Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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Noise reduction in low-dose positron emission tomography with adaptive parameter estimation in sinogram domain

  • Kyu Bom Kim (Department of Radiation Convergence Engineering, College of Health Science, Yonsei University) ;
  • Yeonkyeong Kim (Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine) ;
  • Kyuseok Kim (Department of Biomedical Engineering, Eulji University) ;
  • Su Hwan Lee (Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine)
  • Received : 2024.03.17
  • Accepted : 2024.05.14
  • Published : 2024.10.25
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Abstract

Noise reduction in low-dose positron emission tomography (PET) is a well-researched topic aimed at reducing patient radiation doses and improving diagnosis. Software-based noise reduction mainly improves the contrast between regions by reducing the variation of the acquired image. However, it should be performed under appropriate parameters to reduce discrimination. We propose a method that derives optimal noise-reduction parameters using the multi-scale structural similarity index measure and visual information fidelity, which are metrics for image quality assessment. Simulation and experimental studies demonstrated the viability of the proposed algorithm. The contrast-to-noise ratio value of the denoised reconstruction slice, which was used as the optimal parameter, increased approximately three times compared to that of the low-dose slice while preserving the resolution. The results indicate that the proposed method successfully predicted the parameters according to the noise-reduction algorithm and PET system conditions in the sinogram domain. The proposed algorithm should help prevent misdiagnosis and provide standardized medical images for clinical application by performing appropriate noise reduction.

Keywords

Acknowledgement

This study was supported by the Korea Medical Device Development Fund awarded by the Korean government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety) (Project Number: RS-2020-KD000032), the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2023-00252863), and the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (RS2023-00239193, RS-2023-00243656).

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