Probability image of tissue characteristics for liver fibrosis using multi-Rayleigh model with removal of nonspeckle signals

Shohei Mori, Shinnosuke Hirata, Tadashi Yamaguchi, Hiroyuki Hachiya

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

We have been developing a quantitative diagnostic method for liver fibrosis using an ultrasound image. In our previous study, we proposed a multi- Rayleigh model to express a probability density function of the echo amplitude from liver fibrosis and proposed a probability imaging method of tissue characteristics on the basis of the multi-Rayleigh model. In an evaluation using the multi-Rayleigh model, we found that a modeling error of the multi-Rayleigh model was increased by the effect of nonspeckle signals. In this paper, we proposed a method of removing nonspeckle signals using the modeling error of the multi-Rayleigh model and evaluated the probability image of tissue characteristics after removing the nonspeckle signals. By removing nonspeckle signals, the modeling error of the multi-Rayleigh model was decreased. A correct probability image of tissue characteristics was obtained by removing nonspeckle signals. We concluded that the removal of nonspeckle signals is important for evaluating liver fibrosis quantitatively.

Original languageEnglish
Article number07HF20
JournalJapanese journal of applied physics
Volume54
Issue number7
DOIs
Publication statusPublished - 2015 Jul 1
Externally publishedYes

ASJC Scopus subject areas

  • Engineering(all)
  • Physics and Astronomy(all)

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