Wavelet Transforms in the Analysis of Mechanical Heart Valve Cavitation

2005 ◽  
Vol 128 (2) ◽  
pp. 217-222 ◽  
Author(s):  
Luke H. Herbertson ◽  
Varun Reddy ◽  
Keefe B. Manning ◽  
Joseph P. Welz ◽  
Arnold A. Fontaine ◽  
...  
Keyword(s):  
Wavelet Transforms ◽  
Heart Valves ◽  
Signal To Noise Ratio ◽  
Mechanical Heart Valve ◽  
Analytical Techniques ◽  
Gas Content ◽  
Denoising Method ◽  
Area Of Interest ◽  
Valve Closing Dynamics ◽  
Wavelet Technique

Cavitation is known to cause blood element damage and may introduce gaseous emboli into the cerebral circulation, increasing the patient’s risk of stroke. Discovering methods to reduce the intensity of cavitation induced by mechanical heart valves (MHVs) has long been an area of interest. A novel approach for analyzing MHV cavitation is presented. A wavelet denoising method is explored because currently used analytical techniques fail to suitably unmask the cavitation signal from other valve closing sounds and noise detected with a hydrophone. Wavelet functions are used to denoise the cavitation signal during MHV closure and rebound. The wavelet technique is applied to the signal produced by closure of a 29-mm Medtronic-Hall MHV in degassed water with a gas content of 5ppm. Valve closing dynamics are investigated under loading conditions of 500, 2500, and 4500mmHg∕s. The results display a marked improvement in the quantity and quality of information that can be extracted from acoustic cavitation signals using the wavelet technique compared to conventional analytical techniques. Time and frequency data indicate the likelihood and characteristics of cavitation formation under specified conditions. Using this wavelet technique we observe an improved signal-to-noise ratio, an enhanced time-dependent aspect, and the potential to minimize valve closing sounds, which disguise individual cavitation events. The overall goal of this work is to eventually link specific valves with characteristic waveforms or distinct types of cavitation, thus promoting improved valve designs.

Development of a Wavelet Analytical Tool for Isolating and Evaluating Cavitation-Induced Hemolysis Generated During Mechanical Heart Valve Closure

2007 ◽  
Author(s):  
Luke H. Herbertson ◽  
Steven Deutsch ◽  
Keefe B. Manning
Keyword(s):  
Blood Cells ◽  
Heart Valves ◽  
Pressure Fluctuations ◽  
Mechanical Heart Valve ◽  
Analytical Techniques ◽  
Analytical Tool ◽  
The Body ◽  
Isolation Technique ◽  
Visual Evidence

Cavitation formed during the closure of mechanical heart valves (MHVs) can harm nearby blood cells and valve surfaces. In this study we focus on an approach to accurately measure cavitation energy in order to compare the effectiveness of replacement valves. Cavitation energy is difficult to measure acoustically because it is masked by other pressure fluctuations in the body or system. To improve upon currently used analytical techniques, a wavelet isolation technique was developed to quantify cavitation energy. With this method, acoustic signals captured by a hydrophone are decomposed, denoised, and then reconstructed. Wavelet analysis should prove to be particularly valuable in vivo, where visual evidence of cavitation cannot be obtained.

Observation and Quantification of Gas Bubble Formation on a Mechanical Heart Valve

2000 ◽  
Vol 122 (4) ◽  
pp. 304-309 ◽  
Author(s):  
Hsin-Yi Lin ◽  
Brian A. Bianccucci ◽  
Steven Deutsch ◽  
Arnold A. Fontaine ◽  
J. M. Tarbell
Keyword(s):  
High Speed ◽  
Heart Valves ◽  
Bubble Formation ◽  
Mechanical Heart Valve ◽  
Gas Bubbles ◽  
Co2 Concentration ◽  
Video Camera ◽  
Gas Content ◽  
Gas Bubble ◽  
Cavitation Intensity

Clinical studies using transcranial Doppler ultrasonography in patients with mechanical heart valves (MHV) have detected gaseous emboli. The relationship of gaseous emboli release and cavitation on MHV has been a subject of debate in the literature. To study the influence of cavitation and gas content on the formation and growth of stable gas bubbles, a mock circulatory loop, which employed a Medtronic-Hall pyrolytic carbon disk valve in the mitral position, was used. A high-speed video camera allowed observation of cavitation and gas bubble release on the inflow valve surfaces as a function of cavitation intensity and carbon dioxide CO2 concentration, while an ultrasonic monitoring system scanned the aortic outflow tract to quantify gas bubble production by calculating the gray scale levels of the images. In the absence of cavitation, no stable gas bubbles were formed. When gas bubbles were formed, they were first seen a few milliseconds after and in the vicinity of cavitation collapse. The volume of the gas bubbles detected in the aortic track increased with both increased CO2 and increased cavitation intensity. No correlation was observed between O2 concentration and bubble volume. We conclude that cavitation is an essential precursor to stable gas bubble formation, and CO2, the most soluble blood gas, is the major component of stable gas bubbles. [S0148-0731(00)00204-1]

Image Denoising Using Wavelet Transform and Wavelet Transform with Enhanced Diversity

2011 ◽  
Vol 403-408 ◽  
pp. 866-870
Author(s):  
Vaibhav Nigam ◽  
Smriti Bhatnagar ◽  
Sajal Luthra
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Image Denoising ◽  
Wavelet Transforms ◽  
Signal To Noise Ratio ◽  
Practical Implementation ◽  
Discrete Wavelet ◽  
Maximum Output ◽  
Denoising Method ◽  
Detail Coefficient

This paper is a comparative study of image denoising using previously known wavelet transform and new type of wavelet transform, namely, Diversity enhanced discrete wavelet transform. The Discrete Wavelet Transform (DWT) has two parameters: the mother wavelet and the number of iterations. For every noisy image, there is a best pair of parameters for which we get maximum output Peak Signal to Noise Ratio, PSNR. As the denoising algorithms are sensitive to the parameters of the wavelet transform used, in this paper comparison of DEDWT to DWT has been presented. The diversity is enhanced by computing wavelet transforms with different parameters. After the filtering of each detail coefficient, the corresponding wavelet transforms are inverted and the estimated image, having a higher PSNR, is extracted. To benchmark against the best possible denoising method three thresholding techniques have been compared. In this paper we have presented a more practical, implementation oriented work.

scholarly journals A Local Search Maximum Likelihood Parameter Estimator of Chirp Signal

2021 ◽  
Vol 11 (2) ◽  
pp. 673
Author(s):  
Guangli Ben ◽  
Xifeng Zheng ◽  
Yongcheng Wang ◽  
Ning Zhang ◽  
Xin Zhang
Keyword(s):  
Maximum Likelihood ◽  
Local Search ◽  
Signal To Noise Ratio ◽  
Estimation Accuracy ◽  
Chirp Signal ◽  
Estimation Of Parameters ◽  
Parameter Estimator ◽  
Denoising Method ◽  
Time Frequency ◽  
Approximate Estimation

A local search Maximum Likelihood (ML) parameter estimator for mono-component chirp signal in low Signal-to-Noise Ratio (SNR) conditions is proposed in this paper. The approach combines a deep learning denoising method with a two-step parameter estimator. The denoiser utilizes residual learning assisted Denoising Convolutional Neural Network (DnCNN) to recover the structured signal component, which is used to denoise the original observations. Following the denoising step, we employ a coarse parameter estimator, which is based on the Time-Frequency (TF) distribution, to the denoised signal for approximate estimation of parameters. Then around the coarse results, we do a local search by using the ML technique to achieve fine estimation. Numerical results show that the proposed approach outperforms several methods in terms of parameter estimation accuracy and efficiency.

Research on LIFT Technology for 3D Seismic Denoising in Coalfield

2014 ◽  
Vol 556-562 ◽  
pp. 6328-6331
Author(s):  
Su Zhen Shi ◽  
Yi Chen Zhao ◽  
Li Biao Yang ◽  
Yao Tang ◽  
Juan Li
Keyword(s):  
Signal To Noise Ratio ◽  
High Signal ◽  
3D Seismic ◽  
Signal To Noise ◽  
Denoising Method ◽  
Seismic Processing ◽  
Work Area ◽  
Noise Data ◽  
Noise Ratio ◽  
Effective Signal

The LIFT technology has applied in process of denoising to ensure the imaging precision of minor faults and structure in 3D coalfield seismic processing. The paper focused on the denoising process in two study areas where the LIFT technology is used. The separation of signal and noise is done firstly. Then denoising would be done in the noise data. The Data of weak effective signal that is from the noise data could be blended with the original effective signal to reconstruct the denoising data, so the result which has high signal-to-noise ratio and preserved amplitude is acquired. Thus the fact shows that LIFT is an effective denoising method for 3D seismic in coalfield and could be used widely in other work area.

Design Optimization of a Mechanical Heart Valve for Reducing Valve Thrombogenicity: A Case Study With ATS Valve

2010 ◽  
Author(s):  
Gaurav Girdhar ◽  
Yared Alemu ◽  
Michalis Xenos ◽  
Jawaad Sheriff ◽  
Jolyon Jesty ◽  
...  
Keyword(s):  
Heart Valves ◽  
Thrombus Formation ◽  
Anticoagulation Therapy ◽  
Mechanical Heart Valve ◽  
Ventricular Assist Devices ◽  
Circulatory Support ◽  
Ventricular Assist ◽  
Mechanical Circulatory Support Devices ◽  
Artificial Hearts

Flow past mechanical heart valves (MHV) in mechanical circulatory support devices including total artificial hearts and ventricular assist devices, is primarily implicated in thromboembolism due to non-physiological flow conditions where the elevated stresses and exposure times are sufficiently high to cause platelet activation and thrombus formation. Mitigation of this risk requires lifelong anticoagulation therapy and less thrombogenic MHV designs should therefore be developed by device manufacturers [1].

Argatroban and bivalirudin compared to unfractionated heparin in preventing thrombus formation on mechanical heart valves

2009 ◽  
Vol 101 (06) ◽  
pp. 1163-1169 ◽  
Author(s):  
Torsten Linde ◽  
Thomas Michel ◽  
Kathrin Hamilton ◽  
Ulrich Steinseifer ◽  
Ivar Friedrich ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Unfractionated Heparin ◽  
Continuous Infusion ◽  
Valve Replacement ◽  
Heart Valves ◽  
Thrombus Formation ◽  
Mechanical Heart Valve ◽  
Mechanical Heart Valves ◽  
Valve Prostheses

SummaryPrevention of valve thrombosis in patients after prosthetic mechanical heart valve replacement and heparin-induced thrombocytopenia (HIT) is still an open issue. The aim of the present in-vitro study was to investigate the efficacy of argatroban and bivalirudin in comparison to unfractionated heparin (UFH) in preventing thrombus formation on mechanical heart valves. Blood (230 ml) from healthy young male volunteers was anticoagulated either by UFH, argatroban bolus, argatroban bolus plus continuous infusion, bivalirudin bolus, or bivalirudin bolus plus continuous infusion. Valve prostheses were placed in a newly developed in-vitro thrombosis tester and exposed to the anticoagulated blood samples. To quantify the thrombi, electron microscopy was performed, and each valve was weighed before and after the experiment. Mean thrombus weight in group 1 (UFH) was 117 + 93 mg, in group 2 (argatroban bolus) 722 + 428 mg, in group 3 (bivalirudin bolus) 758 + 323 mg, in group 4 (argatroban bolus plus continuous infusion) 162 + 98 mg, and in group 5 (bivalirudin bolus plus continuous infusion) 166 + 141 mg (p-value <0.001). Electron microscopy showed increased rates of thrombus formation in groups 2 and 3. Argatroban and bivalirudin were as effective as UFH in preventing thrombus formation on valve prostheses in our in-vitro investigation when they were administered continuously. We hypothesise that continuous infusion of argatroban or bivalirudin are optimal treatment options for patients with HIT after mechanical heart valve replacement for adapting oral to parenteral anticoagulation or vice versa.

scholarly journals Adverse Hemodynamic Conditions Associated with Mechanical Heart Valve Leaflet Immobility

2018 ◽  
Vol 5 (3) ◽  
pp. 74 ◽  
Author(s):  
Fardin Khalili ◽  
Peshala Gamage ◽  
Richard Sandler ◽  
Hansen Mansy
Keyword(s):  
Heart Valve ◽  
Heart Valves ◽  
Computational Study ◽  
Cell Damage ◽  
Mechanical Heart Valve ◽  
Flow Characteristics ◽  
Maximal Velocity ◽  
Valve Dysfunction ◽  
Promising Tool ◽  
Artificial Heart Valves

Artificial heart valves may dysfunction, leading to thrombus and/or pannus formations. Computational fluid dynamics is a promising tool for improved understanding of heart valve hemodynamics that quantify detailed flow velocities and turbulent stresses to complement Doppler measurements. This combined information can assist in choosing optimal prosthesis for individual patients, aiding in the development of improved valve designs, and illuminating subtle changes to help guide more timely early intervention of valve dysfunction. In this computational study, flow characteristics around a bileaflet mechanical heart valve were investigated. The study focused on the hemodynamic effects of leaflet immobility, specifically, where one leaflet does not fully open. Results showed that leaflet immobility increased the principal turbulent stresses (up to 400%), and increased forces and moments on both leaflets (up to 600% and 4000%, respectively). These unfavorable conditions elevate the risk of blood cell damage and platelet activation, which are known to cascade to more severe leaflet dysfunction. Leaflet immobility appeared to cause maximal velocity within the lateral orifices. This points to the possible importance of measuring maximal velocity at the lateral orifices by Doppler ultrasound (in addition to the central orifice, which is current practice) to determine accurate pressure gradients as markers of valve dysfunction.

K-L Expansion of a Random Process in Wavelet Bases

1998 ◽  
Author(s):  
Om P. Agrawal ◽  
Shantaram S. Pai
Keyword(s):  
Eigenvalue Problem ◽  
Wavelet Transforms ◽  
Series Representation ◽  
Numerical Algorithms ◽  
Analytical Techniques ◽  
Random Processes ◽  
Random Coefficients ◽  
Engineering Systems ◽  
Wavelet Bases ◽  
Orthogonal Wavelets

Abstract Random processes play a significant role in stochastic analysis of mechanical systems, structures, fluid mechanics, and other engineering systems. In this paper, a numerical method for series representation of random processes, with specified mean and correlation functions, in wavelet bases is presented. In this method, the Karhunen-Loeve expansion approach is used to represent a process as a linear sum of orthonormal eigenfunctions with uncorrelated random coefficients. The correlation and the eigenfunctions are approximated as truncated linear sums of compactly supported orthogonal wavelets. The eigenfunctions satisfy an integral eigenvalue problem. Using the above approximations, the integral eigenvalue problem is converted to a matrix (finite dimensional) eigenvalue problem. Numerical algorithms are discussed to compute one- and two-dimensional wavelet transforms of certain functions, and the resulting equations are solved to obtain the eigenvalues and the eigenfunctions. The scheme provides an improvement over other existing schemes. Two examples are considered to show the feasibility and effectiveness of this method. Numerical studies show that the results obtained using this method compare well with analytical techniques.

scholarly journals Non-local mean filtering algorithm based on deep learning

2018 ◽  
Vol 232 ◽  
pp. 03025
Author(s):  
Baozhong Liu ◽  
Jianbin Liu
Keyword(s):  
Deep Learning ◽  
Noise Reduction ◽  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Denoising Method ◽  
Filtering Algorithm ◽  
Local Mean ◽  
Non Local ◽  
Reduction Effect ◽  
Mean Filtering

Aimed at the problem that the traditional image denoising algorithm is not effective in noise reduction, a new image denoising method is proposed. The method combines deep learning and non-local mean filtering algorithms to denoise the noisy image to obtain better noise reduction effect. By comparing with Gaussian filtering algorithm, median filtering algorithm, bilateral filtering algorithm and early non-local mean filtering algorithm, the noise reduction effect of the new algorithm is better than the traditional method and the peak signal to noise ratio is compared with the early non-local mean algorithm. The performance is better.

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