公开(公告)号：EP2278933B1

公开(公告)日：2019-07-10

申请号：EP09742997.1

申请日：2009-05-04

Applicant: STC.UNM

Inventor： LUAN, Shuang ,  SWANSON, Nathan ,  MA, Lijun

IPC: A61N5/10

公开(公告)号：EP3137166A1

公开(公告)日：2017-03-08

申请号：EP15785977.8

申请日：2015-04-30

Applicant: STC.UNM

Inventor： LUAN, Shuang ,  RIOFRIO ALMEIDA, Daniel Andres

IPC: A61N5/10 ,  A61N5/00 ,  G06N3/00

CPC classification number: A61N5/1031 ,  A61N5/10 ,  A61N5/1001 ,  A61N5/1007 ,  A61N5/103 ,  A61N5/1084 ,  A61N2005/101 ,  A61N2005/1024 ,  A61N2005/1034 ,  G06F19/00 ,  G06F19/325 ,  G06N3/006 ,  G16H20/40

Abstract: An optimization technique for use with radiation therapy planning that combines stochastic optimization techniques such as Particle Swarm Optimization (PSO) with deterministic techniques to solve for optimal and reliable locations for delivery of radiation doses to a targeted tumor while minimizing the radiation dose experienced by the surrounding critical structures such as normal tissues and organs.

Abstract translation: 一种用于放射治疗计划的优化技术，其将随机优化技术（例如粒子群优化（PSO））与确定性技术相结合，以解决最佳可靠的位置以将靶向肿瘤递送辐射剂量，同时最小化周围的辐射剂量 关键结构如正常组织和器官。

公开(公告)号：EP2278933A2

公开(公告)日：2011-02-02

申请号：EP09742997.1

申请日：2009-05-04

Applicant: STC.UNM

Inventor： LUAN, Shuang ,  SWANSON, Nathan ,  MA, Lijun

IPC: A61B19/00 ,  A61B18/00 ,  A61B6/00

CPC classification number: A61N5/1031 ,  A61B34/10 ,  A61B90/10 ,  A61B2090/101 ,  A61N5/103 ,  A61N5/1042 ,  A61N5/1084

Abstract: The present invention is a method and system for developing a dynamic scheme for Gamma Knife radiosurgery based on the concept of "dose-painting" to take advantage of robotic patient positioning systems on the Gamma Knife C and Perfexion units. The spherical high dose volume created by the Gamma Knife unit will be viewed as a 3D spherical "paintbrush", and treatment planning is reduced to finding the best route of this "paintbrush" to "paint" a 3D tumor volume. Under the dose-painting concept, Gamma Knife radiosurgery becomes dynamic, where the patient is moving continuously under the robotic positioning system.

公开(公告)号：WO2009137010A2

公开(公告)日：2009-11-12

申请号：PCT/US2009/002733

申请日：2009-05-04

Applicant: STC.UNM ,  LUAN, Shuang ,  SWANSON, Nathan ,  MA, Lijun

Inventor： LUAN, Shuang ,  SWANSON, Nathan ,  MA, Lijun

IPC: A61B19/00 ,  A61B18/00 ,  A61B6/00

CPC classification number: A61N5/1031 ,  A61B34/10 ,  A61B90/10 ,  A61B2090/101 ,  A61N5/103 ,  A61N5/1042 ,  A61N5/1084

Abstract: The present invention is a method and system for developing a dynamic scheme for Gamma Knife radiosurgery based on the concept of "dose-painting" to take advantage of robotic patient positioning systems on the Gamma Knife C and Perfexion units. The spherical high dose volume created by the Gamma Knife unit will be viewed as a 3D spherical "paintbrush", and treatment planning is reduced to finding the best route of this "paintbrush" to "paint" a 3D tumor volume. Under the dose-painting concept, Gamma Knife radiosurgery becomes dynamic, where the patient is moving continuously under the robotic positioning system.

Abstract translation: 本发明是基于“剂量绘画”的概念，利用伽玛刀C和Perfexion单元上的机器人患者定位系统开发伽玛刀放射外科手术的动态方案和系统。 由伽玛刀单元创建的球形高剂量体积将被视为3D球形“画笔”，并且减少治疗计划以找到该“画笔”最佳路线来“绘制”3D肿瘤体积。 在剂量绘画概念下，伽玛刀放射外科手术变得动态，患者在机器人定位系统下连续移动。

公开(公告)号：WO2015168431A1

公开(公告)日：2015-11-05

申请号：PCT/US2015/028558

申请日：2015-04-30

Applicant: STC.UNM

Inventor： LUAN, Shuang ,  RIOFRIO ALMEIDA, Daniel Andres

IPC: A61N5/10 ,  A61N5/00 ,  G06N3/00

CPC classification number: A61N5/1031 ,  A61N5/10 ,  A61N5/1001 ,  A61N5/1007 ,  A61N5/103 ,  A61N5/1084 ,  A61N2005/101 ,  A61N2005/1024 ,  A61N2005/1034 ,  G06F19/00 ,  G06F19/325 ,  G06N3/006 ,  G16H20/40

Abstract: An optimization technique for use with radiation therapy planning that combines stochastic optimization techniques such as Particle Swarm Optimization (PSO) with deterministic techniques to solve for optimal and reliable locations for delivery of radiation doses to a targeted tumor while minimizing the radiation dose experienced by the surrounding critical structures such as normal tissues and organs.

Abstract translation: 一种用于放射治疗计划的优化技术，其将随机优化技术（例如粒子群优化（PSO））与确定性技术相结合，以解决将靶向肿瘤辐射剂量递送到最佳和可靠的位置，同时最小化周围的辐射剂量 关键结构如正常组织和器官。