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公开(公告)号：US20150226815A1
公开(公告)日：2015-08-13
申请号：US14178741
申请日：2014-02-12
CPC分类号： A61B5/055 , A61B5/7289 , G01R33/4826 , G01R33/5601 , G01R33/56366
摘要： In a method for automatically detecting contrast enhancement at predetermined phases as a contrast agent bolus perfuses a target tissue volume in a patient, a continuous acquisition MRI imaging system is provided for obtaining dynamic contrast enhanced MRI data for use in creating images. The contrast agent bolus is injected into a blood stream of the patient which passes through the target volume. With the imaging system, a center of a k-space of the target volume is repeatedly sampled to obtain k-space data. A bolus time curve signal is automatically extracted from the k-space data which indicates a course of bolus contrast enhancement which is used to automatically pick time frames at the predetermined phases of the perfusion which are then used to identify corresponding key images to be obtained at the time frames.
摘要翻译： 在用于自动检测在预定阶段的对比度增强作为造影剂推注灌注患者中的目标组织体积的方法中，提供连续采集MRI成像系统以获得用于创建图像的动态对比度增强MRI数据。 将造影剂推注注射到穿过目标体积的患者的血液流中。 利用成像系统，重复采样目标体积的k空间的中心以获得k空间数据。 从k空间数据中自动提取推注时间曲线信号，该空间数据指示用于在灌注的预定阶段自动选择时间帧的快速对比度增强过程，然后使用该时间帧来识别将在 时间框架。
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2.发明申请公开(公告)号：US20130265052A1
公开(公告)日：2013-10-10
申请号：US13859946
申请日：2013-04-10
IPC分类号： G01R33/56
CPC分类号： G01R33/56 , G01R33/5611
摘要： In a method and magnetic resonance (MR) apparatus to determine sample points of a random undersampling scheme of k-space to acquire reduced MR data with multiple coils, a set of sample points of the random undersampling scheme to acquire the reduced MR data is determined, and an indicator of a signal noise in reconstructed MR data is calculated. Furthermore, an additional sample point, which is not included in the set of sample points is determined, and a change of the indicator that results by an addition of the additional sample point to the set of sample points is calculated. The additional sample point is selectively added to the set of sample points dependent on the calculated change.
摘要翻译： 在用于确定k空间的随机欠采样方案的采样点以获取具有多个线圈的减小的MR数据的方法和磁共振（MR）装置中，确定用于获取减小的MR数据的随机欠采样方案的一组采样点 ，并且计算重建的MR数据中的信号噪声的指示符。 此外，确定不包括在采样点集合中的附加采样点，并且计算通过将附加采样点添加到采样点集合而导致的指示符的改变。 根据计算的变化，附加采样点被选择性地添加到采样点集合。
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3.发明授权Method to select an undersampling scheme for magnetic resonance imaging, and magnetic resonance imaging method and system using such a selected undersampling scheme 有权选择用于磁共振成像的欠采样方案的方法，以及使用这种选择的欠采样方案的磁共振成像方法和系统公开(公告)号：US09297873B2
公开(公告)日：2016-03-29
申请号：US13627042
申请日：2012-09-26
IPC分类号： G01R33/561
CPC分类号： G01R33/5611
摘要： In a method to select an undersampling scheme of k-space and an associated set of reconstruction kernels to acquire reduced magnetic resonance (MR) data sets with multiple coils, a calibration data set is acquired for each of the respective coils, a noise covariance is determined from autocorrelations and correlations of the noise of the various coils. At least one set of reconstruction kernels is calculated for each of the multiple undersampling schemes from the calibration data sets of the various coils. For each set of reconstruction kernels, a characteristic value is calculated from the noise covariance and the respective reconstruction kernels of the coils, with the characteristic value being proportional to a spatial mean value of a signal noise of an MR image. A selected undersampling scheme and a selected set of reconstruction kernels are selected based on the calculated characteristic values.
摘要翻译： 在选择k空间的欠采样方案和相关联的一组重建内核以获取具有多个线圈的减小的磁共振（MR）数据集的方法中，针对各个线圈获取校准数据集，噪声协方差为 根据各种线圈的噪声的自相关性和相关性确定。 根据各种线圈的校准数据集，对于多个欠采样方案中的每一个计算至少一组重构内核。 对于每组重建内核，根据噪声协方差和线圈的相应重建内核计算特征值，其特征值与MR图像的信号噪声的空间平均值成比例。 基于所计算的特征值来选择所选的欠采样方案和所选择的重构内核集合。
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4.发明申请FAT AND IRON QUANTIFICATION USING A MULTI-STEP ADAPTIVE FITTING APPROACH WITH MULTI-ECHO MAGNETIC RESONANCE IMAGING 有权使用多步自适应拟合方法与多重磁共振成像的脂肪和铁定量公开(公告)号：US20140126795A1
公开(公告)日：2014-05-08
申请号：US14054903
申请日：2013-10-16
CPC分类号： G01R33/4828 , G01R33/50
摘要： A computer-implemented method for quantifying fat and iron in anatomical tissue includes acquiring a plurality of multi-echo signal datasets representative of the anatomical tissue using a magnetic resonance (MR) pulse sequence. A plurality of multi-echo signal datasets are selected from the plurality of multi-echo signal datasets and used to determine a first water magnitude value and a first fat magnitude value. In response to determining that the multi-echo signal datasets include at least three multi-echo datasets, a first stage analysis is performed. This first stage analysis comprises selecting a first effective transverse relaxation rate value. Next, first algorithm inputs comprising the first water magnitude value, the first fat magnitude value, and the first effective transverse relation rate value are created. Then, a non-linear fitting algorithm is performed based on the first algorithm inputs to calculate a second water magnitude value, a second fat magnitude value, and a second effective transverse relaxation rate value. A first proton density fat fraction value is then determined based on the second water magnitude value and the second fat magnitude value.
摘要翻译： 用于在解剖组织中量化脂肪和铁的计算机实现的方法包括使用磁共振（MR）脉冲序列获取代表解剖组织的多个多回波信号数据集。 从多个多回波信号数据集中选择多个多回波信号数据集，并用于确定第一水量值和第一脂肪量值。 响应于确定多回波信号数据集包括至少三个多回波数据集，执行第一阶段分析。 该第一阶段分析包括选择第一有效横向松弛率值。 接下来，创建包括第一水量值，第一脂肪量值和第一有效横向关系速率值的第一算法输入。 然后，基于第一算法输入来执行非线性拟合算法，以计算第二水量值，第二脂肪量值和第二有效横向松弛率值。 然后基于第二水量值和第二脂肪量值来确定第一质子密度脂肪分数值。
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5.发明申请公开(公告)号：US20140125336A1
公开(公告)日：2014-05-08
申请号：US14054914
申请日：2013-10-16
CPC分类号： G01R33/4828 , G01R33/50
摘要： Embodiments relate to evaluating properties of tissues with magnetic resonance imaging (MRI). A MR image is used to measure a characteristic that influences a particular chemical property of a tissue. In an exemplary embodiment, tissue transverse relaxation values or relaxation rates, which can readily be measured from MR images, are used to evaluate iron deposition in tissue. Iron deposition influences the tissue transverse relaxation values (T2 or T2*) or relaxation rates (R2=1/T2 or R2*=1/T2*). A clinically relevant R2CR* map is calculated based on the known values of the effective R2eff*, the water R2w*, and the fat R2f* by incorporating the most relevant value for each individual image element of a plurality of image elements of an MR image of the tissue. The clinically relevant R2CR* map provides an accurate evaluation of iron deposition in any region of the tissue with the use of one map.
摘要翻译： 实施例涉及评估具有磁共振成像（MRI）的组织的性质。 MR图像用于测量影响组织的特定化学性质的特征。 在示例性实施例中，可以容易地从MR图像测量的组织横向松弛值或松弛率用于评估组织中的铁沉积。 铁沉积影响组织横向弛豫值（T2或T2 *）或松弛率（R2 = 1 / T2或R2 * = 1 / T2 *）。 基于有效R2eff *，水R2w *和脂肪R2f *的已知值，通过对MR图像的多个图像元素的每个单独图像元素合并最相关值来计算临床相关的R2CR *图 的组织。 临床上相关的R2CR *图谱使用一张地图提供组织任何区域铁沉积的准确评估。
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6.发明授权公开(公告)号：US09429635B2
公开(公告)日：2016-08-30
申请号：US14054914
申请日：2013-10-16
CPC分类号： G01R33/4828 , G01R33/50
摘要： Embodiments relate to evaluating properties of tissues with magnetic resonance imaging (MRI). A MR image is used to measure a characteristic that influences a particular chemical property of a tissue. In an exemplary embodiment, tissue transverse relaxation values or relaxation rates, which can readily be measured from MR images, are used to evaluate iron deposition in tissue. Iron deposition influences the tissue transverse relaxation values (T2 or T2*) or relaxation rates (R2=1/T2 or R2*=1/T2*). A clinically relevant R2CR* map is calculated based on the known values of the effective R2eff*, the water R2w*, and the fat R2f* by incorporating the most relevant value for each individual image element of a plurality of image elements of an MR image of the tissue. The clinically relevant R2CR* map provides an accurate evaluation of iron deposition in any region of the tissue with the use of one map.
摘要翻译： 实施例涉及评估具有磁共振成像（MRI）的组织的性质。 MR图像用于测量影响组织的特定化学性质的特征。 在示例性实施例中，可以容易地从MR图像测量的组织横向松弛值或松弛率用于评估组织中的铁沉积。 铁沉积影响组织横向弛豫值（T2或T2 *）或松弛率（R2 = 1 / T2或R2 * = 1 / T2 *）。 基于有效R2eff *，水R2w *和脂肪R2f *的已知值，通过对MR图像的多个图像元素的每个单独图像元素合并最相关值来计算临床相关的R2CR *图 的组织。 临床上相关的R2CR *图谱使用一张地图提供组织任何区域铁沉积的准确评估。
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公开(公告)号：US20140088899A1
公开(公告)日：2014-03-27
申请号：US14038958
申请日：2013-09-27
申请人： Jun Liu , Hui Xue , Marcel Dominik Nickel , Ti-chiun Chang , Mariappan S. Nadar , Alban Lefebvre , Edgar Mueller , Qiu Wang , Zhili Yang , Nirmal Janardhanan , Michael Zenge
发明人： Jun Liu , Hui Xue , Marcel Dominik Nickel , Ti-chiun Chang , Mariappan S. Nadar , Alban Lefebvre , Edgar Mueller , Qiu Wang , Zhili Yang , Nirmal Janardhanan , Michael Zenge
IPC分类号： G01R33/24
CPC分类号： G01R33/243 , G01R33/246 , G01R33/5611
摘要： A method for estimating a coil sensitivity map for a magnetic resonance (MR) image includes providing a matrix A of sliding blocks of a 3D image of coil calibration data, calculating a left singular matrix V∥ from a singular value decomposition of A corresponding to τ leading singular values, calculating P=V∥V∥H, calculating a matrix S that is an inverse Fourier transform of a zero-padded matrix P, and solving MHcr=(Sr)Hcr for cr, where cr is a vector of coil sensitivity maps for all coils at spatial location r, and M = ( ( 1 1 … 1 0 0 … 0 … … … 0 0 … 0 ) ( 0 0 … 0 1 1 … 1 … … … 0 0 … 0 ) … ( 0 0 … 0 0 0 … 0 … … … 1 1 … 1 ) ) .
摘要翻译： 用于估计磁共振（MR）图像的线圈灵敏度图的方法包括提供线圈校准数据的3D图像的滑动块的矩阵A，从对应于τ的A的奇异值分解计算左奇异矩阵V∥ 计算P = V || V | H，计算作为零填充矩阵P的傅立叶逆变换的矩阵S，求解cr的MHcr =（Sr）Hcr，其中cr是线圈灵敏度的向量 对于空间位置r处的所有线圈的映射，M =（（1 1 ... 1 0 0 ... 0 ... ... 0 0 ... 0）（0 0 ... 0 1 1 ... 1 ... ... 0 0 ... 0） ...（0 0 ... 0 0 0 ... 0 ...... ... 1 1 ... 1））。
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8.发明申请METHOD TO SELECT AN UNDERSAMPLING SCHEME FOR MAGNETIC RESONANCE IMAGING, AND MAGNETIC RESONANCE IMAGING METHOD AND SYSTEM USING SUCH A SELECTED UNDERSAMPLING SCHEME 有权选择用于磁共振成像的不完善方案的方法和使用这种选择的下列方案的磁共振成像方法和系统公开(公告)号：US20130076352A1
公开(公告)日：2013-03-28
申请号：US13627042
申请日：2012-09-26
IPC分类号： G01R33/58 , G01R33/341
CPC分类号： G01R33/5611
摘要： In a method to select an undersampling scheme of k-space and an associated set of reconstruction kernels to acquire reduced magnetic resonance (MR) data sets with multiple coils, a calibration data set is acquired for each of the respective coils, a noise covariance is determined from autocorrelations and correlations of the noise of the various coils. At least one set of reconstruction kernels is calculated for each of the multiple undersampling schemes from the calibration data sets of the various coils. For each set of reconstruction kernels, a characteristic value is calculated from the noise covariance and the respective reconstruction kernels of the coils, with the characteristic value being proportional to a spatial mean value of a signal noise of an MR image. A selected undersampling scheme and a selected set of reconstruction kernels are selected based on the calculated characteristic values.
摘要翻译： 在选择k空间的欠采样方案和相关联的一组重建内核以获取具有多个线圈的减小的磁共振（MR）数据集的方法中，针对各个线圈获取校准数据集，噪声协方差为 根据各种线圈的噪声的自相关性和相关性确定。 根据各种线圈的校准数据集，对于多个欠采样方案中的每一个计算至少一组重构内核。 对于每组重建内核，根据噪声协方差和线圈的相应重建内核计算特征值，其特征值与MR图像的信号噪声的空间平均值成比例。 基于所计算的特征值来选择所选的欠采样方案和所选择的重构内核集合。
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公开(公告)号：US10001538B2
公开(公告)日：2018-06-19
申请号：US13859946
申请日：2013-04-10
IPC分类号： G01R33/56 , G01R33/561
CPC分类号： G01R33/56 , G01R33/5611
摘要： In a method and magnetic resonance (MR) apparatus to determine sample points of a random undersampling scheme of k-space to acquire reduced MR data with multiple coils, a set of sample points of the random undersampling scheme to acquire the reduced MR data is determined, and an indicator of a signal noise in reconstructed MR data is calculated. Furthermore, an additional sample point, which is not included in the set of sample points is determined, and a change of the indicator that results by an addition of the additional sample point to the set of sample points is calculated. The additional sample point is selectively added to the set of sample points dependent on the calculated change.
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10.发明授权Fat and iron quantification using a multi-step adaptive fitting approach with multi-echo magnetic resonance imaging 有权脂肪和铁定量使用多步自适应拟合方法与多回波磁共振成像公开(公告)号：US09194925B2
公开(公告)日：2015-11-24
申请号：US14054903
申请日：2013-10-16
CPC分类号： G01R33/4828 , G01R33/50
摘要： A computer-implemented method for quantifying fat and iron in anatomical tissue includes acquiring a plurality of multi-echo signal datasets representative of the anatomical tissue using a magnetic resonance (MR) pulse sequence. A plurality of multi-echo signal datasets are selected from the plurality of multi-echo signal datasets and used to determine a first water magnitude value and a first fat magnitude value. In response to determining that the multi-echo signal datasets include at least three multi-echo datasets, a first stage analysis is performed. This first stage analysis comprises selecting a first effective transverse relaxation rate value. Next, first algorithm inputs comprising the first water magnitude value, the first fat magnitude value, and the first effective transverse relation rate value are created. Then, a non-linear fitting algorithm is performed based on the first algorithm inputs to calculate a second water magnitude value, a second fat magnitude value, and a second effective transverse relaxation rate value. A first proton density fat fraction value is then determined based on the second water magnitude value and the second fat magnitude value.
摘要翻译： 用于在解剖组织中量化脂肪和铁的计算机实现的方法包括使用磁共振（MR）脉冲序列获取代表解剖组织的多个多回波信号数据集。 从多个多回波信号数据集中选择多个多回波信号数据集，并用于确定第一水量值和第一脂肪量值。 响应于确定多回波信号数据集包括至少三个多回波数据集，执行第一阶段分析。 该第一阶段分析包括选择第一有效横向松弛率值。 接下来，创建包括第一水量值，第一脂肪量值和第一有效横向关系速率值的第一算法输入。 然后，基于第一算法输入来执行非线性拟合算法，以计算第二水量值，第二脂肪量值和第二有效横向松弛率值。 然后基于第二水量值和第二脂肪量值来确定第一质子密度脂肪分数值。
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