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MRI iterative self-calibration parallel imaging algorithm based on singular value decomposition

A singular value decomposition and self-calibration technology, which is applied in the direction of using nuclear magnetic resonance imaging system for measurement, magnetic resonance measurement, and magnetic variable measurement, can solve problems such as poor image quality and low signal-to-noise ratio of GRAPPA algorithm

Pending Publication Date: 2021-05-25
SUZHOU LONWIN MEDICAL SYST
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Problems solved by technology

However, there are artifacts in the SENSE algorithm, and the signal-to-noise ratio of the GRAPPA algorithm is low, and the obtained image effect is not good.

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  • MRI iterative self-calibration parallel imaging algorithm based on singular value decomposition
  • MRI iterative self-calibration parallel imaging algorithm based on singular value decomposition
  • MRI iterative self-calibration parallel imaging algorithm based on singular value decomposition

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Embodiment Construction

[0036] Such as figure 1 As shown, a kind of MRI iterative self-calibration parallel imaging algorithm (SSCPIiT for short) based on singular value decomposition of the present invention comprises the following steps: (1) memory pre-allocation: (2) read K space data; (3) construct calibration matrix; (4) The calibration matrix and K-space data enter the GPU; (5) Calculate the sensitivity spectrum; (6) CG iteratively obtain the complete K-space; (7) Image reconstruction; (8) The reconstruction result flows back to the CPU. The specific calculation method is as follows.

[0037] (1) In the SPGR and DESPGR sequences, Cartesian acquisition is used to realize magnetic resonance scanning; the present embodiment sets a central calibration line at the center of K space, wherein the central calibration line adopts a full-acquisition acquisition mode, and is interlaced sampling from the center to both sides, and The 2x speed SENSE is the same, except that the center needs part of the ful...

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Abstract

The invention relates to an MRI (Magnetic Resonance Imaging) iterative self-calibration parallel imaging algorithm (SSCPIiT) based on singular value decomposition, which innovatively adds sensitive spectrum information and combines SENSE and GRAPPA parallel acquisition technologies, thereby inheriting the advantages of SENSE and GRAPPA parallel acceleration acquisition and having the advantages of a GRAPPA self-calibration parallel acquisition technology. By combining the advantages of the SENSE technology and the GRAPPA technology, the SSCPIiT parallel acquisition technology is short in acquisition time and higher in signal-to-noise ratio compared with the SENSE technology and the GRAPPA technology. The SSCPIiT method effectively solves the problems that artifacts exist in SENSE and the signal-to-noise ratio of GRAPPA is low. Therefore, the SSCPIiT has a very high clinical application value.

Description

technical field [0001] The invention belongs to the field of magnetic resonance imaging, in particular to an MRI iterative self-calibration parallel imaging algorithm based on singular value decomposition. Background technique [0002] In parallel MRI, data is acquired from multiple receiver coils simultaneously, allowing image reconstruction from undersampled multi-coil data. Each coil exhibits a different spatial sensitivity as an additional spatial encoding function. This can speed up image acquisition by subsampling k-space and reconstructing the image with sensitivity information. Currently used reconstruction algorithms have two different routes: the explicit reconstruction algorithm based on coil sensitivity (SENSE) and the reconstruction algorithm based on local kernels in k-space, which utilize the multiple channels of adjacent points in k-space Correlation between (GRAPPA and SPIRiT). However, there are artifacts in the SENSE algorithm, and the signal-to-noise r...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R33/48G01R33/56
CPCG01R33/48G01R33/56
Inventor 姜忠德丁少伟徐明芳
Owner SUZHOU LONWIN MEDICAL SYST
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