LI Ruijing, CHEN Houjin, PENG Yahui, LI Jupeng. Comparison of Four Forward Models for Breast Imaging in Ultrasound Computed Tomography[J]. Chinese Journal of Electronics, 2019, 28(4): 805-816. DOI: 10.1049/cje.2019.05.008
Citation: LI Ruijing, CHEN Houjin, PENG Yahui, LI Jupeng. Comparison of Four Forward Models for Breast Imaging in Ultrasound Computed Tomography[J]. Chinese Journal of Electronics, 2019, 28(4): 805-816. DOI: 10.1049/cje.2019.05.008

Comparison of Four Forward Models for Breast Imaging in Ultrasound Computed Tomography

  • Ultrasound computed tomography (USCT) is considered to have great potential for breast cancer screening. Compared with ray-based methods, Waveform inversion (WI) methods obtain high spatial resolution images because they consider higher-order diffraction effects. For the WI method, considering more properties of the medium in a forward model can estimate more accurate images. However, longer reconstruction time is required. Therefore, to reduce the reconstruction time, three hypotheses are set in this work to develop the medium under different conditions. We compare the reconstructed images using the four forward models to analyze the effects of the various considered medium properties, which include the sound speed, density of the medium, acoustic absorption and dispersion. To reduce the difficulty of hardware manufacturing, a square border ultrasonic transducer array is adopted in the USCT data acquisition system. Penalized leastsquares optimization problems are constructed to obtain numerical solutions of the sound speed and bulk modulus distributions. The reconstruction of the bulk modulus makes the reconstructed sound speed images more accurate. Computer simulations are conducted to compare reconstructed images using the four forward models under different noise conditions. A numerical breast phantom is used to evaluate the performance. The results suggest that for breast imaging, the forward model (which only considers the heterogeneous sound speed) is a compromise option between image accuracy and computational time.
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