatment image data associated with the first imaging modality and (b) the planning image data associated with the second imaging modality; and

processing, using a deep learning engine, input image data that includes both the transformed image data and the planning image data to generate output data for updating the treatment plan.