公开(公告)号：WO2022129144A1

公开(公告)日：2022-06-23

申请号：PCT/EP2021/085848

申请日：2021-12-15

Applicant: VARIAN MEDICAL SYSTEMS INTERNATIONAL AG

Inventor： BASIRI, Shahab ,  HAKALA, Mikko ,  KUUSELA, Esa ,  CZEIZLER, Elena

IPC: G16H20/40

Abstract: Disclosed herein are systems and methods for iteratively training artificial intelligence models using reinforcement learning techniques. With each iteration, a training agent applies a random radiation therapy treatment attribute corresponding to the radiation therapy treatment attribute associated with previously performed radiation therapy treatments when an epsilon value indicative of a likelihood of exploration and exploitation training of the artificial intelligence model satisfies a threshold. When the epsilon value does not satisfy the threshold, the agent generates (242), using an existing policy, a first predicted radiation therapy treatment attribute, and generates (244), using a predefined model, a second predicted radiation therapy treatment attribute. The agent applies (246) one of the first predicted radiation therapy treatment attribute or the second predicted radiation therapy treatment attribute that is associated with a higher reward. The agent iteratively repeats (248) training the artificial intelligence model until the existing policy satisfies an accuracy threshold.

公开(公告)号：WO2020200849A1

公开(公告)日：2020-10-08

申请号：PCT/EP2020/057847

申请日：2020-03-20

Applicant: VARIAN MEDICAL SYSTEMS INTERNATIONAL AG

Inventor： KUUSELA, Esa ,  LAAKSONEN, Hannu ,  HALKO, Lauri

IPC: A61N5/10

Abstract: Cost functions and cost function gradients for use in radiation treatment planning can be computed based on an approximation of an "isodose" surface. Where a clinical goal is expressed by reference to a threshold isodose surface, a corresponding cost function component can be defined directly by reference to that isodose surface 1004, and a corresponding contribution to the cost function gradient can be approximated by identifying voxels that are intersected by the threshold isodose surface and approximating the gradient of the dose distribution within each such voxel.

公开(公告)号：WO2018054873A2

公开(公告)日：2018-03-29

申请号：PCT/EP2017/073568

申请日：2017-09-19

Applicant: VARIAN MEDICAL SYSTEMS INTERNATIONAL AG

Inventor： OLLILA, Santtu ,  VAINIO, Mikko ,  PELTOLA, Jarkko ,  NORD, Janne ,  KUUSELA, Esa

IPC: A61N5/10

Abstract: Methods and systems are provided for developing radiation therapy treatment plans. A treatment template with radiation fields can be chosen for a patient based on a tumor location. Static radiation field positions can be adjusted for the patient, while arc radiation fields may remain the same. Static radiation field positions can be adjusted using dose gradient, historical patient data, and other techniques.

公开(公告)号：WO2018050883A1

公开(公告)日：2018-03-22

申请号：PCT/EP2017/073422

申请日：2017-09-18

Applicant: VARIAN MEDICAL SYSTEMS INTERNATIONAL AG

Inventor： OLLILA, Santtu ,  VAINIO, Mikko ,  PELTOLA, Jarkko ,  NORD, Janne ,  KUUSELA, Esa ,  KAUPPINEN, Juha ,  PETAJA, Viljo ,  RUSANEN, Marko

IPC: A61N5/10

Abstract: In a radiation treatment plan that includes a plurality of treatment fields of multiple treatment modalities, such as IMRT modality and dynamic treatment path modality (e.g., VMAT and conformal arc therapy), an optimized spatial point sequence may be determined that optimizes the total treatment time, which includes both the beam-on time (i.e., during the delivery of radiation dose) and the beam-off time (i.e., during transitions between consecutive treatment fields). The result is a time-ordered field trajectory that intermixes and interleaves different treatment fields, in one embodiment, a dynamic treatment path may be cut into a plurality of sections, and one or more IMRT fields may be inserted between the plurality of sections.

Abstract translation: 在包括诸如IMRT模态和动态治疗路径模态（例如，VMAT和共形电弧治疗）的多个治疗模态的多个治疗场的放射治疗计划中，优化的空间点序列可以 被确定为优化总治疗时间，其包括射束开启时间（即，在辐射剂量的递送期间）和射束关闭时间（即，在连续治疗区域之间的过渡期间）。 结果是混合并交织不同处理区域的时间有序的场轨迹。在一个实施例中，动态处理路径可以被切割成多个区段，并且可以在多个区段之间插入一个或多个IMRT字段。 / p>

公开(公告)号：WO2022063682A1

公开(公告)日：2022-03-31

申请号：PCT/EP2021/075521

申请日：2021-09-16

Applicant: VARIAN MEDICAL SYSTEMS INTERNATIONAL AG

Inventor： HAKALA, Mikko ,  KUUSELA, Esa ,  CZEIZLER, Elena ,  BASIRI, Shahab

IPC: A61N5/10 ,  A61N5/00

Abstract: These teachings serve to facilitate radiating a treatment target (105) in a patient (104) during a radiation treatment session with a radiation treatment platform (114) having a moving source of radiation (115) and using an optimized radiation treatment plan (113). These teachings in particular provide for configuring the radiation treatment platform (114) in a half-fan trajectory arrangement. These teachings then provide for beginning the radiation treatment session with the source of radiation (115) in a first location and an isocenter (301) for the treatment target (105) in a first position. Then, during the radiation treatment session, these teachings provide for moving the source of radiation (115) from that first location in synchronization with moving the isocenter (301) from the aforementioned first position.

公开(公告)号：WO2018122251A1

公开(公告)日：2018-07-05

申请号：PCT/EP2017/084629

申请日：2017-12-27

Applicant: VARIAN MEDICAL SYSTEMS INTERNATIONAL AG

Inventor： KUUSELA, Esa ,  SIKET-SZASZ, Edit ,  LESSARD, Marco ,  HALKO, Lauri

IPC: A61N5/10

Abstract: In a method of interactive manipulation of the dose distribution of a radiation treatment plan, after an initial candidate treatment plan has been obtained, a set of clinical goals are transferred into a set of constraints. Each constraint may be expressed in terms of a threshold value for a respective quality index of the dose distribution. The dose distribution can then be modified interactively by modifying the threshold values for the set of constraints. Re-optimization may be performed based on the modified threshold values. A user may assign relative priorities among the set of constraints. When a certain constraint is modified, a re-optimized treatment plan may not violate those constraints that have priorities that are higher than that of the modified constraint, but may violate those constraints that have priorities that are lower than that of the modified constraint.

公开(公告)号：WO2018054873A3

公开(公告)日：2018-03-29

申请号：PCT/EP2017/073568

申请日：2017-09-19

Applicant: VARIAN MEDICAL SYSTEMS INTERNATIONAL AG

Inventor： OLLILA, Santtu ,  VAINIO, Mikko ,  PELTOLA, Jarkko ,  NORD, Janne ,  KUUSELA, Esa

IPC: A61N5/10

Abstract: Methods and systems are provided for developing radiation therapy treatment plans. A treatment template with radiation fields can be chosen for a patient based on a tumor location. Static radiation field positions can be adjusted for the patient, while arc radiation fields may remain the same. Static radiation field positions can be adjusted using dose gradient, historical patient data, and other techniques.

Abstract translation: 为开发放射疗法治疗计划提供了方法和系统。 基于肿瘤位置可以为患者选择具有辐射场的治疗模板。 可以为患者调整静态辐射场位置，而电弧辐射场可以保持不变。 可以使用剂量梯度，历史患者数据和其他技术来调整静态辐射场位置。

公开(公告)号：WO2017167439A1

公开(公告)日：2017-10-05

申请号：PCT/EP2017/000370

申请日：2017-03-27

Applicant: VARIAN MEDICAL SYSTEMS INTERNATIONAL AG

Inventor： KUUSELA, Esa ,  CORDERO MARCOS, Maria Isabel ,  NORD, Janne

IPC: A61N5/10

CPC classification number: A61N5/1071 ,  A61N5/103 ,  A61N5/1031 ,  A61N2005/1041 ,  A61N2005/1087

Abstract: A system for estimating a dose from a proton therapy plan includes a memory that stores machine instructions and a processor coupled to the memory that executes the machine instructions to subdivide a representation of a volume of interest in a patient anatomy traversed by a planned proton field into a plurality of voxels. The processor further executes the machine instructions to determine the distance from the source of the planned proton beam to one of the voxels. The processor also executes the machine instructions to compute the discrete contribution at the voxel to an estimated dose received by the volume of interest from the planned proton beam based on the distance between the source and the volume of interest.

Abstract translation: 用于根据质子治疗计划来估计剂量的系统包括存储机器指令的存储器和耦合到存储器的处理器，所述处理器执行机器指令以细分患者中感兴趣体积的表示 被计划的质子场横穿成多个体素的解剖学。 处理器还执行机器指令以确定从计划质子束的源到体素中的一个的距离。 处理器还执行机器指令，以基于源和感兴趣体积之间的距离，计算体素对由计划质子束感兴趣体积接收的估计剂量的离散贡献。

公开(公告)号：WO2017041194A1

公开(公告)日：2017-03-16

申请号：PCT/CH2016/000119

申请日：2016-09-12

Applicant: VARIAN MEDICAL SYSTEMS INTERNATIONAL AG

Inventor： NORD, Janne ,  KUUSELA, Esa ,  PYYRY, Joakim ,  PELTOLA, Jarkko ,  SABEL, Martin

IPC: A61N5/10

CPC classification number: A61N5/1031 ,  A61N5/1038 ,  A61N2005/1041 ,  A61N2005/1074 ,  G06F19/00 ,  G06F19/3481

Abstract: A method of generating a treatment plan for treating a patient with radiotherapy, the method includes obtaining a plurality of sample plans, which are generated by use of a knowledge base comprising historical treatment plans and patient data. The method also includes performing a multi-criteria optimization based on the plurality of sample plans to construct a Pareto frontier, where the plurality of sample plans are evaluated with at least two objectives measuring qualities of the plurality of sample plans such that treatment plans on the constructed Pareto frontier are Pareto optimal with respect to the objectives. The method further includes identifying a treatment plan by use of the constructed Pareto frontier.

Abstract translation: 一种产生用于治疗放射治疗患者的治疗计划的方法，所述方法包括获得通过使用包括历史治疗计划和患者数据的知识库产生的多个样本计划。 该方法还包括基于多个样本计划执行多标准优化以构建帕累托边界，其中多个样本计划被评估至少两个测量多个样本计划的质量的目标，使得在 建造的帕累托边界是帕累托最优的目标。 该方法还包括通过使用构建的帕累托边界来识别治疗计划。

公开(公告)号：WO2022200244A2

公开(公告)日：2022-09-29

申请号：PCT/EP2022/057279

申请日：2022-03-21

Applicant: VARIAN MEDICAL SYSTEMS INTERNATIONAL AG

Inventor： KUUSELA, Esa ,  LAAKSONEN, Hannu

IPC: G06T7/00 ,  G06T7/11 ,  G06N20/00 ,  G06T2207/10072 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30004 ,  G06T2207/30096 ,  G06T7/0012 ,  G16H20/40 ,  G16H30/20

Abstract: Disclosed herein are systems and methods for training a machine learning model for automatic organ segmentation. A processor receives 210 an image of one or more pre-contoured organs, the image comprising a plurality of voxels. The processor executes 220 a machine learning model using the image to output predicted organ labels for the plurality of voxels of the image. The processor determines 230 differences between corresponding predicted organ labels and expected organ labels for the plurality of voxels. The processor determines 240 radiation dose levels that correspond to the plurality of voxels of the image. The processor determines 250 weights for the plurality of voxels based on the radiation dose levels of the respective voxels. The processor then trains 260 the machine learning model based on the differences and the weights for the plurality of voxels.