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PET image reconstruction algorithm for improving spatial resolution uniformity of PET system based on deep learning

An image reconstruction and deep learning technology, applied in the field of biomedical image analysis, to achieve the effect of improving uniformity and solving uneven spatial resolution

Active Publication Date: 2021-06-08
ZHEJIANG UNIV
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Problems solved by technology

[0006] In view of the above, the present invention provides a PET image reconstruction algorithm based on deep learning to improve the uniformity of the spatial resolution of the PET system. Projection and Neural Network Solving

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  • PET image reconstruction algorithm for improving spatial resolution uniformity of PET system based on deep learning
  • PET image reconstruction algorithm for improving spatial resolution uniformity of PET system based on deep learning
  • PET image reconstruction algorithm for improving spatial resolution uniformity of PET system based on deep learning

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Embodiment Construction

[0028] In order to describe the present invention more specifically, the technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0029] Such as figure 1As shown, the deep learning of the present invention improves the image reconstruction algorithm of the spatial resolution uniformity of the PET system, which specifically includes the following steps:

[0030] (1) Collect data. The phantom is injected with a PET radioactive tracer, and the phantom is placed at different positions in the radial distance from the center of field of view (FOV) of the PET device for scanning, and the coincident photons are detected and counted to obtain the results of the phantom at different radial positions. The corresponding original projection data matrix Y at i i .

[0031] (2) According to the principle of PET imaging, the measurement equation model is established:

[0032] Y=GX+R+S

[0033]...

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Abstract

The invention discloses a PET image reconstruction algorithm for improving the uniformity of the spatial resolution uniformity of a PET systembased on deep learning. The algorithm fully utilizes the characteristic that the radial resolution in the FOV space of the PET system is not uniform to solve the mutual depth effect, takes a high-resolution concentration distribution diagram reconstructed by projection data when a phantom is located in the FOV center as a label, adopting a neural network training means to improve the resolution of a reconstructed concentration distribution diagram of the same phantom at an FOV edge position without any novel detector or obtaining any additional information, such as DOI information or PSF information. According to the algorithm, a software means is used for replacing a complex hardware method at the present stage, and the problem that the spatial resolution of the PET system is not uniform is solved.

Description

technical field [0001] The invention belongs to the technical field of biomedical image analysis, and specifically relates to a PET image reconstruction algorithm based on deep learning to improve the spatial resolution uniformity of a PET system. Background technique [0002] Positron emission tomography (PET) is a non-invasive functional imaging technique and one of the important imaging methods in nuclear medicine and molecular imaging. In the early stages of disease, biochemical changes often precede anatomical changes, because PET can utilize 15 O. 18 F and other radionuclide-labeled glucose, protein and other substances are used as tracers to participate in the normal physiological metabolism of organisms, so that they can dynamically and quantitatively reflect the pathophysiological changes and metabolic processes in animals at the molecular level. It plays an irreplaceable role in the early diagnosis and treatment of brain diseases and malignant tumors. When these...

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Application Information

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IPC IPC(8): G06T3/40G06T11/00G06N3/04G06N3/08
CPCG06T3/4053G06T11/005G06N3/08G06N3/045
Inventor 刘华锋林菁
Owner ZHEJIANG UNIV
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