-
1.发明申请System, Method and Computer Accessible Medium for Providing Real-Time Diffusional Kurtosis Imaging and for Facilitating Estimation of Tensors and Tensor- Derived Measures in Diffusional Kurtosis Imaging 有权系统，方法和计算机可访问介质，用于提供实时扩散性血液饱和成像和促进传感器估计和弥漫性高血压成像中的传感器测量公开(公告)号：US20120002851A1
公开(公告)日：2012-01-05
申请号：US13022488
申请日：2011-02-07
申请人： Jens Jensen , Joseph Helpern , Ali Tabesh , Els Fieremans
发明人： Jens Jensen , Joseph Helpern , Ali Tabesh , Els Fieremans
IPC分类号： G06K9/00
CPC分类号： G01R33/56341
摘要： Exemplary method, system, and computer-accessible medium can be provided for determining a measure of diffusional kurtosis by receiving data relating to at least one diffusion weighted image, and determining a measure of a diffusional kurtosis as a function of the received data using a closed form solution procedure. In accordance with certain exemplary embodiments of the present disclosure, provided herein are computer-accessible medium, systems and methods for, e.g., imaging in an MRI system, and, more particularly for facilitating estimation of tensors and tensor-derived measures in diffusional kurtosis imaging (DKI). For example, DKI can facilitate a characterization of non-Gaussian diffusion of water molecules in biological tissues. The diffusion and kurtosis tensors parameterizing the DKI model can typically be estimated via unconstrained least squares (LS) methods. In the presence of noise, motion, and imaging artifacts, these methods can be prone to producing physically and/or biologically implausible tensor estimates. The exemplary embodiments of the present disclosure can address at least this deficiency by formulating an exemplary estimation problem, e.g., as linearly constrained linear LS, where the constraints can ensure acceptable tensor estimates.
摘要翻译： 可以提供示例性方法，系统和计算机可访问介质，用于通过接收与至少一个扩散加权图像相关的数据来确定扩散峰度的度量，并且使用闭合的方法确定作为接收数据的函数的扩散峰度的度量 形式解决程序。 根据本公开的某些示例性实施例，本文提供了用于例如在MRI系统中成像的计算机可访问介质，系统和方法，并且更具体地，用于促进扩散峭度成像中的张量和张量导出测量的估计 （DKI）。 例如，DKI可以促进水分子在生物组织中的非高斯扩散的表征。 参数化DKI模型的扩散和峰度张量通常可以通过非约束最小二乘法（LS）方法估计。 在存在噪声，运动和成像伪像的情况下，这些方法可能容易产生物理和/或生物学不可信的张量估计。 本公开的示例性实施例可以通过制定示例性估计问题（例如线性约束线性LS）来解决至少该缺陷，其中约束可以确保可接受的张量估计。
-
2.发明授权System, method and computer accessible medium for providing real-time diffusional kurtosis imaging and for facilitating estimation of tensors and tensor-derived measures in diffusional kurtosis imaging 有权系统，方法和计算机可访问介质，用于提供实时扩散峰度成像，并有助于估计扩张峰度成像中的张量和张量推导测量公开(公告)号：US08811706B2
公开(公告)日：2014-08-19
申请号：US13022488
申请日：2011-02-07
申请人： Jens Jensen , Joseph Helpern , Ali Tabesh , Els Fieremans
发明人： Jens Jensen , Joseph Helpern , Ali Tabesh , Els Fieremans
CPC分类号： G01R33/56341
摘要： Exemplary method, system, and computer-accessible medium can be provided for determining a measure of diffusional kurtosis by receiving data relating to at least one diffusion weighted image, and determining a measure of a diffusional kurtosis as a function of the received data using a closed form solution procedure. In accordance with certain exemplary embodiments of the present disclosure, provided herein are computer-accessible medium, systems and methods for, e.g., imaging in an MRI system, and, more particularly for facilitating estimation of tensors and tensor-derived measures in diffusional kurtosis imaging (DKI). For example, DKI can facilitate a characterization of non-Gaussian diffusion of water molecules in biological tissues. The diffusion and kurtosis tensors parameterizing the DKI model can typically be estimated via unconstrained least squares (LS) methods. In the presence of noise, motion, and imaging artifacts, these methods can be prone to producing physically and/or biologically implausible tensor estimates. The exemplary embodiments of the present disclosure can address at least this deficiency by formulating an exemplary estimation problem, e.g., as linearly constrained linear LS, where the constraints can ensure acceptable tensor estimates.
摘要翻译： 可以提供示例性方法，系统和计算机可访问介质，用于通过接收与至少一个扩散加权图像相关的数据来确定扩散峰度的度量，并且使用闭合的方法确定作为接收数据的函数的扩散峰度的度量 形式解决程序。 根据本公开的某些示例性实施例，本文提供了用于例如在MRI系统中成像的计算机可访问介质，系统和方法，并且更具体地，用于促进扩散峭度成像中的张量和张量导出测量的估计 （DKI）。 例如，DKI可以促进水分子在生物组织中的非高斯扩散的表征。 参数化DKI模型的扩散和峰度张量通常可以通过非约束最小二乘法（LS）方法估计。 在存在噪声，运动和成像伪像的情况下，这些方法可能容易产生物理和/或生物学不可信的张量估计。 本公开的示例性实施例可以通过制定示例性估计问题（例如线性约束线性LS）来解决至少该缺陷，其中约束可以确保可接受的张量估计。
-
公开(公告)号：US07742649B2
公开(公告)日：2010-06-22
申请号：US11766228
申请日：2007-06-21
申请人： Ali Tabesh , Duanfeng He
发明人： Ali Tabesh , Duanfeng He
CPC分类号： G06T9/005 , G06K9/00221
摘要： An image compression system and method is described, which makes use of the symmetry found in faces and heads to perform the compression. The image is divided along the line of symmetry, and pairs of corresponding pixels on the two divided sides are determined. A weighted average and a weighted variance of the pixel values of the pairs is computed, and is used to encode the image. A transform such as the Karhunen-Loeve transform is used to compute the weighted averages and variances.
摘要翻译： 描述了一种图像压缩系统和方法，其利用在面部和头部中发现的对称性来执行压缩。 图像沿着对称线分割，并且确定两个分开的边上的对应像素对。 计算对的像素值的加权平均和加权方差，并用于对图像进行编码。 使用诸如Karhunen-Loeve变换的变换来计算加权平均值和方差。
-
公开(公告)号：US20070248275A1
公开(公告)日：2007-10-25
申请号：US11766228
申请日：2007-06-21
申请人： Ali Tabesh , Duanfeng He
发明人： Ali Tabesh , Duanfeng He
IPC分类号： G06K9/46
CPC分类号： G06T9/005 , G06K9/00221
摘要： An image compression system and method is described, which makes use of the symmetry found in faces and heads to perform the compression. The image is divided along the line of symmetry, and pairs of corresponding pixels on the two divided sides are determined. A weighted average and a weighted variance of the pixel values of the pairs is computed, and is used to encode the image. A transform such as the Karhunen-Loeve transform is used to compute the weighted averages and variances.
摘要翻译： 描述了一种图像压缩系统和方法，其利用在面部和头部中发现的对称性来执行压缩。 图像沿着对称线分割，并且确定两个分开的边上的对应像素对。 计算对的像素值的加权平均和加权方差，并用于对图像进行编码。 使用诸如Karhunen-Loeve变换的变换来计算加权平均值和方差。
-
公开(公告)号：US07254275B2
公开(公告)日：2007-08-07
申请号：US10321911
申请日：2002-12-17
申请人： Ali Tabesh , Duanfeng He
发明人： Ali Tabesh , Duanfeng He
CPC分类号： G06T9/005 , G06K9/00221
摘要： An image compression system and method is described, which makes use of the symmetry found in faces and heads to perform the compression. The image is divided along the line of symmetry, and pairs of corresponding pixels on the two divided sides are determined. A weighted average and a weighted variance of the pixel values of the pairs is computed, and is used to encode the image. A transform such as the Karhunen-Loeve transform is used to compute the weighted averages and variances.
摘要翻译： 描述了一种图像压缩系统和方法，其利用在面部和头部中发现的对称性来执行压缩。 图像沿着对称线分割，并且确定两个分开的边上的对应像素对。 计算对的像素值的加权平均和加权方差，并用于对图像进行编码。 使用诸如Karhunen-Loeve变换的变换来计算加权平均值和方差。
-
公开(公告)号：US09858389B2
公开(公告)日：2018-01-02
申请号：US12462041
申请日：2009-07-27
申请人： Michael Donovan , Faisal Khan , Gerardo Fernandez , Ali Tabesh , Ricardo Mesa-Tejada , Carlos Cordon-Cardo , Jose Costa , Stephen Fogarasi , Yevgen Vengrenyuk
发明人： Michael Donovan , Faisal Khan , Gerardo Fernandez , Ali Tabesh , Ricardo Mesa-Tejada , Carlos Cordon-Cardo , Jose Costa , Stephen Fogarasi , Yevgen Vengrenyuk
摘要： Clinical information, molecular information and/or computer-generated morphometric information is used in a predictive model for predicting the occurrence of a medical condition. In an embodiment, a model predicts risk of prostate cancer progression in a patient, where the model is based on features including one or more (e.g., all) of preoperative PSA, dominant Gleason Grade, Gleason Score, at least one of a measurement of expression of AR in epithelial and stromal nuclei and a measurement of expression of Ki67-positive epithelial nuclei, a morphometric measurement of average edge length in the minimum spanning tree (MST) of epithelial nuclei, and a morphometric measurement of area of non-lumen associated epithelial cells relative to total tumor area. In some embodiments, the morphometric information is based on image analysis of tissue subject to multiplex immunofluorescence and may include characteristic(s) of a minimum spanning tree (MST) and/or a fractal dimension observed in the images.
-
7.发明授权公开(公告)号：US07761240B2
公开(公告)日：2010-07-20
申请号：US11200758
申请日：2005-08-09
申请人： Olivier Saidi , Ali Tabesh , Mikhail Teverovskiy
发明人： Olivier Saidi , Ali Tabesh , Mikhail Teverovskiy
CPC分类号： G06K9/0014 , G06T7/0012 , G06T2207/30024
摘要： Systems and methods are provided for automated diagnosis and grading of tissue images based on morphometric data extracted from the images by a computer. The morphometric data may include image-level morphometric data such as fractal dimension data, fractal code data, wavelet data, and/or color channel histogram data. The morphometric data may also include object-level morphometric data such as color, structural, and/or textural properties of segmented image objects (e.g., stroma, nuclei, red blood cells, etc.).
摘要翻译： 提供了基于通过计算机从图像提取的形态测量数据来自动诊断和分级组织图像的系统和方法。 形态测量数据可以包括图像级形态测量数据，例如分维数据，分形码数据，小波数据和/或颜色通道直方图数据。 形态测量数据还可以包括对象级形态测量数据，例如分割图像对象（例如，基质，细胞核，红细胞等）的颜色，结构和/或纹理特性。
-
8.发明申请Systems and methods for treating, diagnosing and predicting the occurrence of a medical condition 有权用于治疗，诊断和预测医疗状况发生的系统和方法公开(公告)号：US20100177950A1
公开(公告)日：2010-07-15
申请号：US12462041
申请日：2009-07-27
申请人： Michael Donovan , Faisal Khan , Gerardo Fernandez , Ali Tabesh , Ricardo Mesa-Tejada , Carlos Cordon-Cardo , Jose Costa , Stephen Fogarasi , Yevgen Vengrenyuk
发明人： Michael Donovan , Faisal Khan , Gerardo Fernandez , Ali Tabesh , Ricardo Mesa-Tejada , Carlos Cordon-Cardo , Jose Costa , Stephen Fogarasi , Yevgen Vengrenyuk
摘要： Clinical information, molecular information and/or computer-generated morphometric information is used in a predictive model for predicting the occurrence of a medical condition. In an embodiment, a model predicts risk of prostate cancer progression in a patient, where the model is based on features including one or more (e.g., all) of preoperative PSA, dominant Gleason Grade, Gleason Score, at least one of a measurement of expression of AR in epithelial and stromal nuclei and a measurement of expression of Ki67-positive epithelial nuclei, a morphometric measurement of average edge length in the minimum spanning tree (MST) of epithelial nuclei, and a morphometric measurement of area of non-lumen associated epithelial cells relative to total tumor area. In some embodiments, the morphometric information is based on image analysis of tissue subject to multiplex immunofluorescence and may include characteristic(s) of a minimum spanning tree (MST) and/or a fractal dimension observed in the images.
摘要翻译： 临床信息，分子信息和/或计算机生成的形态学信息用于预测医疗状况发生的预测模型中。 在一个实施方案中，模型预测患者中前列腺癌进展的风险，其中模型基于包括术前PSA，优势Gleason等级，格里森评分（Gleason Score），格里森评分（Gleason Score）的一个或多个（例如，全部） AR在上皮和基质核中的表达以及Ki67阳性上皮细胞核表达的测量，上皮细胞核的最小生成树（MST）中平均边缘长度的形态测量以及非内腔相关区域的形态测量 上皮细胞相对于总肿瘤面积。 在一些实施方案中，形态学信息基于经多重免疫荧光的组织的图像分析，并且可以包括在图像中观察到的最小生成树（MST）和/或分形维数的特征。
-
9.发明申请公开(公告)号：US20060064248A1
公开(公告)日：2006-03-23
申请号：US11200758
申请日：2005-08-09
申请人： Olivier Saidi , Ali Tabesh , Mikhail Teverovskiy
发明人： Olivier Saidi , Ali Tabesh , Mikhail Teverovskiy
CPC分类号： G06K9/0014 , G06T7/0012 , G06T2207/30024
摘要： Systems and methods are provided for automated diagnosis and grading of tissue images based on morphometric data extracted from the images by a computer. The morphometric data may include image-level morphometric data such as fractal dimension data, fractal code data, wavelet data, and/or color channel histogram data. The morphometric data may also include object-level morphometric data such as color, structural, and/or textural properties of segmented image objects (e.g., stroma, nuclei, red blood cells, etc.).
摘要翻译： 提供了基于通过计算机从图像提取的形态测量数据来自动诊断和分级组织图像的系统和方法。 形态测量数据可以包括图像级形态测量数据，例如分维数据，分形码数据，小波数据和/或颜色通道直方图数据。 形态测量数据还可以包括对象级形态测量数据，例如分割图像对象（例如，基质，细胞核，红细胞等）的颜色，结构和/或纹理特性。
-
-
-
-
-
-
-
-
搜索结果
9 条记录 (耗时 0.025 秒)
