Transcript שקופית 1 - Tel Aviv University

Automated Method for Doppler
Echocardiography Analysis in
Patients with Atrial Fibrillation
O. Shechner
H. Greenspan
M. Scheinowitz
The Department of Biomedical Engineering and
M.S. Feinberg
The Heart institute, Sheba Medical Center, Tel Hashomer
Tel Aviv University, Tel Aviv, Israel
Presentation structure
Introduction
Methods
Results
Conclusions
Introduction

Doppler echocardiography:


Non invasive modality for the assessment of cardiac
function
Blood flow velocity tracing through the heart valves
can be obtained by transthoracic Doppler
echocardiography.

Extracted data:
• Maximal Velocity Envelope
(MVE)
• Peak velocity
• Peak and mean pressure
• Velocity-time integral (VTI)
Transvalvular blood flow patterns

MV signals: “M” shape
E
A

TV signals: Gauss shape
Atrial Fibrillation

Atrial Fibrillation (AF) is the most common sustained
cardiac arrhythmia
 AF characterized by irregular heart rate, electrogram
and haemodynamic changes.

MV signals: only E-wave
present due to the loss of
atrial contraction
E
E
E
E
E

TV signals: inter-beat
amplitude variability
Manual methods



Time consuming
Inter and intra observer variability
Difficulties when dealing with AF patients
Early work

Doppler image analysis



MVE estimation by averaging points and fitting into a kinetic
model (Hall et al, 1995-1998)
Edge detection-based algorithm for Brachial artery Doppler
tracings (Tschirren et al, 2000)
Validation using phantoms, simulations and normal
patient groups
Our work

Automated analysis of MV and TV Doppler
signals

Validation on a large dataset of both AF and
non-AF patients
Proposed Framework
Input Image
Image
separation into
ECG and Signals
Signal
enhancement
ECG analysis:
segmentation into
cardiac cycles
Signal processing:
Edge detection
Rough MVE
extraction
Point linking
Parameters
Parameter curve
fitting
Parameter
extraction
Methods
Image separation

Dividing the image into region of interest
(ROI) and ECG signal:


The ECG signal is extracted by its color
The location of the horizontal axis is found using
horizontal projection – ROI extraction
Original Image
ROI
the ROI of the doppler image
the horizontal axis detected
ECG
the ECG wave. it will be later used for syncronization
Methods
Image enhancement

Segmentation of ROI pixels by their gray level into
three clusters (K-means)
 Contrast stretching improves image contrast and
suppresses noise
High threshold
contrast enhancement
7000
1
0.9
6000
0.8
5000
# of pixels
0.7
0.6
0.5
4000
background
3000
0.4
weak signal
2000
strong signal
0.3
1000
0.2
0.1
0
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Low threshold
0.9
1
0
0.1
0.2
0.3
0.4
0.5
0.6
gray level
0.7
0.8
0.9
1
Image enhancement
Methods
Signal processing: Edge detection

Combining the Sobel operator with the nonlinear Laplace operator (NLLAP):
NLLAP( x, y)  GRADMAX( x, y)  GRADMIN( x, y)
GRADMAX ( x, y)  max [I ( x ', y ')  I ( x, y)]| ( x ', y ')  d ( x, y)
GRADMIN ( x, y)  min [ I ( x ', y ')  I ( x, y)]| ( x ', y ')  d ( x, y)



NLLAP introduces adaptive orientation of
the Laplace operator
Edge is detected at places of zero
crossings
Thresholding is applied on the edge
strength
edge strength  min(GRADMAX , GRADMIN )
d(x,,y) –
Neighborhood
of (x,y)
Methods
Edge processing
Sobel
NLLAP
Sobel + NLLAP + Post processing
Methods
Rough MVE extraction

MVE vector is extracted from the edge image:

Using the biggest-gap algorithm a pixel is selected
from each column
150
100
50
0
0
100
200
300
400
500
600
Methods
Linking

Anchor point
The linking process is done beat-wise

maximal vertical value taken as anchor

Ascending and descending slopes are
detected

Vertical “Noise level” is determined “noise level”

Starting slopes are determined; slopes
are interpolated from starting slope to
anchor point
180
160
140
120
100
80
60
40
20
0
280
200
200
180
180
160
160
140
140
120
120
100
100
80
80
60
60
40
40
20
20
0
0
100
200
300
400
500
600
0
0
100
200
290
300
310
300
320
330
400
340
350
500
360
370
380
600
Methods
Parameter fitting

The MVE is fitted into a parameter model
using the Levenberg-Marquardt algorithm
(MSE criteria)
 Partial Fourier series model is used (TV: n=4;
MV: n=5)
N
f (t )   an cos(o  n  t   n )
n 0

Parameter extraction
the MVE with parameter fitting
Methods
Experimental Setup

Dataset: 467 beats from 121 images that were
taken from 45 patients (25 AF, 20 non-AF)

Validation:


Beat-by-beat comparison between the automatically
extracted parameters and the manually extracted
parameters (two technicians)
Via Average-beat (manual vs calculated)
Results

MV results
Non-AF
the MVE with parameter fitting
AF
the MVE with parameter fitting

TV results
Non-AF
the MVE with parameter fitting
AF
the MVE with parameter fitting
Results: Technicians vs. Automatic
Automated Vs Technician 1
non-AF
AF
MV: peak
velocity
0.9927
0.9911
MV: VTI
0.9892
TV : peak
velocity
0.9526
Automated Vs Technician 2
non-AF
AF
MV: peak
velocity
0.9853
0.9751
0.9812
MV: VTI
0.9780
0.9445
TV : peak
velocity
0.9678
Automated Vs Technician avg
non-AF
AF
MV: peak
velocity
0.9925
0.9891
0.9541
MV: VTI
0.9896
0.9754
0.9426
TV: peak
velocity
0.9628
0.9434
Technician 1 Vs Technician 2
non-AF
AF
MV: peak
velocity
0.9895
0.9759
MV: VTI
0.9816
0.9726
TV : peak
velocity
0.9703
0.9537
Results: Technicians vs. Automatic (cont.)
Peak velocity
MV signals
y = 1.02x + 5.50
TV signals
y = 0.95x + 0.097
AF
y = 1.12x + 7.75
y = 1.16x + 0.39
non-AF
Averaged Beat Experiments

Comparing the error between manual average and
automated average to the error between manual
average and representative beat
Non
-AF
AF
Automated /
Manual
Representative /
Manual
Mean error
Mean error
MV: peak velocity
2.9%
6.3%
MV : VTI
6.2%
13.4%
TV : Peak Pressure
4.9%
9.7%
MV: peak velocity
6.8%
8.5%
MV : VTI
4.6%
13.0%
TV : Peak Pressure
9.3%
6.0%
Conclusions

The possibility of automated system for
MV/TV Doppler image analysis was shown

The system is robust and manages to deal
with both AF and non-AF signals with
different morphology

Parameters are extracted from all the beats
in the image, allowing the computation of an
accurate average