Automated Method for Doppler Automated Method for Doppler Echocardiography Analysis in Echocardiography Analysis in Patients with Atrial FibrillationPatients with Atrial Fibrillation

O. ShechnerO. Shechner

H. GreenspanH. Greenspan

M. ScheinowitzM. Scheinowitz

The Department of Biomedical Engineering and The Department of Biomedical Engineering and

M.S. Feinberg M.S. Feinberg

The Heart institute, Sheba Medical Center, Tel HashomerThe Heart institute, Sheba Medical Center, Tel Hashomer

Tel Aviv University, Tel Aviv, IsraelTel Aviv University, Tel Aviv, Israel

Presentation structurePresentation structure

Results

Methods

Introduction

Conclusions

IntroductionIntroduction Doppler echocardiography:Doppler echocardiography:

Non invasive modality for the assessment of cardiac Non invasive modality for the assessment of cardiac functionfunction

Blood flow velocity tracing through the heart valves Blood flow velocity tracing through the heart valves can be obtained by transthoracic Doppler can be obtained by transthoracic Doppler echocardiography.echocardiography.

Extracted data:Extracted data:• Maximal Velocity Envelope Maximal Velocity Envelope

(MVE)(MVE)

• Peak velocityPeak velocity

• Peak and mean pressurePeak and mean pressure

• Velocity-time integral (VTI)Velocity-time integral (VTI)

Transvalvular blood flow patternsTransvalvular blood flow patterns MV signals: “M” shapeMV signals: “M” shape TV signals: Gauss shapeTV signals: Gauss shape

E

A

Atrial FibrillationAtrial Fibrillation

MV signals: only E-wave MV signals: only E-wave present due to the loss of present due to the loss of atrial contractionatrial contraction

TV signals: inter-beat TV signals: inter-beat amplitude variabilityamplitude variability

Atrial Fibrillation (AF) is the most common sustained Atrial Fibrillation (AF) is the most common sustained cardiac arrhythmiacardiac arrhythmia

AF characterized by irregular heart rate, electrogram AF characterized by irregular heart rate, electrogram and haemodynamic changes.and haemodynamic changes.

E E E EE

Time consumingTime consuming Inter and intra observer variabilityInter and intra observer variability Difficulties when dealing with AF patientsDifficulties when dealing with AF patients

Doppler image analysisDoppler image analysis MVE estimation by averaging points and fitting into a kinetic MVE estimation by averaging points and fitting into a kinetic

model (Hall model (Hall et al, 1995-1998et al, 1995-1998)) Edge detection-based algorithm for Brachial artery Doppler Edge detection-based algorithm for Brachial artery Doppler

tracings (tracings (Tschirren Tschirren et al, 2000et al, 2000))

Validation using phantoms, simulations and normal Validation using phantoms, simulations and normal patient groupspatient groups

Manual methodsManual methods

Early workEarly work

Our workOur work

Automated analysis of MV and TV Doppler Automated analysis of MV and TV Doppler signalssignals

Validation on a large dataset of both AF and Validation on a large dataset of both AF and non-AF patientsnon-AF patients

Proposed FrameworkProposed Framework

Image separation into ECG and Signals

Signal enhancement

Signal processing: Edge detection

Rough MVE extraction

ECG analysis: segmentation into cardiac cycles

Point linking

Parameter curve fitting

Parameter extraction

Input Image

Parameters

Image separationImage separation Dividing the image into region of interest Dividing the image into region of interest

(ROI) and ECG signal:(ROI) and ECG signal: The ECG signal is extracted by its colorThe ECG signal is extracted by its color The location of the horizontal axis is found using The location of the horizontal axis is found using

horizontal projection – ROI extractionhorizontal projection – ROI extraction

the horizontal axis detectedthe ROI of the doppler image

the ECG wave. it will be later used for syncronization

MethodsMethods

ROI

ECG

Original Image

Image enhancementImage enhancement Segmentation of ROI pixels by their gray level into Segmentation of ROI pixels by their gray level into

three clusters (K-means)three clusters (K-means) Contrast stretching improves image contrast and Contrast stretching improves image contrast and

suppresses noisesuppresses noise

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

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High threshold

Low threshold

MethodsMethods

background

weak signal strong signal

Image enhancementImage enhancement

Signal processing: Edge detectionSignal processing: Edge detection

Combining the Sobel operator with the non-Combining the Sobel operator with the non-linear Laplace operator (NLLAP):linear Laplace operator (NLLAP):

),(),(),( yxGRADMINyxGRADMAXyxNLLAP ( , ) max [ ( ', ') ( , )] | ( ', ') ( , )GRADMAX x y I x y I x y x y d x y

( , ) min [ ( ', ') ( , )] | ( ', ') ( , )GRADMIN x y I x y I x y x y d x y

min( , )edge strength GRADMAX GRADMIN

MethodsMethods

NLLAP introduces adaptive orientation of the NLLAP introduces adaptive orientation of the Laplace operatorLaplace operator

Edge is detected at places of zero crossingsEdge is detected at places of zero crossings Thresholding is applied on the edge strengthThresholding is applied on the edge strength

d(x,,y) – Neighborhoodof (x,y)

Edge processingEdge processing

Sobel NLLAP

Sobel + NLLAP + Post processing

MethodsMethods

Rough MVE extractionRough MVE extraction MVE vector is extracted from the edge image:MVE vector is extracted from the edge image:

Using the biggest-gap algorithm a pixel is selected Using the biggest-gap algorithm a pixel is selected from each columnfrom each column

MethodsMethods

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LinkingLinking The linking process is done beat-wiseThe linking process is done beat-wise

maximal vertical value taken as anchormaximal vertical value taken as anchor Ascending and descending slopes are Ascending and descending slopes are

detecteddetected Vertical “Noise level” is determinedVertical “Noise level” is determined Starting slopes are determined; slopes Starting slopes are determined; slopes

are interpolated from starting slope to are interpolated from starting slope to anchor pointanchor point

“noise level”

Anchor point

MethodsMethods

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the MVE with parameter fitting

Parameter fittingParameter fitting The MVE is fitted into a parameter model The MVE is fitted into a parameter model

using the Levenberg-Marquardt algorithm using the Levenberg-Marquardt algorithm (MSE criteria)(MSE criteria)

Partial Fourier series model is used (TV: n=4; Partial Fourier series model is used (TV: n=4; MV: n=5)MV: n=5)

Parameter extractionParameter extraction

N

nnon tnatf

0

)cos()(

MethodsMethods

Experimental SetupExperimental Setup Dataset: 467 beats from 121 images that were Dataset: 467 beats from 121 images that were

taken from 45 patients (25 AF, 20 non-AF)taken from 45 patients (25 AF, 20 non-AF)

Validation:Validation: Beat-by-beat comparison between the automatically Beat-by-beat comparison between the automatically

extracted parameters and the manually extracted extracted parameters and the manually extracted parameters (two technicians)parameters (two technicians)

Via Average-beat (manual vs calculated)Via Average-beat (manual vs calculated)

MethodsMethods

ResultsResults MV resultsMV results TV resultsTV results

the MVE with parameter fitting

the MVE with parameter fittingthe MVE with parameter fitting

the MVE with parameter fittingNon-AFNon-AFNon-AFNon-AF

AFAF AFAF

Results: Technicians vs. AutomaticResults: Technicians vs. Automatic

non-AFnon-AFAFAF

MVMV: : peak peak velocityvelocity0.99270.99270.99110.9911

MVMV: : VTIVTI0.98920.98920.98120.9812

TV TV : : peak peak velocityvelocity0.95260.95260.94450.9445

non-AFnon-AFAFAF

MVMV: : peak peak velocityvelocity0.98530.98530.97510.9751

MVMV: : VTIVTI0.97800.97800.95410.9541

TV TV : : peak peak velocityvelocity0.96780.96780.94260.9426

Automated Vs Technician 1 Automated Vs Technician 2

non-AFnon-AFAFAF

MVMV: : peak peak velocityvelocity0.99250.99250.98910.9891

MVMV: : VTIVTI0.98960.98960.97540.9754

TVTV: : peak peak velocityvelocity0.96280.96280.94340.9434

Automated Vs Technician avg

non-AFnon-AFAFAF

MVMV: : peak peak velocityvelocity0.98950.98950.97590.9759

MVMV: : VTIVTI0.98160.98160.97260.9726

TV TV : : peak peak velocityvelocity0.97030.97030.95370.9537

Technician 1 Vs Technician 2

Results: Technicians vs. Automatic (cont.)Results: Technicians vs. Automatic (cont.)

MV signals TV signals

AF

non-AF

y = 0.95x + 0.097y = 0.95x + 0.097y = 1.02x + 5.50y = 1.02x + 5.50

y = 1.12x + 7.75y = 1.12x + 7.75 y = 1.16x + 0.39y = 1.16x + 0.39

Peak velocityPeak velocity

Averaged Beat ExperimentsAveraged Beat Experiments Comparing the error between manual average and Comparing the error between manual average and

automated average to the error between manual automated average to the error between manual average and representative beataverage and representative beat

Automated / Automated / ManualManual

Representative / Representative / ManualManual

Mean errorMean errorMean errorMean error

NonNon-AF-AF

MV: peak velocityMV: peak velocity2.9%2.9%6.3%6.3%MV : VTIMV : VTI6.2%6.2%13.4%13.4%

TV : Peak PressureTV : Peak Pressure4.9%4.9%9.7%9.7%

AFAF

MV: peak velocityMV: peak velocity6.8%6.8%8.5%8.5%MV : VTIMV : VTI4.6%4.6%13.0%13.0%

TV : Peak PressureTV : Peak Pressure9.3%9.3%6.0%6.0%

ConclusionsConclusions The possibility of automated system for The possibility of automated system for

MV/TV Doppler image analysis was shownMV/TV Doppler image analysis was shown

The system is robust and manages to deal The system is robust and manages to deal with both AF and non-AF signals with with both AF and non-AF signals with different morphologydifferent morphology

Parameters are extracted from all the beats Parameters are extracted from all the beats in the image, allowing the computation of an in the image, allowing the computation of an accurate averageaccurate average