Partial K space sequence image reconstruction method based on self-adapted double-dictionary learning

A sequential image and K-space technology, applied in the field of medical image processing, can solve problems such as low image resolution, image motion blur, reconstructed image artifacts, etc., to achieve rich information, improve robustness, and good reconstruction effect Effect

Active Publication Date: 2013-07-24
XIDIAN UNIV
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Problems solved by technology

However, the higher the sampling rate, the longer the acquisition time required, and the imaged person has to stay in the imaging instrument for a long time, and the motion of the imaged person will cause motion blur in the image
However, further downsampling can cause artifacts in the reconstructed image and low image resolution

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  • Partial K space sequence image reconstruction method based on self-adapted double-dictionary learning
  • Partial K space sequence image reconstruction method based on self-adapted double-dictionary learning
  • Partial K space sequence image reconstruction method based on self-adapted double-dictionary learning

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Embodiment Construction

[0027] Refer to attached figure 1 , the concrete steps of the present invention include as follows:

[0028] Step 1. Synthesize complete K-space data to obtain training images

[0029] 1a) Collect N pieces of partial K-space data, and use the N pieces of partial K-space data to synthesize n pieces of complete K-space data Q i , i=1,2,...,n, methods for synthesizing data include pixel-level synthesis methods, feature-level synthesis methods, and decision-based synthesis methods, etc. This example uses but is not limited to pixel-based The synthetic method of grade, its synthetic process is as follows:

[0030] Synthesize a piece of K-space data with N / n pieces of partial K-space data, taking the first piece of partial K-space data of the N / n pieces of partial K-space data as a standard;

[0031] Add the data collected in the second partial K-space and not collected in the first partial K-space to the first partial K-space;

[0032] Add the data collected in the third partia...

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Abstract

The invention discloses a partial K space sequence image reconstruction method based on self-adapted double-dictionary learning, and the method is mainly used for solving the problems of an existing method that the quality of a reconstructed image is more seriously reduced under the condition of sampling under 10 times. The partial K space sequence image reconstruction method comprises the following main steps of: collecting partial K space data and utilizing the correlation between the partial K space data to be integrated into complete K space data; obtaining a training image by the complete K space data; utilizing a KSVD (Kernel Singular Value Decomposition) algorithm to train the training image to obtain dictionaries with high and low resolution ratios; and utilizing a relation between the dictionaries with the high and low resolution ratios to reconstruct the input partial K space data, and carrying out residual error compensation on the reconstructed image to obtain a more accurate reconstruction result. According to the partial K space sequence image reconstruction method disclosed by the invention, the quality of the reconstructed image can be effectively improved under the condition of sampling under 10 times; and the partial K space sequence image reconstruction method can be used for reconstructing MRI (Magnetic Resonance Imaging) sequence images of a plurality of parts.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to a medical image processing method, and can be used for MRI image reconstruction of multiple parts. Background technique [0002] Partial K-space image reconstruction is a problem proposed to reduce the amount of data acquisition to reconstruct high-resolution images in order to speed up the imaging speed of magnetic resonance images. In order to solve this problem, many classic methods have been proposed: [0003] The first is the most commonly used zero-filling method, that is, the unacquired K-space data is filled with zeros, and then inverse Fourier transform is performed to obtain the imaging method of the image space. This imaging method can improve the imaging speed. The defect is that there are Artifacts. [0004] The second is the phase correction method, which assumes that the phase of the magnetic resonance image space is slowly changing. It uses part of the image...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/50
Inventor 缑水平刘芳唐晓焦李成盛珂吴建设王爽马文萍马晶晶
Owner XIDIAN UNIV
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