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51. 发明申请

US20190378311A1 Machine-Learned Network for Fourier Transform in Reconstruction for Medical Imaging 审中-公开
公开(公告)号：US20190378311A1
公开(公告)日：2019-12-12
申请号：US16150304
申请日：2018-10-03
申请人： Siemens Healthcare GmbH
发明人： Boris Mailhe , Simon Arberet , Florent Billy Romaric Gbelidji , Mariappan S. Nadar
IPC分类号： G06T11/00 , G06N3/04 , G06N3/08 , G16H30/40 , G06F17/14
摘要： For low-complexity to learned reconstruction and/or learned Fourier transform-based operators for reconstruction, a neural network is used for the transform operators. The network architecture is modeled on the Cooley-Tukey fast Fourier transform (FFT) approach. By splitting input data before recursive calls in the network architecture, the network may be trained to perform the transform with similar complexity as FFT. The learned operators may be used in a trained network for reconstruction, such as with a learned iterative framework and image regularizer.

52. 发明授权

US10267883B2 System and method for motion resolved MRI 有权
公开(公告)号：US10267883B2
公开(公告)日：2019-04-23
申请号：US15361670
申请日：2016-11-28
申请人： Siemens Healthcare GmbH
发明人： Xiao Chen , Mariappan S. Nadar , Marcel Dominik Nickel , Boris Mailhe , Qiu Wang
IPC分类号： G01R33/563 , G01R33/48 , A61B5/055 , A61B5/00 , G01R33/56 , G06T7/00 , G06T11/00 , G01R33/567 , G01R33/485
摘要： A method comprises acquiring navigation data during navigation scans at each of a plurality of points in time. A plurality of magnetic resonance imaging (MRI) k-space data corresponding to an imaged object are acquired at the plurality of points in time using Cartesian sampling, the k-space data including at least two spatial dimensions, a time. The respective motion state for each of the k-space data are computed based on the navigation data. At least one image is reconstructed from the plurality MRI k-space data using k-space data corresponding to at least two motion states and the same point in time to reconstruct the at least one image.

53. 发明授权

US10247800B2 MRI pulse sequence design 有权
公开(公告)号：US10247800B2
公开(公告)日：2019-04-02
申请号：US15655936
申请日：2017-07-21
申请人： Siemens Healthcare GmbH
发明人： Xiao Chen , Mariappan S. Nadar , Benjamin L. Odry , Boris Mailhe
IPC分类号： G01R33/54 , A61B5/055 , G01R33/56 , G01R33/563 , A61B5/00
摘要： A method uses an artificial neural network (ANN) to automatically produce a magnetic resonance (MR) pulse sequence. A first MR signal corresponding to a first tissue and a second MR signal corresponding to a second tissue are identified. An RF pulse to be applied to the first and second tissues is selected. Based on at least the first MR signal, the second MR signal, and the RF pulse, an updated first MR signal and an updated second MR signal are determined. A difference is computed between the updated first MR signal and the updated second MR signal. The difference is added to an accumulated difference. The RF pulse selecting, updated first and second MR signal determination, difference computation and adding are repeated. The ANN is controlled to use reinforcement learning to select the MR imaging pulse sequence based, at least in part, on the accumulated difference.

54. 发明申请

US20190086488A1 Magnetic Resonance Radio Frequency Pulse Design using Machine Learning 审中-公开
公开(公告)号：US20190086488A1
公开(公告)日：2019-03-21
申请号：US15705889
申请日：2017-09-15
申请人： Siemens Healthcare GmbH
发明人： Xiao Chen , Mariappan S. Nadar , Benjamin L. Odry , Boris Mailhe
IPC分类号： G01R33/34 , G01R33/54
摘要： Systems and methods are provided for automatically designing RF pulses using a reinforcement machine-learnt classifier. Data representing an object and a selected outcome is accessed. A reinforcement learnt method identifies the RF pulse sequence that generates a result within a predefined value of the selected outcome. An MRI scanner images the object using the RF pulse sequence.

55. 发明申请

US20190025392A1 MRI Pulse Sequence Design 审中-公开
公开(公告)号：US20190025392A1
公开(公告)日：2019-01-24
申请号：US15655936
申请日：2017-07-21
申请人： Siemens Healthcare GmbH
发明人： Xiao Chen , Mariappan S. Nadar , Benjamin L. Odry , Boris Mailhe
IPC分类号： G01R33/54 , A61B5/055 , A61B5/00 , G01R33/56 , G01R33/563
摘要： A method uses an artificial neural network (ANN) to automatically produce a magnetic resonance (MR) pulse sequence. A first MR signal corresponding to a first tissue and a second MR signal corresponding to a second tissue are identified. An RF pulse to be applied to the first and second tissues is selected. Based on at least the first MR signal, the second MR signal, and the RF pulse, an updated first MR signal and an updated second MR signal are determined. A difference is computed between the updated first MR signal and the updated second MR signal. The difference is added to an accumulated difference. The RF pulse selecting, updated first and second MR signal determination, difference computation and adding are repeated. The ANN is controlled to use reinforcement learning to select the MR imaging pulse sequence based, at least in part, on the accumulated difference.

56. 发明申请

US20180232878A1 Image Quality Assessment System And Method 审中-公开
公开(公告)号：US20180232878A1
公开(公告)日：2018-08-16
申请号：US15892746
申请日：2018-02-09
申请人： Siemens Healthcare GmbH
发明人： Sandro Braun , Xiaoguang Lu , Boris Mailhe , Benjamin L. Odry , Xiao Chen , Mariappan S. Nadar
IPC分类号： G06T7/00 , G06T7/20 , G06T7/194
CPC分类号： G06T7/0012 , G06T7/0002 , G06T7/194 , G06T7/20 , G06T2207/10088 , G06T2207/20081 , G06T2207/20084 , G06T2207/30016 , G06T2207/30168
摘要： A system and method includes generation of one or more motion-corrupted images based on each of a plurality of reference images, and training of a regression network to determine a motion score, where training of the regression network includes input of a generated motion-corrupted image to the regression network, reception of a first motion score output by the regression network in response to the input image, and determination of a loss by comparison of the first motion score to a target motion score, the target motion score calculated based on the input motion-corrupted image and a reference image based on which the motion-corrupted image was generated.

57. 发明申请

US20170168129A1 System and Method for Motion Resolved MRI 审中-公开
公开(公告)号：US20170168129A1
公开(公告)日：2017-06-15
申请号：US15361670
申请日：2016-11-28
申请人： Siemens Healthcare GmbH
发明人： Xiao Chen , Mariappan S. Nadar , Marcel Dominik Nickel , Boris Mailhe , Qiu Wang
IPC分类号： G01R33/563 , G01R33/48 , G06T7/00 , A61B5/055 , G06T11/00 , G01R33/56 , A61B5/00
CPC分类号： G01R33/56308 , A61B5/004 , A61B5/055 , A61B5/4244 , A61B5/7207 , A61B5/7225 , A61B2576/02 , G01R33/4818 , G01R33/482 , G01R33/485 , G01R33/56 , G01R33/5601 , G01R33/5607 , G01R33/5608 , G01R33/5635 , G01R33/5676 , G06T7/0012 , G06T11/003 , G06T2207/10088 , G06T2207/30056
摘要： A method comprises acquiring navigation data during navigation scans at each of a plurality of points in time. A plurality of magnetic resonance imaging (MM) k-space data corresponding to an imaged object are acquired at the plurality of points in time using Cartesian sampling, the k-space data including at least two spatial dimensions, a time. The respective motion state for each of the k-space data are computed based on the navigation data. At least one image is reconstructed from the plurality MM k-space data using k-space data corresponding to at least two motion states and the same point in time to reconstruct the at least one image.

58. 发明申请

US20170115368A1 Iterative Magnetic Resonance Fingerprinting Reconstruction 审中-公开
公开(公告)号：US20170115368A1
公开(公告)日：2017-04-27
申请号：US15332336
申请日：2016-10-24
申请人： Siemens Healthcare GmbH
发明人： Xiao Chen , Mariappan S. Nadar , Christopher Cline , Boris Mailhe , Qiu Wang
IPC分类号： G01R33/56 , G01R33/48 , G01R33/563
CPC分类号： G01R33/4824 , G01R33/50 , G01R33/5611
摘要： Disclosed herein is a method obtaining a magnetic resonance image of an object, comprising obtaining a first time evolution signal from a magnetic resonance signal from the object; performing a search of a compressed dictionary of magnetic resonance fingerprints to select a magnetic resonance fingerprint representative of the first time evolution signal, wherein the selected magnetic resonance fingerprint is an exact or approximate nearest neighbor match to the first time evolution signal; obtaining a magnetic resonance parameter associated with the selected fingerprint; generating the magnetic resonance image of the object from the obtained magnetic resonance parameter; and performing a second search of the compressed dictionary using the magnetic resonance image.

59. 发明申请

US20170103512A1 LEARNING-BASED FRAMEWORK FOR PERSONALIZED IMAGE QUALITY EVALUATION AND OPTIMIZATION 有权
公开(公告)号：US20170103512A1
公开(公告)日：2017-04-13
申请号：US14881599
申请日：2015-10-13
申请人： Siemens Healthcare GmbH
发明人： Boris Mailhe , Mariappan S. Nadar
IPC分类号： G06T7/00 , G06K9/66 , G06T5/00
CPC分类号： G06K9/66 , G06K9/036 , G06T5/001 , G06T7/0002 , G06T2207/10088 , G06T2207/20081 , G06T2207/20084 , G06T2207/30168
摘要： A computer-implemented method for providing image quality optimization individualized for a user includes a computer receiving raw image data acquired from an image scanner and identifying one or more raw image quality features based on the raw image data. The computer automatically determines one or more target image quality features by applying one or more user preferences to the one or more raw image quality features. The computer also automatically determines one or more processing parameters based on the one or more target image quality features. The computer may then process the raw image data using the one or more processing parameters to yield an image.

60. 发明申请

US20170069082A1 Parameter-Free Denoising of Complex MR Images by Iterative Multi-Wavelet Thresholding 有权
标题翻译：通过迭代多小波阈值对复杂MR图像进行无参数去噪
公开(公告)号：US20170069082A1
公开(公告)日：2017-03-09
申请号：US14849391
申请日：2015-09-09
申请人： Siemens Healthcare GmbH
发明人： Boris Mailhe , Mariappan S. Nadar , Stephan Kannengiesser
IPC分类号： G06T7/00 , G06T11/00 , G06T5/10 , G06T5/00 , G06T5/20
CPC分类号： G06T5/10 , G01R33/5608 , G01R33/5611 , G06T5/002 , G06T5/20 , G06T2207/10088
摘要： A method for denoising Magnetic Resonance Imaging (MRI) data includes receiving a noisy image acquired using an MRI imaging device and determining a noise model comprising a non-diagonal covariance matrix based on the noisy image and calibration characteristics of the MRI imaging device. The noisy image is designated as the current best image. Then, an iterative denoising process is performed to remove noise from the noisy image. Each iteration of the iterative denoising process comprises (i) applying a bank of heterogeneous denoisers to the current best image to generate a plurality of filter outputs, (ii) creating an image matrix comprising the noisy image, the current best image, and the plurality of filter outputs, (iii) finding a linear combination of elements of the image matrix which minimizes a Stein Unbiased Risk Estimation (SURE) value for the linear combination and the noise model, (iv) designating the linear combination as the current best image, and (v) updating each respective denoiser in the bank of heterogeneous denoisers based on the SURE value. Following the iterative denoising process, the current best image is designated as a final denoised image.
摘要翻译：用于去噪磁共振成像（MRI）数据的方法包括接收使用MRI成像装置获取的噪声图像，并且基于噪声图像和MRI成像装置的校准特性来确定包括非对角协方差矩阵的噪声模型。嘈杂的图像被指定为当前最佳图像。然后，执行迭代去噪处理以从噪声图像中去除噪声。迭代去噪过程的每次迭代包括（i）将一组异构去噪器应用于当前最佳图像以产生多个滤波器输出，（ii）创建包括噪声图像，当前最佳图像和多个滤波器输出的图像矩阵的滤波器输出，（iii）找到使线性组合和噪声模型的斯坦因无偏差风险估计（SURE）值最小化的图像矩阵的元素的线性组合，（iv）将线性组合指定为当前最佳图像，和（v）基于SURE值来更新异构去噪器组中的每个相应的去噪器。在迭代去噪过程之后，将当前最佳图像指定为最终的去噪图像。

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