CHAPTER 5

EFFICIENT APPROACHES FOR QUANTIFICATION OF AORTIC

REGURGITATION USING PROXIMAL ISOVELOCITY SURFACE AREA

PROCESS

5.1. Introduction

Aortic Regurgitation is also known as Aortic Insufficiency (AI). It indicates the inability

of the aortic valve. During diastole, the aortic valve allows blood flow in the reverse direction

from aorta into the left ventricle. Regurgitation is due to incompetence of the aortic valve or any

disorder of the valvular apparatus (e.g., leaflets, annulus of the aorta) ensuing in diastolic flow of

blood into the left ventricular chamber. AR force an accurate amount overload to the left

ventricle (LV) which results in dilation, eccentric hypertrophy and finally loss of function. The

integral part of aorta is aortic valve, which is tubular-like structure. During systole and diastole,

the valve apparatus includes three distinct leaflets with definitive passive motion. Due to

relatively high systolic and diastolic pressure the aortic valve is challenged with a relatively high

mechanical stress and, in terms of morphology, a consideration is to be made of the relation to

origin of the coronary arteries. During systole and diastole, the movement of the tube is

insufficient in all directions [18]. Echocardiography which is followed by MR imaging (or

contrast aortography) produces all valuable parameters. Additionally the key considerations are

the diameter of the AV orifice and of the neighboring ascending aorta segment diameter. Of late

in clinical cardiology an all-round estimation of Valvular Regurgitation is an essential objective

to be carried out by the cardiac surgery.

Evaluation of the severity of regurgitation is supreme to clinical decision making in

patients with AR., because patients with severe Aortic Regurgitation often need surgical

treatment. Semi-quantitative position of Aortic Regurgitation with color and spectral Doppler

echo or with angiography is generally used. But both the processes are delayed by some specific

restrictions or reasons. Invasive and Noninvasive quantitative evaluation of Regurgitant Volume

and Regurgitant Fraction are obtainable, but Regurgitant Volume and Regurgitation Fraction

depend on loading circumstances. It was proposed in recent times that noninvasive calculation of

aortic ERO was a measure of lesion severity in AR [63]. The EROA is a basic descriptor of

Aortic Regurgitation that confirms the effect of AR on the LV and offers information additional

to the volume overload measurements like Regurgitation Fraction. But the measurement of the

EROA by quantitative Doppler Echocardiography is not always possible to attain high degree of

reliability; a grouping of methods is desirable (very much suggested). In order to measure the

severity of valvular and congenital heart diseases the clinicians have great interest in PISA

methods only. Depending on the conservation of mass, for evaluating (or scheming) the effective

orifice area in Valvular Regurgitation the PISA method has been faithfully adopted [64]. In table

5.1, Echocardiographic and Doppler parameters used in the evaluation of Aortic Regurgitation

severity are described.

Table 5.1 Echocardiographic and Doppler parameters used to evaluate AR severity:

Utility, Advantages, and Limitations

Utility/Advantages Limitations

Left Ventricle size Enlargement sensitive for

chronic significant AR.

Normal size virtually excludes

significant chronic AR.

Enlargement seen in other

conditions. Normal in acute

significant AR.

Aortic cusps alterations Simple, usually abnormal in

severe AR; Flail valve denotes

severe AR.

Poor accuracy

Doppler parameters

Jet width or jet cross-

sectional area in

*LVOT- Color Flow

Simple, very sensitive, quick

screen for AR.

Expands unpredictably below

the orifice. Inaccurate for

eccentric jets.

Vena Contracta Width Simple, quantitative to identify

mild or severe AR.

Not useful for multiple AR

jets.

PISA method Quantification both EROA and

Rvol.

Feasibility limited by aortic

valve calcifications. Not valid

for multiple jets, less accurate

in eccentric jets.

Flow quantification-PW Quantitative, valid with

multiple jets and eccentric jets.

Provides both lesion severity

i.e. EROA, RF and volume

overload, Rvol.

Not valid for combined MR

and AR, unless pulmonic site

is used.

Jet density-CW Simple. Incomplete jet

compatible with mild AR.

Qualitative. Overlap between

moderate and severe AR.

Jet deceleration rate

(*PHT)- CW

Simple Qualitative; affected by

changes in LV and aortic

diastolic pressures.

*LVOT – Left Ventricle Outflow Tract, *PHT – Pressure Half Time

The Proximal Isovelocity Surface Area method [60, 26] depends on the continuity

principle and assumes that blood flow converging in the direction of a flat orifice forms

hemispherical isovelocity shells. It has been proved that the PISA method is precise and

reproducible. This method is frequently applied in medical science because the proximal

convergence method can be easily visualized and it is the only probable method currently easy to

have it. Despite the hypothetical advantages in comparison to Valvular Regurgitation the PISA is

seldom used routinely for the evaluation of AR severity. The chief purpose of the present

research is to give an efficient method based on image processing methods. And these can

exactly evaluate the EROA using Doppler Echocardiography image with the aid of PISA.

Considerable interest is shown in the PISA method to estimate the severity of valvular and

congenital heart diseases. In this Part I and part II contain the details of the methodology for

quantification of AR that has been carried out.

In Part I, in preprocessing stage the RGB color Doppler Echocardiography image has

been subjected to Wiener filtering. Then the filtered image has been quantized with the aid of

color quantization using NBS / ISCC color space which makes the image more precise. Besides

those the PISA method is used for calculating the quantitative parameters like VC, R vol, RF,

Effective regurgitant orifice (ERO) and mostly AR. The PFC method is pursued to quantify

Aortic Regurgitation by analyzing the converging flow field proximal to estimate the mildness

severity and eccentricity of an AR lesion.

Similarly in part II, an efficient method for quantifying the EROA in AR using clustering

based image segmentation processing methods on Doppler Echocardiographic image and helped

by PISA method has been presented. Considerable attention has been received by PISA method

by clinicians for assessing the severity of valvular and congenital heart diseases. In the

preprocessing stage the Gaussian filter is used to reduce the noise in color Doppler

echocardiographic image. Accordingly it has been improved with the support of image contrast

enhancement by using Contrast-Limited Adaptive Histogram Equalization (CLAHE). As a

sequel, the image is subjected to image segmentation based on Fuzzy k-means clustering to make

the quantification more precise. Clustering is a method for grouping an image into units that are

reliable to one or more characteristics. Besides those the PISA method is used for computing the

quantitative parameters like VC, R vol, RF, ERO and mostly AR. The PFC method is pursued to

quantify VR with the help of analyzing the converging flow field proximal to estimate the

mildness severity and eccentricity of an AR lesion. This research also offers a survey of

qualitative and quantitative parameters for rating AR severity, utility, advantages and limitations

of Echocardiography along with Doppler parameters which are made use of in the estimation of

MR severity.

5.2. Technical Terms

Aortic Regurgitation – It indicates the inability of the aortic valve. During diastole, the

aortic valve allows blood flow in the reverse direction from aorta into the left ventricle.

Wiener Filtering – It is one of most significant techniques. This filtering is used for

removing the blur in images because of linear motion or unfocussed optics.

Color Quantization – color image quantization is a method that reduces the number of

distinct colors in an image so that the new image will looks like the original image.

NBS/ISCC Color Space – ISCC–NBS System of Color Designation is a system used to

name the colors. The colors are named based on a set of 12 basic or fundamental color terms and

a few set of adjective modifiers.

Gaussian Filtering – It is one of the filters whose impulse response is a Gaussian

function. The main intention of this filter is to provide no overshoot to a step function input

while reducing the rise and fall time.

Image Enhancement – In the image enhancement, the perception of information in

images for human viewers are enhanced and it offers `better' input for further image processing

methods.

Clustering – It is one of the most significant unconfirmed learning problems; it deals with

finding a structure in a set of unlabeled data, since other problems are of this kind.

Fuzzy k Means – Fuzzy K-Means is also known as Fuzzy C-Means. It is an extension of

K-Means, the popular simple clustering method .The K-Means discovers hard clusters, where

Fuzzy K-Means is more statistically formalized method than K-Means and discovers soft

clusters. Hard cluster denotes a point which belongs to only one cluster, but the soft cluster

denotes a particular point which belongs to more than one cluster with certain probability.

5.3. Review of Related Researches

A brief review of recent researches related to quantification of Aortic Regurgitation is

described below.

Thomas Wittlinger et al. [65] have presented a study that the MRI was related to both

angiography and echocardiography in estimating the RV and AR in patients. At 1.5 T, forty

patients were taken under examination. The regurgitant jet was located with the aid of a gradient-

echo sequence. To compute the left ventricular function, cine measurements were accomplished.

For flow estimation, a velocity-encoded breath-hold phase-difference magnetic resonance

sequence was made familiar. The severity of AR is calculated by making use of MRI accepted

with that of angiography in 28 of 40 patients, and with the Echocardiography result in 80%. As a

result the correlation between calculated stroke volume by the two methods that is magnetic

resonance cine and flow measurements was very excellent (r > 0.9).

Quantification of aortic regurgitation using Echocardiography is still stimulating. In spite

of rheological characteristics which, have been granted by Chen Li et al. [33], a novel

echocardiographic method called Vector Flow Mapping (VFM) directly measures blood flow

volume. They have in turn, projected to estimate the accuracy of VFM in quantifying chronic

AR. Therefore for AR quantification the clinicians feel that a highly reproducible parameter with

good precision has been discovered RegR evaluated by VFM-as a novel Doppler method that

allows quantitative analysis of FV despite the presence of turbulent flow.

Leopoldo Pérez de Isla et al. [66] have proposed detailed study so that the measurements

of LVOT area are arrived at by using 3D-echo which is highly reliable compared to those which

are made by using 2D-echo. Both the methods i.e. 2-D echo and 3-D echo are used to measure

the LVOT area and the circularity index was measured using 3D-echo exclusively. Moreover the

severity of valvular aortic stenosis was classified using both 2D-echo and 3D-echo. Therefore for

assessing the LVOT area, the 3D-echo may be a superior technique. The additional advantage is

that the LVOT is elliptical in shape, but not related to its circularity in size. Also for

distinguishing the severity of valvular aortic stenosis, the 3D-echo could be supportive.

Anne-catherine Pouleur, MD et al. [67] have presented a study to appraise the correctness

of multidetector CT compared with cine MR imaging, TTE and TEE in the measurement of

aortic valve area (AVA) of patients undergoing cardiac surgery. After examining 48 patients (

out of which 33 are men and 15 are women under ages 62 ±13), the accuracy of multidetector

CT for detection of aortic stenosis was compared with that of TTE. The results demonstrated

that, the multidetector CT could be an alternative to TTE in patients with poor acoustic windows.

The multidetector CT can be used to measure the AVA and detect aortic stenosis at the time of

noninvasive coronary imaging with accuracy similar to that of TTE and MRI.

An extensive attention is shown to Non-invasive management of AV disease. Actually a

lot of current publications have formerly reported its use in clinical practice. The major issue is

to obtain considerate pathophysiological processes and, most significantly, extensive

experimental activity. In addition to testing of various animal models, technical and material

features are also being intensively investigated. Clinicians are dubious about the applicability

and durability of this positive improvement whether it can be equated with the standard of

present cardiac surgery. Certainly it is justified that the full use of certain models as a tentative

measure to help to progress the circulatory status, may not permit the safe surgery. A tiny

analysis of the above mentioned issue has been granted by Sochman and Peregrin [18].

5.4. The Proposed Methodology – Part I

A major goal of the clinical cardiology is quantifying the severity of AR and it is

extremely old clinical decision making. Very often the screening for the existence of AR is

preceded with the help of color Doppler flow mapping. Several echocardiographic methods have

been published to improve the quantification of valvular incompetence. Although the primary

method used for estimating AR severity has been found to be less precise than the latter

counterparts and therefore PFC method using color Doppler has been recognized as a precise

(exact) and consistent quantitative approach. For the quantification of aortic regurgitant, here we

present an efficient mode by uniting image processing method which exactly quantifies the

EROA which strengthen the PISA method to estimate the severity of AR lesion. More than this

here we deal with the consistency of the PISA method for computing ERO of AR. The present

approach mainly includes two modules:

i. Preprocessing

ii. Quantification using Proximal Isovelocity Surface Area (PISA)

5.4.1. Preprocessing

Initially in the preprocessing step the color Doppler Echocardiography AR image is

subjected to Wiener filtering, that reduces the noise quantity in the image in comparison with an

estimation of the preferred noiseless image.

Wiener Filtering: The purpose of this filter is to reduce the amount of noise present in a

signal. The Wiener filter is the Mean Squared Error optimal stationary linear filter for images

despoiled by additive noise and blurring. In order to calculate the Wiener filter, the signal and

noise processes are assumed to be second order stationary. Presuming stationary the nature of the

involved signals, the average squared distance between filter output and a preferred signal is

minimized by calculating the Wiener filter coefficients that can be made perfect in the frequency

domain producing easily:

))(/)(()( fPfPfW YYDY …(5.1)

Where )( fD is the desired signal, )()()( fYfWfS

is the Wiener filter output, )( fY the

Wiener filter input and )( fPYY , )( fPDY are the power spectrum of )( fY and the cross power

spectrum of )( fY , )( fD respectively.

The AR color flow Doppler TEE image in apical view showing Proximal Flow

Convergence is shown in figure 5.1(a), figure 5.1(b) shows its filtered output.

Figure 5.1 a) AR Color flow Doppler image (TEE) in apical view showing Proximal FC and b) its

filtered output

[Image Courtesy: Journal of American College of Cardiology, Clinical Studies article]

After that the filtered color Doppler Echocardiographic image is subjected to color

quantization. Then the color quantization fetches down the numerous colors used in an image,

usually with the intention that the new image has to be as visually similar as possible to the

original image.

Color Quantization: Color quantization is the technique used for minimizing the

number of colors in a digital image by changing them with a specific color selected from a

palette [68]. It is broadly used nowadays because it decreases the work load of huge image data

on storage and transmission bandwidth in several multimedia applications. Accordingly the main

inducement of color image quantization is for mapping the set of colors in the original color

image to a smaller set of colors in the quantized image so that this mapping reduces the variation

between the original and the quantized images, as mentioned earlier [45]. With the help of

NBS/ISCC color space color quantization is carried out as our contribution..

Imagine that the color Doppler Echocardiographic image as iI where, 10 ini .

Consequently, to accomplish the quantization, the RGB color space values of the image are

represented by vectors, that is,iRI ,

iGI andiBI correspondingly with size NM . Where NM is

resample into RR images (generally R =192). Therefore, from the color space imagesiRI ,

iGI

andiBI , the sampled image

iRS , iGS and

iBS are obtained. After that, with the help ofiRS ,

iGS ,

iBS and Color Look Up Table (CLUT), the color quantization is implemented. The matrix

representation of the CLUT can be specified as

111

222

111

000

nnn

lt

BGR

BGR

BGR

BGR

C

…(5.2)

From the above matrix representation, the CLUT has n different probable colors created

by various, xR , yG and zB transformations. After that by manipulating the Euclidean distance

between every pixel value of the re-sampled color space images and the ltC value, the database

images are color quantized. As a result the quantized image IQ is obtained. Then the quantized

image IQ consists of quantized RGB color space values RQ , GQ and BQ which are formulated

as

222 )),(()),(()),((min),( jBjGjRR BbaSGbaSRbaSbaQ …(5.3)

where, 10 nj , 10 ra and 10 rb . This is the same for the quantization

of other color space images GQ and BQ also (i.e. BGR QQQ ).

Figure 5.2 Color Quantization Outputs a) Quantized view b) Binary output c)

Segmented output

5.4.2. Quantification using Proximal Isovelocity Surface Area

After preprocessing stage the color Doppler Echocardiographic image is subjected to

efficient quantification of AR along with the help of PISA method. The PISA method which is

derived from the analysis of the Flow Convergence (FC) region proximal to the regurgitant

orifice and from the conservation of mass has already been previously illustrated in reference

sited [69 – 71]. The regurgitant jet size of LV cavity, LVOT width of the jet and the pressure

half-time are calculated by using CW Doppler. The Echocardiography calculates R vol or RF as

the total stroke volume through the AV is equal to the forward stroke volume plus R vol. Basing

on

these methods the degree of severity of AR can be understood as mild, moderate, severe or

eccentric. The Qualitative and quantitative standard parameters that are used in grading of

severity is illustrated in the table 5.2.

Table 5.2 Qualitative and quantitative parameters useful in grading Aortic Regurgitation

severity

Mild Moderate Severe

Structural Parameters

LA size Normal* Normal / dilated Generally dilated

Aortic leaflets Normal /

abnormal

Normal / abnormal Abnormal/flail,

or wide

coaptation defect

Doppler Parameters

Jet width in LVOT-Color

Flow ξ

Small in central

jets

Intermediate Large in central

jets; variable in

eccentric jets

Jet density-CW Incomplete Thick/Dense Thick/Dense

Jet deceleration rate-CW

(PHT, ms)ψ

Slow>500 Medium 500-200 Steep<200

Quantitative Parameters φ

VC width, cm ξ <0.3 0.3-0.60 >0.6

Jet width/LVOT width, % ξ <25 25-45 46-64 65

Jet CSA/LVOT CSA, % ξ <5 5-20 21-59 60

R vol, ml/beat <30 30-44 45-59 60

RF, % <30 30-39 40-49 50

EROA, cm2 <0.10 0.10-0.19 0.20-0.29 0.30

*Unless there are other reasons for LV dilation. Normally 2D values; LV minor axis

2.8cm/m2, LV end-diastolic volume 82ml/m

2(2).; ξ With a Nyquist limit of 50-60 cm/s. ψ

PHT is shortened with increasing LV diastolic pressure and vasodilator therapy, and may be

lengthened in chronic adaptation to severe AR. (AHA, ACC and ESC recommended values).

Understanding the hemispheric shape of the PISA, the diastolic aortic regurgitant flow

FlowR , is measured as

rFlow VrR 22 …(5.4)

Where in early diastole, the radius r of the FC is calculated and rV represents the

equivalent aliasing velocity. The aortic regurgitant ERO area is then calculated as

VelFlow RRPISAERO /)( …(5.5)

Here VelR represents the maximal velocity of the aortic regurgitant jet in early diastole

recorded with continuous wave Doppler Echocardiography from the apical, par apical or right

parasternal transducer position. Color-flow methods comprise the measurement of the maximal

anteroposterior diameter (height) of the regurgitant jet at the junction of the LV Outflow Tract

(LVOT) and the aortic annulus in parasternal long-axis view, and the maximum height of the LV

outflow tract at the same site. Continuous Doppler-wave imaging of AR allows quantification of

both the slope and pressures half-time. Regurgitant Volume ( VolR ) which is calculated as:

flow mitral - flow aorticVolR (5.6)

VelRA )785.0*(D flow ortic 2

LVOT (5.7)

Here LVOTD represents the diameter of LV Outflow Tract (LVOT). Regurgitant Fraction (

RF ) was calculated asflow aortic

R VolRF . A Regurgitant Fraction above 40% to 50 % accuses

more severe AR.

The quantitatively obtained values of mild and eccentric (figure 5.2) are shown in table

5.3.

Table 5.3 Measured parameter values of Mild and Eccentric Aortic Regurgitation

Quantitative Parameters Mild Eccentric

Radius r (cm) 0.6085 1.32292

Vena Contracta Width (cm) 0.3175 0.635

Jet Width (cm) 0.9525 1.56104

LVOT width (cm) 1.2964 2.2754

Regurgitant Flow Rate (cm2) 111.6868 483.8353

EROA (cm2) 0.22337 0.96767

Aortic flow (cm3) 152.658 267.9303

Rvol (cm3) 21.456 57.9303

Regurgitant Fraction (%) 14.05 21.621

5.5. The Proposed Methodology – Part II

In part II also it is restated that the PFC method using color Doppler has been recognized

as a faithful and accurate quantitative approach for quantifying AR by combining different

image processing techniques which duly quantify the EROA in assessing the degree of severity

of an aortic regurgitant lesion. The approach which is offered for the quantification of AR has

three modules:

i. Preprocessing

ii. Image Segmentation

iii. Quantification using Proximal Isovelocity Surface Area (PISA)

5.5.1. Preprocessing

In preprocessing stage, primarily the color Doppler Echocardiography AR image is

subjected to Gaussian filtering which is used for reducing the noise present in the image. After

filtering make use of image enhancement for enhancing and improving the excellence i.e contrast

and brightness of the image for human viewing.

5.5.1.1. Gaussian Filtering

It is one of the linear filters used in different contexts of image processing. A Gaussian

filter merges both differential and low pass filtering which identify the edges or computing

orientation of features in digital images. If the Gaussian filter is used for low-pass filtering, then

the smooth impulse response is obtained which approximates the Gaussian derivative. The

weighted mean of the input values gives the output of the Gaussian filter at any time t . The

weights are specified by the formula

,...,1,0,1...,;2

exp).()(2

2

Cw (5.8)

Here represents distance in time lapsed from the current moment, is the Gaussian

filter parameter and the sum of all weights is made equal to unit value by the normalization

constant. )(C . The parameter represents the whole Gaussian filter.

Figure 5.3 shows a central severe Aortic Regurgitation image in short axes view and its filtered

output.

Figure 5.3 Aortic Regurgitation image along with their filtered output

[Image Courtesy : www.mpoullis.net (timed and dated 09:30, 04.03.2003)]

5.5.1.2. Image Enhancement

Image enhancement is used for the benefit of human viewers to understand or perceive

the information in images and it offers better input for further image processing methods. After

filtering, the image is subjected to image contrast enhancement. Contrast enhancement is

commonly done by converting an image to a color space where image intensity is one of the

main components. One such color space is *b*a*L [72]. Firstly, therefore, we have to create a

color transformation structure. The said structure decides the specific color space conversion by

using color transform functions to transform the image from RGB to *b*a*L color space, and

afterwards work on the luminosity layer 'L*' of the image.

The values of luminosity which can distance a wide range from 0 to 100 is scaled

between [0 1] as shown below,

100

0i

iLab

100)(I

L …(5.9)

Later the luminosity layer is changed with the processed data and is converted back to the

RGB color space with the help of Contrast-Limited Adaptive Histogram Equalization. This

CLAHE functions on small data regions instead of on the entire image unlike the histogram. The

contrast of each region is improved and so the histogram of each output region almost nearly

equals the specified histogram. (uniform distribution by default). In order to avoid the amplifying

noise that is present in the image, the contrast enhancement can be limited. Figure 5.4 shows

color space conversion output and image contrast enhancement output.

(a) (b)

Figure 5.4 a) Color Space Conversion Output b) Image contrast Enhancement Output

5.5.2. Image Segmentation using Clustering

Image segmentation is comprised as a significant part in low-level vision, image analysis,

and so on. More over the image segmentation decides the quality of the final results of image

analysis, and it is a hard and challenging task in image processing. Normally in image

segmentation, the processes of partitioning an image into several regions are uniform or similar.

But the union of any two adjacent regions is not homogenous (i.e., regions that belong to

individual surfaces by clustering pixels) [73]. In packages, numerous image segmentation

methods are used as per availability. Here based on the Fuzzy K means clustering, the image

segmentation is employed. Fuzzy K means clustering is a pixel-based segmentation that groups

objects which are more similar to each other.

The fuzzy clustering algorithm is one of the most important techniques which is used in

unsupervised pattern recognition. In this way the first step was taken by Ressini after that Zadeh

defined the fuzzy sets. A wide range of applications besides fuzzy control and machine vision

utilized this technique in recent times. K-means is the most famous technique out of the many

fuzzy clustering methods. The main aim of fuzzy clustering (or grouping) is to analyze the data

Sn RYY },{ 1 based on minimum distance measure criterion into a few c clusters as shown

below,

n

k

pc

i ikmikm vYvuJ

1 1|||| ),( …(5.10)

Here the fuzzification parameter is m which obtains a value greater than unity. iv is thi

cluster center. To each cluster, ]1,0[ik is the degree of membership of data and p is the

power of Euclidian distance. By use of optimization algorithm, the optimal values

}{ },,,2 ,1 ,{ miki UciVV are computed.

Figure 5.5 Segmentation Output

5.5.3. Quantification using Proximal Isovelocity Surface Area

For the efficient quantification of Aortic Regurgitation, the preprocessed color Doppler

image was utilized with the aid of PISA method. The evaluation of the flow convergence region

close to the regurgitant orifice and the theory of conservation of mass, lead to the growth of

PISA method [69–71]. The severity of AR was calculated

by means of M-mode, 2-D and Doppler echocardiography and it was graded reliable with the

recommendations of the American Society of Echocardiography (ASE). In Doppler

echocardiography, Aortic Regurgitation is approximated by the size of the regurgitant jet in the

LV cavity, the jet width in the LVOT and the pressure half-time measured by the continuous

wave Doppler. Calculation of R vol or RF is also possible by Echocardiography, because the

sum of forward stroke volume and R vol provides the total stroke volume through the aortic

valve. Basing on these methods, the clinicians can grade the severity of AR whether mild,

moderate, severe or eccentric (See detailed in section 5.4.2).

Table 5.4 Measured values of the above mentioned parameters of Mild and Eccentric

Aortic Regurgitation

Quantitative Parameters Mild Eccentric

Radius r (cm) 0.6085 1.3224

Aliasing Velocity (cm/s) 44 47

Vena Contracta Width (cm) 0.2915 0.6435

Jet Width (cm) 0.2263 0.9125

LVOT width (cm) 0.9652 1.1564

Jet width/LVOT width (%) 23.44 78

Regurgitant Flow Rate (cm2) 102.3136 516.1586

EROA (cm2) 0.0989 0.96767

Aortic flow (cm3) 152.658 158.1882

Rvol (cm3) 21.456 57.9303

Regurgitant Fraction (%) 14.05 36.621

5.6. Conclusions

In this chapter the researcher has presented an efficient quantification of Aortic

Regurgitation. For quantification the Doppler Echocardiographic image is processed by utilizing

image processing techniques. The greater precision was achieved rationally while quantifying

AR Doppler image. In part I, we have engaged Weiner filtering and color quantization in the

preprocessing stage and quantification of AR is done with the help of PISA method. Similarly in

part II, for improving the accuracy of AR to a greater extent we have used Gaussian filtering and

contrast image enhancement in the preprocessing stage. The Aortic Regurgitation quantification

is made very exact by using image segmentation. The preprocessed color Doppler

Echocardiographic image is segmented using fuzzy K-means clustering method. The

enhancements in the quantifying of Valvular Regurgitation are finally led based on

improvements in imaging technologies which assist to improve the measurement of flow

convergence, Vena Contracta and regurgitant jet. Therefore from our research we can conclude

that it is advantageous to find out cardiac output non-invasively by Doppler Echocardiography

with the assistance of flow convergence method. These experimental results are found correlated

with several other procedures that exist for measuring cardiac output.