Recovery processing method for diffusion tensor magnetic resonance image

Abstract

The invention discloses a method for recovery processing of a diffusion tensor magnetic resonance image, relating to the medical diagnosis image processing technical field. The technical proposal which is provided in order to overcome the defect in the prior art that a great deal of useful information is lost is as follows: firstly, image information is acquired; secondly, the image information is converted into vector image information; thirdly, layer number, a layer number counter, steplength, cycle index and so on are designed; fourthly, a cycle counter is designed to be 1; fifthly, H and J of the current layer number and the current cycle index are calculated; sixthly, affine invariable gradient Gaff and Euclid curvature k are calculated; seventhly, affine invariable anisotropy smooth values of a vector image are determined; eighthly, restored image information is calculated; ninthly to thirteenthly, the cycle index and the processing layer number are calculated and controlled, and then determination is made whether to stop. The invention has the advantages that: more useful information is reserved during the process of recovery processing of the image, and the image is made to be clearer and simpler and has more details.

Description

technical field [0001] The invention relates to the technical field of medical diagnosis image processing, in particular to a method for restoring a diffusion tensor magnetic resonance image. Background technique [0002] Diffusion tensor imaging (DTI) is a newly developed imaging technique that enables the non-invasive study of human tissue structures (for example, nerve fibers in the human brain) and diseases such as multiple sclerosis and stroke diagnosis is possible. In clinical practice, DTI images are polluted by Rice noise due to the influence of thermal noise in the patient's body. In order to facilitate post-processing such as tensor calculation and fiber tracking, the image needs to be restored first. [0003] At present, the mainstream of DTI image restoration is based on partial differential equation (partial differential equation, PDE) method. Now, the restoration of DTI images using PDE is based on the Euclidean gradient. Although the image restoration meth...

AI Technical Summary

Problems solved by technology

Using this method to restore DTI vector images requires processing each element of the vector separately; the defect of this method is that since each element of the vector evolves independently, the correlation between elements cannot be preserved, resulting in the loss of a lot of useful information of the original image

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