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1. 发明公开

US20240278039A1 TRIGGERED TREATMENT SYSTEMS AND METHODS 审中-公开
公开(公告)号：US20240278039A1
公开(公告)日：2024-08-22
申请号：US18637764
申请日：2024-04-17
申请人： Varian Medical Systems, Inc. , Siemens Healthineers International AG.
发明人： Christel SMITH , Corey ZANKOWSKI , Jan Hein TIMMER , Wolfgang KAISSL , Deepak KHUNTIA , Eric ABEL , Josh STAR-LACK , Camille NOEL
IPC分类号： A61N5/10 , A61B90/00
CPC分类号： A61N5/1069 , A61N5/1049 , A61N5/1067 , A61N5/1077 , A61B2090/0481 , A61N2005/1061 , A61N2005/1094
摘要： In various embodiments, a radiation therapy method can include loading a planning image of a target in a human. In addition, the position of the target can be monitored. A computation can be made of an occurrence of substantial alignment between the position of the target and the target of the planning image. Furthermore, after the computing, a beam of radiation is triggered to deliver a dosage to the target in a short period of time (e.g., less than a second).

2. 发明申请

US20170072220A1 KNOWLEDGE-BASED SPATIAL DOSE METRICS AND METHODS TO GENERATE BEAM ORIENTATIONS IN RADIOTHERAPY 审中-公开
标题翻译：基于知识的空间剂量度量及其在放射治疗中生成光束取向的方法
公开(公告)号：US20170072220A1
公开(公告)日：2017-03-16
申请号：US14850625
申请日：2015-09-10
申请人： Varian Medical Systems, Inc.
发明人： Corey ZANKOWSKI , Janne NORD , Maria Isabel Cordero MARCOS , Joona HARTMAN , Jarkko PELTOLA , Esa KUUSELA
IPC分类号： A61N5/10
CPC分类号： A61N5/1031
摘要： A system for estimating a dose from a radiation therapy plan includes a memory that stores machine-readable instructions and a processor communicatively coupled to the memory, the processor operable to execute the instructions to subdivide a representation of a volume of interest into voxels. The processor also determines distances between a planned radiation field origin and each respective voxel. The processor further computes geometry-based expected (GED) metrics based on the distances, a plan parameter, and a field strength parameter. The processor sums the metrics to yield an estimated dose received by the volume of interest from the planned radiation field.
摘要翻译：用于从辐射治疗计划估计剂量的系统包括存储机器可读指令的存储器和通信地耦合到存储器的处理器，所述处理器可操作以执行指令以将感兴趣体积的表示细分为体素。处理器还确定计划辐射场原点与每个相应体素之间的距离。处理器还基于距离，计划参数和场强参数来计算基于几何的预期（GED）度量。处理器对指标进行总和，以产生由计划辐射场感兴趣的体积所接收的估计剂量。

3. 发明申请

US20170004267A1 METHODS AND SYSTEMS FOR RADIOTHERAPY TREATMENT PLANNING 审中-公开
标题翻译：放射治疗计划的方法和系统
公开(公告)号：US20170004267A1
公开(公告)日：2017-01-05
申请号：US14754742
申请日：2015-06-30
申请人： VARIAN MEDICAL SYSTEMS, INC.
发明人： Michelle SVATOS , Corey ZANKOWSKI
IPC分类号： G06F19/00 , G06F17/18 , G06N7/00
CPC分类号： G06F19/3481 , A61N5/10 , G06N7/005 , G16H50/20
摘要： Example methods and systems for determining a treatment plan for a radiotherapy treatment system to deliver radiotherapy treatment to a patient are disclosed. In one embodiment, the method may comprise performing a particle transport simulation to initiate a first set of particle histories and a second set of particle histories from a radiation source of the radiotherapy treatment system to a treatment volume with multiple voxels representing an anatomy of the patient. The method may also comprise determining first cumulative costs associated with respective particle histories in the first set and second cumulative costs associated with respective particle histories in the second set. The method may further comprise selecting the first set or second set based on the first cumulative costs and second cumulative costs and determining the treatment plan based on the selected first set or second set.
摘要翻译：公开了用于确定用于对患者进行放射治疗治疗的放射疗法治疗系统的治疗计划的示例性方法和系统。在一个实施例中，该方法可以包括执行粒子传输模拟以从放射治疗处理系统的辐射源到具有表示患者的解剖结构的多个体素的治疗体积的第一组粒子历史和第二组粒子历史。该方法还可以包括确定与第一组中的各个粒子历史相关联的第一累积成本以及与第二组中的各个粒子历史相关联的第二累积成本。该方法还可以包括基于第一累积成本和第二累积成本来选择第一集合或第二集合，并且基于所选择的第一集合或第二集合确定治疗计划。

4. 发明申请

US20220414428A1 ARTIFICIAL INTELLIGENCE ENABLED PREFERENCE LEARNING 有权
公开(公告)号：US20220414428A1
公开(公告)日：2022-12-29
申请号：US17362728
申请日：2021-06-29
申请人： VARIAN MEDICAL SYSTEMS, INC.
发明人： Simant DUBE , Oskar RADERMECKER , Parin DALAL , Corey ZANKOWSKI
IPC分类号： G06N3/04 , G06T7/00 , G06K9/46 , G06N3/08 , G06T7/149
摘要： Embodiments described herein provide for training an artificial intelligence model to become a preference-aware model. The artificial intelligence model preferences as the artificial intelligence model trains. Reinforcement learning is used to train experts in the artificial intelligence model such that each expert is trained to converge to a unique preference. The architecture of the artificial intelligence model is highly flexible. Upon executing a trained model, users can select automatically images according to various preferences based on medical professional preferences, geographic preferences, patient anatomy, and institutional guidelines.

5. 发明申请

US20150045604A1 SYSTEM AND METHOD FOR PATIENT-SPECIFIC MOTION MANAGEMENT 有权
标题翻译：患者特定运动管理系统与方法
公开(公告)号：US20150045604A1
公开(公告)日：2015-02-12
申请号：US13960642
申请日：2013-08-06
申请人： Varian Medical Systems, Inc.
发明人： Daren SAWKEY , Michelle M. SVATOS , Corey ZANKOWSKI
IPC分类号： A61N5/10
CPC分类号： A61N5/1068
摘要： A medical method includes: determining a first probability density function related to a first uncertainty in hitting a target in a treatment of the target; determining a second probability density function related to a second uncertainty, wherein the first uncertainty is attributable to a first source of uncertainty, and the second uncertainty is attributable to a second source of uncertainty that is different from the first source of uncertainty; processing at least the first probability density function and the second probability density function using a processing unit; and outputting a result of the processing.
摘要翻译：医疗方法包括：确定与目标治疗中击中目标的第一不确定性有关的第一概率密度函数; 确定与第二不确定性相关的第二概率密度函数，其中所述第一不确定性归因于第一不确定性来源，并且所述第二不确定性归因于不同于所述第一不确定性来源的第二不确定源; 使用处理单元处理至少第一概率密度函数和第二概率密度函数; 并输出处理结果。

6. 发明申请

US20190051398A1 AGGREGATION OF ARTIFICIAL INTELLIGENCE (AI) ENGINES 审中-公开
公开(公告)号：US20190051398A1
公开(公告)日：2019-02-14
申请号：US16102770
申请日：2018-08-14
申请人： VARIAN MEDICAL SYSTEMS, INC.
发明人： Corey ZANKOWSKI , Charles ADELSHEIM , Joakim PYYRY , Esa KUUSELA
IPC分类号： G16H20/40 , G06N99/00
摘要： Example methods and systems for generating an aggregated artificial intelligence (AI) engine for radiotherapy treatment planning are provided. One example method may include obtaining multiple AI engines associated with respective multiple treatment planners; generating multiple sets of output data using the multiple AI engines associated with the respective multiple treatment planners: comparing the multiple AI engines associated with the respective multiple treatment planners based on the multiple sets of output data; and based on the comparison, aggregating at least some of the multiple AI engines to generate the aggregated AI engine for performing the particular treatment planning step. The multiple AI engines may be trained to perform a particular treatment planning step, and each of the multiple AI engines is trained to emulate one of the multiple treatment planners performing the particular treatment planning step.

7. 发明申请

US20160364879A1 KNOWLEDGE-BASED AUTOMATIC IMAGE SEGMENTATION 审中-公开
标题翻译：基于知识的自动图像分割
公开(公告)号：US20160364879A1
公开(公告)日：2016-12-15
申请号：US15250287
申请日：2016-08-29
申请人： Varian Medical Systems, Inc.
发明人： Corey ZANKOWSKI
IPC分类号： G06T7/00
CPC分类号： G06T7/143 , G06T7/11 , G06T2207/20128 , G06T2207/30004 , G06T2207/30061 , G06T2207/30081 , G06T2207/30096 , G06T2207/30204
摘要： A method for medical image segmentation. The method includes accessing and updating a knowledge-base in accordance with embodiments of the present invention. The techniques include: receiving a medical image and computing a sparse landmark signature based on the medical image content. Next, a knowledge-base is searched for representative matches to form a base set, wherein the base set comprises a plurality of reference image sets. A portion of the plurality of reference image sets of the base set is deformed to generate mappings from the base set to the medical image set. Finally a weighted average segmentation for each structure of interest of the medical image set is determined.
摘要翻译：一种医学图像分割方法。该方法包括根据本发明的实施例访问和更新知识库。所述技术包括：基于医学图像内容接收医学图像和计算稀疏地标签名。接下来，搜索知识库以进行代表性匹配以形成基本集合，其中基本集合包括多个参考图像集合。基本组的多个参考图像组的一部分被变形以产生从基本集合到医学图像集合的映射。最后，确定医学图像集的感兴趣结构的加权平均分割。

8. 发明申请

US20150302603A1 KNOWLEDGE-BASED AUTOMATIC IMAGE SEGMENTATION 审中-公开
标题翻译：基于知识的自动图像分割
公开(公告)号：US20150302603A1
公开(公告)日：2015-10-22
申请号：US14698479
申请日：2015-04-28
申请人： Varian Medical Systems, Inc.
发明人： Corey ZANKOWSKI
IPC分类号： G06T7/00
CPC分类号： G06T7/149 , G06T7/33 , G06T2207/20128 , G06T2207/30004
摘要： A method for medical image segmentation includes accessing and updating a knowledge-base. A medical image is received and a sparse landmark signature is computed based on the medical image. Either a representative or a cohort average reference image set is selected. A portion of either representative reference image set or the cohort average reference image set is deformed to generate mappings to the medical image set. A segmentation for each structure of interest of the medical image set is determined. The knowledge-base is searched for representative matches to form a plurality of sub-volume base sets comprising a plurality of reference image set sub-volumes. A portion of the plurality of reference image set sub-volumes is deformed to generate mappings from the plurality of sub-volume base sets to corresponding structures of interest of the medical image set. A weighted-average segmentation for the structures of interest in the medical image set is calculated.
摘要翻译：用于医学图像分割的方法包括访问和更新知识库。接收医学图像，并且基于医学图像计算稀疏地标签名。选择代表性或队列平均参考图像集合。代表参考图像集或队列平均参考图像集合的一部分变形以产生对医学图像集合的映射。确定医学图像集合的感兴趣的每个结构的分割。搜索知识库以进行代表性匹配以形成包括多个参考图像集子卷的多个子卷基本集合。多个参考图像集子体积的一部分被变形以产生从多个子体积基本集合到医学图像集的感兴趣的相应结构的映射。计算医学图像集中感兴趣结构的加权平均分割。

9. 发明申请

US20200269068A1 MATERIAL INSERTS FOR RADIATION THERAPY 审中-公开
公开(公告)号：US20200269068A1
公开(公告)日：2020-08-27
申请号：US16436671
申请日：2019-06-10
申请人： Varian Medical Systems, Inc. , Varian Medical Systems International AG , Varian Medical Systems Particle Therapy GmbH
发明人： Eric ABEL , Corey ZANKOWSKI , Jessica PEREZ , Anthony MAGLIARI , Christel SMITH , MIchael FOLKERTS , Bill HANSEN , Reynald VANDERSTRAETEN , Timo KOPONEN
IPC分类号： A61N5/10
摘要： A system for treating a patient during radiation therapy is disclosed. The system includes a shell, a plurality of material inserts disposed in the shell, where each material insert of the plurality of material inserts respectively shapes a distribution of a dose delivered to the patient by a respective beam of a plurality of beams emitted from a nozzle of a radiation treatment system, and a scaffold component disposed in the shell that holds the plurality material inserts in place relative to the patient such that each material insert lies on a path of at least one of the beams.

10. 发明申请

US20190074079A1 RADIOTHERAPY TREATMENT PLANNING USING ARTIFICIAL INTELLIGENCE (AI) ENGINES 审中-公开
公开(公告)号：US20190074079A1
公开(公告)日：2019-03-07
申请号：US16102767
申请日：2018-08-14
申请人： VARIAN MEDICAL SYSTEMS, INC.
发明人： Corey ZANKOWSKI , Charles ADELSHEIM , Joakim PYYRY , Esa KUUSELA
IPC分类号： G16H20/40 , G06K9/62 , A61N5/10 , G16H50/20 , G06N5/02
摘要： Example methods and systems for radiotherapy treatment planning are provided. One example method may comprise obtaining image data associated with a patient; and processing the image data to generate a treatment plan for the patient using an inferential chain that includes multiple AI engines that are trained separately to perform respective multiple treatment planning steps. A first treatment planning step may be performed using a first AI engine to generate first output data based on at least one of: (i) the image data, and (ii) first input data generated based on the image data. A second treatment planning step may be performed using a second AI engine to generate the treatment plan based on at least one of: (i) the first output data, and (ii) second input data generated based on the first output data.

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