A Non-Negative High-Order Tensor Quasi-Newton Search Method for Fiber Orientation Distribution Estimation

Abstract

Provided is a fiber direction distribution estimating method based on nonnegative higher-order tensor quasi-Newton searching. The method includes the followings steps: reading brain magnetic resonance data, obtaining a magnetic resonance signal S(g) in a gradient applying direction g and a magnetic resonance signal S0 in a gradient non-applying direction as well as gradient direction data, selecting an interested area that is needed, and calculating a diffusion attenuation signal S(g) / S0 in the area; modeling the diffusion attenuation signals S(g) / S0 of all voxels in the interested area one by one to form an ellipsoid distribution model with a diffusion form; and obtaining a diffusion function D(v) by calculating a coefficient vector c of a tensor, then calculating a diffusion function value of each sampling point, fitting the diffusion function values into a diffusion model, and searching an extremum and calculating the fiber direction. A higher-order Cartesian tensor is utilized to achieve fitting of a fiber direction distribution function square root, all fiber direction distribution functions are guaranteed to be nonnegative, the angle resolution is high, and a great experiment effect is achieved.

Description

technical field [0001] The invention relates to the fields of medical imaging, image processing, numerical analysis, three-dimensional reconstruction, computer science, neuroanatomy, etc., especially a high-order Cartesian diffusion tensor imaging method for non-negative fiber direction distribution of brain white matter fiber imaging . Background technique [0002] White matter fiber imaging is a non-invasive information technology that obtains the microstructure information of voxel nerve fibers in the white matter area of ​​the human brain and performs three-dimensional reconstruction and display. The main method is to perform voxel modeling on the original diffusion-weighted magnetic resonance data, obtain the distribution of fiber directions in each voxel, and form an anatomically meaningful fiber space microstructure. Through a lot of research, scholars in this field have achieved a series of achievements in brain fiber microstructure reconstruction algorithm, uncerta...

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Problems solved by technology

[0003] In order to overcome the problem that the existing fiber imaging method cannot guarantee the non-negativity of the diffusion function, the present invention proposes a non-negative fiber direction distribution estimation method based on quasi-Newton search oriented by high-order tensors

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