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Positron emission tomography image reconstruction using feature extraction.

Juan Gao1, Qiyang Zhang1,2, Qiegen Liu3

  • 1Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.

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|August 6, 2019
PubMed
Summary

This study introduces a feature refinement (FR) approach to improve positron emission tomography (PET) imaging from low-sampled data. The new MLEM-TV-FR method recovers fine image features lost in traditional algorithms, enhancing image quality and efficiency.

Keywords:
Positron emission tomography (PET)feature extractionlow-sampled datamaximum likelihood-expectation maximization (MLEM)total variation (TV)

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Area of Science:

  • Medical Imaging
  • Image Reconstruction
  • Positron Emission Tomography (PET)

Background:

  • Positron emission tomography (PET) systems aim to reduce costs through image reconstruction algorithms for low-sampled data.
  • Current Maximum Likelihood-Expectation Maximization with Total Variation (MLEM-TV) algorithms struggle to differentiate true structures from noise, leading to loss of fine image features.

Purpose of the Study:

  • To recover fine image features lost during the MLEM-TV algorithm when reconstructing PET images from low-sampled data.
  • To enhance the image quality and diagnostic value of low-sampled PET scans.

Main Methods:

  • A feature refinement (FR) approach, previously used in computed tomography (CT), was adapted for PET imaging.
  • The MLEM-TV algorithm was augmented with an FR step after each iteration to preserve structural information.
  • A specialized feature descriptor was developed to distinguish structures from noise and artifacts, with an optimal 7x7 patch size selected.

Main Results:

  • The MLEM-TV-FR algorithm demonstrated improved peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) compared to conventional MLEM-TV.
  • Significant reduction in required sampling numbers was achieved, leading to enhanced computational efficiency.
  • Successful application to simulated brain PET, Shepp-Logan phantom, and mouse imaging.

Conclusions:

  • The MLEM-TV-FR algorithm yields superior image quality over MLEM and MLEM-TV by preserving fine structures and suppressing artifacts.
  • This method shows significant potential for improving low-sampled PET imaging, offering better diagnostic capabilities.
  • The approach effectively addresses limitations of existing algorithms in reconstructing high-fidelity PET images from sparse data.