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公开(公告)号：US20240241194A1
公开(公告)日：2024-07-18
申请号：US18559973
申请日：2022-05-13
申请人： Elekta, Inc.
CPC分类号： G01R33/1276 , A61N5/1071 , A61N2005/1087
摘要： Systems and techniques may be used for generating an image using one or more protons. For example, a technique may include detecting, over a time period using two orthogonal two-dimensional detector arrays, a magnetic field corresponding to a proton in motion. The technique may include determining a trajectory of the proton based on the magnetic field over the period of time, and generating a two-dimensional proton image using the trajectory. The two-dimensional proton image may be output for display.
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公开(公告)号：US11954761B2
公开(公告)日：2024-04-09
申请号：US16949720
申请日：2020-11-11
申请人： Elekta, Inc.
发明人： Xiao Han
CPC分类号： G06T11/00 , A61B5/055 , A61B5/7267 , G01R33/4812 , G01R33/5608 , A61B6/032 , A61B6/037 , A61B8/00 , G01R33/481 , G01R33/4814
摘要： Systems, computer-implemented methods, and computer readable media for generating a synthetic image of an anatomical portion based on an origin image of the anatomical portion acquired by an imaging device using a first imaging modality are disclosed. These systems may be configured to receive the origin image of the anatomical portion acquired by the imaging device using the first imaging modality, receive a convolutional neural network model trained for predicting the synthetic image based on the origin image, and convert the origin image to the synthetic image through the convolutional neural network model. The synthetic image may resemble an imaging of the anatomical portion using a second imaging modality differing from the first imaging modality.
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公开(公告)号：US11679276B2
公开(公告)日：2023-06-20
申请号：US17302254
申请日：2021-04-28
申请人： Elekta, Inc.
CPC分类号： A61N5/1039 , A61N5/1037 , A61N5/1067 , A61N5/1081 , G06N20/10 , A61N2005/1041
摘要： Systems and methods are disclosed for monitoring anatomic position of a human subject and modifying a radiotherapy treatment based on anatomic position changes, as determined with a regression model trained to estimate movement of a region of interest. Example operations for movement monitoring and therapy control include: obtaining 3D image data for a subject, which provides a reference volume and at least one defined region of interest; obtaining real-time 2D image data corresponding to the subject, captured during the radiotherapy treatment session; extracting features from the 2D image data; producing a relative motion estimation of a region of interest with a machine learning regression model, the model trained to estimate a spatial transformation from the 2D image data based on training from the reference volume; and controlling a radiotherapy beam of a radiotherapy machine used in the radiotherapy session, based on the relative motion estimation.
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公开(公告)号：US11517768B2
公开(公告)日：2022-12-06
申请号：US15658484
申请日：2017-07-25
申请人： Elekta, Inc.
发明人： Lyndon S. Hibbard
摘要： Systems and methods can include a method for training a deep convolutional neural network to provide a patient radiation treatment plan, the method comprising collecting patient data from a group of patients, the patient data including at least one image of patient anatomy and a prior treatment plan, wherein the treatment plan includes predetermined machine parameters, and training a deep convolution neural network for regression by using the prior treatment plans and the corresponding collected patient data to determine a new treatment plan. Systems and methods can also include a method of using a deep convolutional neural network to provide a radiation treatment plan, the method comprising retrieving a trained deep convolution neural network previously trained on patient data from a group of patients, collecting new patient data, wherein the new patient data includes at least one image of patient anatomy, and determining a new treatment plan for the new patient using the trained deep convolutional neural network for regression, wherein the new treatment plan has a new set of machine parameters.
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公开(公告)号：US11369804B2
公开(公告)日：2022-06-28
申请号：US17302225
申请日：2021-04-27
申请人： Elekta, Inc.
发明人： Martin Soukup , Kun-Yu Tsai
摘要： System and methods may be used for arc fluence optimization without iteration to arc sequence generation. A method may include defining a particle arc range for a radiotherapy treatment of a patient, and generating an arc sequence, including a set of parameters for delivering the radiotherapy treatment, without requiring a dose calculation. The method may include optimizing fluence of the arc sequence for the radiotherapy treatment without iterating back to arc sequence generation, and outputting the fluence optimized arc sequence for use in the radiotherapy treatment.
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公开(公告)号：US11318327B2
公开(公告)日：2022-05-03
申请号：US16665360
申请日：2019-10-28
申请人： Elekta, Inc.
摘要： The present disclosure relates to a method for use in adaptive radiotherapy and a treatment planning device. The method may comprise accessing a first medical image and a second medical image that represent a region of interest of a patient at different times. Each medical image is segmented into a target region and at least one non-target region. The method may further comprise accessing a deformation vector field including a plurality of vectors, wherein each vector defines a geometric transformation to map a respective voxel in the first medical image to a corresponding voxel in the second medical image. The method may further comprise generating a modified deformation vector field by: identifying a first vector in the deformation vector field that maps a voxel in the first medical image to a voxel that is in a non-target region in the second medical image; and determining whether the first vector causes a distance between the mapped voxel and the target region to increase and, if so, reducing the magnitude of the first vector. The method may further comprise post-processing the modified deformation vector field to compensate for changes in the shape or size of the target region.
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公开(公告)号：US20210339046A1
公开(公告)日：2021-11-04
申请号：US17305772
申请日：2021-07-14
申请人： Elekta, Inc.
摘要： Systems and techniques may be used to estimate a patient state during a radiotherapy treatment. For example, a method may include generating a dictionary of expanded potential patient measurements and corresponding potential patient states using a preliminary motion model. The method may include training, using a machine learning technique, a correspondence motion model relating an input patient measurement to an output patient state using the dictionary. The method may include estimating, using a processor, the patient state corresponding to an input image using the correspondence motion model.
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公开(公告)号：US10886026B2
公开(公告)日：2021-01-05
申请号：US15556676
申请日：2016-03-10
申请人： ELEKTA, INC.
发明人： Johannes Ferdinand Van Der Koijk , Scot Evan Hogan , Colin Raymond Winfield , Alexis Nicolaas Thomas Jozef Kotte
摘要： This disclosure relates generally to treatment management systems, which may include a clinical database for storing therapeutic protocols. The system may also include a treatment engine operatively connected to the clinical database. The treatment engine may obtain diagnostic information and select a first plurality of therapeutic protocols from the clinical database based on the obtained diagnostic information and reference protocol data. The treatment engine may calculate a treatment efficacy probability for each protocol using the reference protocol data. The treatment engine may develop a first treatment plan and evaluate intermediate data indicating an altered patient state due to the first treatment plan. The treatment engine may select, based on reference protocol data and adaptive protocol data, a second treatment plan using a second plurality of therapeutic protocols. The selected second treatment plan is adapted based on the clinical objective, the reference protocol data, and the treatment efficacy information.
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公开(公告)号：US10668300B2
公开(公告)日：2020-06-02
申请号：US15836474
申请日：2017-12-08
申请人： Elekta, Inc.
发明人： Sami Hissoiny , Michel Moreau
摘要： Radiation treatment planning and administration can include a Monte Carlo computer simulation tool to simulate photo-generated electrons in tissue. In the simulation, electrons that have left tissue voxels and entered air voxels can be evaluated to identify electrons that are circling along a spiraling trajectory in the air voxels. After at least one full spiraling circumference or other specified distance has been traversed using a detailed electron transport model, a simpler linear ballistic motion model can be instituted. This speeds simulation while accurately accounting for spiraling electrons that re-enter tissue voxels.
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公开(公告)号：US10664723B2
公开(公告)日：2020-05-26
申请号：US16122331
申请日：2018-09-05
申请人： Elekta, Inc.
发明人： Xiao Han
IPC分类号： G06K9/62 , G06N20/00 , G06F16/51 , G06N5/04 , G06T7/00 , A61N5/10 , G06T5/00 , G06K9/46 , G06K9/66 , A61B5/055 , G01R33/48 , A61B6/03 , A61B6/00
摘要： Systems and methods are provided for generating a pseudo-CT prediction model using multi-channel MR images. An exemplary system may include a processor configured to retrieve training data including multiple MR images and at least one CT image for each of a plurality of training subjects. For each training subject, the processor may determine at least one tissue parameter map based on the multiple MR images and obtain CT values based on the at least one CT image. The processor may also generate the pseudo-CT prediction model based on the tissue parameter maps and the CT values of the plurality of training subjects.
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