专利快速检索-快速检索全球专利，免费商用专利数据库-IPRDB

41. 发明申请

WO2021159143A1 ADVERSARIAL PREDICTION OF RADIOTHERAPY TREATMENT PLANS 审中-公开
公开(公告)号：WO2021159143A1
公开(公告)日：2021-08-12
申请号：PCT/US2021/070119
申请日：2021-02-03
申请人： ELEKTA, INC.
发明人： HIBBARD, Lyndon Stanley
IPC分类号： A61N5/10
摘要： Systems and methods are disclosed for generating radiotherapy treatment machine parameters based on projection images of a target anatomy. The systems and methods include operations including receiving a set of pairs of image data for each gantry angle of a radiotherapy treatment machine, wherein each pair of the set of pairs comprises a given projection image that represents a view of an anatomy of a subject from a given gantry angle and a given graphical aperture image of multi-leaf collimator (MLC) leaf positions at the given gantry angle based on the given projection image; training a generative adversarial network (GAN) model based on the set of pairs of image data for each gantry angle; and using the trained GAN model to predict an aperture image of MLC leaf positions for a desired gantry angle based on a projection image that represents a view of an anatomical region of interest.

42. 发明申请

WO2019199699A1 IMAGE SYNTHESIS USING ADVERSARIAL NETWORKS 审中-公开
公开(公告)号：WO2019199699A1
公开(公告)日：2019-10-17
申请号：PCT/US2019/026400
申请日：2019-04-08
申请人： ELEKTA, INC.
发明人： HAN, Xiao
IPC分类号： G06K9/62
摘要： A statistical learning technique that does not rely upon paired imaging information is described herein. The technique may be computer-implemented and may be used in order to train a statistical learning model to perform image synthesis, such as in support of radiation therapy treatment planning. In an example, a trained statistical learning model may include a convolutional neural network established as a generator convolutional network, and the generator may be trained at least in part using a separate convolutional neural network established as a discriminator convolutional network. The generator convolutional network and the discriminator convolutional network may form an adversarial network architecture for use during training. After training, the generator convolutional network may be provided for use in synthesis of images, such as to receive imaging data corresponding to a first imaging modality type, and to synthesize imaging data corresponding to a different, second imaging modality type.

43. 发明申请

WO2019005180A1 METHOD FOR IMPROVING CONE-BEAM CT IMAGE QUALITY USING A DEEP CONVOLUTIONAL NEURAL NETWORK 审中-公开
公开(公告)号：WO2019005180A1
公开(公告)日：2019-01-03
申请号：PCT/US2017/043479
申请日：2017-07-24
申请人： ELEKTA, INC.
发明人： XU, Jiaofeng , HAN, Xiao
IPC分类号： G06T11/00 , G06T5/00
摘要： Systems and methods can include training a DCNN to reduce one or more artifacts using a projection space approach or an image space approach. A projection space approach can include collecting an artifact contaminated CBCT projection space image, and a corresponding artifact reduced, CBCT projection space image from each patient in a group of patients, and using the artifact contaminated CBCT projection space image and the corresponding artifact reduced, CBCT projection space image collected from each patient in the group of patients to train a DCNN to reduce one or more artifacts in a projection space image. An image space approach can include collecting a plurality of CBCT patient anatomical images and corresponding registered CT anatomical images from a group of patients, and using the plurality of CBCT anatomical images and corresponding artifact reduced CT anatomical images to train a DCNN to remove artifacts from a CBCT anatomical image.

44. 发明申请

WO2018048575A1 SYSTEM AND METHOD FOR LEARNING MODELS OF RADIOTHERAPY TREATMENT PLANS TO PREDICT RADIOTHERAPY DOSE DISTRIBUTIONS 审中-公开
标题翻译：用于学习放射治疗计划模型以预测放射治疗剂量分布的系统和方法
公开(公告)号：WO2018048575A1
公开(公告)日：2018-03-15
申请号：PCT/US2017/046608
申请日：2017-08-11
申请人： ELEKTA, INC.
发明人： HIBBARD, Lyndon S.
IPC分类号： A61N5/10 , G06N3/04 , G06N3/08 , G06N3/02 , G06F19/00
摘要： The present disclosure relates to systems and methods for developing radiotherapy treatment plans though the use of machine learning approaches and neural network components. A neural network is trained using one or more three-dimensional medical images, one or more three-dimensional anatomy maps, and one or more dose distributions to predict a fluence map or a dose map. During training the neural network receives a predicted dose distribution determined by the neural network that is compared to an expected dose distribution. Iteratively the comparison is performed until a predetermined threshold is achieved. The trained neural network is then utilized to provide a three-dimensional dose distribution.
摘要翻译：本公开涉及通过使用机器学习方法和神经网络组件来开发放射治疗计划的系统和方法。使用一个或多个三维医学图像，一个或多个三维解剖图，以及一个或多个剂量分布来预测注量图或剂量图来训练神经网络。在训练期间，神经网络接收由神经网络确定的与预期剂量分布相比较的预测剂量分布。迭代地进行比较直到达到预定的阈值。训练的神经网络然后被用来提供三维剂量分布。

45. 发明申请

WO2018039368A1 IMAGE SEGMENTATION USING NEURAL NETWORK METHOD 审中-公开
标题翻译：用神经网络方法分割图像
公开(公告)号：WO2018039368A1
公开(公告)日：2018-03-01
申请号：PCT/US2017/048245
申请日：2017-08-23
申请人： ELEKTA, INC.
发明人： XU, Jiaofeng , HAN, Xiao
IPC分类号： G06T7/11
摘要： The present disclosure relates to systems, methods, devices, and non-transitory computer-readable storage medium for segmenting three-dimensional images. In one implementation, a computer-implemented method for segmenting a three-dimensional image is provided. The method may include receiving a three-dimensional image acquired by an imaging device, and selecting a plurality of stacks of adjacent two-dimensional images from the three-dimensional image. The method may further include segmenting, by a processor, each stack of adjacent two-dimensional images using a neural network model. The method may also include determining, by the processor, a label map for the three-dimensional image by aggregating the segmentation results from the plurality of stacks.
摘要翻译：本公开涉及用于分割三维图像的系统，方法，设备和非临时性计算机可读存储介质。在一个实现中，提供了用于分割三维图像的计算机实现的方法。该方法可以包括接收由成像装置获取的三维图像，并且从三维图像中选择多个相邻二维图像的堆叠。该方法可以进一步包括由处理器使用神经网络模型来分割相邻二维图像的每个堆叠。该方法还可以包括通过聚集来自多个堆叠的分割结果来由处理器确定三维图像的标签图。

你已经成功收藏专利！

检索式保存成功!

IPRDB

热门服务

关于我们

友情链接

联系方式