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    GENERATING TIME-EFFICIENT TREATMENT FIELD TRAJECTORIES FOR EXTERNAL-BEAM RADIATION TREATMENTS
    2.
    发明申请
    GENERATING TIME-EFFICIENT TREATMENT FIELD TRAJECTORIES FOR EXTERNAL-BEAM RADIATION TREATMENTS 审中-公开
    生成用于外照射治疗的时效治疗野外运动

    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>

    IMAGE DEFORMATION USING MULTIPLE IMAGE REGIONS
    3.
    发明申请
    IMAGE DEFORMATION USING MULTIPLE IMAGE REGIONS 审中-公开
    使用多个图像区域的图像变形

    公开(公告)号：WO2008116565A3

    公开(公告)日：2008-10-02

    申请号：PCT/EP2008/002056

    申请日：2008-03-14

    Applicant: VARIAN MEDICAL SYSTEMS INTERNATIONAL AG ,  NORD, Janne Llmari ,  PELTOLA, Jarkko

    Inventor： NORD, Janne Llmari ,  PELTOLA, Jarkko

    IPC: G06T7/00

    Abstract: Disclosed are systems for and methods of registering (i.e., aligning) a deformable image with a reference image subject to a plurality of regions within the deformable and reference images. Different members of the plurality of regions may be used in different phases of a deformation algorithm and the identity of these regions may change between different iterations of the deformation algorithm. In some embodiments, most of an image is used for calculation of the internal force of the demons algorithm while a smaller subset of the image is used for calculating the opposing external force.

    Abstract translation: 公开了将可变形图像与经历可变形图像和参考图像内的多个区域的参考图像配准（即，对齐）的系统和方法。 多个区域中的不同成员可以用于变形算法的不同阶段，并且这些区域的身份可以在变形算法的不同迭代之间改变。 在一些实施例中，图像的大部分用于计算恶魔算法的内部力，而图像的较小子集用于计算相反的外力。

    SYSTEMES AND METHODS FOR AUTOMATIC CREATION OF DOSE PREDICTION MODELS AND THERAPY TREATMENT PLANS AS A CLOUD SERVICE
    5.
    发明申请
    SYSTEMES AND METHODS FOR AUTOMATIC CREATION OF DOSE PREDICTION MODELS AND THERAPY TREATMENT PLANS AS A CLOUD SERVICE 审中-公开
    用于自动创建剂量预测模型和治疗方案作为云服务的系统和方法

    公开(公告)号：WO2014187866A1

    公开(公告)日：2014-11-27

    申请号：PCT/EP2014/060461

    申请日：2014-05-21

    Applicant: VARIAN MEDICAL SYSTEMS INTERNATIONAL AG

    Inventor： HARTMAN, Joona ,  CORDERO MARCOS, Maria ,  KUUSELA, Esa ,  PELTOLA, Jarkko ,  NORD, Janne

    IPC: A61N5/10

    CPC classification number: A61N5/1031 ,  A61N5/103 ,  A61N5/1043 ,  A61N2005/1041 ,  A61N2005/1074 ,  A61N2005/1087 ,  G06F19/00 ,  G06F19/3418 ,  G06F19/3481

    Abstract: The present invention proposes a method for automatically creating a dose prediction model based on existing clinical knowledge that is accumulated from multiple sources without collaborators establishing communication links between each other. According to embodiments of the claimed subject matter, clinics can collaborate in creating a dose prediction model by submitting their treatment plans into a remote computer system (such as a cloud-based system) which aggregates information from various collaborators and produces a model that captures clinical information from all submitted treatment plans. According to further embodiments, the method may contain a step where all patient data submitted by a clinic is made anonymous or the relevant parameters are extracted and condensed prior to submitting them over the communications link in order to comply with local regulations.

    Abstract translation: 本发明提出了一种基于从多个来源积累的现有临床知识自动创建剂量预测模型的方法，没有协作者建立彼此之间的通信链路。 根据所要求保护的主题的实施例，诊所可以通过将其治疗计划提交到远程计算机系统（例如基于云的系统）中来协作来创建剂量预测模型，所述远程计算机系统聚合来自各种协作者的信息并产生捕获临床的模型 所有提交的治疗计划的资料。 根据另外的实施例，该方法可以包括一个步骤，其中由诊所提交的所有患者数据是匿名的，或者在通过通信链路提交相关参数之前提取和浓缩相关参数以符合当地法规。

    APPARATUS AND METHOD PERTAINING TO DETERMINING A SPATIALLY-VARIANT NORMAL TISSUE CONSTRAINT AS A FUNCTION OF DOSE DISTRIBUTION
    6.
    发明申请
    APPARATUS AND METHOD PERTAINING TO DETERMINING A SPATIALLY-VARIANT NORMAL TISSUE CONSTRAINT AS A FUNCTION OF DOSE DISTRIBUTION 审中-公开
    用于确定空间变异的正常组织约束的装置和方法作为剂量分布的功能

    公开(公告)号：WO2014009564A1

    公开(公告)日：2014-01-16

    申请号：PCT/EP2013/064903

    申请日：2013-07-15

    Applicant: VARIAN MEDICAL SYSTEMS INTERNATIONAL AG

    Inventor： NORD, Janne ,  PELTOLA, Jarkko

    IPC: A61N5/10

    CPC classification number: A61N5/1031 ,  A61N2005/1041

    Abstract: A control circuit optimizes a radiation-treatment plan (as regards treating at least one target volume for a given patient) by automatically determining a spatially-variant normal tissue constraint as a function, at least in part, of dose distribution for normal tissue that is proximal to the target volume. If desired, the control circuit can repeatedly determine spatially-variant normal tissue constraints while optimizing the radiation-treatment plan. This automatic determination can comprise evaluating dose distributions at specific different distances from the target volume. So configured, the control circuit can effect such evaluation by penalizing, during the optimization of the radiation-treatment plan, dose distribution levels that exceed a predetermined distribution property (such as an aggregation value for the dose values including, but not limited to, an average value of dose values for each of the given specific different distances) at a given one of the specific different distances.

    Abstract translation: 控制电路通过自动确定空间变异的正常组织约束作为至少部分由正常组织的剂量分布的函数来优化放射治疗计划（关于给定患者的至少一个目标体积） 靠近目标体积。 如果需要，控制电路可以在优化放射治疗计划的同时重复地确定空间变异的正常组织约束。 该自动确定可以包括在与目标体积不同的距离处评估剂量分布。 如此配置，控制电路可以通过在放射治疗计划的优化期间惩罚超过预定分布特性的剂量分布水平（例如剂量值的聚集值，包括但不限于 在给定的特定不同距离之间的给定特定不同距离的每个的剂量值的平均值）。

    ARTIFICIAL INTELLIGENCE MODELING FOR RADIATION THERAPY DOSE DISTRIBUTION ANALYSIS
    7.
    发明申请
    ARTIFICIAL INTELLIGENCE MODELING FOR RADIATION THERAPY DOSE DISTRIBUTION ANALYSIS 审中-公开

    公开(公告)号：WO2022200178A1

    公开(公告)日：2022-09-29

    申请号：PCT/EP2022/057042

    申请日：2022-03-17

    Applicant: VARIAN MEDICAL SYSTEMS INTERNATIONAL AG

    Inventor： PELTOLA, Jarkko ,  RUSANEN, Marko ,  PIETILA, Ville

    IPC: A61N5/10

    Abstract: Disclosed herein are methods and systems to optimize a radiation therapy treatment plan using dose distribution values predicted via a trained artificial intelligence model. A server trains the AI model using a training dataset comprising data associated with a plurality of previously implemented radiation therapy treatments on a plurality of previous patients and dose distributions associated with one or more organs of each previous patient. The server then executes (420) the trained AI model to predict dose distribution for a patient. The server then displays a heat map illustrating the predicted values, transmits the predicted values to a plan optimizer to generate an optimized treatment plan for the patient, and/or transmits (430) an alert when a treatment plan generated by a plan optimizer deviates from rules and thresholds indicated within the patient's plan objectives.

    CONTROLLING AND SHAPING THE DOSE DISTRIBUTION OUTSIDE TREATMENT TARGETS IN EXTERNAL-BEAM RADIATION TREATMENTS
    8.
    发明申请
    CONTROLLING AND SHAPING THE DOSE DISTRIBUTION OUTSIDE TREATMENT TARGETS IN EXTERNAL-BEAM RADIATION TREATMENTS 审中-公开
    控制和塑造外部辐射治疗中的剂量分布外部治疗目标

    公开(公告)号：WO2018054907A1

    公开(公告)日：2018-03-29

    申请号：PCT/EP2017/073652

    申请日：2017-09-19

    Applicant: VARIAN MEDICAL SYSTEMS INTERNATIONAL AG

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

    IPC: A61N5/10

    Abstract: Streamlined and partially automated methods of setting normal tissue objectives in radiation treatment planning are provided. These methods may be applied to multiple-target cases as well as single-target cases. The methods can impose one or more target-specific dose falloff constraints around each target, taking into account geometric characteristics of each target such as target volume and shape. In some embodiments, methods can also take into account a planner's preferences for target dose homogeneity. In some embodiments, methods can generate additional dose falloff constraints in locations between two targets where dose bridging is likely to occur.

    Abstract translation: 提供了在放射治疗计划中设定正常组织目标的简化和部分自动化的方法。 这些方法可能适用于多目标案例以及单目标案例。 考虑到每个目标的几何特征，诸如目标体积和形状，该方法可以在每个目标周围施加一个或多个目标特定剂量衰减约束。 在一些实施例中，方法还可以考虑计划者对目标剂量均匀性的偏好。 在一些实施方案中，方法可以在可能发生剂量桥接的两个目标之间的位置产生额外的剂量衰减约束。

    ARTIFICIAL INTELLIGENCE MODELING FOR RADIATION THERAPY DOSE DISTRIBUTION ANALYSIS
    10.
    发明申请
    ARTIFICIAL INTELLIGENCE MODELING FOR RADIATION THERAPY DOSE DISTRIBUTION ANALYSIS 审中-公开

    公开(公告)号：WO2022200181A1

    公开(公告)日：2022-09-29

    申请号：PCT/EP2022/057059

    申请日：2022-03-17

    Applicant: VARIAN MEDICAL SYSTEMS INTERNATIONAL AG

    Inventor： PELTOLA, Jarkko ,  RUSANEN, Marko ,  PIETILA, Ville

    IPC: A61N5/10

    Abstract: Disclosed herein are methods and systems to optimize a radiation therapy treatment plan using dose distribution values predicted via a trained artificial intelligence model. A server trains the Al model using a training dataset comprising data associated with a plurality of previously implemented radiation therapy treatments on a plurality of previous patients and dose distributions associated with one or more organs of each previous patient. The server then executes (210) the trained Al model to predict dose distribution for a patient. The server then displays (220) a heat map illustrating the predicted values, transmits the predicted values to a plan optimizer to generate an optimized treatment plan for the patient, and/or transmits an alert when a treatment plan generated by a plan optimizer deviates from rules and thresholds indicated within the patient's plan objectives.

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