Cover Image
QR Code

Simultaneous Denoising of Dynamic PET Images Based on Deep Image Prior.

Parametric imaging obtained from kinetic modeling analysis of dynamic positron emission tomography (PET) data is a useful tool for quantifying tracer kinetics. However, pixel-wise time-activity curves have high noise levels which lead to poor quality of parametric images. To solve this limitation, w...

Full description

Published in:Journal of Digital Imaging Vol. 35; no. 4; pp. 834 - 846
Main Authors: Yang, Cheng-Hsun, Huang, Hsuan-Ming
Format: diagnostic images equations & formulas research tables/charts Journal Article
Published: Springer Nature Aug2022
Online Access:View this record in EBSCOhost
fields @attributes:
  recordID: 1
pdfLink:
plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=159195616&site=ehost-live
header:
  @attributes:
    shortDbName: ccm
    uiTerm: 159195616
    longDbName: CINAHL Complete
    uiTag: AN
  controlInfo:
    bkinfo:
    dissinfo:
    jinfo:
      jid:
        08971889
        DOQ
      jtl: Journal of Digital Imaging
      issn: 08971889
      maglogo: N
    pubinfo:
      dt: Aug2022
      vid: 35
      iid: 4
      pid: 237
      pub: Springer Nature
      place: New York, New York
    artinfo:
      ui:
        159195616
        155542687
        159195616
        159195616
        10.1007/s10278-022-00606-x
        159195616
      ppf: 834
      ppct: 12
      formats:
        fmt:
          – @attributes:
              type: T
          – @attributes:
              type: P
      tig:
        atl: Simultaneous Denoising of Dynamic PET Images Based on Deep Image Prior.
      aug:
        au:
          Yang, Cheng-Hsun
          Huang, Hsuan-Ming
        affil: Institute of Medical Device and Imaging, College of Medicine, National Taiwan University, No.1, Sec. 1, Jen Ai Rd., Zhongzheng Dist., Taipei City 100, Taiwan
      sug:
        subj:
          Positron-Emission Tomography Methods
          Noise Prevention and Control
          Deep Learning
          Human
          Computer Simulation
          Equipment Reliability
      ab: Parametric imaging obtained from kinetic modeling analysis of dynamic positron emission tomography (PET) data is a useful tool for quantifying tracer kinetics. However, pixel-wise time-activity curves have high noise levels which lead to poor quality of parametric images. To solve this limitation, we proposed a new image denoising method based on deep image prior (DIP). Like the original DIP method, the proposed DIP method is an unsupervised method, in which no training dataset is required. However, the difference is that our method can simultaneously denoise all dynamic PET images. Moreover, we propose a modified version of the DIP method called double DIP (DDIP), which has two DIP architectures. The additional DIP model is used to generate high-quality input data for the second DIP model. Computer simulations were performed to evaluate the performance of the proposed DIP-based methods. Our simulation results showed that the DDIP method outperformed the single DIP method. In addition, the DDIP method combined with data augmentation could generate PET parametric images with superior image quality compared to the spatiotemporal-based non-local means filtering and high constrained backprojection. Our preliminary results show that our proposed DDIP method is a novel and effective unsupervised method for simultaneously denoising dynamic PET images.
      pubtype: Academic Journal
      doctype:
        diagnostic images
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
    refInfo:
    holdings:
      @attributes:
        islocal: N