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21. 发明授权

US07508968B2 Image compounding based on independent noise constraint 失效
标题翻译：基于独立噪声约束的图像复合
公开(公告)号：US07508968B2
公开(公告)日：2009-03-24
申请号：US11229106
申请日：2005-09-16
申请人： Yunqiang Chen , Hongcheng Wang , Tong Fang , Jason Jenn-Kwei Tyan
发明人： Yunqiang Chen , Hongcheng Wang , Tong Fang , Jason Jenn-Kwei Tyan
IPC分类号： G06K9/00 , G06K9/40 , G06K9/36
CPC分类号： G06T5/002 , G06T5/50 , G06T2207/10081 , G06T2207/10132 , G06T2207/20076 , G06T2207/20192 , G06T2207/30004
摘要： A method and system for improving image quality by compounding a plurality of images to mitigate the effects of image noise. The method utilizes the independency between noise components for multiple image compounding. An effective measurement is designed to regularize the independency between noise in a traditional generative model based filtering framework, thereby enabling a more robust algorithmic solution to inaccurate signal/noise modeling. The method generally comprises selecting a plurality of images, calculating the residual error on each image; calculating the noise likelihood of each image, calculating the signal likelihood of the image, performing an independence analysis to regularize an independence constraint between the residual errors of the images, and summing the signal likelihood, noise likelihood and pairwise independency to approximate the joint independency between the residual errors.
摘要翻译：一种用于通过复合多个图像来减轻图像噪声的影响来提高图像质量的方法和系统。该方法利用多重图像复合的噪声分量之间的独立性。设计了一种有效的测量方法来规范传统的基于生成模型的滤波框架中的噪声之间的独立性，从而实现了更为鲁棒的算法解决方案，使信号/噪声建模不准确。该方法通常包括选择多个图像，计算每个图像上的残差; 计算每个图像的噪声可能性，计算图像的信号可能性，执行独立性分析以使图像的残差误差之间的独立性约束正规化，并且将信号似然性，噪声似然性和成对独立性相加以近似于剩余错误。

22. 发明申请

US20070189607A1 SYSTEM AND METHOD FOR EFFICIENT FEATURE DIMENSIONALITY AND ORIENTATION ESTIMATION 审中-公开
标题翻译：用于有效特征尺度和方位估计的系统和方法
公开(公告)号：US20070189607A1
公开(公告)日：2007-08-16
申请号：US11548714
申请日：2006-10-12
申请人： Yunqiang Chen , Tong Fang , Jason Tyan
发明人： Yunqiang Chen , Tong Fang , Jason Tyan
IPC分类号： G06K9/48
CPC分类号： G06K9/46 , G06T7/73
摘要： A method of automatically detecting features in an image includes: designing a gradient detection filter and a line detection filter; applying the gradient detection filter and line detection filter to detect structures in an image; and estimating feature dimensionality and orientation of the detected structures in the image. The computation cost of gradient detection and line detection when applied on an image is a constant number of operations independent of the size of the gradient and line detection filters.
摘要翻译：一种自动检测图像中的特征的方法包括：设计梯度检测滤波器和线路检测滤波器; 应用梯度检测滤波器和线检测滤波器来检测图像中的结构; 并且估计图像中检测到的结构的特征维度和取向。应用于图像时梯度检测和线检测的计算成本是与梯度和线检测滤波器的大小无关的常数运算。

23. 发明申请

US20070140407A1 Reduction of Streak Artifacts In Low Dose CT Imaging through Multi Image Compounding 有权
标题翻译：通过多图像复合减少低剂量CT成像中的条纹伪像
公开(公告)号：US20070140407A1
公开(公告)日：2007-06-21
申请号：US11539665
申请日：2006-10-09
申请人： Yunqiang Chen , Tong Fang
发明人： Yunqiang Chen , Tong Fang
IPC分类号： H05G1/60 , A61B6/00 , G01N23/00 , G21K1/12
CPC分类号： G06T11/005 , A61B6/5258 , G06T2211/421
摘要： Disclosed is a method and system for constructing, from a computerized tomography (CT) scan, an image relating to a physical structure. Projection data associated with the image is obtained and divided into a plurality of subsets. Filtered back projection (FBP) is then applied to each subset in the plurality of subsets. The image is constructed based on the application of the FBP to each subset in the plurality of subsets.
摘要翻译：公开了一种用于从计算机断层摄影（CT）扫描构建与物理结构有关的图像的方法和系统。获取与图像相关联的投影数据并将其分成多个子集。然后将过滤后投影（FBP）应用于多个子集中的每个子集。基于对多个子集中的每个子集的FBP的应用构建图像。

24. 发明申请

US20070133736A1 Devices, systems, and methods for imaging 审中-公开
标题翻译：用于成像的设备，系统和方法
公开(公告)号：US20070133736A1
公开(公告)日：2007-06-14
申请号：US11548863
申请日：2006-10-12
申请人： Yunqiang Chen , Hao Wu , Tong Fang
发明人： Yunqiang Chen , Hao Wu , Tong Fang
IPC分类号： H05G1/60 , A61B6/00 , G01N23/00 , G21K1/12
CPC分类号： A61B6/00 , A61B6/482 , A61B6/5258 , G01N2223/206 , G01N2223/423
摘要： Certain exemplary embodiments comprise a method, which can comprise determining an image of a predetermined physiological structure of a patient. The image can be determined based upon a first set of image data of the predetermined physiological structure of the patient. The image can be based upon a second set of image data of the predetermined physiological structure of the patient. The image can be determined based upon an iteratively adjusted movement of the patient.
摘要翻译：某些示例性实施例包括可以包括确定患者的预定生理结构的图像的方法。可以基于患者的预定生理结构的第一组图像数据来确定图像。图像可以基于患者的预定生理结构的第二组图像数据。可以基于患者的迭代调整的运动来确定图像。

25. 发明授权

US08005288B2 Layer reconstruction from dual-energy image pairs 有权
标题翻译：从双能图像对重建层
公开(公告)号：US08005288B2
公开(公告)日：2011-08-23
申请号：US12101487
申请日：2008-04-11
申请人： Yunqiang Chen , Tong Fang
发明人： Yunqiang Chen , Tong Fang
IPC分类号： G06K9/00 , G06K9/36 , G06K9/32
CPC分类号： G06T5/50 , G06T5/005 , G06T7/33 , G06T2207/10116 , G06T2207/10144 , G06T2207/30004
摘要： A system and method for layer reconstruction from dual-energy image pairs are provided, the method including: receiving a pair of dual-energy images, one having a relatively high energy dose and the other having a relatively low energy dose; ascertaining that a first relatively motionless layer is substantially aligned between the high and low dose images; computing a preliminary image of a second layer that has non-rigid motion relative to the first layer; detecting the relative motion of the second layer relative to the first layer; generating a mask in accordance with the detected motion; filling the motion area corresponding to the mask with gradients of the high-dose image; removing the first layer; and inpainting the motion area.
摘要翻译：提供了一种用于从双能量图像对进行层重构的系统和方法，所述方法包括：接收一对双能量图像，一个具有相对较高的能量剂量，另一个具有相对低的能量剂量; 确定第一相对静止层基本上在高剂量图像和低剂量图像之间对准; 计算相对于第一层具有非刚性运动的第二层的初步图像; 检测第二层相对于第一层的相对运动; 根据检测到的运动产生掩模; 用高剂量图像的梯度填充对应于掩模的运动区域; 去除第一层; 并修复运动区域。

26. 发明申请

US20110064292A1 SYSTEM AND METHOD FOR MULTI-IMAGE BASED VIRTUAL NON-CONTRAST IMAGE ENHANCEMENT FOR DUAL SOURCE CT 有权
标题翻译：用于双图源虚拟非对映图像增强的系统和方法
公开(公告)号：US20110064292A1
公开(公告)日：2011-03-17
申请号：US12854341
申请日：2010-08-11
申请人： Yunqiang Chen , Tong Fang , Bernhard Schmidt , Rainer Raupach
发明人： Yunqiang Chen , Tong Fang , Bernhard Schmidt , Rainer Raupach
IPC分类号： G06T5/00
CPC分类号： G06T5/002 , G06T5/50 , G06T2207/10072 , G06T2207/20076 , G06T2207/20192 , G06T2207/30004 , G06T2207/30016
摘要： A method for enhancing a virtual non-contrast image, includes receiving a pair of dual scan CT images and calculating a virtual non-contrast image from the pair of CT images using known tissue attenuation coefficients. A conditional probability distribution is estimated for tissue at first and second points in each of the pair of CT images and the virtual non-contrast image as being the same type. A conditional probability distribution for tissue is estimated at the first and second points in each of the pair of CT images and the virtual non-contrast image as being of different types. An a posteriori probability of the tissue at the first and second points as being the same type is calculated from the conditional probability distributions, and an enhanced virtual non-contrast image is calculated using the a posteriori probability of the tissue at the first and second points as being the same type.
摘要翻译：一种用于增强虚拟非对比度图像的方法，包括接收一对双扫描CT图像，并使用已知的组织衰减系数从所述一对CT图像计算虚拟非对比度图像。对于一对CT图像和虚拟非对比度图像中的每一个中的第一和第二点处的组织估计为相同类型的条件概率分布。在一对CT图像和虚拟非对比度图像中的每一个中的第一和第二点处估计组织的条件概率分布为不同类型。根据条件概率分布计算第一和第二点处的组织作为相同类型的后验概率，并且使用在第一和第二点处的组织的后验概率来计算增强的虚拟非对比度图像作为同一类型。

27. 发明授权

US07783096B2 Device systems and methods for imaging 有权
标题翻译：用于成像的设备系统和方法
公开(公告)号：US07783096B2
公开(公告)日：2010-08-24
申请号：US11675258
申请日：2007-02-15
申请人： Yunqiang Chen , Hao Wu , Tong Fang
发明人： Yunqiang Chen , Hao Wu , Tong Fang
IPC分类号： G06K9/00
CPC分类号： A61B6/00 , A61B6/482 , G01N2223/206 , G01N2223/423 , G06T7/207 , G06T2207/20016 , G06T2207/30008
摘要： Certain exemplary embodiments comprise a method, which can comprise determining an image of a predetermined physiological structure of a patient. The image can be determined based upon a first set of image data of the predetermined physiological structure of the patient. The image can be based upon a second set of image data of the predetermined physiological structure of the patient. The image can be determined based upon an iteratively adjusted movement of the patient.
摘要翻译：某些示例性实施例包括可以包括确定患者的预定生理结构的图像的方法。可以基于患者的预定生理结构的第一组图像数据来确定图像。图像可以基于患者的预定生理结构的第二组图像数据。可以基于患者的迭代调整的运动来确定图像。

28. 发明申请

US20090116722A1 Method and system for soft tissue image reconstruction in gradient domain 审中-公开
标题翻译：梯度域软组织图像重建方法与系统
公开(公告)号：US20090116722A1
公开(公告)日：2009-05-07
申请号：US12287551
申请日：2008-10-10
申请人： Yunqiang Chen , Tong Fang
发明人： Yunqiang Chen , Tong Fang
IPC分类号： H05G1/64 , G06K9/00
CPC分类号： G06T11/008 , G06T5/005 , G06T2207/20016
摘要： A method and system for soft tissue image reconstruction for dual x-ray imaging is disclosed. A multigrid PDE solver is used for solving a Poisson equation for soft tissue image reconstruction based on a soft tissue gradient field extracted from dual energy x-ray images. The divergence of the soft tissue gradient field is downsampled to a coarsest resolution level, and a soft tissue image is generated based on the divergence of the soft tissue gradient field at the coarsest level. The soft tissue image is interpolated to a next finest resolution level, and refined by at least one coarse grid correction cycle at the current resolution level. The coarse grid correction cycle calculates a defect based on the current soft tissue image, downsamples the defect to the coarsest level, calculates a correction based on the defect at the coarsest level, and upsamples the correction to the current resolution level to refine the current soft tissue image. The interpolation and refinement of the soft tissue image is repeated until the soft tissue image is refined at the finest resolution level.
摘要翻译：公开了一种用于双重X射线成像的软组织图像重建的方法和系统。基于从双能量x射线图像提取的软组织梯度场，多重PDE求解器用于求解软组织图像重建的泊松方程。软组织梯度场的发散度被下采样到最粗分辨率水平，并且基于最粗糙度级别的软组织梯度场的发散度产生软组织图像。将软组织图像插值到下一个最佳分辨率级别，并通过至少一个粗网格校正周期以当前分辨率级别进行细化。粗网格校正周期基于当前软组织图像计算缺陷，将缺陷下采样到最粗水平，基于最粗糙级别的缺陷计算校正，并将校正上采样到当前分辨率水平以细化当前软组织图像重复软组织图像的插值和细化，直到软组织图像以最好的分辨率水平被精炼。

29. 发明申请

US20070196007A1 Device Systems and Methods for Imaging 有权
标题翻译：设备系统和成像方法
公开(公告)号：US20070196007A1
公开(公告)日：2007-08-23
申请号：US11675258
申请日：2007-02-15
申请人： Yunqiang Chen , Hao Wu , Tong Fang
发明人： Yunqiang Chen , Hao Wu , Tong Fang
IPC分类号： G06K9/00
CPC分类号： A61B6/00 , A61B6/482 , G01N2223/206 , G01N2223/423 , G06T7/207 , G06T2207/20016 , G06T2207/30008
摘要： Certain exemplary embodiments comprise a method, which can comprise determining an image of a predetermined physiological structure of a patient. The image can be determined based upon a first set of image data of the predetermined physiological structure of the patient. The image can be based upon a second set of image data of the predetermined physiological structure of the patient. The image can be determined based upon an iteratively adjusted movement of the patient.
摘要翻译：某些示例性实施例包括可以包括确定患者的预定生理结构的图像的方法。可以基于患者的预定生理结构的第一组图像数据来确定图像。图像可以基于患者的预定生理结构的第二组图像数据。可以基于患者的迭代调整的运动来确定图像。

30. 发明申请

US20060078181A1 Image compounding based on independent noise constraint 失效
公开(公告)号：US20060078181A1
公开(公告)日：2006-04-13
申请号：US11229106
申请日：2005-09-16
申请人： Yunqiang Chen , Hongcheng Wang , Tong Fang , Jason Tyan
发明人： Yunqiang Chen , Hongcheng Wang , Tong Fang , Jason Tyan
IPC分类号： G06K9/00
CPC分类号： G06T5/002 , G06T5/50 , G06T2207/10081 , G06T2207/10132 , G06T2207/20076 , G06T2207/20192 , G06T2207/30004
摘要： A method and system for improving image quality by compounding a plurality of images to mitigate the effects of image noise. The method utilizes the independency between noise components for multiple image compounding. An effective measurement is designed to regularize the independency between noise in a traditional generative model based filtering framework, thereby enabling a more robust algorithmic solution to inaccurate signal/noise modeling. The method generally comprises selecting a plurality of images, calculating the residual error on each image; calculating the noise likelihood of each image, calculating the signal likelihood of the image, performing an independence analysis to regularize an independence constraint between the residual errors of the images, and summing the signal likelihood, noise likelihood and pairwise independency to approximate the joint independency between the residual errors.

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