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：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.

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.

公开(公告)号：WO2018050886A1

公开(公告)日：2018-03-22

申请号：PCT/EP2017/073441

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: An optimized radiation treatment plan may be developed in which the total monitor unit (MU) count is taken into account, A planner may specify a maximum treatment time. An optimization algorithm may convert the specified maximum treatment time to a maximum total MU count, which is then used as a constraint in the optimization process. A cost function for the optimization algorithm may include a term that penalizes any violation of the upper constraint for the MU count.

Abstract translation: 可以开发优化的放射治疗计划，其中考虑总监测单位（MU）计数，计划者可以指定最大治疗时间。 优化算法可以将指定的最大处理时间转换为最大总MU计数，然后将其用作优化处理中的约束。 优化算法的成本函数可能包含一个惩罚任何违反MU计数上限约束的术语。