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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.
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公开(公告)号：US20220001205A1
公开(公告)日：2022-01-06
申请号：US16919746
申请日：2020-07-02
摘要： A control circuit accesses historical information regarding previously optimized radiation treatment plans for different patients and processes that information to determine the relative importance of different clinical goals. The circuit then facilitates development of a particular plan for a particular patient as a function of the relative importance of the clinical goals. By one approach the control circuit can be configured as a radiation treatment plan recommendation resource that accesses a database of radiation treatment plan formulation content items including at least one of a radiation treatment plan template, an auto-planning algorithm, and an auto-segmentation algorithm. By one approach the control circuit can be configured to, when presenting automatically-generated radiation treatment plans to a user, also co-present an opportunity for the user to signal to a remote entity that none of the plans are acceptable and that the user will instead employ a user-generated plan for the particular patient.
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公开(公告)号：US20210299476A1
公开(公告)日：2021-09-30
申请号：US16836411
申请日：2020-03-31
摘要： A control circuit accesses patient information including anatomical image information of the patient, segmentation information corresponding to the anatomical image information, and a dose map for the radiation treatment plan. The control circuit then generates at least one organ-specific three-dimensional risk map as a function of the patient information and presents that risk map to a user via a display.
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4.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.
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5.发明申请公开(公告)号：US20190060671A1
公开(公告)日：2019-02-28
申请号：US15690525
申请日：2017-08-30
IPC分类号： A61N5/10
摘要： A control circuit provides an opportunity via a user interface for a user to specify at least one custom DVH estimation model training feature. The control circuit then combines a predetermined set of DVH estimation model training features with a user-specified customer DVH estimation model training feature to provide a combined feature set. The control circuit uses the combined feature set to train a knowledge-based DVH estimation model which is then used to provide a DVH estimation for use when developing/optimizing a radiation treatment plan. That resultant radiation treatment plan then controls a radiation-administration platform to provide a therapeutic radiation dose to a patient.
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公开(公告)号：US20230095485A1
公开(公告)日：2023-03-30
申请号：US17485794
申请日：2021-09-27
发明人： Elena Czeizler , Mikko Hakala , Shahab Basiri , Hannu Laaksonen , Maria Isabel Cordero Marcos , Christopher Boylan , Jarkko Y. Peltola , Ville Pietilä , Esa Kuusela
摘要： A radiation treatment plan three-dimensional dose prediction machine learning model is trained using a training corpus that includes a plurality of radiation treatment plans that are not specific to a particular patient and wherein the training corpus includes some, but not all, possible patient volumes of interest. Information regarding the patient (including information regarding at least one volume of interest for the patient that was not represented in the training corpus) is input to the radiation treatment plan three-dimensional dose prediction machine model. The latter generates predicted three-dimensional dose distributions that include a predicted three-dimensional dose distribution for the at least one volume of interest that was not represented in the training corpus.
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7.发明授权公开(公告)号：US11238580B2
公开(公告)日：2022-02-01
申请号：US16554742
申请日：2019-08-29
发明人： Hannu Laaksonen , Janne Nord , Jan Schreier
摘要： One or more medical images of a patient are processed by a first neural network model to determine a region-of-interest (ROI) or a cut-off plane. Information from the first neural network model is used to crop the medical images, which serves as input to a second neural network model. The second neural network model processes the cropped medical images to determine contours of anatomical structures in the medical images of the patient. Each of the first and second neural network models are deep neural network models. By use of cropped images in the training and inference phases of the second neural network model, contours are produced with sharp edges or flat surfaces.
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公开(公告)号：US10850120B2
公开(公告)日：2020-12-01
申请号：US15391058
申请日：2016-12-27
发明人： Hannu Laaksonen , Esa Kuusela , Janne Nord , Joakim Pyyry , Perttu Niemela
IPC分类号： A61N5/10
摘要： A clinical goal for radiation treatment of a patient is set. A dose prediction model is selected from a number of dose prediction models based on the clinical goal. A radiation treatment plan is then generated for the patient using the dose prediction model that was selected based on the clinical goal.
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公开(公告)号：US10653893B2
公开(公告)日：2020-05-19
申请号：US15690525
申请日：2017-08-30
IPC分类号： A61N5/10
摘要： A control circuit provides an opportunity via a user interface for a user to specify at least one custom DVH estimation model training feature. The control circuit then combines a predetermined set of DVH estimation model training features with a user-specified customer DVH estimation model training feature to provide a combined feature set. The control circuit uses the combined feature set to train a knowledge-based DVH estimation model which is then used to provide a DVH estimation for use when developing/optimizing a radiation treatment plan. That resultant radiation treatment plan then controls a radiation-administration platform to provide a therapeutic radiation dose to a patient.
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公开(公告)号：US10346593B2
公开(公告)日：2019-07-09
申请号：US15784200
申请日：2017-10-16
摘要： Example methods for radiotherapy treatment planning are provided. One example method may include obtaining training data that includes multiple treatment plans associated with respective multiple past patients; and processing the training data to determine, from each of the multiple treatment plans, at least one of the following: first data associated with a particular past patient or a radiotherapy system for delivering radiotherapy treatment to the particular past patient, second data associated with treatment planning trade-off selected for the particular past patient and third data associated with radiation dose for delivery to the particular past patient. The method may also comprise: based on at least one of the first data, the second data and the third data, identifying one or more sub-optimal characteristics associated with the training data, obtaining improved training data and generating a dose estimation model based on the improved training data.
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