On ultrasound image reconstruction by tissue density estimation

An inherent property of medical ultrasound imaging is the speckle noise that generally obscures the image and reduces the diagnostic image resolution and contrast. Consequently, substantial improvement of ultrasound images is an important prerequisite for ultrasound imaging. Some recent research has suggested that the spatial distribution of tissue densities may be used in ultrasound imaging to reconstruct images with fewer speckles. This new approach is based on the direct estimation of tissue density from the radio frequency echo signals received by ultrasound probe. This paper presents a mathematical analysis of this approach and derives a simplified model for issue estimation. This model simplifies the computation involved in density estimation and provides deeper insight into the problem. It shows that image reconstruction by tissue density estimation may have limited effect on speckle noise embedded in the RF echo signal. The analysis results are verified by numerical calculations and simulation examples.

[1]  T. Taxt,et al.  Restoration of medical ultrasound images using two-dimensional homomorphic deconvolution , 1995, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[2]  J A Jensen,et al.  Estimation of pulses in ultrasound B-scan images. , 1991, IEEE transactions on medical imaging.

[3]  Oleg V. Michailovich,et al.  Blind Deconvolution of Medical Ultrasound Images: A Parametric Inverse Filtering Approach , 2007, IEEE Transactions on Image Processing.

[4]  J A Jensen,et al.  A model for the propagation and scattering of ultrasound in tissue. , 1991, The Journal of the Acoustical Society of America.

[5]  Jørgen Arendt Jensen,et al.  Ultrasound fields in an attenuating medium , 1993 .

[6]  F. Lingvall,et al.  On Time-Domain Model-Based Ultrasonic Array Imaging , 2007, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[7]  P. Stepanishen,et al.  Pulsed transmit/receive response of ultrasonic piezoelectric transducers , 1981 .

[8]  R. Jirik,et al.  High-resolution ultrasonic imaging using two-dimensional homomorphic filtering , 2006, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[9]  Torfinn Taxt,et al.  High-resolution ultrasonic imaging using fast two-dimensional homomorphic filtering. , 2006, IEEE transactions on ultrasonics, ferroelectrics, and frequency control.

[10]  Cishen Zhang,et al.  Discrete echo signal modeling of ultrasound imaging systems , 2008, SPIE Medical Imaging.

[11]  T. Taxt,et al.  Two-dimensional noise-robust blind deconvolution of ultrasound images , 2001, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[12]  R. Jirik,et al.  Two-dimensional blind Bayesian deconvolution of medical ultrasound images , 2008, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[13]  A. Tannenbaum,et al.  Despeckling of medical ultrasound images , 2006, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[14]  K. Boone,et al.  Effect of skin impedance on image quality and variability in electrical impedance tomography: a model study , 1996, Medical and Biological Engineering and Computing.

[15]  Oleg V. Michailovich,et al.  A novel approach to the 2-D blind deconvolution problem in medical ultrasound , 2005, IEEE Transactions on Medical Imaging.

[16]  R. Jirik,et al.  Superresolution of ultrasound images using the first and second harmonic signal , 2004, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[17]  Jørgen Arendt Jensen Estimation of in Vivo Pulses in Medical Ultrasound , 1994 .

[18]  P. Stepanishen Transient Radiation from Pistons in an Infinite Planar Baffle , 1970 .