Parallel magnetic resonance imaging device and parallel magnetic resonance imaging method

Abstract

A parallel magnetic resonance imaging device comprises a plurality of imaging channels, a collection module, an initialization module, an operation module and a reconstruction module, wherein the collection module is used for collecting an undersampling matrix di (i is the serial number of each imaging channel and i > 0) from the plurality of imaging channels in an undersampling method and according to undersampling factors, the initialization module is used for acquiring an initialization image matrix rho and an initialization sensitivity matrix si (i is the serial number of each imaging channel and i > 0) of the imaging channels, the operation module is used for optimization iteration solving of a constraint function by means of the conjugate gradient algorithm according to the undersampling matrix di, the initialization image matrix rho and the initialization sensitivity matrix si to obtain a reconstructed image matrix rho and a sensitivity matrix si, and the reconstruction module is used for reconstructing an image according to the reconstructed image matrix rho and the sensitivity matrix si. When the parallel magnetic resonance imaging device is used for image reconstruction, imaging speed is effectively increased, and signal to noise ratio loss is small.

Description

technical field [0001] The invention relates to medical technology, in particular to a parallel magnetic resonance imaging device and an imaging method thereof. Background technique [0002] MRI has become one of the important means of clinical medical examination, providing very valuable diagnostic information for clinical medicine. Compared with other medical imaging technologies, MRI technology has the advantages of no radiation hazard, multi-directional and multi-parameter imaging, etc. It is very sensitive to the examination of soft tissues, and can not only display the morphological information of human anatomical structures, but also reflect certain aspects of human tissues. Some physiological and biochemical information. However, the imaging speed of MRI is slow, and the physiological movement of the subject's body during the imaging process will blur the image and distort the contrast, which cannot meet the requirements of rapid imaging such as cardiac dynamic imag...

AI Technical Summary

Problems solved by technology

We divide this combination method into three categories: one is L1 norm-limited reconstruction using the coil sensitivity map as prior information, such as sparseSENSE, in sparseSENSE, the coil sensitivity value is the same as the traditional Sense reconstruction, first put The sensitivity matrix is ​​obtained by using under-sampled data or separated pre-scan data, and then combined with CS sparse constraints to reconstruct the image. However, this method, like the traditional Sense, still has a large Artifacts

The second is to add CS sparse constraints in GRAPPA. This method is to use the prior collection data to obtain some weight values ​​to estimate the uncollected data. However, it is not very accurate to use the intermediate full collection data to estimate the edge uncollected data. estimated

The third is like sparseSENSE, except that the image and the sensitivity matrix are reconstructed at the same time. According to the different prior constraints, this class can be divided into many methods. In the reconstruction formula, some use the sparsity of the final image as a priori Constraints, some use coil sensitivity smoothness or sparsity as prior constraints, but the loss of image-to-noise ratio is large

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