Development of a Feasible Elastography Framework for Portable Ultrasound

Portable wireless ultrasound is emerging as a new ultrasound device due to the advantages such as small size, lightweight and affordable price. Its high portability allows practitioners to make diagnostic and therapeutic decisions in real-time without having to take the patients out of their environment. Recent portable ultrasound devices are equipped with sophisticated processors and image processing algorithms providing high image quality. Some of them are able to deliver multiple ultrasound modes including color Doppler, echocardiography, and endovaginal examination. Nevertheless, they are still lack of elastography functions due to the limitations in computational performance and data transfer speed via wireless communication. In order to implement the elastography function in the wireless portable ultrasound devices, this thesis proposes a new strain estimation method to significantly reduce the computation time and a compressive sensing framework to minimize the data transfer size. Firstly, a robust phase-based strain estimator (RPSE) is developed to overcome the limited hardware performance of portable ultrasound. The RPSE is not only computationally efficient but also robust to variations of the speed of sound, sampling frequency and pulse repetition. The RPSE has been compared with other representative strain estimators including time-delay, displacement-gradient, and conventional phase-based strain estimators (TSE, DSE and PSE, respectively). It has been shown that the RPSE is superior in several elastographic image quality measures, including signal-to-noise (SNRe) and contrast-to-noise (CNRe), and the computational efficiency. The study indicates that the RPSE method can deliver the acceptable level of elastography and fast computational speed for the ultrasound echo data sets from the numerical and experimental phantoms. According to the results from the numerical phantom experiment, RPSE can achieve highest values of SNRe and CNRe (around 5.22 and 47.62 dB) among all strain estimators tested, and almost 100 times higher computational efficiency than TSE and DSE (around 0.06 vs. 5.76 seconds per frame for RPSE and TSE, respectively). Secondly, as a means to reduce the large amount of ultrasound measurement data that has to be transmitted via wireless communication, the compressive sensing (CS) framework has been applied to elastography. The performance of CS is highly dependent on the

[1]  Jørgen Arendt Jensen,et al.  Ultrasonic colour Doppler imaging , 2011, Interface Focus.

[2]  J. Ophir,et al.  Reduction of signal decorrelation from mechanical compression of tissues by temporal stretching: applications to elastography. , 1997, Ultrasound in medicine & biology.

[3]  F. Kallel,et al.  Elastography: A systems approach , 1997, Int. J. Imaging Syst. Technol..

[4]  E. Madsen,et al.  An anthropomorphic ultrasound breast phantom containing intermediate-sized scatterers. , 1982, Ultrasound in medicine & biology.

[5]  Caroline Ewertsen,et al.  Strain Elastography Ultrasound: An Overview with Emphasis on Breast Cancer Diagnosis , 2013, Diagnostics.

[6]  G. R. Suresh,et al.  Speckle Noise Reduction in Ultrasound Images by Wavelet Thresholding based on Weighted Variance , 2009 .

[7]  D. Thelen,et al.  Tendon motion and strain patterns evaluated with two-dimensional ultrasound elastography. , 2012, Journal of biomechanics.

[8]  E. Ritenour.  Doppler Ultrasound: Physics, Instrumentation and Clinical Applications , 1990 .

[9]  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.

[10]  P N Wells,et al.  Doppler ultrasound in medical diagnosis. , 1989, The British journal of radiology.

[11]  Bonghun Shin,et al.  Application of Eshelby’s Solution to Elastography for Diagnosis of Breast Cancer , 2016, Ultrasonic imaging.

[12]  Hai-Dong Liang,et al.  Medical ultrasound: imaging of soft tissue strain and elasticity , 2011, Journal of The Royal Society Interface.

[13]  Yougun Han.  Development of Methodologies for Strain Measurement and Surface Energy Characterization , 2011 .

[14]  H. Kwon,et al.  Low-cost quasi-real-time elastography using B-mode ultrasound images. , 2014, Bio-medical materials and engineering.

[15]  F. Kallel,et al.  A Least-Squares Strain Estimator for Elastography , 1997, Ultrasonic imaging.

[16]  Denis Friboulet,et al.  Pre-beamformed RF signal reconstruction in medical ultrasound using compressive sensing. , 2013, Ultrasonics.

[17]  Emmanuel J. Candès,et al.  Decoding by linear programming , 2005, IEEE Transactions on Information Theory.

[18]  E. Candès,et al.  Stable signal recovery from incomplete and inaccurate measurements , 2005, math/0503066.

[19]  W. Walker,et al.  A comparison of the performance of time-delay estimators in medical ultrasound , 2003, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[20]  Brian M. Lempriere,et al.  Ultrasound and Elastic Waves: Frequently Asked Questions , 2002 .

[21]  P. Crawford,et al.  Ultrasound in the austere environment: a review of the history, indications, and specifications. , 2013, Military medicine.

[22]  Howard L. Weinert,et al.  Efficient computation for Whittaker-Henderson smoothing , 2007, Comput. Stat. Data Anal..

[23]  Denis Friboulet,et al.  Compressed Sensing Doppler Ultrasound Reconstruction Using Block Sparse Bayesian Learning , 2016, IEEE Transactions on Medical Imaging.

[24]  Yonina C. Eldar,et al.  Compressed Sensing with Coherent and Redundant Dictionaries , 2010, ArXiv.

[25]  Andrew H. Gee,et al.  Efficient elimination of dropouts in displacement tracking , 2006 .

[26]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[27]  Joel A. Tropp,et al.  Just relax: convex programming methods for identifying sparse signals in noise , 2006, IEEE Transactions on Information Theory.

[28]  Grace Wahba,et al.  Spline Models for Observational Data , 1990 .

[29]  J. Ophir,et al.  Methods for Estimation of Subsample Time Delays of Digitized Echo Signals , 1995 .

[30]  Abbas Samani,et al.  Towards clinical prostate ultrasound elastography using full inversion approach. , 2014, Medical physics.

[31]  J Ophir,et al.  Elastographic Imaging Using Staggered Strain Estimates , 2002, Ultrasonic imaging.

[32]  Aziza I. Hussein,et al.  Compressive Sensing Algorithms for Signal Processing Applications: A Survey , 2015 .

[33]  Andrew R Anton,et al.  Use of prehospital ultrasound in North America: a survey of emergency medical services medical directors , 2014, BMC Emergency Medicine.

[34]  A. Støylen,et al.  Real-time strain rate imaging of the left ventricle by ultrasound. , 1998, Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography.

[35]  Jonathan Ophir,et al.  Elastography: Imaging the elastic properties of soft tissues with ultrasound , 2002, Journal of Medical Ultrasonics.

[36]  Matthew Lyon,et al.  Transfer of Real-time Ultrasound Video of FAST Examinations from a Simulated Disaster Scene Via a Mobile Phone , 2014, Prehospital and Disaster Medicine.

[37]  Tzyy-Ping Jung,et al.  Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Noninvasive Fetal ECG Via Block Sparse Bayesian Learning , 2012, IEEE Transactions on Biomedical Engineering.

[38]  Stuart Bradley,et al.  A Comparison of Time Delay Estimation Methods for Periodic Signals , 2009 .

[39]  Joel A. Tropp,et al.  Greed is good: algorithmic results for sparse approximation , 2004, IEEE Transactions on Information Theory.

[40]  Jørgen Grythe,et al.  Improving Elastography using SURF Imaging for Suppression of Reverberations , 2010 .

[41]  João M. Sanches,et al.  Medical Image Noise Reduction Using the Sylvester–Lyapunov Equation , 2008, IEEE Transactions on Image Processing.

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

[43]  T. Krouskop,et al.  Elastography: Ultrasonic estimation and imaging of the elastic properties of tissues , 1999, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine.

[44]  Kaamran Raahemifar,et al.  New sampling approach for wireless ECG systems with compressed sensing theory , 2013, 2013 IEEE International Symposium on Medical Measurements and Applications (MeMeA).

[45]  M. Doyley,et al.  Strain imaging using combined RF and envelope autocorrelation processing , 1996, 1996 IEEE Ultrasonics Symposium. Proceedings.

[46]  Lihong V Wang,et al.  Compressed sensing in photoacoustic tomography in vivo. , 2010, Journal of biomedical optics.

[47]  Robert D. Nowak,et al.  Signal Reconstruction From Noisy Random Projections , 2006, IEEE Transactions on Information Theory.

[48]  C.E. Shannon,et al.  Communication in the Presence of Noise , 1949, Proceedings of the IRE.

[49]  W.F. Walker,et al.  A spline-based algorithm for continuous time-delay estimation using sampled data , 2005, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[50]  Hui Zhi,et al.  Ultrasonic elastography in breast cancer diagnosis: strain ratio vs 5-point scale. , 2010, Academic radiology.

[51]  Aarnout Brombacher,et al.  Probability... , 2009, Qual. Reliab. Eng. Int..

[52]  Michael Elad,et al.  On the Uniqueness of Nonnegative Sparse Solutions to Underdetermined Systems of Equations , 2008, IEEE Transactions on Information Theory.

[53]  Juin-Jet Hwang,et al.  Portable ultrasound device for battlefield trauma , 1998, 1998 IEEE Ultrasonics Symposium. Proceedings (Cat. No. 98CH36102).

[54]  Xiaoming Huo,et al.  Uncertainty principles and ideal atomic decomposition , 2001, IEEE Trans. Inf. Theory.

[55]  Yongmin Kim,et al.  Angular strain estimation method for elastography , 2007, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[56]  Jeffrey W. Eberhard,et al.  A New Time Domain Technique for Velocity Measurements Using Doppler Ultrasound , 1985, IEEE Transactions on Biomedical Engineering.

[57]  Terence Tao,et al.  The Dantzig selector: Statistical estimation when P is much larger than n , 2005, math/0506081.

[58]  H. Ermert,et al.  A new system for the acquisition of ultrasonic multicompression strain images of the human prostate in vivo , 1999, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[59]  T. Loupas,et al.  Multifrequency Doppler: improving the quality of spectral estimation by making full use of the information present in the backscattered RF echoes , 1994, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[60]  B. P. Nelson,et al.  Out of hospital point of care ultrasound: current use models and future directions , 2016, European Journal of Trauma and Emergency Surgery.

[61]  K. W. Lai,et al.  Ultrasonic Elastography and Breast Imaging , 2014 .

[62]  Thomas Kristoffersen Børstad.  Intraoperative Ultrasound Strain Imaging of Brain Tumors , 2011 .

[63]  B. Friemel,et al.  Relative performance of two-dimensional speckle-tracking techniques: normalized correlation, non-normalized correlation and sum-absolute-difference , 1995, 1995 IEEE Ultrasonics Symposium. Proceedings. An International Symposium.

[64]  Sebastian Wojcinski,et al.  Significant differentiation of focal breast lesions: calculation of strain ratio in breast sonoelastography. , 2010, Academic radiology.

[65]  J. Ophir,et al.  Elastography: A Quantitative Method for Imaging the Elasticity of Biological Tissues , 1991, Ultrasonic imaging.

[66]  Laurent Demanet,et al.  Wave atoms and time upscaling of wave equations , 2009, Numerische Mathematik.

[67]  Lili Yuan,et al.  Analytical phase-tracking-based strain estimation for ultrasound elasticity , 2015, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.

[68]  M. O’Donnell,et al.  Measurement of arterial wall motion using Fourier based speckle tracking algorithms , 1991, IEEE 1991 Ultrasonics Symposium,.

[69]  A. Baghani.  A wave equation approach to ultrasound elastography , 2010 .

[70]  Michael Elad,et al.  A generalized uncertainty principle and sparse representation in pairs of bases , 2002, IEEE Trans. Inf. Theory.

[71]  I Céspedes,et al.  Noise reduction in elastograms using temporal stretching with multicompression averaging. , 1996, Ultrasound in medicine & biology.

[72]  Jie Tang,et al.  Prior image constrained compressed sensing (PICCS): a method to accurately reconstruct dynamic CT images from highly undersampled projection data sets. , 2008, Medical physics.

[73]  Tal Shimon Schwartz,et al.  Data-guided statistical sparse measurements modeling for compressive sensing , 2013 .

[74]  T. Hall,et al.  2-D companding for noise reduction in strain imaging , 1998, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[75]  K J Parker,et al.  Multilevel and motion model-based ultrasonic speckle tracking algorithms. , 1998, Ultrasound in medicine & biology.

[76]  Yonina C. Eldar,et al.  Introduction to Compressed Sensing , 2022 .

[77]  Hyock-Ju Kwon,et al.  Application of digital image cross-correlation and smoothing function to the diagnosis of breast cancer. , 2012, Journal of the mechanical behavior of biomedical materials.

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

[79]  J. Ophir,et al.  Reduction of Image Noise in Elastography , 1993 .

[80]  M. Unser.  Sampling-50 years after Shannon , 2000, Proceedings of the IEEE.

[81]  Joel A. Tropp,et al.  Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.

[82]  T. Matsumura,et al.  Breast disease: clinical application of US elastography for diagnosis. , 2006, Radiology.

[83]  D. Donoho,et al.  Sparse MRI: The application of compressed sensing for rapid MR imaging , 2007, Magnetic resonance in medicine.

[84]  E. Candes,et al.  11-magic : Recovery of sparse signals via convex programming , 2005 .

[85]  D. Arola,et al.  Applications of digital image correlation to biological tissues. , 2004, Journal of biomedical optics.

[86]  P. Chaturvedi,et al.  Testing the limitations of 2-D companding for strain imaging using phantoms , 1998, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[87]  L. Demanet,et al.  Wave atoms and sparsity of oscillatory patterns , 2007 .

[88]  Damien Garcia,et al.  Robust smoothing of gridded data in one and higher dimensions with missing values , 2010, Comput. Stat. Data Anal..

[89]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[90]  Pengfei Song,et al.  Ultrasound elastography: the new frontier in direct measurement of muscle stiffness. , 2014, Archives of physical medicine and rehabilitation.

[91]  Gregory D. Hager,et al.  Ablation Monitoring with Elastography: 2D In-vivoand 3D Ex-vivoStudies , 2008, MICCAI.

[92]  Christopher Kovalchick,et al.  Application of digital image correlation method to biogel , 2010 .

[93]  P. Suetens,et al.  Comparison of time-domain displacement estimators for two-dimensional RF tracking. , 2003, Ultrasound in medicine & biology.

[94]  J. P. Lewis,et al.  Fast Template Matching , 2009 .

[95]  Hua Xie,et al.  P4B-5 The Role of Local Center Frequency Estimation in Doppler-Based Strain Imaging , 2007, 2007 IEEE Ultrasonics Symposium Proceedings.

[96]  Septimiu E. Salcudean,et al.  Motion Estimation in Ultrasound Images Using Time Domain Cross Correlation With Prior Estimates , 2006, IEEE Transactions on Biomedical Engineering.

[97]  T. Varghese,et al.  Enhancement of echo-signal correlation in elastography using temporal stretching , 1997, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[98]  Armando Manduca,et al.  Highly Undersampled Magnetic Resonance Image Reconstruction via Homotopic $\ell_{0}$ -Minimization , 2009, IEEE Transactions on Medical Imaging.

[99]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[100]  M. Bilgen,et al.  Error analysis in acoustic elastography. II. Strain estimation and SNR analysis. , 1997, The Journal of the Acoustical Society of America.

[101]  Jonathan Ophir,et al.  Elastography: A decade of progress (2000-2010) , 2011 .

[102]  Eiji Itoi,et al.  Ultrasound elastography-based assessment of the elasticity of the supraspinatus muscle and tendon during muscle contraction. , 2015, Journal of shoulder and elbow surgery.

[103]  R. Adler,et al.  An easily made, low‐cost, tissue‐like ultrasound phantom material , 1995, Journal of clinical ultrasound : JCU.

[104]  Gilbert Strang,et al.  The Discrete Cosine Transform , 1999, SIAM Rev..

[105]  T. Krouskop,et al.  Elastic Moduli of Breast and Prostate Tissues under Compression , 1998, Ultrasonic imaging.

[106]  L S Wilson,et al.  Description of broad-band pulsed Doppler ultrasound processing using the two-dimensional Fourier transform. , 1991, Ultrasonic imaging.

[107]  Graham M. Treece,et al.  Real-time quasi-static ultrasound elastography , 2011, Interface Focus.

[108]  Kaamran Raahemifar,et al.  Statistical finite element method for real-time tissue mechanics analysis , 2012, Computer methods in biomechanics and biomedical engineering.

[109]  E. Candès.  The restricted isometry property and its implications for compressed sensing , 2008 .

[110]  J.T. Powers,et al.  An axial velocity estimator for ultrasound blood flow imaging, based on a full evaluation of the Doppler equation by means of a two-dimensional autocorrelation approach , 1995, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[111]  H. Nyquist,et al.  Certain Topics in Telegraph Transmission Theory , 1928, Transactions of the American Institute of Electrical Engineers.

[112]  T. Varghese,et al.  A theoretical framework for performance characterization of elastography: the strain filter , 1997, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[113]  Bhaskar D. Rao,et al.  Extension of SBL Algorithms for the Recovery of Block Sparse Signals With Intra-Block Correlation , 2012, IEEE Transactions on Signal Processing.

[114]  H. Ermert,et al.  A time-efficient and accurate strain estimation concept for ultrasonic elastography using iterative phase zero estimation , 1999, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[115]  J. Jensen,et al.  Duplex scanning using sparse data sequences , 2008, 2008 IEEE Ultrasonics Symposium.

[116]  W. Beyer.  CRC Standard Mathematical Tables and Formulae , 1991 .