Graph regularization sparse coding-based magnetic resonance super-undersampled K data imaging method

Abstract

The invention discloses a graph regularization sparse coding-based magnetic resonance super-undersampled K data imaging method which comprises the following steps: (a) performing graph regularization sparse coding expression on a double-layer Bergman iteration frame to obtain an image sparse model; (b) updating a learning dictionary and a sparse coefficient on the inner-layer iteration of double-layer Bergman iteration by introducing an auxiliary variable and an alternate solving technology; (c) performing image updating on the outer-layer iteration of the double-layer Bergman iteration to obtain an imaging result by utilizing a part of super-undersampled K data as constraints. According to the method, a proximity graph is established to code local structural data and dig geometric data constraints thereof by introducing adaptive dictionary learning into graph regularization sparse coding, so that image data can be sparsely expressed better; in addition, an image with more complex local geometric characteristics can be processed, a local image structure can be effectively captured, more image details can be recovered, and an obtained image result is higher in fidelity.

Description

technical field [0001] The invention belongs to the field of medical imaging, in particular to magnetic resonance imaging. Background technique [0002] Magnetic resonance imaging is an important medical diagnostic tool, providing clinicians with important anatomy, especially in the absence of ionization. Although magnetic resonance imaging enables high-resolution images to differentiate and represent soft tissues, its imaging speed is limited by physical and physiological limitations. The slow imaging speed is the main disadvantage of the MRI system, which greatly reduces the indications for MRI examinations. Furthermore, it is not suitable for the examination of moving organs and critically ill patients. Increasing the scan duration may cause some physiological problems. motion artifacts etc. Therefore, since the appearance of magnetic resonance imaging, people have been working on improving the imaging speed and imaging quality. [0003] MRI is slow in relation to scan...

AI Technical Summary

Problems solved by technology

[0019] The technical problem to be solved in the present invention is to use the graph regularization sparse coding method to utilize its advantages in geometric feature data constraints to establish a neighboring graph to encode local structural data, which can better describe structural information and realize magnetic resonance image blocks. Reconstruction recognition, to solve the defect that the existing magnetic resonance imaging method cannot ideally represent the image structure sparsely and quickly and accurately reconstruct the magnetic resonance image

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