-
1.发明授权公开(公告)号：US11475991B2
公开(公告)日：2022-10-18
申请号：US16145673
申请日：2018-09-28
发明人： Hannu Laaksonen , Janne Nord , Sami Petri Perttu
摘要： Example methods for adaptive radiotherapy treatment planning using deep learning engines are provided. One example method may comprise obtaining treatment image data associated with a first imaging modality and planning image data associated with a second imaging modality. The treatment image data may be acquired during a treatment phase of a patient. Also, planning image data associated with a second imaging modality may be acquired prior to the treatment phase to generate a treatment plan for the patient. The method may also comprise: in response to determination that an update of the treatment plan is required, processing, using the deep learning engine, the treatment image data and the planning image data to generate output data for updating the treatment plan.
-
公开(公告)号：US11386592B2
公开(公告)日：2022-07-12
申请号：US16722017
申请日：2019-12-20
发明人： Pascal Paysan , Benjamin M Haas , Janne Nord , Sami Petri Perttu , Dieter Seghers , Joakim Pyyry
摘要： Example methods and systems for tomographic data analysis are provided. One example method may comprise: obtaining first three-dimensional (3D) feature volume data and processing the first 3D feature volume data using an AI engine that includes multiple first processing layers, an interposing forward-projection module and multiple second processing layers. Example processing using the AI engine may involve: generating second 3D feature volume data by processing the first 3D feature volume data using the multiple first processing layers, transforming the second 3D volume data into 2D feature data using the forward-projection module and generating analysis output data by processing the 2D feature data using the multiple second processing layers.
-
公开(公告)号：US11436766B2
公开(公告)日：2022-09-06
申请号：US16722004
申请日：2019-12-20
发明人： Pascal Paysan , Benjamin M Haas , Janne Nord , Sami Petri Perttu , Dieter Seghers , Joakim Pyyry
摘要： Example methods and systems for tomographic image reconstruction are provided. One example method may comprise: obtaining two-dimensional (2D) projection data and processing the 2D projection data using the AI engine that includes multiple first processing layers, an interposing back-projection module and multiple second processing layers. Example processing using the AI engine may involve: generating 2D feature data by processing the 2D projection data using the multiple first processing layers, reconstructing first three-dimensional (3D) feature volume data from the 2D feature data using the back-projection module; and generating second 3D feature volume data by processing the first 3D feature volume data using the multiple second processing layers.
-
公开(公告)号：US11282192B2
公开(公告)日：2022-03-22
申请号：US16721876
申请日：2019-12-19
摘要： Example methods and systems for training deep learning engines for radiotherapy treatment planning are provided. One example method may comprise: obtaining a set of training data that includes unlabeled training data and labeled training data; and configuring a deep learning engine to include (a) a primary network and (b) a deep supervision network that branches off from the primary network. The method may further comprise: training the deep learning engine to perform the radiotherapy treatment planning task by processing training data instance to generate (a) primary output data and (b) deep supervision output data; and updating weight data associated with at least some of the multiple processing layers based on the primary output data and/or the deep supervision output data. The deep supervision network may be pruned prior to applying the primary network to perform the radiotherapy treatment planning task for a patient.
-
5.发明授权公开(公告)号：US11013936B2
公开(公告)日：2021-05-25
申请号：US16228800
申请日：2018-12-21
IPC分类号： A61N5/10
摘要： Example methods and systems for generating dose estimation models for radiotherapy treatment planning are provided. One example method may comprise obtaining model configuration data that specifies multiple anatomical structures based on which dose estimation is performed by a dose estimation model. The method may also comprise obtaining training data that includes a first treatment plan associated with a first past patient and multiple second treatment plans associated with respective second past patients. The method may further comprise: in response to determination that automatic segmentation is required for the first treatment plan, performing automatic segmentation on image data associated with the first past patient to generate an improved first treatment plan, and generating the dose estimation model based on the improved first treatment plan and the multiple second treatment plans.
-
6.发明授权公开(公告)号：US10984902B2
公开(公告)日：2021-04-20
申请号：US16145606
申请日：2018-09-28
发明人： Hannu Laaksonen , Janne Nord , Sami Petri Perttu
摘要： Example methods for adaptive radiotherapy treatment planning using deep learning engines are provided. One example method may comprise obtaining treatment image data associated with a first imaging modality. The treatment image data may be acquired during a treatment phase of a patient. Also, planning image data associated with a second imaging modality may be acquired prior to the treatment phase to generate a treatment plan for the patient. The method may also comprise: in response to determination that an update of the treatment plan is required, transforming the treatment image data associated with the first imaging modality to generate transformed image data associated with the second imaging modality. The method may further comprise: processing, using the deep learning engine, the transformed image data to generate output data for updating the treatment plan.
-
-
-
-
-
搜索结果
6 条记录 (耗时 0.017 秒)
