Diffusion-tensor imaging method and system

Abstract

The invention provides a diffusion-tensor imaging method, which comprises the following steps of: respectively performing K space under sampling on an imaged target in each diffusion gradient direction in the same variable density sampling form to acquire K space under sampling data of each diffusion gradient direction; selecting the K space under sampling data of any diffusion gradient direction in the K space under sampling data of each diffusion gradient direction as reference K space data, and converting the reference K space data to acquire a reference image; making a difference between the K space under sampling data of each diffusion gradient direction and the reference K space data to acquire differential chart K space under sampling data of each diffusion gradient direction; rebuilding the differential chart K space under sampling data of each diffusion gradient direction to acquire a differential chart of each diffusion gradient direction; and combining the differential chart of each diffusion gradient direction and the reference image to acquire a diffusion-tensor image in each diffusion gradient direction. The invention also provides a diffusion-tensor imaging system at the same time.

Description

【Technical field】 [0001] The invention relates to magnetic resonance technology, in particular to a diffusion tensor imaging method and system. 【Background technique】 [0002] Diffusion tensor imaging (DTI) is a new method developed on the basis of diffusion weighted imaging (DWI). magnetic resonance imaging technique. The calculation of tensor D in DTI requires a b0 map without diffusion gradient and multiple b1~bn maps with diffusion gradient applied. By solving the eigenvalues ​​and eigenvectors of this symmetric matrix D, some common parameters for analyzing DTI can be obtained, such as fractional anisotropy (FA), relative anisotropy (RA) and apparent diffusion coefficient ( apparent diffusion coefficient, ADC) and so on. [0003] In the traditional diffusion tensor imaging process, the calculation of tensor D is a least squares fitting process. In order to make the fitting effect better, in addition to applying more diffusion gradients, we usually use multiple b valu...

AI Technical Summary

Problems solved by technology

The result of this series of measures is that the total sampling time is too long, resulting in too long time for the entire diffusion tensor imaging, which will exceed the tolerance range of the patient

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