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Multi-channel magnetic resonance image reconstruction method based on deep learning

A magnetic resonance image and deep learning technology, applied in image data processing, 2D image generation, instruments, etc., can solve the problems of not considering multi-channel magnetic resonance image reconstruction, and the inability to widely apply commercial equipment

Active Publication Date: 2019-10-25
XIAMEN UNIV
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Problems solved by technology

However, the methods proposed by Wang et al. did not consider the use of multi-channel MRI images for reconstruction, and cannot be widely applied to current commercial equipment.

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  • Multi-channel magnetic resonance image reconstruction method based on deep learning
  • Multi-channel magnetic resonance image reconstruction method based on deep learning
  • Multi-channel magnetic resonance image reconstruction method based on deep learning

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Embodiment Construction

[0045] The following embodiments will further illustrate the present invention with reference to the accompanying drawings. The embodiment of the present invention is a specific process of reconstructing multi-channel brain data using deep learning methods.

[0046] Embodiments of the present invention include the following steps:

[0047] Step 1: Obtain multi-channel MRI images as a training set

[0048] In the embodiment of the present invention, a magnetic resonance apparatus with a magnetic field strength of 3 Tesla was used to image the brains of 4 volunteers. The sequence parameters used in this embodiment are: sequence echo time TE=6900ms, repetition time TR=2500ms, field of view is 256mm×256mm, layer thickness is 3mm, and the number of coil channels is 12. 4 volunteers were scanned by magnetic resonance equipment The final brain images are used as the training set and test set of the network. The training set comes from 360 multi-channel MRI images of 3 volunteers, an...

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Abstract

The invention discloses a multi-channel magnetic resonance image reconstruction method based on deep learning, and relates to a multi-channel magnetic resonance image reconstruction method. The methodcomprises the following steps: acquiring multi-channel magnetic resonance images, and forming a training set by a sensitivity mapping graph, an under-sampled zero-filled multi-channel magnetic resonance image and a full-sampled synthetic image; establishing a multi-channel deep learning network model for magnetic resonance image reconstruction; constructing a loss function of the network; training multi-channel magnetic resonance image reconstruction network model parameters; reconstructing the target under-sampling multi-channel magnetic resonance image; obtaining an end-to-end mapping function from the under-sampled multi-channel image to the complete magnetic resonance image corresponding to the network model and a network model loss function by adopting a residual connection mode forthe iteration block; training magnetic resonance image reconstruction network model parameters of a residual connection mode; and reconstructing the target under-sampling multi-channel magnetic resonance image by using the residual connected network model. The method has the advantages of high reconstruction speed and good reconstruction effect.

Description

technical field [0001] The present invention relates to a reconstruction method of a multi-channel magnetic resonance image, in particular to a multi-channel magnetic resonance image reconstruction method based on deep learning that utilizes an iterative network to reconstruct a multi-channel magnetic resonance image. Background technique [0002] Magnetic resonance imaging (Magnetic Resonance Imaging, MRI) is an important clinical medical diagnostic tool. Due to its advantages of no radiation and high contrast in soft tissue imaging, it is widely used in the diagnosis of brain, cardiovascular and abdomen. [0003] In the process of clinical diagnosis, high-resolution magnetic resonance images can show tiny lesions, which is helpful for the discovery and treatment of diseases. However, high-resolution MRI images generally require longer scan times, which can increase patient discomfort during the scan. Accelerating scanning through multi-channel parallel imaging and sparse...

Claims

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Application Information

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IPC IPC(8): G06T11/00G06N3/04
CPCG06T11/003G06N3/048G06N3/045
Inventor 屈小波
Owner XIAMEN UNIV
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