Advertisement

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

  • Min-Hee Lee
    Affiliations
    Institute of Human Genomic Study, College of Medicine, Korea University Ansan Hospital, 123, Jeokgeum-ro, Danwon-gu, Ansan-si, Gyeonggi-do, Republic of Korea
    Search for articles by this author
  • Chang-Soo Yun
    Affiliations
    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
    Search for articles by this author
  • Kyuseok Kim
    Correspondence
    Corresponding authors.
    Affiliations
    Department of Integrative Medicine, Major in Digital Healthcare, Yonsei University College of Medicine, Unju-ro, Gangman-gu, Seoul, Republic of Korea
    Search for articles by this author
  • Youngjin Lee
    Correspondence
    Corresponding authors.
    Affiliations
    Department of Radiological Science, College of Health Science, Gachon University, 191, Hambakmoero, Yeonsu-gu, Incheon, Republic of Korea
    Search for articles by this author
  • 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 (adni.loni.usc.edu). 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: http://adni.loni.usc.edu/wpcontent/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.
  • Author Footnotes
    1 Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). 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: http://adni.loni.usc.edu/wpcontent/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.
Published:November 10, 2022DOI:https://doi.org/10.1016/j.ejmp.2022.10.016

      Highlights

      • 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.

      Abstract

      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.

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Physica Medica: European Journal of Medical Physics
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Park C.R.
        • Kang S.H.
        • Lee Y.
        Feasibility of total variation noise reduction algorithm according to various MR-based PET images in a simultaneous PET/MR system: A phantom study.
        Diagnostics. 2021; 11https://doi.org/10.3390/diagnostics11020319
        • Schramek G.G.R.
        • Stoevesandt D.
        • Reising A.
        • Kielstein J.T.
        • Hiss M.
        • Kielstein H.
        Imaging in anatomy: a comparison of imaging techniques in embalmed human cadavers.
        BMC Med Educ. 2013; 13https://doi.org/10.1186/1472-6920-13-143
        • Lammertsma A.A.
        PET/SPECT: functional imaging beyond flow.
        Vision Res. 2001; 41: 1277-1281
        • Kim S.J.W.
        • Seo S.
        • Kim H.S.
        • Kim D.Y.
        • Kang K.W.
        • Min J.J.
        • et al.
        Multi-atlas cardiac PET segmentation.
        Physica Med. 2019; 58: 32-39
        • Won J.Y.
        • Park H.
        • Lee S.
        • Son J.W.
        • Chung Y.
        • Ko G.B.
        • et al.
        Development and initial results of a brain PET insert for simultaneous 7-Tesla PET/MRI using an FPGA-only signal digitization method.
        IEEE Trans Med Imaging. 2021; 40: 1579-5790
        • Presotto L.
        • Iaccarino L.
        • Sala A.
        • Vanoli E.G.
        • Muscio C.
        • Nigri A.
        • et al.
        Low-dose CT for the spatial normalization of PET images: a validation procedure for amyloid-PET semi-quantification.
        Clinl NeuroImage. 2018; 20: 153-160
        • Sousa J.M.
        • Appel L.
        • Engstrom M.
        • Papadimitriou S.
        • Nyholm D.
        • Ahlstrom H.
        • et al.
        Composite attenuation correction method using a 68Ge-transmission multi-atlas for quantitative brain PET/MR.
        Physica Med. 2022; 97: 36-43
        • Nordberg A.
        • Rinne J.O.
        • Dadir A.
        • Langstrom B.
        The use of PET in Alzheimer disease.
        Nature Rev Neurol. 2020; 6: 78-87
        • Marcus C.
        • Mena E.
        • Subramaniam R.M.
        Brain PET in the diagnosis of Alzheimer’s disease.
        Clin Nucl Med. 2014; 39: e413-e426
        • Ghetelat G.
        • Arbizu J.
        • Barthel H.
        • Garibotto V.
        • Law I.
        • Morbelli S.
        • et al.
        Amyloid-PET and 18F-FDG-PET in the diagnostic investigation of Alzheimer’s disease and other dementias.
        Lancet. 2020; 19: 951-962
        • Kas A.
        • Migliaccio R.
        • Tavitian B.
        A future for PET imaging in Alzheimer’s disease.
        Eur J Nucl Med Mol Imaging. 2020; 47: 231-234
        • Yamao T.
        • Miwa K.
        • Wagatsuma K.
        • Shigemoto Y.
        • Sato N.
        • Akamatsu G.
        • et al.
        Centiloid scale analysis for 18F-THK5351 PET imaging in Alzheimer’s disease.
        Physica Med. 2021; 82: 249-254
        • Schrevens L.
        • Lorent N.
        • Dooms C.
        • Vansteenkiste J.
        The role of PET scan in diagnosis, staging, and management of non-small cell lung cancer.
        Oncologist. 2004; 9: 633-643
        • Zhu A.
        • Lee D.
        • Shim H.
        Metabolic PET imaging in cancer detection and therapy response.
        Semin Oncol. 2011; 38: 55-69
        • Wallitt K.L.
        • Khan S.R.
        • Dubash S.
        • Tam H.H.
        • Khan S.
        • Barwick T.D.
        Clinical PET imaging in prostate cancer.
        RadioGraphics. 2017; 37: 1512-1536
        • Vasudevan P.
        • Gabel R.
        • Stenzel J.
        • Forster J.
        • Kurth J.
        • Vollmar B.
        • et al.
        18F-FDG PET-based imaging of myocardial inflammation following acute myocardial infarction in a mouse model.
        Internat J Mol Sci. 2020; 21https://doi.org/10.3390/ijms21093340
        • Morooka M.
        • Kubota K.
        • Kadowaki H.
        • Ito K.
        • Okazaki O.
        • Kashida M.
        • et al.
        11C-Methionine PET of acute myocardial infarction.
        J Nucl Med. 2009; 50: 1283-1287
        • Glenner G.G.
        • Wong C.W.
        Alzheimer’s disease: initial report of the purification and characterization of a novel cerebrovascular amyloid protein.
        Biochem Biophys Res Commun. 1984; 120: 885-890
        • Chen C.F.
        • Xu T.H.
        • Yan Y.
        • Zhou Y.R.
        • Jiang Y.
        • Melcher K.
        • et al.
        Amyloid beta: structure, biology and structure-based therapeutic development.
        Acta Pharmacol Sin. 2017; 38: 1205-1235
        • Morgan C.A.
        • Roberts R.P.
        • Chaffey T.
        • Tahara-Eckl L.
        • van der Meer M.
        • Gunther M.
        • et al.
        Reproducibility and repeatability of magnetic resonance imaging in dementia.
        Physica Med. 2022; 101: 8-17
        • Mosconi L.
        • Tsui W.H.
        • Herholz K.
        • Pupi A.
        • Drzezga A.
        • Lucignani G.
        • et al.
        Multicenter standardized 18F-FDG PET diagnosis of mild cognitive impairment, Alzheimer’s disease, and other dementias.
        J Nucl Med. 2008; 49: 390-398
        • Landau S.M.
        • Breault C.
        • Joshi A.D.
        • Pontecorvo M.
        • Mathis C.A.
        • Jagust W.J.
        • et al.
        Amyloid-b imaging with pittsburgh compound B and florbetapir: comparing radiotracers and quantification methods.
        J Nucl Med. 2013; 54: 70-77
        • Villemagne V.L.
        • Mulligan R.S.
        • Pejoska S.
        • Ong K.
        • Jones G.
        • O’Keefe G.
        • et al.
        Comparison of 11C-PiB and 18F-florbetaben for Aβ imaging in ageing and Alzheimer’s disease.
        Eur J Nucl Med Mol Imaging. 2012; 39: 983-989
        • Hashimoto F.
        • Ohba H.
        • Ote K.
        • Teramoto A.
        • Tsukada H.
        Dynamic PET image denoising using deep convolutional neural networks without prior training datasets.
        IEEE Access. 2019; 7: 96594-96603
        • Cui J.
        • Gong K.
        • Guo N.
        • Wu C.
        • Meng X.
        • Kim K.
        • et al.
        PET image denoising using unsupervised deep learning.
        Eur J Nucl Med Mol Imaging. 2019; 46: 2780-2789
        • Kim K.
        • Lee Y.
        Improvement of signal and noise performance using single image super-resolution based on deep learning in single photon-emission computed tomography imaging system.
        Nucl Eng Technol. 2021; 53: 2341-2347
        • Song T.A.
        • Yang F.
        • Chowdhury S.R.
        • Kim K.
        • Johnson K.A.
        • Fakhri G.E.
        • et al.
        PET image deblurring and super-resolution with an MR-based joint entropy prior.
        IEEE Trans Comput Imaging. 2019; 5: 530-539
      1. Cadena L, Zotin A, Cadena F, Espinosa N. Espinosa, Noise removal of the x-ray medical image using fast spatial filters and GPU. Proceeding of SPIE 10752, Applications of Digital Image Processing XLI 2019;35:176-186.

        • Rudin L.I.
        • Osher S.
        • Fatemi E.
        Nonlinear total variation based noise removal algorithms.
        Physica D. 1992; 60: 259-268
        • Osher S.
        • Burger M.
        • Goldfarb J.
        • Xu J.
        • Yin W.
        An iterative regularization method for total variation-based image restoration.
        Multiscale Model Simul. 2005; 4: 460-489
        • Zhou M.X.
        • Yan X.
        • Xie H.B.
        • Zheng H.
        • Xu D.
        • Yang G.
        Evaluation of non-local means based denoising filters for diffusion kurtosis imaging using a new phantom.
        PLoS ONE. 2015; 10: e0116986
      2. Pal C, Chakrabarti A, Ghosh R. A brief survey of recent edge-preserving smoothing algorithms on digital images 2015: arXiv:1503.072976.

      3. Wang L, Lu J, Li Y, Yahagi T, Okamoto T. Noise reduction using wavelet with application to medical x-ray image. 2015 IEEE International Conference on Industrial Technology, Hong Kong, China 2005: doi:10.1109/ICIT.2005.1600606.

        • Cunha A.
        • Zhou J.
        • Do M.
        The nonsubsampled contourlet transform: theory, design, and applications.
        IEEE Trans Image Process. 2006; 15: 3089-3101
        • Jing-Yi L.
        • Hong L.
        • Dong Y.
        • Yan-Sheng Z.
        A new wavelet threshold function and denoising application.
        Math Probl Eng. 2016; https://doi.org/10.1155/2016/3195492
        • Zhang K.
        • Zho W.
        • Chen Y.
        • Meng D.
        • Zhang L.
        Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising.
        IEEE Trans Image Process. 2017; 26: 3142-3155
        • Kang E.
        • Min J.
        • Ye J.
        A deep convolutional neural network using directional wavelets for low-dose x-ray CT reconstruction.
        Med Phys. 2018; 44: e360-e375
        • Tang S.
        • Gong W.
        • Li W.
        • Wang W.
        Non-blind image deblurring method by local and nonlocal total variation models.
        Signal Process. 2014; 94: 339-349
        • Ruiz P.
        • Zhou X.
        • Mateos J.
        • Molina R.
        • Katsaggelos K.
        Variational Bayesian blind image deconvolution: A review.
        Digital Signal Process. 2015; 47: 116-127
      4. Chen L, Zhang J, Lin S, Fang F, Ren JS. Blind deblurring for saturated images. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, virtual 2021:6308-6316.

      5. Levin A, Weiss Y, Durand F, Freeman WT. Understanding and evaluating blind deconvolution algorithm. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA 2009:1964-1971.

      6. Xu L, Zheng S, Jia J. Unnatural L0 sparse representation for natural image deblurring. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA 2016:1628-1636.

      7. Krishnan D, Tay T, Fergus R. Blind deconvolution using a normalized sparsity measure. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, CO, USA 2011:20-25.5.

        • Ren W.
        • Cao X.
        • Pan J.
        • Guo X.
        Image deblurring via enhanced low-rank prior.
        IEEE Trans Image Process. 2016; 25: 3426-3437
        • Dong J.
        • Pan J.
        • Su Z.
        Blur kernel estimation via salient edges and low rank prior for blind image deblurring.
        Signal Process Image Commun. 2017; 58: 134-145
      8. Sun J, Cao W, Xu Z, Ponce J. Learning a convolutional neural network for non-uniform motion blur removal. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA 2015:769-777.

        • Tang Y.
        • Xue Y.
        • Chen Y.
        • Zhou L.
        Blind deblurring with sparse representation via external patch priors.
        Digital Signal Process. 2018; 78: 322-331
      9. Sun S, Xu Z, Zhang J. Spectral norm regularization for blind image deblurring. Symmetry 2021;13: doi.org/10.3390/sym13101856.

      10. Ren D, Zhang K, Wang Q, Hu Q, Zuo W. Neural blind deconvolution using deep priors. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, virtual 2020: doi:10.1109/CVPR42600.2020.00340.

        • Tai Y.
        • Yang J.
        • Liu X.
        Image super-resolution via deep recursive residual network.
        in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA. 2017: 3147-3155
        • Liu R.
        • Cheng S.
        • He Y.
        • Fan X.
        • Lin Z.
        • Luo Z.
        On the convergence of learning-based iterative methods for nonconvex inverse problems.
        IEEE Trans Pattern Anal Machin Intell. 2020; 42: 3027-3039
      11. Chakrabarti A. A neural approach to blind motion deblurring. In European Conference on Computer Vision, Amsterdam, Netherlands 2016;221-235.

        • Tao X.
        • Gao H.
        • Shen X.
        • Wang J.
        • Jia J.
        Scale-recurrent network for deep image deblurring.
        in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA. 2018: 8174-8182
        • Arabi H.
        • Zaidi H.
        Non-local mean denoising using multiple PET reconstructions.
        Ann Nucl Med. 2021; 35: 176-186
      12. Buades A, Coll B, Morel JM. A non-local algorithm for image denoising. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), San Diego, CA, USA 2005: doi:10.1109/CVPR.2005.38.

        • Dutta J.
        • Leahy R.M.
        • Li Q.
        Non-local means denoising of dynamic PET images.
        PLoS ONE. 2013; 8: e81390
        • Manjon J.V.
        • Carbonell-Caballero J.
        • Lull J.J.
        • Garcia-Marti G.
        • Marti-Bonmati L.
        • Robles M.
        MRI denoising using non-local means.
        Med Image Anal. 2008; 12: 514-523
        • Kim K.
        • Kim W.
        • Kang S.
        • Park C.
        • Lee D.
        • Cho H.
        • et al.
        A blind-deblurring method based on a compressed-sensing scheme in digital breast tomosynthesis.
        Opt Lasers Eng. 2018; 110: 228-235
        • Louchet C.
        • Mosian L.
        Total variation as a local filter.
        SIAM J Imag Sci. 2011; 4: 651-694
        • Chan S.H.
        • Khoshabeh R.
        • Gibson K.B.
        • Gill P.E.
        • Nguyen T.Q.
        An augmented lagrangian method for total variation video restoration.
        IEEE Trans Image Process. 2011; 20: 3097-3111
        • Mittal A.
        • Soundararaja R.
        • Bovik A.C.
        Making a “Completely Blind” image quality analyzer.
        IEEE Signal Process Lett. 2013; 20: 209-212
        • Mittal A.
        • Moorthy A.K.
        • Bovik A.C.
        No-reference image quality assessment in the spatial domain.
        IEEE Trans Image Process. 2012; 21: 4695-4708
        • Shim J.
        • Yoon M.
        • Lee M.J.
        • Lee Y.
        Utility of fast non-local means (FNLM) filter for detection of pulmonary nodules in chest CT for pediatric patient.
        Phys Med. 2021; 81: 52-59
        • Yang J.
        • Hu C.
        • Guo N.
        • Dutta J.
        • Vaina L.M.
        • Jonson K.A.
        • Sepulcre J.
        • Fakhri G.E.
        • Li Q.
        Partial volume correction for PET quantification and its impact on brain network in Alzheimer’s disease.
        Sci Rep. 2017; 7https://doi.org/10.1038/s41598-017-13339-7
        • Kim J.Y.
        • Kim K.
        • Lee Y.
        Application of blind deconvolution based on the new weighted L1-norm regularization with alternating direction method of multipliers in light microscopy images.
        Microsc Microanal. 2020; 26: 929-937
        • Park J.
        • Kang C.K.
        • Lee Y.
        Quantitative evaluation of the image quality using the fast nonlocal means denoising approach in diffusion-weighted magnetic resonance imaging with high b-value.
        J Korean Phys Soc. 2021; 78: 244-250