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A 3D reconstruction solution to ultrasound Joule heat density tomography based on acousto-electric effect: A simulation study

  • R. Yang
  • , A. Song
  • , X. D. Li
  • , Y. Lu
  • , R. Yan
  • , B. Xu
  • , X. Li
  • Jinan University
  • Southeast University, Nanjing
  • Johns Hopkins University

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

A 3D reconstruction solution to ultrasound Joule heat density tomography based on acousto-electric effect by deconvolution is proposed for noninvasive imaging of biological tissue. Compared with ultrasound current source density imaging, ultrasound Joule heat density tomography doesn't require any priori knowledge of conductivity distribution and lead fields, so it can gain better imaging result, more adaptive to environment and with wider application scope. For a general 3D volume conductor with broadly distributed current density field, in the AE equation the ultrasound pressure can't simply be separated from the 3D integration, so it is not a common modulation and basebanding (heterodyning) method is no longer suitable to separate Joule heat density from the AE signals. In the proposed method the measurement signal is viewed as the output of Joule heat density convolving with ultrasound wave. As a result, the internal 3D Joule heat density can be reconstructed by means of Wiener deconvolution. A series of computer simulations set for breast cancer imaging applications, with consideration of ultrasound beam diameter, noise level, conductivity contrast, position dependency and size of simulated tumors, have been conducted to evaluate the feasibility and performance of the proposed reconstruction method. The computer simulation results demonstrate that high spatial resolution 3D ultrasound Joule heat density imaging is feasible using the proposed method, and it has potential applications to breast cancer detection and imaging of other organs.

Original languageEnglish
Article numberP10004
JournalJournal of Instrumentation
Volume9
Issue number10
DOIs
StatePublished - 1 Oct 2014
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Image reconstruction in medical imaging
  • Medical-image reconstruction methods and algorithms, computer-aided diagnosis
  • Medical-image reconstruction methods and algorithms, computer-aided software
  • Multi-modality systems

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