Fourier parallel magnetic resonance imaging method based on one-dimensional part of deep convolutional network

Abstract

The invention relates to a Fourier parallel magnetic resonance imaging method based on a one-dimensional part of a deep convolutional network, and belongs to the technical field of magnetic resonance imaging. The method comprises the following steps: a sample set for training and a sample label set are created; an initial deep convolutional network is built; a training sample of the sample set is input into an initial deep convolutional network model to perform forward propagation, an output result of the forward propagation is compared with an expect result in the sample label set, and training is performed using a gradient descent algorithm until various layer parameters maximizing the consistency between the output result and the expect result are obtained; an optimal deep convolutional network model is established by utilizing the obtained various layer parameters; and a multi-coil under-sampling image obtained through online sampling is input into the optimal deep convolutional network model, forward propagation is performed on the optimal deep convolutional network model, and a rebuilt single-channel whole-sampling image is output. A noise of the rebuilt image can be removed well, a magnetic resonance image having a good visual effect is rebuilt, and the Fourier parallel magnetic resonance imaging method has high practical value.

Description

technical field [0001] The invention relates to the technical field of magnetic resonance imaging, in particular to a one-dimensional partial Fourier parallel magnetic resonance imaging method based on a deep convolutional network. Background technique [0002] Parallel imaging technology is usually used to accelerate the scanning imaging of clinical magnetic resonance imaging equipment. This technology uses multiple receiving coil arrays to collect data at the same time. While maintaining the spatial resolution without attenuation, the number of phase encoding steps is reduced, and the k-space is underdeveloped. Sampling greatly shortens the scanning time of magnetic resonance and improves the imaging speed. Parallel imaging technology needs to perform various transformations on each coil data and use reconstruction algorithms for image reconstruction to obtain the desired image. Therefore, accurate multi-coil undersampling MRI image reconstruction methods are very importan...

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Problems solved by technology

[0010] At present, the popular MRI reconstruction methods GRAPPA and SPIRiT have achieved good results when using the three-times one-dimensional uniform undersampling mode, but the reconstruction speed of these two methods is too slow, and the reconstructed image contains a lot of noise. not satisfactory

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