Joint Deconvolution of Fundamental and Harmonic Ultrasound Images

This paper studies the interest of using harmonic ultrasound (US) images in the process of tissue reflectivity function restoration from RF data. To this end, two direct models (one for fundamental and another for harmonique images) derived from the equation of US wave propagation are proposed. In particular, an axially varying attenuation matrix is used within the harmonic image model in order to account for the attenuation of harmonic echoes. Based on these two image formation models, a joint deconvolution problem is investigated. The solution of this problem is obtained by minimizing a cost function composed of two data fidelity terms representing the linear and non-linear model components,regularized by an ℓ1-norm regularization. The tissue reflectivity function minimizing this function is finally determined using an alternating direction method of multipliers. The performance of the proposed algorithm is quantitatively and qualitatively evaluated on synthetic data, and compared with a classical restoration method used for US images.

[1]  J. Jensen,et al.  Calculation of pressure fields from arbitrarily shaped, apodized, and excited ultrasound transducers , 1992, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[2]  B. Mercier,et al.  A dual algorithm for the solution of nonlinear variational problems via finite element approximation , 1976 .

[3]  P. Burns,et al.  Pulse inversion Doppler: a new method for detecting nonlinear echoes from microbubble contrast agents , 1997, 1997 IEEE Ultrasonics Symposium Proceedings. An International Symposium (Cat. No.97CH36118).

[4]  G. Treece,et al.  Modeling ultrasound imaging as a linear, shift-variant system , 2006, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

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

[6]  Adrian Basarab,et al.  Compressive Deconvolution in Medical Ultrasound Imaging , 2015, IEEE Transactions on Medical Imaging.

[7]  A. Marion,et al.  CREASIMUS: a fast simulator of ultrasound image sequences using 3D tissue motion , 2009, 2009 IEEE International Ultrasonics Symposium.

[8]  Thomas L. Szabo,et al.  Diagnostic Ultrasound Imaging: Inside Out , 2004 .

[9]  J. D'hooge,et al.  A fast convolution-based methodology to simulate 2-Dd/3-D cardiac ultrasound images , 2009, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[10]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

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

[12]  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.

[13]  M. Averkiou,et al.  A new imaging technique based on the nonlinear properties of tissues , 1997, 1997 IEEE Ultrasonics Symposium Proceedings. An International Symposium (Cat. No.97CH36118).

[14]  Pierrick Lotton,et al.  Nonlinear System Identification Using Exponential Swept-Sine Signal , 2010, IEEE Transactions on Instrumentation and Measurement.

[15]  Stéphanie Bidon,et al.  Semi-blind deconvolution for resolution enhancement in ultrasound imaging , 2013, 2013 IEEE International Conference on Image Processing.

[16]  J. Arendt Paper presented at the 10th Nordic-Baltic Conference on Biomedical Imaging: Field: A Program for Simulating Ultrasound Systems , 1996 .

[17]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

[18]  A N Venetsanopoulos,et al.  Modelling and restoration of ultrasonic phased-array B-scan images. , 1985, Ultrasonic imaging.