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1.发明授权公开(公告)号：US11285339B2
公开(公告)日：2022-03-29
申请号：US16356804
申请日：2019-03-18
发明人： Santtu Ollila , Mikko Vainio , Jarkko Peltola , Janne Nord , Esa Kuusela , Juha Kauppinen , Viljo Petäjä , Marko Rusanen
IPC分类号： A61N5/10
摘要： 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.
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2.发明授权公开(公告)号：US11238580B2
公开(公告)日：2022-02-01
申请号：US16554742
申请日：2019-08-29
发明人： Hannu Laaksonen , Janne Nord , Jan Schreier
摘要： One or more medical images of a patient are processed by a first neural network model to determine a region-of-interest (ROI) or a cut-off plane. Information from the first neural network model is used to crop the medical images, which serves as input to a second neural network model. The second neural network model processes the cropped medical images to determine contours of anatomical structures in the medical images of the patient. Each of the first and second neural network models are deep neural network models. By use of cropped images in the training and inference phases of the second neural network model, contours are produced with sharp edges or flat surfaces.
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公开(公告)号：US10850120B2
公开(公告)日：2020-12-01
申请号：US15391058
申请日：2016-12-27
发明人： Hannu Laaksonen , Esa Kuusela , Janne Nord , Joakim Pyyry , Perttu Niemela
IPC分类号： A61N5/10
摘要： A clinical goal for radiation treatment of a patient is set. A dose prediction model is selected from a number of dose prediction models based on the clinical goal. A radiation treatment plan is then generated for the patient using the dose prediction model that was selected based on the clinical goal.
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公开(公告)号：US10589127B2
公开(公告)日：2020-03-17
申请号：US14039413
申请日：2013-09-27
发明人： Janne Nord , Juha Kauppinen , Jarkko Peltola
IPC分类号： A61N5/10
摘要： A control circuit has access to information that correlates a plurality of patient-experience outcomes with corresponding delivered-radiation metrics and uses that plurality of patient-experience outcomes in conjunction with developing a radiation-therapy treatment plan for a particular patient. Those patient-experience outcomes can comprise, for example, information regarding how various patients fared post-radiation treatment as regards such things as post-radiation treatment life expectancy, tumor eradication success, and one or more other quality-of-life metrics. These teachings will accommodate using a particular one of the patient-experience outcomes as an optimization goal when optimizing candidate radiation-therapy treatment plans to develop the radiation-therapy treatment plan. In such a case these teachings will further accommodate, if desired, permitting a user to modify one or more optimization objectives while displaying intermediate optimization results.
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5.发明授权公开(公告)号：US10512791B2
公开(公告)日：2019-12-24
申请号：US15890051
申请日：2018-02-06
发明人： Esa Kuusela , Emmi Ruokokoski , Santtu Ollila , Janne Nord , Jarkko Peltola
IPC分类号： A61N5/10
摘要： A cost function is constructed so as to guide an optimization process to achieve similar coverage for all targets simultaneously in a concurrent radiation treatment of multiple targets, so that a single scaling factor may be used in a plan normalization to achieve the desired coverage for all the targets. The cost function includes a component that favors a solution that attains similar target coverages for all targets, as well as a component that favors a solution that approaches the desired target coverage value for each individual target. The cost function includes a max term relating to deficiencies of actual target coverages with respect to a desired target coverage, or alternatively a soft-max term relating to deviations of actual target coverages with respect to an average target coverage, as well as to deficiencies of actual target coverages with respect to a desired target coverage.
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公开(公告)号：US10449389B2
公开(公告)日：2019-10-22
申请号：US15831210
申请日：2017-12-04
发明人： Santtu Ollila , Mikko Vainio , Jarkko Peltola , Janne Nord
IPC分类号： A61N5/10
摘要： A method for determining MLC leaf sequences for radiation treatment includes obtaining BEV projections of a first target volume and a second target volume along one or more treatment paths of a radiation treatment plan, analyzing the BEV projections to determine one or more contiguous ranges of spatial points where there exists an interstitial region between the first target volume and the second target volume in the direction of MLC leaf motion, and determining a first set of MLC leaf sequences such that an aperture formed by the MLC in a first portion of the one or more contiguous ranges of spatial points exposes radiation to the first target volume but not the second target volume, and an aperture formed by the MLC in a second portion of the one or more contiguous ranges of spatial points exposes radiation to the second target volume but not the first target volume.
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7.发明申请公开(公告)号：US20190240507A1
公开(公告)日：2019-08-08
申请号：US15890051
申请日：2018-02-06
发明人： Esa Kuusela , Emmi Ruokokoski , Santtu Ollila , Janne Nord , Jarkko Peltola
IPC分类号： A61N5/10
CPC分类号： A61N5/1031 , A61N5/1036 , A61N5/1045 , A61N5/1047 , A61N5/1069
摘要： A cost function is constructed so as to guide an optimization process to achieve similar coverage for all targets simultaneously in a concurrent radiation treatment of multiple targets, so that a single scaling factor may be used in a plan normalization to achieve the desired coverage for all the targets. The cost function includes a component that favors a solution that attains similar target coverages for all targets, as well as a component that favors a solution that approaches the desired target coverage value for each individual target. The cost function includes a max term relating to deficiencies of actual target coverages with respect to a desired target coverage, or alternatively a soft-max term relating to deviations of actual target coverages with respect to an average target coverage, as well as to deficiencies of actual target coverages with respect to a desired target coverage.
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公开(公告)号：US20190076672A1
公开(公告)日：2019-03-14
申请号：US16189807
申请日：2018-11-13
发明人： Janne Nord , Joakim Pyyry , Adam Harrington , Sean Herries , Joseph Schumm , Reto Filiberti , Kari Jyrkkälä , Sylvie Spiessens , Roland Meier , Dominique Gasser
IPC分类号： A61N5/10
摘要： Collision free regions are predetermined for one or more class solutions. Each class solution has defined limits for allowed field geometry variations. Collision free regions in planning can be defined as a set of allowed isocenter positions relative to a fixation device. The collision free regions may be visualized by a user to plan for field geometry and isocenter position tradeoffs. Collision free regions in delivery can be defined as a set of allowed couch support coordinates. The treatment fields in a radiation treatment plan can be checked against the collision free regions in delivery to determine whether they will pose any collision risks.
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公开(公告)号：US20180243584A1
公开(公告)日：2018-08-30
申请号：US15441898
申请日：2017-02-24
发明人： Janne Nord , Joakim Pyyry , Adam Harrington , Sean Herries , Joseph Schumm , Reto Filiberti , Kari Jyrkkälä , Sylvie Spiessens , Roland Meier , Dominique Gasser
IPC分类号： A61N5/10
CPC分类号： A61N5/1039 , A61N5/103 , A61N5/1036 , A61N5/1048 , A61N5/1081
摘要： Collision free regions are predetermined for one or more class solutions. Each class solution has defined limits for allowed field geometry variations. Collision free regions in planning can be defined as a set of allowed isocenter positions relative to a fixation device. The collision free regions may be visualized by a user to plan for field geometry and isocenter position tradeoffs. Collision free regions in delivery can be defined as a set of allowed couch support coordinates. The treatment fields in a radiation treatment plan can be checked against the collision free regions in delivery to determine whether they will pose any collision risks.
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10.发明授权Systems and methods for automatic creation of dose prediction models and therapy treatment plans as a cloud service 有权自动创建剂量预测模型和治疗计划作为云服务的系统和方法公开(公告)号：US09409039B2
公开(公告)日：2016-08-09
申请号：US14040618
申请日：2013-09-27
发明人： Joona Hartman , Maria Cordero Marcos , Esa Kuusela , Jarkko Peltola , Janne Nord
CPC分类号： A61N5/1031 , A61N5/103 , A61N5/1043 , A61N2005/1041 , A61N2005/1074 , A61N2005/1087 , G06F19/00 , G06F19/3418 , G06F19/3481
摘要： 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.
摘要翻译： 本发明提出了一种基于现有临床知识自动创建剂量预测模型的方法，所述临床知识从多个来源累积，而不需要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。 根据进一步的实施例，该方法可以包括以下步骤：由临床提交的所有患者数据被匿名化，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，以便符合当地法规。
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