公开(公告)号：US10765332B2

公开(公告)日：2020-09-08

申请号：US15569535

申请日：2015-04-29

Applicant: Brainlab AG

Inventor： Christian Harrer ,  Stephan Mittermeyer ,  Bálint Varkuti

IPC: A61B5/00 ,  A61B5/024 ,  A61B5/0452 ,  A61B5/0245

Abstract: The invention relates to a computer-implemented medical data processing method for determining a heartbeat signal describing the heartbeat of a patient in the time domain, the method comprising executing, on a processor of a computer, steps of: a) acquiring, at the processor, acceleration measurement data describing an acceleration in the time domain of an anatomical body part measured on an external surface of the anatomical body part; b) determining, by the processor, component analysis data describing a result of an independent component analysis in the time domain of the acceleration measurement data; c) acquiring, at the processor, heartbeat template data describing template shapes of heartbeat in the time domain; d) determining, by the processor and based on the component analysis data and the heartbeat template data, recurrent shape data describing a recurrence of certain signal shapes in the component analysis data; e) determining, based on the recurrent shape data, heartbeat signal data describing a time series of the heartbeat.

公开(公告)号：US12285628B2

公开(公告)日：2025-04-29

申请号：US17429839

申请日：2019-03-01

Applicant: Brainlab AG

Inventor： Christian Harrer ,  Wolfgang Ullrich

IPC: A61N5/10 ,  G16H20/40

Abstract: By using the Al module, the method of the present invention calculates, i.e. predicts, the dependency Ci (pi) of a radiotherapy (RT) quality criterion C, from an adjustment of such a radiotherapy planning parameter pi. In this way, the decision making process in RT treatment plan optimization is streamlined by prediction of promising settings of one or more radiotherapy planning parameters p, before the actual time intensive iterative optimization process is carried out. This is achieved by applying an Al module, which has been trained to predict the specific behaviour of the dose optimization algorithm, i.e. the optimizer, with respect to geometric patient data, dose prescription and treatment indication data. Thus, a computer-implemented medical method of predicting a dependency Ci (pi) of a radiotherapy (RT) quality criterion Ci from an adjustment of a radiotherapy planning parameter p, is presented. The method comprises the following steps of providing geometric patient data geometrically describing an area of a patient, which is to be irradiated according to a radiotherapy treatment plan (step S1), providing dose prescription data and treatment indication data for said patient (step S2), and predicting with a trained Artificial Intelligence (Al) module the dependency Ci (pi) of the radiotherapy quality criterion Ci from the radiotherapy planning parameter p, when adjusting said radiotherapy planning parameter pi, thereby using the geometric patient data, the dose prescription data and the treatment indication data as input for the Al module (step S3).

公开(公告)号：US20220126116A1

公开(公告)日：2022-04-28

申请号：US17429839

申请日：2019-03-01

Applicant: Brainlab AG

Inventor： Christian Harrer ,  Ullrich Wolfgang

IPC: A61N5/10 ,  G16H20/40

Abstract: By using the Al module, the method of the present invention calculates, i.e. predicts, the dependency Ci (pi) of a radiotherapy (RT) quality criterion C, from an adjustment of such a radiotherapy planning parameter pi. In this way, the decision making process in RT treatment plan optimization is streamlined by prediction of promising settings of one or more radiotherapy planning parameters p, before the actual time intensive iterative optimization process is carried out. This is achieved by applying an Al module, which has been trained to predict the specific behaviour of the dose optimization algorithm, i.e. the optimizer, with respect to geometric patient data, dose prescription and treatment indication data. Thus, a computer-implemented medical method of predicting a dependency Ci (pi) of a radiotherapy (RT) quality criterion Ci from an adjustment of a radiotherapy planning parameter p, is presented. The method comprises the following steps of providing geometric patient data geometrically describing an area of a patient, which is to be irradiated according to a radiotherapy treatment plan (step S1), providing dose prescription data and treatment indication data for said patient (step S2), and predicting with a trained Artificial Intelligence (Al) module the dependency Ci (pi) of the radiotherapy quality criterion Ci from the radiotherapy planning parameter p, when adjusting said radiotherapy planning parameter pi, thereby using the geometric patient data, the dose prescription data and the treatment indication data as input for the Al module (step S3).