Unsupervised training method and unsupervised training device for magnetic resonance parameter imaging model

An imaging model and training method technology, applied in neural learning methods, biological neural network models, image data processing, etc., can solve the problems of difficulty in obtaining full-collected data and unfavorable network training, and achieve the effect of unsupervised training.

Pending Publication Date: 2021-02-05
SHENZHEN INST OF ADVANCED TECH
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Problems solved by technology

In the existing deep learning MRI parametric imaging method, a fully acquired parameter map is required as a reference image, but it is often difficult to obtain full-acquired data in actual scanning, which is not conducive to network training

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  • Unsupervised training method and unsupervised training device for magnetic resonance parameter imaging model
  • Unsupervised training method and unsupervised training device for magnetic resonance parameter imaging model
  • Unsupervised training method and unsupervised training device for magnetic resonance parameter imaging model

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Embodiment Construction

[0042] In order to make the object, technical solution and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0043] Before describing the various embodiments of the present application in detail, first briefly describe the inventive concept of the present application: the MRI parametric imaging model based on deep learning often needs to use fully acquired k-space data during training, and it is often difficult to Acquire all k-space data. The application adopts the loss function based on the parameter weighted image, and the calculation of the loss function can be completed by using the under-sampled k-space data and the parameter-weighted image data obtained according to the under-sampled k-space dat...

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Abstract

The invention discloses an unsupervised training method and an unsupervised training device for a magnetic resonance parameter imaging model. The unsupervised training method comprises the following steps: inputting acquired under-acquisition k space data into a to-be-trained magnetic resonance parameter imaging model to obtain parameter weighted image data; updating a loss function according to the acquired under-acquisition k space data and the parameter weighted image data; and updating the network parameters of the to-be-trained magnetic resonance parameter imaging model according to the updated loss function. According to the method and device, the loss function based on the parameter weighted image is introduced, calculation of the loss function can be completed by utilizing the under-collected k space data and the parameter weighted image data obtained according to the under-collected k space data, full-collected k space data does not need to be adopted, and unsupervised training of the model is achieved.

Description

technical field [0001] The invention belongs to the technical field of image reconstruction of magnetic resonance parametric imaging signals, and in particular relates to an unsupervised training method, a training device, a computer-readable storage medium and computer equipment for a magnetic resonance parametric imaging model. Background technique [0002] Quantitative Magnetic Resonance Parametric Mapping is an emerging tool for assessing and determining the fundamental biological properties of tissues. It is designed to measure absolute relaxation at magnetic resonance, thus providing comparable measurements across sites and time points. The most common way to obtain MR parametric maps is to acquire weighted images with varying imaging parameters (e.g., inversion time (TI) in T1 mapping, echo time (TE) in T2 mapping, or spin in T1ρ mapping. Time to Lock (TSL). The parametric map is then estimated by fitting these images pixel-by-pixel with the corresponding physical in...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04G06N3/08G06T11/00
CPCG06N3/088G06T11/006G06T11/008G06N3/045
Inventor 梁栋程静朱燕杰刘新郑海荣
Owner SHENZHEN INST OF ADVANCED TECH
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