scholarly journals High-Resolution Ultrasound Imaging Enabled by Random Interference and Joint Image Reconstruction

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6434
Author(s):  
Pavel Ni ◽  
Heung-No Lee
Keyword(s):  
High Resolution ◽  
Image Reconstruction ◽  
Ultrasound Imaging ◽  
Signal To Noise Ratio ◽  
Reconstruction Method ◽  
Signal To Noise ◽  
Joint Reconstruction ◽  
Reconstruction Scheme ◽  
Reconstruction Methods ◽  
Noise Ratio

In ultrasound, wave interference is an undesirable effect that degrades the resolution of the images. We have recently shown that a wavefront of random interference can be used to reconstruct high-resolution ultrasound images. In this study, we further improve the resolution of interference-based ultrasound imaging by proposing a joint image reconstruction scheme. The proposed reconstruction scheme utilizes radio frequency (RF) signals from all elements of the sensor array in a joint optimization problem to directly reconstruct the final high-resolution image. By jointly processing array signals, we significantly improved the resolution of interference-based imaging. We compare the proposed joint reconstruction method with popular beamforming techniques and the previously proposed interference-based compound method. The simulation study suggests that, among the different reconstruction methods, the joint reconstruction method has the lowest mean-squared error (MSE), the best peak signal-to-noise ratio (PSNR), and the best signal-to-noise ratio (SNR). Similarly, the joint reconstruction method has an exceptional structural similarity index (SSIM) of 0.998. Experimental studies showed that the quality of images significantly improved when compared to other image reconstruction methods. Furthermore, we share our simulation codes as an open-source repository in support of reproducible research.

scholarly journals Multiplane wave imaging increases signal-to-noise ratio in ultrafast ultrasound imaging

2015 ◽  
Vol 60 (21) ◽  
pp. 8549-8566 ◽  
Author(s):  
Elodie Tiran ◽  
Thomas Deffieux ◽  
Mafalda Correia ◽  
David Maresca ◽  
Bruno-Felix Osmanski ◽  
...  
Keyword(s):  
Ultrasound Imaging ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Wave Imaging ◽  
Noise Ratio

MULTI-OBJECTIVE GA-OPTIMIZED INTERPOLATION KERNELS FOR RECONSTRUCTION OF HIGH RESOLUTION EMR IMAGES FROM LOW-SAMPLED K-SPACE DATA

2009 ◽  
Vol 08 (02) ◽  
pp. 127-140 ◽  
Author(s):  
D. BALASUBRAMANIAN ◽  
MURALI C. KRISHNA ◽  
R. MURUGESAN
Keyword(s):  
Image Reconstruction ◽  
Signal To Noise Ratio ◽  
Fitness Function ◽  
Computing Time ◽  
Reference Image ◽  
Signal To Noise ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Noise Ratio ◽  
Space Data

The low-frequency instrumentation and imaging capabilities facilitate electron magnetic resonance imaging (EMRI) as an emerging non-invasive imaging technology for mapping free radicals in biological systems. Unlike MRI, EMRI is implemented as a pure phase–phase encoding technique. The fast bio-clearance of the imaging agent and the requirement to reduce radio frequency power deposition dictate collection of reduced k-space samples, compromising the quality and resolution of the EMR images. The present work evaluates various interpolation kernels to generate larger k-space samples for image reconstruction, from the acquired reduced k-space samples. Using k-space EMR data sets, acquired for phantom as well as live mice, the proposed technique is critically evaluated by computing quality metrics viz. signal-to-noise ratio (SNR), standard deviation error (SDE), root mean square error (RMSE), peak signal-to-noise ratio (PSNR), contrast-to-noise ratio (CNR) and Lui's error function (F(I)). The quantitative evaluation of 24 different interpolation functions (including piecewise polynomial functions and many windowed sinc functions) to upsample the k-space data for the Fourier EMR image reconstruction shows that at the expense of a slight increase in computing time, the reconstructed images from upsampled data, produced using Spline-sinc, Welch-sinc, and Gaussian-sinc kernels, are closer to reference image with minimal distortion. Support of the interpolating kernel is a characteristic parameter deciding the quality of the reconstructed image and the time complexity. In this paper, a method to optimize the kernel support using genetic algorithm (GA) is also explored. Maximization of the fitness function has two conflicting objectives and it is approached as a multi-objective optimization problem.

scholarly journals Fast Compressed Sensing MRI Based on Complex Double-Density Dual-Tree Discrete Wavelet Transform

2017 ◽  
Vol 2017 ◽  
pp. 1-13
Author(s):  
Shanshan Chen ◽  
Bensheng Qiu ◽  
Feng Zhao ◽  
Chao Li ◽  
Hongwei Du
Keyword(s):  
Wavelet Transform ◽  
Compressed Sensing ◽  
Discrete Wavelet Transform ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Discrete Wavelet ◽  
Translation Invariance ◽  
Signal To Noise ◽  
Reconstruction Methods ◽  
Noise Ratio

Compressed sensing (CS) has been applied to accelerate magnetic resonance imaging (MRI) for many years. Due to the lack of translation invariance of the wavelet basis, undersampled MRI reconstruction based on discrete wavelet transform may result in serious artifacts. In this paper, we propose a CS-based reconstruction scheme, which combines complex double-density dual-tree discrete wavelet transform (CDDDT-DWT) with fast iterative shrinkage/soft thresholding algorithm (FISTA) to efficiently reduce such visual artifacts. The CDDDT-DWT has the characteristics of shift invariance, high degree, and a good directional selectivity. In addition, FISTA has an excellent convergence rate, and the design of FISTA is simple. Compared with conventional CS-based reconstruction methods, the experimental results demonstrate that this novel approach achieves higher peak signal-to-noise ratio (PSNR), larger signal-to-noise ratio (SNR), better structural similarity index (SSIM), and lower relative error.

scholarly journals Signal to Noise Ratio of APD-Based Monolithic Scintillator Detectors for High Resolution PET

2008 ◽  
Vol 55 (3) ◽  
pp. 842-852 ◽  
Author(s):  
M.C. Maas ◽  
D.R. Schaart ◽  
D.J. van der Laan ◽  
H.T. van Dam ◽  
J. Huizenga ◽  
...  
Keyword(s):  
High Resolution ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Scintillator Detectors

scholarly journals Development Of A Nonlinear Frequency Compounding Method With Applications In Ultrasound Imaging And Thermometry

2021 ◽  
Author(s):  
Tyler Hornsby
Keyword(s):  
Ultrasound Imaging ◽  
Focused Ultrasound ◽  
Ex Vivo ◽  
Signal To Noise Ratio ◽  
Single Frequency ◽  
Temperature Sensitive ◽  
Nonlinear Frequency ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Temperature Maps

<div>Frequency compounding is an ultrasound imaging technique used to reduce artifacts and improve signal-to-noise-ratio (SNR). In this work a new nonlinear frequency compounding (NLFC) method was introduced, and its application in B-mode imaging and noninvasive thermometry was investigated. NLFC input frequencies were optimized to maximize speckle-signal-to-noise-ratio (SSNR) in a tissue mimicking phantom, and the method was then used to produce maps of the temperature sensitive change in backscattered energy of acoustic harmonics (<i>h</i>CBE) during heating of ex vivo porcine tissue with a focused ultrasound transducer. A <i>h</i>CBE-to-temperature calibration was also performed and temperature maps produced. Lastly, a comparative study of the NLFC and previously used nonlinear single frequency (NLSF) method was completed. By using the NLFC method it was concluded that SSNR of B-mode and backscattered energy images, SNR of <i>h</i>CBE maps, and temperature map agreement with a theoretical COMSOL based model were improved over the previously used NLSF method.</div>

Sign in / Sign up

Export Citation Format

Share Document