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Spiral CT image reconstruction method and device based on neural network and storage medium

A neural network, CT image technology, applied in the field of radiation imaging, to achieve the effect of suppressing noise

Pending Publication Date: 2020-12-15
TSINGHUA UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, there is no related research on the application of neural networks to reconstruct spiral CT images.

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  • Spiral CT image reconstruction method and device based on neural network and storage medium
  • Spiral CT image reconstruction method and device based on neural network and storage medium
  • Spiral CT image reconstruction method and device based on neural network and storage medium

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Embodiment Construction

[0034] Specific embodiments of the present disclosure will be described in detail below, and it should be noted that the embodiments described here are only for illustration, and are not intended to limit the embodiments of the present disclosure. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the disclosed embodiments. It will be apparent, however, to one of ordinary skill in the art that these specific details need not be employed to practice the disclosed embodiments. In other instances, well-known structures, materials or methods have not been described in detail to avoid obscuring embodiments of the present disclosure.

[0035] Throughout this specification, reference to "one embodiment," "an embodiment," "an example," or "an example" means that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in the present disclosure. In at least o...

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Abstract

The invention provides a spiral CT image reconstruction device and method based on a neural network. The equipment comprises a memory used for storing an instruction and three-dimensional projection data of an inspected object from spiral CT equipment, and the inspected object is preset as a multi-layer cross section; the processor is configured to execute the instruction so as to perform image reconstruction on each layer of cross section, and for reconstruction of each layer of cross section, the method comprises the steps of inputting the three-dimensional projection data related to the to-be-reconstructed cross section to a trained neural network model to obtain a cross section reconstruction image; and forming a three-dimensional reconstructed image according to the reconstructed image with the multi-layer cross section. According to the equipment disclosed by the invention, by combining the advantages of the deep neural network and the particularity of the spiral CT imaging problem, the three-dimensional data projection data can be reconstructed into a three-dimensional reconstructed image with more information and less noise.

Description

technical field [0001] The present disclosure relates to radiation imaging, in particular to a neural network-based spiral CT image reconstruction method, device and storage medium. Background technique [0002] X-ray CT (Computed-Tomography) imaging systems are widely used in medical, security inspection, industrial non-destructive testing and other fields. The ray source and detector collect a series of projection data according to a certain orbit, and the three-dimensional spatial distribution of the linear attenuation coefficient of the object under the ray energy can be obtained through the restoration of the image reconstruction algorithm. CT image reconstruction is to restore the linear attenuation coefficient distribution from the projection data collected by the detector, which is the core step of CT imaging. At present, analytical reconstruction algorithms such as Filtered Back-Projection (FBP), Feldkmap-Davis-Kress (FDK) and iterative reconstruction methods such ...

Claims

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Application Information

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IPC IPC(8): G06T17/00G06N3/04
CPCG06T17/00G06T2207/10081G06T2207/20081G06N3/045G06N3/04
Inventor 邢宇翔张丽郑奡高河伟梁凯超陈志强李亮
Owner TSINGHUA UNIV
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