A deep learning high under-sampling hyperpolarized gas lung MRI reconstruction method

A deep learning and undersampling technology, applied in neural learning methods, 2D image generation, image data processing, etc., can solve problems such as difficulty in accurately estimating noise

Active Publication Date: 2019-03-01
INNOVATION ACAD FOR PRECISION MEASUREMENT SCI & TECH CAS
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Problems solved by technology

But in MRI, accurate noise estimation is a difficult task, because the noise may be non-stationary Rician noise

Method used

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  • A deep learning high under-sampling hyperpolarized gas lung MRI reconstruction method
  • A deep learning high under-sampling hyperpolarized gas lung MRI reconstruction method
  • A deep learning high under-sampling hyperpolarized gas lung MRI reconstruction method

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Embodiment 1

[0050] A high undersampling hyperpolarized gas lung MRI reconstruction method based on deep learning, comprising the following steps:

[0051] Step 1, constructing hyperpolarized gas lung MRI image training set, hyperpolarized gas in this embodiment is 129 Xe, the training set of hyperpolarized gas lung MRI images is 129 Xe lung MRI image training set;

[0052] In step 1.1, fully sampled hyperpolarized gas lung magnetic resonance k-space data and corresponding proton images were collected from 72 volunteers. The full-sampled hyperpolarized gas lung magnetic resonance k-space data were acquired by 3D bSSFP sequence, the sampling matrix size was 96×84, and the number of slices was 24. Fast Fourier transform is performed on the full-sampled hyperpolarized gas lung magnetic resonance k-space data, and the obtained reconstruction result is used as the reference image y, and the images whose signal area of ​​the lung is smaller than 10% of the image size are eliminated, and a tota...

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Abstract

The invention discloses a deep learning high under-sampling hyperpolarized gas lung MRI reconstruction method. The method comprises the following steps: constructing and constructing a hyperpolarizedgas lung MRI image training set; according to the method, the cascade CNN model is used, lung contour information is added into a loss function, an accurate reconstructed image can be obtained under the high under-sampling multiple, and the imaging speed is remarkably increased.

Description

technical field [0001] The present invention relates to the technical fields of magnetic resonance imaging (Magnetic Resonance Imaging, MRI), deep learning, undersampling reconstruction, etc., and specifically relates to a high undersampling hyperpolarized gas lung MRI reconstruction method for deep learning, which is suitable for accelerating hyperpolarization Inert gases (such as 129 Xe, 3 He et al.) the imaging speed of lung MRI and improve the imaging quality. Background technique [0002] MRI is a non-invasive, ionizing radiation-free imaging method that can provide high-resolution structural and functional information for clinical use. Traditional MRI focuses on hydrogen protons, while the lungs are mainly composed of cavities with a low density of hydrogen protons, so the lungs are a blind area of ​​traditional MRI. Spin-exchange optical pumping technology can convert noble gases (such as 3 He, 129 Xe, etc.) Polarizability increased by 10 3 ~10 5 times, thus ma...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T11/00G06T7/11G06N3/08
CPCG06N3/08G06T11/005G06T2207/10088G06T2207/20081G06T7/11
Inventor 周欣段曹辉邓鹤肖洒李海东孙献平叶朝辉
Owner INNOVATION ACAD FOR PRECISION MEASUREMENT SCI & TECH CAS
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