Ultrasonic elastography is an imaging technique providing information about the relative stiffness of biological tissues. In general, elastography suffers from noise artifacts, which degrade lesion detectability and increase the likelihood of misdiagnosis. This paper proposes a method called transmit- side frequency compounding for elastography (TSFC). Beamforming is modified to transmit frames with N alternating center frequencies. Pairs of frames with the same center frequency are used to calculate sub-elastograms that are then averaged to produce one compounded elastogram. Simulation results based on an uniformly elastic tissue model demonstrate the decorrelation among sub-elastograms and the improvement in elastographic signal-to-noise ratio (SNRe) achieved by compounding sub-elastograms. An elastic phantom experiment further validates the noise reduction obtained by the proposed technique.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1109/TUFFC.2011.1835 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
The Clinical Value of the MAR+ Metal Artifact Reduction Algorithm for Postoperative Assessment of Lumbar Internal Fixation.
J Comput Assist Tomogr
January 2025
Department of Radiology, the Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University.
Background: With the widespread use of lumbar pedicle screws for internal fixation, the morphology of the screws and the surrounding tissues should be evaluated. The metal artifact reduction (MAR) technique can reduce the artifacts caused by pedicle screws, improve the quality of computed tomography (CT) images after pedicle fixation, and provide more imaging information to the clinic.
Purpose: To explore whether the MAR+ method, a projection-based algorithm for correcting metal artifacts through multiple iterative operations, can reduce metal artifacts and have an impact on the structure of the surrounding metal.
Raising the operating temperature of mid-wavelength infrared detectors is critical for meeting the low size, weight, and power (SWaP) demands of infrared imaging systems. In this work, we report and analyze a high operating temperature (HOT) InAsSb nBn mid-wave infrared (MWIR) focal plane array (FPA) and single element photodetectors with AlAs/AlSb superlattices as the electron barrier. Under an applied bias of -350 mV, the nBn photodetectors demonstrate a dark current density of 2.
View Article and Find Full Text PDFThe electron-bombarded active pixel sensor (EBAPS) is a highly sensitive vacuum-solid hybrid low-light imaging device capable of functioning in ultra-low illumination environments as low as 10-4 lx. However, this high sensitivity also causes problems, such as a low signal-to-noise ratio and complex noise. To enhance the quality of low-light night vision images captured by EBAPS and achieve effective imaging in ultra-low illumination, this study proposes a noise reduction algorithm based on the noise characteristics of EBAPS images.
View Article and Find Full Text PDFFor wavelength division multiplexing (WDM) systems, excessive linear and nonlinear noise will seriously decrease the quality of optical signals, and the effective joint monitoring scheme can prevent the degradation of system performance due to noise accumulation. In this paper, we propose a probability information assisted knowledge distillation (PIAKD) scheme that achieves intelligent joint monitoring for linear signal-to-noise ratio (SNRL) and nonlinear signal-to-noise ratio (SNRNL) in WDM systems. Under the condition of multi-task regression, outputs are independent and continuous, PIAKD addresses the longstanding challenge that the student model fails to effectively learn knowledge from the teacher model by introducing probability information into the loss function.
View Article and Find Full Text PDFA hybrid model for carbon price forecasting based on SSA-NSTransformer: Considering the role of multi-stage carbon reduction targets.
J Environ Manage
January 2025
School of Economics and Management, North China Electric Power University, Beijing, China. Electronic address:
In order to reduce the unpredictability of carbon prices caused by their increasingly prominent environmental and market attributes, and to minimize their negative impact on carbon trading, further research on forecasting models for carbon price is urgently needed. To improve the accuracy of prediction, this paper proposes a carbon price forecasting method based on SSA-NSTransformer. The method includes four main steps: Firstly, decomposition of carbon price signals, using Singular Spectrum Analysis to remove noise signals; Secondly, analysis of influencing factors, using Random Forest to identify and select key influencing factors of carbon price signal components from energy price, financial market, socio-economic, and environmental aspects; Furthermore, influencing factors prediction, considering the impact of different carbon reduction targets and predicting future trends of influencing factors; And finally, carbon price prediction, considering the impact of factors based on multi-stage carbon reduction targets, using Non-stationary Transformer to predict the signal components of carbon prices, reconstructing the carbon price time series, and testing the model accuracy.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!