Image restoration algorithm incorporating methods to remove noise and blurring from positron emission tomography imaging for Alzheimer's disease diagnosis

  • Min-Hee Lee
    Institute of Human Genomic Study, College of Medicine, Korea University Ansan Hospital, 123, Jeokgeum-ro, Danwon-gu, Ansan-si, Gyeonggi-do, Republic of Korea
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  • Chang-Soo Yun
    Department of Radiation Convergence Engineering, College of Software and Digital Healthcare Convergence, Yonsei University, 1, Yeonsedae-gil, Heungeop-myeon, Wonju-si, Gangwon-do, Republic of Korea
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  • Kyuseok Kim
    Corresponding authors.
    Department of Integrative Medicine, Major in Digital Healthcare, Yonsei University College of Medicine, Unju-ro, Gangman-gu, Seoul, Republic of Korea
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  • Youngjin Lee
    Corresponding authors.
    Department of Radiological Science, College of Health Science, Gachon University, 191, Hambakmoero, Yeonsu-gu, Incheon, Republic of Korea
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  • for the Alzheimer Disease Neuroimaging Initative
    Author Footnotes
    1 Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database ( As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at:
  • Author Footnotes
    1 Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database ( As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at:
Published:November 10, 2022DOI:


      • Image restoration algorithm to incorporate denoising and deblurring methods.
      • Our proposed algorithm can improve diagnostic accuracy for Alzheimer’s disease.
      • Improving the image quality of Alzheimer's disease using the PET image with ADNI.
      • Numerous evaluations in the field are taking advantage of our algorithm.


      The aim of this study was to design an image restoration algorithm that combined denoising and deblurring and to confirm its applicability in positron emission tomography (PET) images of patients with Alzheimer’s disease (AD). PET images of patients with AD obtained using 18F-AV-45, which have a lot of noise, and 18F-FDG, which have a lot of blurring, were available in the Alzheimer's Disease Neuroimaging Initiative open dataset. The proposed framework performed image restoration incorporating blind deconvolution after noise reduction using a non-local means (NLM) approach to improve the PET image quality. We found that the coefficient of variation result after denoising and deblurring of the 18F-AV-45 image was improved 1.34 times compared to that for the degraded image. In addition, the profile result of the 18F-FDG PET image of patients with AD, which had a relatively large amount of blurring, showed a gentle shape when deblurring was performed after denoising. The overall no-reference-based evaluation results showed different results according to the degree of noise and blurring in the PET images. In conclusion, the applicability of the deconvolution deblurring algorithm to AD PET images after NLM denoising processing was demonstrated in this study.


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