Lung multi-core MRI double-domain super-resolution reconstruction method based on artificial intelligence

Abstract

The invention provides a lung multi-core MRI (Magnetic Resonance Imaging) double-domain super-resolution reconstruction method based on artificial intelligence. The method comprises the step of constructing a k-space reconstruction network and an image domain reconstruction network. And reconstructing the up-sampled multi-core low-resolution k-space domain data by using the k-space reconstruction network, and obtaining a super-resolution multi-core image by using the k-space reconstruction network and the image domain reconstruction network. According to the method, a double-domain reconstruction network is adopted, and respective characteristics of data in a k space and an image domain are effectively utilized; the image fusion technology is adopted, and the relevance of multi-core data in the space is effectively utilized. Compared with a traditional super-resolution reconstruction method, richer features can be learned, and the super-resolution reconstruction effect of the lung multi-nuclear magnetic resonance image is effectively improved.

Description

technical field [0001] The invention belongs to the field of imaging technology, and in particular relates to an artificial intelligence-based double-domain super-resolution reconstruction method of lung multi-core MRI. Background technique [0002] Magnetic resonance imaging (Magnetic Resonance Imaging, MRI) is non-invasive and non-radioactive, and can provide rich structural and functional information at the overall level, and has important and extensive applications in the fields of clinical diagnosis and preclinical research [Mehmet A , Steen M, Sebastian W, et al. Magn. Reson. Med., 2019, 439:453.]. Proton ( 1 H) is the MRI observable nucleus with the highest content in the living body, and has the highest gyromagnetic ratio. Classical MRI usually uses protons ( 1 H) as an imaging observation nucleus. compared to multi-core elements 1 H has a larger chemical shift range, which can provide more comprehensive functional and metabolic information. In recent years, the...

AI Technical Summary

Problems solved by technology

Although deep learning algorithms can obtain high-quality super-resolution reconstruction images, a large number of high-resolution images are still required as labels. Multi-core MRI is susceptible to noise and artifacts, and there are few high-quality multi-core MRI images. It is difficult to extract rich image features from the training set data, which affects the final super-resolution reconstruction results

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