Shin, BonghunJeon, SooJeongwon, RyuKwon, Hyock Ju2018-01-162018-01-162017-07-0101617346https://doi.org/10.1177/0161734617716938http://hdl.handle.net/10012/12864Bonghun Shin, Soo Jeon, Jeongwon Ryu and Hyock Ju Kwon, “Compressed Sensing for Elastography in Portable Ultrasound,” Ultrasonic Imaging, 39(6), pp. 393-413, Copyright © The Author(s) 2017. Reprinted by permission of SAGE Publications. https://doi.org/10.1177/0161734617716938Portable wireless ultrasound has many advantages such as high portability, easy connectivity, strong individuality, as well as on-site diagnostic ability in real-time. Some of the modern portable ultrasound devices offer high image quality and multiple ultrasound modes comparable to console style ultrasound, however, none of them provides ultrasound elastography function that enables the diagnosis of malignant legions using elastic properties. This is mainly due to the limitations of hardware performance and wireless data transfer speed for processing the large amount of data for elastography. Therefore, reduction of the data transfer size is one of the feasible solutions to overcome these limitations. Recently compressive sensing (CS) theory has been rigorously studied as a means to break the conventional Nyquist sampling rate and thus can significantly decrease the amount of measurement signals without sacrificing signal quality. In this research, we implemented various CS reconstruction frameworks and comparatively evaluated their reconstruction performance for realizing ultrasound elastography function on portable ultrasound. Combinations of three most common model bases (FT, DCT, and WA) and two reconstruction algorithms (l_1 minimization and BSBL) were considered for CS frameworks. Two kinds of numerical phantoms, echoic and elastography phantoms, were developed to evaluate performance of CS on B-mode images and elastograms, respectively. To assess the reconstruction quality, mean absolute error (MAE), signal-to-noise (SNRe) and contrast-to-noise (CNRe) were measured on the B-mode images and elastograms from CS reconstructions. Results suggest that CS reconstruction adopting BSBL algorithm with DCT model basis can yield the best results for all the measures tested, and the maximum data reduction rate for producing readily discernable elastograms is around 60%.enCompressive sensingmodel basisl_1 minimizationBayesian learningelastographyportable ultrasoundArticle