公开(公告)号：EP3157627A1

公开(公告)日：2017-04-26

申请号：EP15731400.6

申请日：2015-06-11

申请人： Elekta AB (PUBL) ,  Impac Medical Systems, Inc.

发明人： SJÖLUND, Jens Olof ,  HAN, Xiao

IPC分类号： A61N5/10

CPC分类号： A61N5/1038 ,  A61B6/032 ,  A61N5/103 ,  A61N5/1039 ,  A61N2005/1041 ,  G06N5/04 ,  G06N7/005 ,  G06N99/005

摘要： The present disclosure relates to systems, methods, and computer-readable storage media for radiotherapy. Embodiments of the present disclosure may receive a plurality of training data and determine one or more predictive models based on the training data. The one or more predictive models may be determined based on at least one of a conditional probability density associated with a selected output characteristic given one or more selected input variables or a joint probability density. Embodiments of the present disclosure may also receive patient specific testing data. In addition, embodiments of the present disclosure may predict a probability density associated with a characteristic output based on the one or more predictive models and the patient specific testing data. Moreover, embodiments of the present disclosure may generate a new treatment plan based on the prediction and may use the new treatment plan to validate a previous treatment plan.

摘要翻译： 本公开涉及用于放疗的系统，方法和计算机可读存储介质。 本公开的实施例可以接收多个训练数据并基于训练数据确定一个或多个预测模型。 该一个或多个预测模型可基于与给定一个或多个所选输入变量或联合概率密度的选定输出特性相关联的条件概率密度中的至少一个来确定。 本公开的实施例还可以接收患者特定测试数据。 另外，本公开的实施例可以基于一个或多个预测模型和患者特定测试数据来预测与特征输出相关联的概率密度。 此外，本公开的实施例可以基于预测生成新的治疗计划并且可以使用新的治疗计划来验证先前的治疗计划。

公开(公告)号：EP3980912A1

公开(公告)日：2022-04-13

申请号：EP19761804.4

申请日：2019-08-27

申请人： Elekta AB (Publ)

发明人： FAY, Dominik ,  SJÖLUND, Jens Olof

IPC分类号： G06F21/62 ,  A61N5/10 ,  G06N3/08

公开(公告)号：EP3695882A1

公开(公告)日：2020-08-19

申请号：EP20157132.0

申请日：2020-02-13

申请人： Elekta AB (PUBL)

发明人： ERIKSSON, Markus ,  SJÖLUND, Jens Olof ,  ÖSTRÖM, Linn ,  TILLY, David Andreas ,  KIMSTRAND, Peter ,  ADLER, Jonas Anders

IPC分类号： A61N5/10

摘要： Systems and methods for calculating radiotherapy dose distribution are provided. The systems and methods include operations for receiving data representing at least one of particle trajectories or a dose deposition pattern in a simulated delivery of a radiotherapy plan; applying a dose calculation process to the received data to generate a first radiotherapy dose distribution having a first level of detail; and processing the first radiotherapy dose distribution using a trained machine learning technique to generate a second radiotherapy dose distribution having a second level of detail that enhances the first level of detail.