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21.发明授权公开(公告)号：US07282917B1
公开(公告)日：2007-10-16
申请号：US11278039
申请日：2006-03-30
IPC分类号： G01V3/00
CPC分类号： G01R33/5611
摘要： The present invention provides a system and method for parallel imaging that performs auto-calibrating reconstructions with a 2D (for 2D imaging) or 3D kernel (for 3D imaging) that exploits the computational efficiencies available when operating in certain data “domains” or “spaces”. The reconstruction process of multi-coil data is separated into a “training phase” and an “application phase” in which reconstruction weights are applied to acquired data to synthesize (replace) missing data. The choice of data space, i.e., k-space, hybrid space, or image space, in which each step occurs is independently optimized to reduce total reconstruction time for a given imaging application. As such, the invention retains the image quality benefits of using a 2D k-space kernel without the computational burden of applying a 2D k-space convolution kernel.
摘要翻译： 本发明提供了一种用于并行成像的系统和方法，其使用2D（用于2D成像）或3D内核（用于3D成像）执行自动校准重建，其利用在某些数据“域”或“空间”中操作时可用的计算效率 “。 多线圈数据的重构过程分为“训练阶段”和“应用阶段”，其中重建权重应用于获取的数据以合成（替换）缺失数据。 每个步骤发生的数据空间的选择，即k空间，混合空间或图像空间被独立地优化以减少给定成像应用的总重构时间。 因此，本发明保留了使用2D k空间内核的图像质量优点，而没有应用2D k空间卷积内核的计算负担。
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公开(公告)号：US08890522B2
公开(公告)日：2014-11-18
申请号：US13051924
申请日：2011-03-18
IPC分类号： G01V3/00 , G01R33/563 , G01R33/561 , G01R33/48
CPC分类号： G01R33/56308 , G01R33/4818 , G01R33/5611
摘要： The present disclosure is intended to describe embodiments for improving image data acquisition and processing in accelerated dynamic magnetic resonance imaging sequences. One embodiment is described where a method includes an acquisition sequence configured to acquire an undersampled set of magnetic resonance data. The undersampled set of magnetic resonance data has a pseudo-random sampling pattern within a data space acquired at a first time, the pseudo-random sampling pattern being influenced by other pseudo-random sampling patterns within the data space arising from the acquisition of additional undersampled sets of magnetic resonance data at respective times. In some embodiments, the pseudo-random sampling patterns of the undersampled sets of magnetic resonance data interleave to yield a desired sampling pattern.
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23.发明授权公开(公告)号：US08653817B2
公开(公告)日：2014-02-18
申请号：US13051952
申请日：2011-03-18
IPC分类号： G01V3/00
CPC分类号： G01R33/56308 , G01R33/4818 , G01R33/5611
摘要： The present disclosure is intended to describe embodiments for improving image data acquisition and processing in accelerated dynamic magnetic resonance imaging sequences. One embodiment is described where a method includes an acquisition sequence configured to acquire an undersampled set of magnetic resonance data. The undersampled set of magnetic resonance data has a pseudo-random sampling pattern within a data space acquired at a first time, the pseudo-random sampling pattern being influenced by other pseudo-random sampling patterns within the data space arising from the acquisition of additional undersampled sets of magnetic resonance data at respective times. In some embodiments, the pseudo-random sampling patterns of the undersampled sets of magnetic resonance data interleave to yield a desired sampling pattern.
摘要翻译： 本公开旨在描述用于改进加速动态磁共振成像序列中的图像数据采集和处理的实施例。 描述一个实施例，其中方法包括被配置为获取欠采样的磁共振数据集合的获取序列。 欠采样的磁共振数据集合在第一时间获取的数据空间内具有伪随机采样模式，伪随机采样模式受数据空间内的其他伪随机采样模式的影响，这是由于获取额外的欠采样 各磁共振数据组。 在一些实施例中，欠采样的磁共振数据组的伪随机采样模式交织以产生期望的采样模式。
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公开(公告)号：US08116541B2
公开(公告)日：2012-02-14
申请号：US11744949
申请日：2007-05-07
CPC分类号： G06K9/28 , G01R33/3415 , G01R33/4828 , G01R33/5611 , G01R33/5619
摘要： A method for determining weights (or coefficients) for synthesizing k-space data for autocalibrated parallel imaging (API) combines training data sets (including k-space data such as autocalibrating signals (ACS)) acquired at multiple successive time points. Combining training data sets from multiple successive time points together to determine a set of weights increases the accuracy of the calculated weights. The weights may be applied to k-space data from a single or multiple time points. The method retains the phase information of the individual time point images and may thus be applied, for example, to phase-sensitive multi-point imaging such as chemical species separation studies.
摘要翻译： 用于确定用于自动校准并行成像（API）的k空间数据的权重（或系数）的方法组合在多个连续时间点获取的训练数据集（包括诸如自动校准信号（ACS）的k空间数据）。 将来自多个连续时间点的训练数据集合在一起以确定一组权重增加计算权重的精度。 权重可以从单个或多个时间点应用于k空间数据。 该方法保留了各个时间点图像的相位信息，因此可以应用于例如化学物质分离研究的相敏多点成像。
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25.发明授权公开(公告)号：US07692425B2
公开(公告)日：2010-04-06
申请号：US12363346
申请日：2009-01-30
IPC分类号： G01V3/00
CPC分类号： G01R33/5611
摘要： The present invention provides a system and method for parallel imaging that performs auto-calibrating reconstructions with a 2D (for 2D imaging) or 3D kernel (for 3D imaging) that exploits the computational efficiencies available when operating in certain data “domains” or “spaces”. The reconstruction process of multi-coil data is separated into a “training phase” and an “application phase” in which reconstruction weights are applied to acquired data to synthesize (replace) missing data. The choice of data space, i.e., k-space, hybrid space, or image space, in which each step occurs is independently optimized to reduce total reconstruction time for a given imaging application. As such, the invention retains the image quality benefits of using a 2D k-space kernel without the computational burden of applying a 2D k-space convolution kernel.
摘要翻译： 本发明提供了一种用于并行成像的系统和方法，其使用2D（用于2D成像）或3D内核（用于3D成像）执行自动校准重建，其利用在某些数据“域”或“空间”中操作时可用的计算效率 “。 多线圈数据的重构过程分为“训练阶段”和“应用阶段”，其中重建权重应用于获取的数据以合成（替换）丢失的数据。 每个步骤发生的数据空间的选择，即k空间，混合空间或图像空间被独立地优化以减少给定成像应用的总重构时间。 因此，本发明保留了使用2D k空间内核的图像质量优点，而没有应用2D k空间卷积内核的计算负担。
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26.发明授权公开(公告)号：US07486075B2
公开(公告)日：2009-02-03
申请号：US11867186
申请日：2007-10-04
IPC分类号： G01V3/00
CPC分类号： G01R33/5611
摘要： The present invention provides a system and method for parallel imaging that performs auto-calibrating reconstructions with a 2D (for 2D imaging) or 3D kernel (for 3D imaging) that exploits the computational efficiencies available when operating in certain data “domains” or “spaces”. The reconstruction process of multi-coil data is separated into a “training phase” and an “application phase” in which reconstruction weights are applied to acquired data to synthesize (replace) missing data. The choice of data space, i.e., k-space, hybrid space, or image space, in which each step occurs is independently optimized to reduce total reconstruction time for a given imaging application. As such, the invention retains the image quality benefits of using a 2D k-space kernel without the computational burden of applying a 2D k-space convolution kernel.
摘要翻译： 本发明提供了一种用于并行成像的系统和方法，其使用2D（用于2D成像）或3D内核（用于3D成像）执行自动校准重建，其利用在某些数据“域”或“空间”中操作时可用的计算效率 “。 多线圈数据的重构过程分为“训练阶段”和“应用阶段”，其中重建权重应用于获取的数据以合成（替换）丢失的数据。 每个步骤发生的数据空间的选择，即k空间，混合空间或图像空间被独立地优化以减少给定成像应用的总重构时间。 因此，本发明保留了使用2D k空间内核的图像质量优点，而没有应用2D k空间卷积内核的计算负担。
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27.发明申请公开(公告)号：US20080297152A1
公开(公告)日：2008-12-04
申请号：US11757571
申请日：2007-06-04
IPC分类号： G01R33/56
CPC分类号： G01R33/5611
摘要： An RF coil assembly includes a plurality of RF source coils and an RF target coil separate from the plurality of RF source coils. A computer is programmed to acquire MR data of an imaging object from each of the plurality of RF source coils and to acquire MR data of the imaging object from the RF target coil. The computer is further programmed to calculate a set of weights based on a relationship between MR data acquired from each RF source coil and MR data acquired from the RF target coil and to reconstruct an image based on an application of the set of weights to at least a portion of the MR data acquired from each of the plurality of RF source coils.
摘要翻译： RF线圈组件包括多个RF源线圈和与多个RF源线圈分离的RF目标线圈。 计算机被编程为从多个RF源线圈中的每一个获取成像对象的MR数据，并从RF目标线圈获取成像对象的MR数据。 计算机还被编程为基于从每个RF源线圈获取的MR数据与从RF目标线圈获取的MR数据之间的关系来计算一组权重，并且基于该组权重的应用至少重构图像 从多个RF源线圈中的每一个获取的MR数据的一部分。
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公开(公告)号：US20080279433A1
公开(公告)日：2008-11-13
申请号：US11744949
申请日：2007-05-07
IPC分类号： G06K9/00
CPC分类号： G06K9/28 , G01R33/3415 , G01R33/4828 , G01R33/5611 , G01R33/5619
摘要： A method for determining weights (or coefficients) for synthesizing k-space data for autocalibrated parallel imaging (API) combines training data sets (including k-space data such as autocalibrating signals (ACS)) acquired at multiple successive time points. Combining training data sets from multiple successive time points together to determine a set of weights increases the accuracy of the calculated weights. The weights may be applied to k-space data from a single or multiple time points. The method retains the phase information of the individual time point images and may thus be applied, for example, to phase-sensitive multi-point imaging such as chemical species separation studies.
摘要翻译： 用于确定用于自动校准并行成像（API）的k空间数据的权重（或系数）的方法组合在多个连续时间点获取的训练数据集（包括诸如自动校准信号（ACS）的k空间数据）。 将来自多个连续时间点的训练数据集合在一起以确定一组权重增加计算权重的精度。 权重可以从单个或多个时间点应用于k空间数据。 该方法保留了各个时间点图像的相位信息，因此可以应用于例如化学物质分离研究的相敏多点成像。
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