FUNDAMENTAL STUDY OF SPECTRAL DISTORTIONS CORRECTION USING MACHINE LEARNING TECHNIQUES IN PHOTON-COUNTING X-RAY CT

DOI IR (HANDLE)

Bibliographic Information

Other Title
  • 機械学習を用いたフォトンカウンティング型X線CTにおけるスペクトル歪み補正の基礎的研究

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Description

Owing to the spectral capabilities, a photon counting CT system has generated considerable recent research interest. It can distinguish materials of target objects and enables us to perform a K-edge imaging. However, the photon-counting CT has a critical issue; spectral distortion mainly due to a pulse-pileup effect. In this work, we focus on the capability of nonlinear regression of machine learning and propose a neural network-based method to correct the spectral distortion. The aim of this study is to develop a neural-network based correction method of spectral distortion due to pulse-pileup effects for a photon-counting CT. We investigated the feasibility of our method with a simulation.

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Details 詳細情報について

  • CRID
    1390009225532480128
  • NII Article ID
    120007119605
  • NII Book ID
    AA12677220
  • DOI
    10.15002/00023970
  • HANDLE
    10114/00023970
  • ISSN
    21879923
  • Text Lang
    ja
  • Article Type
    departmental bulletin paper
  • Data Source
    • JaLC
    • IRDB
    • CiNii Articles
  • Abstract License Flag
    Allowed

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