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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>

    TOMOGRAPHIC IMAGE PROCESSING USING ARTIFICIAL INTELLIGENCE (AI) ENGINES
    4.
    发明申请
    TOMOGRAPHIC IMAGE PROCESSING USING ARTIFICIAL INTELLIGENCE (AI) ENGINES 审中-公开

    公开(公告)号：WO2021122364A1

    公开(公告)日：2021-06-24

    申请号：PCT/EP2020/085719

    申请日：2020-12-11

    Applicant: VARIAN MEDICAL SYSTEMS INTERNATIONAL AG

    Inventor： NORD, Janne ,  PERTTU, Sami, Petri ,  PAYSAN, Pascal ,  HASS, Benjamin M ,  SEGHERS, Dieter ,  PYYRY, Joakim

    IPC: G06T11/00 ,  G06T11/006 ,  G06T2211/421

    Abstract: Example methods and systems for tomographic image reconstruction are provided. One example method may comprise: obtaining two-dimensional (2D) projection data (310) and processing the 2D projection data using an AI engine (301) that includes multiple first processing layers (311), an interposing back-projection module (312) and multiple second processing layers (313). Example processing using the AI engine may involve: generating 2D feature data (320) by processing the 2D projection data using the multiple first processing layers, reconstructing first three-dimensional (3D) feature volume data (330) from the 2D feature data using the back-projection module; and generating second 3D feature volume data (340) by processing the first 3D feature volume data using the multiple second processing layers. Methods and systems for tomographic data analysis are also provided.

    METHODS AND SYSTEMS FOR RADIOTHERAPY TREATMENT PLANNING BASED ON DEEP TRANSFER LEARNING
    5.
    发明申请
    METHODS AND SYSTEMS FOR RADIOTHERAPY TREATMENT PLANNING BASED ON DEEP TRANSFER LEARNING 审中-公开

    公开(公告)号：WO2020126122A1

    公开(公告)日：2020-06-25

    申请号：PCT/EP2019/064145

    申请日：2019-05-30

    Applicant: VARIAN MEDICAL SYSTEMS INTERNATIONAL AG

    Inventor： LAAKSONEN, Hannu ,  PERTTU, Sami Petri ,  NORD, Janne ,  SCHREIER, Jan ,  RUOKOLA, Tomi

    IPC: G16H20/40 ,  G16H30/40

    Abstract: Example methods and systems for deep transfer learning for radiotherapy treatment planning are provided. One example method may comprise: obtaining (310) a base deep learning engine that is pre-trained to perform a base radiotherapy treatment planning task; and based on the base deep learning engine, generating a target deep learning engine to perform a target radiotherapy treatment planning task. The target deep learning engine may be generated by configuring (330) a variable base layer among multiple base layers of the base deep learning engine, and generating (340) one of multiple target layers of the target deep learning engine by modifying the variable base layer. Alternatively or additionally, the target deep learning engine may be generated by configuring (350) an invariable base layer among the multiple base layers, and generating (360) one of multiple target layers of the target deep learning engine based on feature data generated using the invariable base layer.

    CONTROLLING AND SHAPING THE DOSE DISTRIBUTION OUTSIDE TREATMENT TARGETS IN EXTERNAL-BEAM RADIATION TREATMENTS
    6.
    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。

    METHOD AND APPARATUS PERTAINING TO TREATMENT PLANS FOR DYNAMIC RADIATION-TREATMENT PLATFORMS
    8.
    发明申请
    METHOD AND APPARATUS PERTAINING TO TREATMENT PLANS FOR DYNAMIC RADIATION-TREATMENT PLATFORMS 审中-公开
    治疗放射治疗平台治疗方案的方法与装置

    公开(公告)号：WO2012156494A1

    公开(公告)日：2012-11-22

    申请号：PCT/EP2012/059220

    申请日：2012-05-17

    Applicant: VARIAN MEDICAL SYSTEMS INTERNATIONAL AG ,  VARIAN MEDICAL SYSTEMS CANADA, INC. ,  NORD, Janne ,  PESONEN, Olavi ,  ARCHAMBAULT, Yves

    Inventor： NORD, Janne ,  PESONEN, Olavi ,  ARCHAMBAULT, Yves

    IPC: A61N5/10

    CPC classification number: A61N5/1047 ,  A61N5/1038

    Abstract: A control circuit accesses patient information and treatment-platform information and uses that information to automatically suggest a treatment plan having at least one of a given number of treatment-pathway traversals wherein the given number is permitted to be greater than one and sub -treatment-pathway traversal-based physical alterations to at least one of the dynamic elements of the dynamic radiation-treatment platform. By one approach the aforementioned patient information can refer, at least in part, to the patient's external contour and a treatment target's size and position with respect to the patient. The patient information regarding the treatment target can represent the latter as a simple symmetrical geometric shape (such as a cuboid). The treatment-platform information, in turn, can refer, at least in part, to dynamic elements of the dynamic radiation-treatment platform itself.

    Abstract translation: 控制电路访问患者信息和治疗平台信息，并使用该信息来自动建议具有给定数量的治疗通路遍历中的至少一个的治疗计划，其中给定的数量被允许大于1并且次治疗 - 基于路径遍历的物理改变到动态辐射处理平台的至少一个动态元件。 通过一种方法，上述患者信息可以至少部分地参考患者的外部轮廓和相对于患者的治疗目标的大小和位置。 关于治疗目标的患者信息可以将后者表示为简单的对称几何形状（例如长方体）。 治疗平台信息反过来可以至少部分地涉及动态辐射治疗平台本身的动态元件。

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