automated method for doppler echocardiography analysis in patients with atrial fibrillation
Post on 02-Jan-2016
22 Views
Preview:
DESCRIPTION
TRANSCRIPT
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
1000
2000
3000
4000
5000
6000
7000
gray level
# o
f pix
els
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1contrast enhancement
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
0 100 200 300 400 500 6000
50
100
150
0 100 200 300 400 500 600
0
20
40
60
80
100
120
140
160
180
200
280 290 300 310 320 330 340 350 360 370 3800
20
40
60
80
100
120
140
160
180
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
0 100 200 300 400 500 6000
20
40
60
80
100
120
140
160
180
200
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
top related