REAL-TIME DECONVOLUTION IN ULTRASONIC IMAGING SYSTEMS

2001 ◽  
Vol 09 (03) ◽  
pp. 745-755 ◽  
Author(s):  
G. CINCOTTI ◽  
R. CAROTENUTO ◽  
G. CARDONE ◽  
P. GORI ◽  
M. PAPPALARDO
Keyword(s):  
Additive Noise ◽  
Ultrasonic Imaging ◽  
Computational Cost ◽  
Imaging Systems ◽  
Imaging Data ◽  
Regularization Technique ◽  
Restoration Technique ◽  
Spread Function ◽  
Standard Regularization ◽  
Ultrasonic Images

We address the problem of improving the lateral resolution of ultrasonic images by a regularization technique in the wavelet domain. With a very low additional computational cost, the proposed approach increases the efficiency of the standard regularization technique because it efficiently remove the additive noise. Under the assumption that the point spread function is known, we applied our restoration technique to both synthetic and real ultrasonic imaging data. Moreover, experimental results show that the proposed method reduces also speckle artifacts, which generally are enhanced by the deconvolution.

scholarly journals Fractional ridge regression: a fast, interpretable reparameterization of ridge regression

GigaScience ◽  
2020 ◽  
Vol 9 (12) ◽  
Author(s):  
Ariel Rokem ◽  
Kendrick Kay
Keyword(s):  
Ridge Regression ◽  
Large Scale ◽  
Imaging Data ◽  
Regularization Technique ◽  
Large Scale Data ◽  
Novel Approach ◽  
Manual Exploration ◽  
L2 Norm ◽  
Software Implementations ◽  
Brain Imaging Data

Abstract Background Ridge regression is a regularization technique that penalizes the L2-norm of the coefficients in linear regression. One of the challenges of using ridge regression is the need to set a hyperparameter (α) that controls the amount of regularization. Cross-validation is typically used to select the best α from a set of candidates. However, efficient and appropriate selection of α can be challenging. This becomes prohibitive when large amounts of data are analyzed. Because the selected α depends on the scale of the data and correlations across predictors, it is also not straightforwardly interpretable. Results The present work addresses these challenges through a novel approach to ridge regression. We propose to reparameterize ridge regression in terms of the ratio γ between the L2-norms of the regularized and unregularized coefficients. We provide an algorithm that efficiently implements this approach, called fractional ridge regression, as well as open-source software implementations in Python and matlab (https://github.com/nrdg/fracridge). We show that the proposed method is fast and scalable for large-scale data problems. In brain imaging data, we demonstrate that this approach delivers results that are straightforward to interpret and compare across models and datasets. Conclusion Fractional ridge regression has several benefits: the solutions obtained for different γ are guaranteed to vary, guarding against wasted calculations; and automatically span the relevant range of regularization, avoiding the need for arduous manual exploration. These properties make fractional ridge regression particularly suitable for analysis of large complex datasets.

EXPRESS: Design Considerations for Discrete Frequency Infrared Microscopy Systems

2021 ◽  
pp. 000370282110133
Author(s):  
Rohit Bhargava ◽  
Yamuna Dilip Phal ◽  
Kevin Yeh
Keyword(s):  
Imaging System ◽  
Chemical Imaging ◽  
Optical Component ◽  
Imaging Systems ◽  
Imaging Data ◽  
Discrete Frequency ◽  
Figures Of Merit ◽  
Design Considerations ◽  
Hardware Implementations ◽  
Measurement Capabilities

Discrete frequency infrared (DFIR) chemical imaging is transforming the practice of microspectroscopy by enabling a diversity of instrumentation and new measurement capabilities. While a variety of hardware implementations have been realized, considerations in the design of all-IR microscopes have not yet been compiled. Here we describe the evolution of IR microscopes, provide rationales for design choices, and the major considerations for each optical component that together comprise an imaging system. We analyze design choices in illustrative examples that use these components to optimize performance, under their particular constraints. We then summarize a framework to assess the factors that determine an instrument’s performance mathematically. Finally, we summarize the design and analysis approach by enumerating performance figures of merit for spectroscopic imaging data that can be used to evaluate the capabilities of imaging systems or suitability for specific intended applications. Together, the presented concepts and examples should aid in understanding available instrument configurations, while guiding innovations in design of the next generation of IR chemical imaging spectrometers.

A Quantitative Approach to Speckle Reduction via Frequency Compounding

1986 ◽  
Vol 8 (3) ◽  
pp. 151-164 ◽  
Author(s):  
G.E. Trahey ◽  
J.W. Allison ◽  
S.W. Smith ◽  
O.T. von Ramm
Keyword(s):  
Speckle Pattern ◽  
Imaging Systems ◽  
Center Frequency ◽  
Speckle Reduction ◽  
Optimal Method ◽  
Pattern Change ◽  
Multiple Imaging ◽  
Speckle Patterns ◽  
Contrast Reduction ◽  
Ultrasonic Images

Coherent speckle is a source of image noise in ultrasonic B-mode imaging. The use of multiple imaging frequencies has been suggested as a technique for speckle contrast reduction. This technique involves the averaging of images whose speckle patterns have been modified by a change in the spectrum of the transmitted or received acoustical pulse. We have measured the rate of this speckle pattern change in ultrasonic images as a function of the change in center frequency of the transmitted acoustical pulse. This data is used to quantitatively describe the trade-off of resolution loss versus speckle reduction encountered when frequency compounding is employed and to derive the optimal method of frequency compounding. These results are then used as a basis for describing the overall advisability of frequency compounding in ultrasonic imaging systems. Our analysis indicates that simple frequency compounding is counterproductive in improving image quality.

A new beamforming technique for ultrasonic imaging systems

Ultrasonics ◽  
2000 ◽  
Vol 38 (1-8) ◽  
pp. 156-160
Author(s):  
G. Cincotti ◽  
G. Cardone ◽  
P. Gori ◽  
M. Pappalardo
Keyword(s):  
Ultrasonic Imaging ◽  
Imaging Systems ◽  
Beamforming Technique
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