Technology for reconstructing MRI fingerprint identification based on sliding window

Abstract

The invention relates to the technical field of information processing, and in particular to a technology for reconstructing MRI fingerprint identification based on a sliding window. The technology reconstructs an image sequence based the sliding window in an acquisition signal time axis and generates a dictionary element corresponding with a window width. The technology can shorten total acquisition time, can obtain a more accurate quantitative parameter imaging result and smaller errors provide with same acquisition time. In addition, since the method is a non-iteration method, the method requires similar time for reconstruction as traditional MRI fingerprint identification technology, thus having greater application value.

Description

technical field [0001] The invention relates to the technical field of information processing, in particular to a sliding window-based magnetic resonance fingerprint identification and reconstruction technology. Background technique [0002] Magnetic resonance imaging (MRI) is a multi-parameter imaging technique without ionizing radiation, which has a wide range of medical and clinical applications. As a research direction of magnetic resonance technology, magnetic resonance quantitative imaging mainly uses the change of received signal intensity after excitation by different radio frequency pulse sequences to calculate the T of different tissues in the body. 1 , T 2 Maps of quantitative parameters such as relaxation time and proton density. This quantitative map can intuitively reflect the different physiological and physical properties of tissues, so it has potential application value in medical diagnosis. Since conventional quantitative imaging techniques need to desig...

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Problems solved by technology

[0004] However, because the traditional magnetic resonance fingerprinting technology only acquires one helix in each TR, the acquisition signal is highly under-sampled for each excitation, and the reconstructed image is full of irrelevant under-sampling artifacts.

This noise-like undersampling artifact will affect the accuracy of dictionary recognition. Therefore, traditional magnetic resonance fingerprint recognition technology needs to ensure a sufficiently large number of excitations (generally, the number of excitations L is greater than 1000) to ensure accurate multi-parameter identification results. The total collection time will increase

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