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An iterative self-consistency parallel imaging reconstruction method based on transformation learning and joint sparsity

A joint sparse and consistent technology, used in 2D image generation, image data processing, instrumentation, etc.

Active Publication Date: 2019-06-25
KUNMING UNIV OF SCI & TECH
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Problems solved by technology

Although the TLMRI method can effectively improve the image quality and processing speed compared with other methods, there is still room for improvement in the image quality and processing speed of this method in multi-coil MR imaging.

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  • An iterative self-consistency parallel imaging reconstruction method based on transformation learning and joint sparsity
  • An iterative self-consistency parallel imaging reconstruction method based on transformation learning and joint sparsity
  • An iterative self-consistency parallel imaging reconstruction method based on transformation learning and joint sparsity

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Embodiment Construction

[0051] The technical scheme of the present invention is further described in detail below in conjunction with the accompanying drawings: an iterative self-consistent parallel imaging reconstruction method based on transformation learning and joint sparsity, the specific steps of the method are as follows:

[0052] Assumption represents all coil images to be reconstructed, x=(x 1 ,...,x c ,...,x Nc ), Represents the c-th column of x, that is, the vectorized representation of the c-th coil image, N=n x ×n y Indicates the number of pixels in a single image, n x and n y Represent the number of rows and columns of a single image, respectively, N c Indicates the total number of coil images. Partial Fourier measurements or k-space subsampled data for all coil images is given by:

[0053] y=Ax (1)

[0054] in, represents the cth column of y, that is, the partial Fourier measurement data of the cth coil image; A is a partial Fourier transform matrix, and is an N×N ...

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Abstract

The invention relates to an iterative self-consistency parallel imaging reconstruction method based on transformation learning and joint sparsity, and belongs to the technical field of medical magnetic resonance imaging. Based on an iteration self-consistency parallel imaging reconstruction problem, the invention provides a Cartesian iteration self-consistency parallel magnetic resonance imaging reconstruction method combining transformation learning and sparse regularization terms. and carrying out solution by using a variable separation (VS) technology and an alternating direction multipliermethod (ADMM) technology. The method comprises the following steps of: carrying out solution by using an ADMM method, Simulation experiments on the two actual data sets show that compared with othercomparison methods, the new algorithm provided by the invention can obtain better reconstruction quality.

Description

technical field [0001] The invention relates to an iterative self-consistent parallel imaging reconstruction method based on transformation learning and joint sparsity, belonging to the technical field of medical magnetic resonance imaging. Background technique [0002] Parallel magnetic resonance (Magnetic Resonance, MR) imaging is a well-known accelerated imaging method. Its advantage is to reduce the sampling time of MR imaging by receiving the spatial sensitivity information through the multi-coil array receiver. In the past two decades, many parallel imaging reconstruction methods have been proposed, and these methods are different due to different ways of using the sensitivity information; for example, the Sensitivity Encoding (SENSitivity Encoding, SENSE) method uses explicit sensitivity information to perform Refactored. The main limitation of this method in practical application is that it is difficult to measure the sensitivity information accurately. The other ...

Claims

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Application Information

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IPC IPC(8): G06T11/00
Inventor 段继忠鲍中文
Owner KUNMING UNIV OF SCI & TECH
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