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Non-local low-rank constrained self-calibration parallel magnetic resonance imaging reconstruction method

A magnetic resonance imaging, low-rank constraint technology, applied in the field of self-calibration parallel magnetic resonance imaging reconstruction, can solve problems such as image quality needs to be improved, and achieve the effect of improving quality

Active Publication Date: 2021-06-18
KUNMING UNIV OF SCI & TECH
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Problems solved by technology

However, the existing methods only use a simple joint total variation (JTV) and joint L1 norm (Joint L1-norm, JL1) regularization term, and the image quality of the reconstructed image of the algorithm needs to be improved.

Method used

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  • Non-local low-rank constrained self-calibration parallel magnetic resonance imaging reconstruction method
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  • Non-local low-rank constrained self-calibration parallel magnetic resonance imaging reconstruction method

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Embodiment 1

[0056] Embodiment 1: The present invention is an efficient reconstruction method proposed based on the SPIRiT framework.

[0057] suppose represents the multi-coil image to be reconstructed, Represents undersampled k-space data. N represents the number of pixels of the single-coil image to be reconstructed, M represents the number of k-space undersampled projection data of the single-coil image, and C is the number of coils. Each column of X and Y is obtained by stacking single coil images column by column. The undersampled k-space data Y of the multi-coil image is given by:

[0058] Y=AX (1)

[0059] Among them, the matrix Represents an operator for undersampling projection on multi-coil data X, is an undersampling operator, is the Fourier transform.

[0060] SPIRiT performs consistency between each point of the image grid and its entire neighborhood points (whether they are acquired or not), and the calibration consistency formula for all k-space positions is: ...

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Abstract

The invention relates to a non-local low-rank constrained self-calibration parallel magnetic resonance imaging reconstruction method, and belongs to the technical field of magnetic resonance imaging. Parallel magnetic resonance imaging reconstruction is always a research hotspot in recent years. The invention provides a high-quality magnetic resonance imaging reconstruction model of image non-local low-rank constraint (NLR) based on an SPIRIT reconstruction framework, the high-quality magnetic resonance imaging reconstruction model is named as NLR-SPIRiT, and solving is carried out by using an alternating direction method of multipliers (ADMM). According to an experiment, an iterative self-consistent parallel imaging JTV-SPIRIT model which is combined with a joint total variation (JTV) regular term is compared. Experimental results show that the image reconstructed by the NLR-SPIRiT algorithm has better reconstruction quality, and under-sampling artifacts can be better removed.

Description

technical field [0001] The invention relates to a non-local low-rank constrained self-calibration parallel magnetic resonance imaging reconstruction method, which belongs to the technical field of magnetic resonance imaging. Background technique [0002] Magnetic resonance imaging (MRI) is a major medical imaging technique that is of great value for both academic research and clinical applications. Compressed sensing (Compressed Sensing, CS) and parallel imaging (Parallel Imaging, PI) technology can speed up the speed of MRI by reducing the amount of k-space data acquisition. How to reconstruct high-quality images from sampling data lower than the Nyquist sampling rate has always been a research hotspot in the field of magnetic resonance imaging. [0003] In compressed sensing reconstruction, regularization constraints are used to explore image prior information, and images are reconstructed by nonlinear reconstruction algorithms. The regularization for exploring image pri...

Claims

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Application Information

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IPC IPC(8): G06T11/00
CPCG06T11/003
Inventor 段继忠潘婷
Owner KUNMING UNIV OF SCI & TECH
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