Magnetic resonance diffusion imaging method for integration and reconstruction based on Gaussian model acting as instance

Abstract

The invention discloses a magnetic resonance diffusion imaging method for integration and reconstruction based on a Gaussian diffusion model acting as an instance. The method comprises the steps that signal acquisition is performed on a tested target based on multilayer simultaneously excited preset sequences; phase estimation is performed on the acquired under-sampled signals through a parallel imaging technology; the Gaussian diffusion model is established through the estimated phase, the acquired under-sampled signals and a reference image without diffusion weight; the under-sampled signals of all the directions are integrated according to the Gaussian diffusion model, and a target equation is established; the target equation is iteratively solved by using a nonlinear conjugate gradient algorithm so as to obtain a diffusion tensor parameter; and a diffusion coefficient and a diffusion weight image are calculated according to the diffusion tensor parameter. Therefore, high acceleration acquisition of magnetic resonance diffusion tensor imaging can be realized so that the acquisition time can be effectively reduced, the diffusion tensor parameter can be accurately estimated to obtain the diffusion image of high signal-to-noise ratio and high resolution, and the requirement of clinical application can be met.

Description

technical field [0001] The invention relates to the technical field of magnetic resonance imaging, in particular to a method for magnetic resonance diffusion imaging based on integrated reconstruction using a Gaussian diffusion model as an example. Background technique [0002] Magnetic resonance diffusion tensor imaging can non-invasively detect the microscopic Brownian motion of human water molecules, and provide structural information of human fiber connections and functional information of tissues. It is an important neuroimaging technology and has been widely used in clinical and research . Currently, magnetic resonance diffusion tensor imaging is the only imaging technique capable of non-invasively detecting human nerve fiber tracts. [0003] The contrast mechanism of magnetic resonance diffusion imaging can adopt a Gaussian diffusion model, and the diffusion tensor parameter in the Gaussian diffusion model reflects the diffusion properties of tissues. Through the di...

AI Technical Summary

Problems solved by technology

In terms of resolution, multi-shot echo-planar sequence acquisition can achieve higher resolution diffusion images than traditional single-shot techniques, but multi-shot acquisition also results in longer acquisition times

Therefore, although high-resolution, multi-diffusion gradient acquisition can achieve more accurate high-quality diffusion imaging, the required image scanning time is much longer than the traditional methods currently used in clinical practice, which limits the clinical application and development of neuroimaging diagnostic techniques.

[0005] At present, the technologies for accelerated MRI diffusion tensor imaging are mainly divided into two categories: 1) Parallel imaging technology, which uses multi-channel coils to collect signals with spatial sensitivity encoding, and through a de-aliasing algorithm, the under-sampled The signal is restored to obtain a complete image; however, the current parallel imaging technology is affected by the number of channel coils and the signal-to-noise ratio, and the acceleration factor is usually 2 to 3 times, and the signal-to-noise ratio of the image is low

(2) Compressed sensing technology. Compressed sensing technology uses the sparsity of diffuse image data to reduce the number of acquired signals and speed up acquisition by imposing sparsity constraints. However, excessive reliance on compressed sensing technology often leads to problems such as image smoothness and loss of details. And usually requires random sampling, it is difficult to apply clinically

Images