公开(公告)号：US20250108233A1

公开(公告)日：2025-04-03

申请号：US18851085

申请日：2023-02-07

Applicant: RAYSEARCH LABORATORIES AB

Inventor： Rasmus HELANDER ,  Mats HOLMSTROM

IPC: A61N5/10

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.

公开(公告)号：US20240033539A1

公开(公告)日：2024-02-01

申请号：US18256427

申请日：2021-11-29

Applicant: RAYSEARCH LABORATORIES AB

Inventor： Mats HOLMSTROM ,  David ANDERSSON ,  Gabriel CARRIZO ,  Adnan HOSSAIN

IPC: A61N5/10 ,  G16H20/40

CPC classification number: A61N5/1038 ,  A61N5/1039 ,  G16H20/40

Abstract: An estimated or predicted dose for radiotherapy treatment may be generated based on a partial dose map including dose information only for one or more regions of interest within a treatment site, by use of a properly trained machine learning system such as a U-Net or a V-Net. Said partial dose map typically set to fulfil clinical goals. A method of training such a machine learning system is also disclosed.

公开(公告)号：US20230381539A1

公开(公告)日：2023-11-30

申请号：US18255152

申请日：2021-11-18

Applicant: RaySearch Laboratories AB (Publ)

Inventor： Albin FREDRIKSSON ,  Hanna GRUSELIUS ,  Mats HOLMSTROM ,  David ANDERSSON

IPC: A61N5/10

CPC classification number: A61N5/1031 ,  A61N2005/1034

Abstract: A method (100) for generating a treatment plan specifying an irradiation of a patient, the method comprising: a dose inference stage (112), including using a model to infer a spatial dose from patient data; a dose mimicking stage (116), including executing a robust optimization process to generate a deliverable treatment plan which is consistent with the inferred spatial dose, wherein the robust optimization considers a plurality of scenarios relating to patient data uncertainty.