Multi-layer magnetic resonance fast imaging method based on adjacent layer information and undersampling
An imaging method and under-sampling technology, applied in medical science, sensors, diagnostic recording/measurement, etc., can solve problems such as slow speed of magnetic resonance imaging, and achieve the effects of improving use efficiency, saving use cost, and speeding up
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Embodiment 1
[0026] A multilayer magnetic resonance fast imaging method based on adjacent layer information and undersampling, comprising the following steps:
[0027] Step 1. Perform a full sampling scan on the first layer of the layer sequence that needs to be scanned, and reconstruct the image through Fourier transform;
[0028] Step 2, enter the scanning of the next layer, analyze the K-space data of the previous layer adjacent to the current scanning layer, and obtain the position information of the local large signal point and the global large signal point;
[0029] Step 3. According to the position information of the global large signal point and the local large signal point obtained in step 2, plan the undersampling scanning trajectory of the current level;
[0030] Step 4. Scan the current level according to the under-sampled scanning trajectory obtained in step 3 to obtain the under-sampled K-space data of the current level;
[0031] Step 5, the undersampled K-space data obtaine...
Embodiment 2
[0039] Step 1. The number of mouse head scanning layers is 5 layers, the thickness of each layer is 0.4mm, and the resolution is 128*128. Firstly, for the first layer that needs to scan the layer sequence, use the spin echo (SE) sequence, set TR=2s, TE=8.667ms, and the sampling trajectory is Cartesian sampling method, and perform full sampling scanning to obtain the K-space data. Fourier transform reconstruction image;
[0040] Step 2. Enter the scan of the next layer, calculate the amplitude value of the K-space data of the previous layer adjacent to the current scanning layer, and obtain the local large point positions of the eight neighborhoods and the global large point in the K-space of the previous layer through statistics. point location. After statistics, the number of local large signal points in the eight neighborhoods in K space accounts for 15% of the total number of points, and the number of selected global large signal points is consistent with the number of loc...
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