Orthogonal sparse dictionary based on Chirp echo for ultrasound imaging

Abstract High-end ultrasound imaging system always requires high-speed data acquisition and processing system, which often leads to high-cost issues of ultrasound hardware. Fortunately, the compressed sensing theory offers a perspective of reducing the data sampling rate. To obtain better-reconstructed ultrasound imaging quality at a low sampling rate, this paper explores coding technology with compressed sensing and proposes a kind of Orthogonal Sparse Dictionary based on Chirp echo (OSD). The simulation experiments of scattering point targets, cyst phantoms, and geabr_0 data were conducted, and a series of performance index parameters about reconstructed image quality were calculated and analyzed. The simulation results indicate that the image quality reconstructed by the proposed OSD is superior to traditional sparse dictionaries with the same reconstruction algorithm and sampling rate. By applying the proposed OSD, there is no obvious distortion about the image quality at 30% sampling rate.

[1]  Xiaojun Yuan,et al.  Turbo Compressed Sensing with Partial DFT Sensing Matrix , 2014, IEEE Signal Processing Letters.

[2]  Adrian Basarab,et al.  Compressed sensing of ultrasound images: Sampling of spatial and frequency domains , 2010, 2010 IEEE Workshop On Signal Processing Systems.

[3]  Denis Friboulet,et al.  Compressed sensing reconstruction of 3D ultrasound data using dictionary learning , 2015, 2014 IEEE International Conference on Image Processing (ICIP).

[4]  Tai-Kyong Song,et al.  Coded excitation for ultrasound tissue harmonic imaging. , 2010, Ultrasonics.

[5]  Jianwen Luo,et al.  A Compressed Sensing Strategy for Synthetic Transmit Aperture Ultrasound Imaging , 2017, IEEE Transactions on Medical Imaging.

[6]  Ping Wang,et al.  Sparse dictionary for synthetic transmit aperture medical ultrasound imaging. , 2017, The Journal of the Acoustical Society of America.

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

[8]  Jafar Saniie,et al.  Block sparse compressed sensing in ultrasonic NDE echo analysis and parameter estimation , 2017, 2017 IEEE International Ultrasonics Symposium (IUS).

[9]  William J. Caputi,et al.  Stretch: A Time-Transformation Technique , 1971, IEEE Transactions on Aerospace and Electronic Systems.

[10]  Yücel Altunbasak,et al.  Approximation and Compression With Sparse Orthonormal Transforms , 2015, IEEE Transactions on Image Processing.

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

[12]  Y. Yoo,et al.  A New Dynamic Complex Baseband Pulse Compression Method for Chirp-Coded Excitation in Medical Ultrasound Imaging , 2017, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.

[13]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

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

[15]  Ivan Volaric,et al.  Sparse time–frequency distributions based on the $$\ell _1$$ℓ1-norm minimization with the fast intersection of confidence intervals rule , 2018, Signal Image Video Process..

[16]  J. Romberg,et al.  Imaging via Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[17]  Sungho Tak,et al.  A Data-Driven Sparse GLM for fMRI Analysis Using Sparse Dictionary Learning With MDL Criterion , 2011, IEEE Transactions on Medical Imaging.

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

[19]  Piotr Indyk,et al.  Sparse Recovery Using Sparse Matrices , 2010, Proceedings of the IEEE.

[20]  Helmut Essen,et al.  High range resolution by means of synthetic bandwidth generated by frequency-stepped chirps , 2003 .

[21]  G. Schmitz,et al.  Fast pulse-echo ultrasound imaging employing compressive sensing , 2011, 2011 IEEE International Ultrasonics Symposium.

[22]  Ran Tao,et al.  A Novel SAR Imaging Algorithm Based on Compressed Sensing , 2015, IEEE Geoscience and Remote Sensing Letters.

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

[24]  Andreas Mandelis,et al.  High-Frame-Rate Synthetic Aperture Ultrasound Imaging Using Mismatched Coded Excitation Waveform Engineering: A Feasibility Study , 2016, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.

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

[26]  Yi Shen,et al.  Dictionaries Construction Using Alternating Projection Method in Compressive Sensing , 2011, IEEE Signal Processing Letters.

[27]  Colas Schretter,et al.  Ultrasound Imaging From Sparse RF Samples Using System Point Spread Functions , 2018, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.

[28]  Kainam Thomas Wong,et al.  Viable/Inviable Polynomial-Phase Modulations for "Stretch Processing" , 2012, IEEE Transactions on Aerospace and Electronic Systems.