Imaging method and device using a mobile magnetic resonance apparatus, storage medium, and terminal

An imaging device using a mobile magnetic resonance apparatus, comprising:

• a target K-space data generation module, which is configured to randomly sample an object-to-be-scanned using a mobile magnetic resonance apparatus to acquire encoding of the frequency and phase of the tissue of the object-to-be-scanned, and obtain target K-space data;
• a data input module, which is configured to input the target K-space data and pre-generated denoising reconstruction network parameters into a pre-trained denoising reconstruction network;
• wherein the pre-trained denoising reconstruction network is generated based on fully sampled training data;
• and
• a magnetic resonance image output module, which is configured to output a magnetic resonance image corresponding to the object-to-be-scanned,
• wherein the pre-trained denoising reconstruction network is generated according to the following steps, which comprise:
• using the mobile magnetic resonance apparatus to perform full sampling on multiple scanning objects to acquire the encoding of the frequency and phase of the tissue of each scanning object, and obtain multiple pieces of K-space training data;
• constructing fully sampled training data based on the multiple pieces of K-space training data;
• constructing a target denoising reconstruction network, inputting the fully sampled training data into the target denoising reconstruction network, and outputting a network cost value;
• and
• generating a pre-trained denoising reconstruction network and pre-generated denoising reconstruction network parameters when the network cost value reaches its minimum;
• wherein the constructing fully sampled training data based on the multiple pieces of K- space training data comprises:
• performing image reconstruction based on each K-space data to obtain the magnetic resonance image of each K-space data;
• associating each K-space data with its corresponding magnetic resonance image to obtain a K-space image dataset;
• and
• randomly dividing the K-space image dataset into equal parts, and determining a preset number of parts of the K-space image dataset as the fully sampled training data.