Fast and precise MRI (Magnetic Resonance Imaging) reconstruction method

Abstract

The invention discloses a fast and precise MRI (Magnetic Resonance Imaging) reconstruction method. The method comprises steps: scanning is carried out in a target visual field to obtain K space full sampling data; the K space full sampling data are sampled by using radial sampling to obtain under sampling data; the under sampling data are subjected to multi-loop data recovery to obtain an MRI image; and double-density dual-tree complex wavelet transform is used as a sparse basis, and according to the priori information of the MRI image under the double-density dual-tree complex wavelet transform, an FISTA algorithm is combined for image reconstruction. The reconstruction method can better depict image details and information, and the quality of the recovered image is improved.

Description

technical field [0001] The invention relates to the technical field of medical image processing, in particular to a fast and accurate reconstruction method of MRI images. Background technique [0002] Magnetic Resonance Imaging (MRI) has no ionizing radiation, has extremely high soft tissue contrast and spatial resolution, and uses the resonance principle of hydrogen nuclei under the action of an external magnetic field for imaging. It has been widely used in medical testing. However, due to the limitations of the hardware scanning system and the traditional Nyquist sampling theorem in MRI, the sampling time of MRI in K space is too long, and the imaging speed is slow, and its long imaging time will introduce motion artifacts and distortion of the original image. Contrast distortion restricts its development in clinical medicine. [0003] In 2004, the compressed sensing (Compressed sensing) theory proposed by Candes et al. broke through the limitations of the traditional Ny...

AI Technical Summary

Problems solved by technology

The dual-tree complex wavelet provides information descriptions in six main directions such as ±15°, ±45° and ±75°, but for some complex images, due to its limited direction selectivity, there are derivatives at the edge, Can not accurately describe the details of the original image

[0007] In addition, the existing restoration CS-MRI reconstruction algorithm conjugate gradient method, iterative shrinkage threshold method, two-step iterative shrinkage threshold method, fast iterative shrinkage threshold method (fast iterative shrinkage / soft algorithm, FISTA), etc., although they can restore images, However, the sampling time is long, and the wavelet is used as the sparse basis, which cannot describe the details of the original image.

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