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    • 41. 发明申请
    • Method For Image Reconstruction From Undersampled Medical Imaging Data
    • 从未采样的医学成像数据进行图像重建的方法
    • US20100207629A1
    • 2010-08-19
    • US12705818
    • 2010-02-15
    • Joshua D TrzaskoArmando Manduca
    • Joshua D TrzaskoArmando Manduca
    • G01R33/48
    • G01R33/561G01R33/5608G01R33/5611
    • A method for image reconstruction that utilizes a generalization of compressed sensing is provided. More particularly, a method for homotopic l0 minimization is provided, in which a series of subproblems that asymptotically approach a solution to the l0 minimization are iteratively solved. These subproblems include utilizing concave metric prior functionals in the traditional compressed sensing framework. Substantially undersampled image data is acquired from a subject, for example, with a medical imaging system, such as a magnetic resonance imaging (“MRI”) system or a computed tomography (“CT”) system. Using the provided method, undersampling on the order of around 96 percent can be achieved while still producing clinically acceptable images.
    • 提供了一种利用压缩感知泛化的图像重建方法。 更具体地,提供了一种用于同位素最小化的方法,其中迭代地解决渐近地接近10个最小化的解的一系列子问题。 这些子问题包括在传统的压缩感知框架中利用凹度度量先验函数。 例如使用诸如磁共振成像(“MRI”)系统或计算机断层扫描(“CT”)系统的医学成像系统从受试者获取基本欠采样的图像数据。 使用所提供的方法,可以在仍然产生临床可接受的图像的同时实现大约96%的低采样。
    • 46. 发明授权
    • Method for compressed sensing image reconstruction using a priori knowledge of spatial support
    • 使用先验空间支持知识的压缩感测图像重建方法
    • US08897515B2
    • 2014-11-25
    • US12877638
    • 2010-09-08
    • Joshua D TrzaskoArmando Manduca
    • Joshua D TrzaskoArmando Manduca
    • G06K9/00G06T11/00
    • G06T11/006G06T2211/424G06T2211/436
    • A method for image reconstruction that utilizes the benefits of compressed sensing (“CS”) while incorporating a priori knowledge of object spatial support into the image reconstruction is provided. Image data is acquired from a subject, for example, with a medical imaging system, such as a magnetic resonance imaging (“MRI”) system or a computed tomography (“CT”) system. An estimate of the spatial support of the subject is produced, for example, using a low resolution image of the subject, or an image reconstructed from undersampled image data in a traditional sense. An estimate image of the subject is also produced by using traditional image reconstruction methods on the acquired image data. An image of the subject is then reconstructed using the produced estimate image and produced spatial support estimate. This method allows for the reconstruction of quality images from undersampled image data in a computationally efficient manner.
    • 提供了一种利用压缩感知(“CS”)的优点,同时将对象空间支持的先验知识并入图像重建中的图像重建方法。 例如,利用诸如磁共振成像(“MRI”)系统或计算机断层扫描(“CT”)系统的医学成像系统从受试者获取图像数据。 产生对象的空间支持的估计,例如,使用对象的低分辨率图像,或从传统意义上的欠采样图像数据重建的图像。 通过对所获取的图像数据使用传统的图像重建方法也产生对象的估计图像。 然后使用所产生的估计图像重建受试者的图像并产生空间支持估计。 该方法允许以计算有效的方式重建来自欠采样图像数据的质量图像。
    • 47. 发明授权
    • Method for image reconstruction from undersampled medical imaging data
    • 从欠采样的医学成像数据进行图像重建的方法
    • US08310233B2
    • 2012-11-13
    • US12705818
    • 2010-02-15
    • Joshua D TrzaskoArmando Manduca
    • Joshua D TrzaskoArmando Manduca
    • G01V3/00G06K9/00
    • G01R33/561G01R33/5608G01R33/5611
    • A method for image reconstruction that utilizes a generalization of compressed sensing is provided. More particularly, a method for homotopic l0 minimization is provided, in which a series of subproblems that asymptotically approach a solution to the l0 minimization are iteratively solved. These subproblems include utilizing concave metric prior functionals in the traditional compressed sensing framework. Substantially undersampled image data is acquired from a subject, for example, with a medical imaging system, such as a magnetic resonance imaging (“MRI”) system or a computed tomography (“CT”) system. Using the provided method, undersampling on the order of around 96 percent can be achieved while still producing clinically acceptable images.
    • 提供了一种利用压缩感知泛化的图像重建方法。 更具体地,提供了一种用于同位素最小化的方法,其中迭代地解决渐近地接近10个最小化的解的一系列子问题。 这些子问题包括在传统的压缩感知框架中利用凹度度量先验函数。 例如使用诸如磁共振成像(MRI)系统或计算机断层摄影(CT)系统的医学成像系统从受试者获取基本欠采样的图像数据。 使用所提供的方法,可以在仍然产生临床可接受的图像的同时实现大约96%的低采样。