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公开(公告)号：US08938110B2
公开(公告)日：2015-01-20
申请号：US13508751
申请日：2010-10-29
申请人： Liran Goshen , Kevin M. Brown , Stanislav Zabic , Jens Wiegert , Asher Gringauz
发明人： Liran Goshen , Kevin M. Brown , Stanislav Zabic , Jens Wiegert , Asher Gringauz
CPC分类号： G06T5/002 , G06T5/003 , G06T2207/10081 , G06T2207/20012 , G06T2207/30004
摘要： A method includes generating enhanced image data based on lower dose image data and a predetermined image quality threshold, wherein an image quality of the enhanced image data is substantially similar to an image quality of higher dose image data, and a system includes an image quality enhancer (128) that generates enhanced image data based on lower dose image data and a predetermined image quality threshold, wherein an image quality of the enhanced image data is substantially similar to an image quality of higher dose image data.
摘要翻译： 一种方法包括基于较低剂量图像数据和预定图像质量阈值生成增强图像数据，其中增强图像数据的图像质量基本上类似于较高剂量图像数据的图像质量，并且系统包括图像质量增强器 （128），其基于较低剂量图像数据和预定图像质量阈值生成增强图像数据，其中所述增强图像数据的图像质量基本上类似于较高剂量图像数据的图像质量。
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公开(公告)号：US20140133729A1
公开(公告)日：2014-05-15
申请号：US14232292
申请日：2012-07-10
申请人： Liran Goshen
发明人： Liran Goshen
IPC分类号： G06T5/00
CPC分类号： G06T5/002 , A61B6/032 , A61B6/481 , A61B6/482 , A61B6/5205 , A61B6/5258 , G06T2200/04 , G06T2207/10072 , G06T2207/10081 , G06T2207/30004 , G06T2207/30008
摘要： A method includes estimating structure models for a voxel(s) of a spectral image based on a noise model, fitting structure models to a 3D neighborhood about the voxel(s), selecting one of the structure models for the voxel(s) based on the fittings and predetermined model selection criteria, and de-noising the voxel(s) based on the selected structure model, producing a set of de-noised spectral images. Another method includes generating a virtual contrast enhanced intermediate image for each energy image of a set of spectral images corresponding to different energy ranges based on de-noised spectral images, decomposed de-noised spectral images, an iodine map, and a contrast enhancement factor; and generating final virtual contrast enhanced images by incorporating a simulated partial volume effect with the intermediate virtual contrast enhanced images. Also described herein are approaches for generating a virtual non-contrasted image, a bone and calcification segmentation map, and an iodine map for multi-energy imaging studies.
摘要翻译： 一种方法包括基于噪声模型来估计用于光谱图像的体素的结构模型，将关于体素的3D邻域拟合结构模型，基于以下方式选择体素的结构模型之一： 配件和预定模型选择标准，以及基于所选择的结构模型去除体素，产生一组去噪的光谱图像。 另一种方法包括基于去噪频谱图像，分解的去噪频谱图像，碘图谱和对比度增强因子，生成对应于不同能量范围的一组光谱图像的每个能量图像的虚拟对比度增强中间图像; 并通过将模拟部分体积效应与中间虚拟对比度增强图像相结合来生成最终的虚拟对比度增强图像。 本文还描述了用于生成虚拟非对比图像，骨骼和钙化分割图以及用于多能量成像研究的碘图的方法。
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公开(公告)号：US09247919B2
公开(公告)日：2016-02-02
申请号：US13982275
申请日：2012-01-18
申请人： Liran Goshen
发明人： Liran Goshen
CPC分类号： A61B6/52 , G06T11/005 , G06T2211/408
摘要： A method and system for dual energy CT image reconstruction are provided. In one aspect, a fast kVp switching x-ray source is used during an imaging scan to produce a low energy x-ray beam for L consecutive projection angles, and then to produce a high energy x-ray beam for H consecutive projection angles, wherein L is substantially less than H. Various methods are provided for estimating the resulting undersampled data in the low energy projection data set and the high energy projection data set. The missing low energy projection data may be estimated from the known high energy projection data using any one of several disclosed structural propagation embodiments.
摘要翻译： 提供了一种用于双能CT图像重建的方法和系统。 在一个方面，在成像扫描期间使用快速的kVp切换x射线源来产生用于L个连续投影角度的低能X射线束，然后产生用于H个连续投影角的高能x射线束， 其中L基本上小于H.提供用于估计低能量投影数据集和高能量投影数据集中的所得欠采样数据的各种方法。 可以使用几个公开的结构传播实施例中的任何一个，从已知的高能量投影数据估计丢失的低能量投影数据。
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公开(公告)号：US09159124B2
公开(公告)日：2015-10-13
申请号：US13989158
申请日：2011-11-16
申请人： Liran Goshen , Asher Gringauz , Yechiel Lamash , Andrei Feldman , Guido Pardo-Roques , Jonathan Sapir
发明人： Liran Goshen , Asher Gringauz , Yechiel Lamash , Andrei Feldman , Guido Pardo-Roques , Jonathan Sapir
摘要： A method includes enhancing a contrast to noise ratio (CNR) of image data, generating CNR enhanced image data, wherein the CNR enhanced image data has a substantially same image quality as the image data. A computing system (118) includes a computer readable storage medium (122) encoded with computer readable instructions for enhancing a contrast to noise ratio (CNR) of image data and one or more processors (120), which, when executing the computer readable instructions, causes the computing system to enhance the CNR of the image data. A method includes generating CNR enhanced image data, wherein CNR enhanced image data has a substantially same noise level, noise power spectrum, and spatial resolution of the image data.
摘要翻译： 一种方法包括提高图像数据的对比度（CNR），生成CNR增强图像数据，其中CNR增强图像数据具有与图像数据基本相同的图像质量。 计算系统（118）包括用计算机可读指令编码的用于增强图像数据的对比度（CNR）和一个或多个处理器（120）的计算机可读存储介质（122），当执行计算机可读指令时 ，使计算系统增强图像数据的CNR。 一种方法包括生成CNR增强图像数据，其中CNR增强图像数据具有基本相同的噪声水平，噪声功率谱和图像数据的空间分辨率。
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公开(公告)号：US20130308745A1
公开(公告)日：2013-11-21
申请号：US13982275
申请日：2012-01-18
申请人： Liran Goshen
发明人： Liran Goshen
IPC分类号： A61B6/00
CPC分类号： A61B6/52 , G06T11/005 , G06T2211/408
摘要： A method and system for dual energy CT image reconstruction are provided. In one aspect, a fast kVp switching x-ray source is used during an imaging scan to produce a low energy x-ray beam for L consecutive projection angles, and then to produce a high energy x-ray beam for H consecutive projection angles, wherein L is substantially less than H. Various methods are provided for estimating the resulting undersampled data in the low energy projection data set and the high energy projection data set. The missing low energy projection data may be estimated from the known high energy projection data using any one of several disclosed structural propagation embodiments.
摘要翻译： 提供了一种用于双能CT图像重建的方法和系统。 在一个方面，在成像扫描期间使用快速的kVp切换x射线源来产生用于L个连续投影角度的低能X射线束，然后产生用于H个连续投影角的高能x射线束， 其中L基本上小于H.提供用于估计低能量投影数据集和高能量投影数据集中的所得欠采样数据的各种方法。 可以使用几个公开的结构传播实施例中的任何一个，从已知的高能量投影数据估计丢失的低能量投影数据。
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公开(公告)号：US20120224760A1
公开(公告)日：2012-09-06
申请号：US13508751
申请日：2010-10-29
申请人： Liran Goshen , Kevin M. Brown , Stanislav Zabic , Jens Wiegert , Asher Gringauz
发明人： Liran Goshen , Kevin M. Brown , Stanislav Zabic , Jens Wiegert , Asher Gringauz
CPC分类号： G06T5/002 , G06T5/003 , G06T2207/10081 , G06T2207/20012 , G06T2207/30004
摘要： A method includes generating enhanced image data based on lower dose image data and a predetermined image quality threshold, wherein an image quality of the enhanced image data is substantially similar to an image quality of higher dose image data, and a system includes an image quality enhancer (128) that generates enhanced image data based on lower dose image data and a predetermined image quality threshold, wherein an image quality of the enhanced image data is substantially similar to an image quality of higher dose image data.
摘要翻译： 一种方法包括基于较低剂量图像数据和预定图像质量阈值生成增强图像数据，其中增强图像数据的图像质量基本上类似于较高剂量图像数据的图像质量，并且系统包括图像质量增强器 （128），其基于较低剂量图像数据和预定图像质量阈值生成增强图像数据，其中所述增强图像数据的图像质量基本上类似于较高剂量图像数据的图像质量。
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公开(公告)号：US09547889B2
公开(公告)日：2017-01-17
申请号：US14232292
申请日：2012-07-10
申请人： Liran Goshen
发明人： Liran Goshen
CPC分类号： G06T5/002 , A61B6/032 , A61B6/481 , A61B6/482 , A61B6/5205 , A61B6/5258 , G06T2200/04 , G06T2207/10072 , G06T2207/10081 , G06T2207/30004 , G06T2207/30008
摘要： A method includes estimating structure models for a voxel(s) of a spectral image based on a noise model, fitting structure models to a 3D neighborhood about the voxel(s), selecting one of the structure models for the voxel(s) based on the fittings and predetermined model selection criteria, and de-noising the voxel(s) based on the selected structure model, producing a set of de-noised spectral images. Another method includes generating a virtual contrast enhanced intermediate image for each energy image of a set of spectral images corresponding to different energy ranges based on de-noised spectral images, decomposed de-noised spectral images, an iodine map, and a contrast enhancement factor; and generating final virtual contrast enhanced images by incorporating a simulated partial volume effect with the intermediate virtual contrast enhanced images. Also described herein are approaches for generating a virtual non-contrasted image, a bone and calcification segmentation map, and an iodine map for multi-energy imaging studies.
摘要翻译： 一种方法包括基于噪声模型来估计用于光谱图像的体素的结构模型，将关于体素的3D邻域拟合结构模型，基于以下方式选择体素的结构模型之一： 配件和预定模型选择标准，以及基于所选择的结构模型去除体素，产生一组去噪的光谱图像。 另一种方法包括基于去噪频谱图像，分解的去噪频谱图像，碘图谱和对比度增强因子，生成对应于不同能量范围的一组光谱图像的每个能量图像的虚拟对比度增强中间图像; 并通过将模拟部分体积效应与中间虚拟对比度增强图像相结合来生成最终的虚拟对比度增强图像。 本文还描述了用于生成虚拟非对比图像，骨骼和钙化分割图以及用于多能量成像研究的碘图的方法。
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公开(公告)号：US20130243348A1
公开(公告)日：2013-09-19
申请号：US13989158
申请日：2011-11-16
申请人： Liran Goshen , Asher Gringauz , Yechiel Lamash , Andrei Feldman , Guido Pardo-Roques , Jonathan Sapir
发明人： Liran Goshen , Asher Gringauz , Yechiel Lamash , Andrei Feldman , Guido Pardo-Roques , Jonathan Sapir
IPC分类号： G06T5/00
摘要： A method includes enhancing a contrast to noise ratio (CNR) of image data, generating CNR enhanced image data, wherein the CNR enhanced image data has a substantially same image quality as the image data. A computing system (118) includes a computer readable storage medium (122) encoded with computer readable instructions for enhancing a contrast to noise ratio (CNR) of image data and one or more processors (120), which, when executing the computer readable instructions, causes the computing system to enhance the CNR of the image data. A method includes generating CNR enhanced image data, wherein CNR enhanced image data has a substantially same noise level, noise power spectrum, and spatial resolution of the image data.
摘要翻译： 一种方法包括提高图像数据的对比度（CNR），生成CNR增强图像数据，其中CNR增强图像数据具有与图像数据基本相同的图像质量。 计算系统（118）包括用计算机可读指令编码的用于增强图像数据的对比度（CNR）和一个或多个处理器（120）的计算机可读存储介质（122），当执行计算机可读指令时 ，使计算系统增强图像数据的CNR。 一种方法包括生成CNR增强图像数据，其中CNR增强图像数据具有基本相同的噪声水平，噪声功率谱和图像数据的空间分辨率。
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