Dynamic PET Imaging Using Dual Texture Features.

Zhanglei Ouyang,Dong Liang,Zhanli Hu,Na Zhang,Zixiang Chen,Zhaoping Cheng,Yanhua Duan,Shujun Zhao

doi:10.3389/fncom.2021.819840

Abstract

Purpose: This study aims to explore the impact of adding texture features in dynamic positron emission tomography (PET) reconstruction of imaging results.Methods: We have improved a reconstruction method that combines radiological dual texture features. In this method, multiple short time frames are added to obtain composite frames, and the image reconstructed by composite frames is used as the prior image. We extract texture features from prior images by using the gray level-gradient cooccurrence matrix (GGCM) and gray-level run length matrix (GLRLM). The prior information contains the intensity of the prior image, the inverse difference moment of the GGCM and the long-run low gray-level emphasis of the GLRLM.Results: The computer simulation results show that, compared with the traditional maximum likelihood, the proposed method obtains a higher signal-to-noise ratio (SNR) in the image obtained by dynamic PET reconstruction. Compared with similar methods, the proposed algorithm has a better normalized mean squared error (NMSE) and contrast recovery coefficient (CRC) at the tumor in the reconstructed image. Simulation studies on clinical patient images show that this method is also more accurate for reconstructing high-uptake lesions.Conclusion: By adding texture features to dynamic PET reconstruction, the reconstructed images are more accurate at the tumor.

Highlights

Positron emission tomography (PET) imaging works by imaging an injected radioactive tracer that combines with negative electrons to produce annihilating photons (Zhang et al, 2019; Hu et al, 2020; Zeng et al, 2020)
To evaluate the performance of the proposed method, we conducted a computerized simulation experiment and compared the visual effects and quantitative indexes of the images reconstructed by the proposed method with those of the images reconstructed by other methods
Compared with the signal-to-noise ratio (SNR) of the image reconstructed by the maximum likelihood expectation maximization (MLEM) method, the SNR of the image reconstructed by the kernelized expectation-maximization (KEM) method has been improved to a large extent, but the edge preservation is inadequate

Summary

Introduction

Positron emission tomography (PET) imaging works by imaging an injected radioactive tracer that combines with negative electrons to produce annihilating photons (Zhang et al, 2019; Hu et al, 2020; Zeng et al, 2020). PET imaging provides functional information on a wide range of biochemical and physiological processes (Delcroix et al, 2021; Doyen et al, 2021). Scholars have proposed introducing a prior image into PET reconstruction (Green, 1990; Nuyts et al, 2002; Wang and Qi, 2015). Gao proposed applying the texture information of the prior image to PET reconstruction (Gao et al, 2021)

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Results

Conclusion