mina emad azmy research assistant – signal and image processing lab

Toward Automated Assessment of Heart Regional Function

Mina Emad AzmyResearch Assistant – Signal and Image Processing Lab

Significance

Coronary artery disease is the leading

cause of death

Early diagnosis can help in preventing

heart attacks by providing better

diagnosis and treatment

Assessment of Regional Function using Cardiac MRI

Comprehensive method for

assessment of cardiac

regional function.

Provides great flexibility in

imaging the structure and

anatomy of the heart.

Considered the gold

standard for assessing Left

Ventricle regional function.

Tagged MR Parallel Planes of saturated

magnetization Applied orthogonal to the

imaging plane Move with the motion of the

heart• Permit quantification of its mechanical strain in three-coordinate directions (circumferential, longitudinal, and radial)

Harmonic Phase (HARP) MethodAutomatic tracking of cardiac

motion from a tagged MR image

sequence

Based on the idea that the phase of

each point is invariant with time

Easy to compute the strain after

acquiring these points with time

HARP Output

Peak Strain

Systolic Rate

Diastolic Rate

Objective: To extract diagnostic information from the strain curvesHARP Images Analysis

(Raw Strain Measurements)

Images Acquisition(Cardiac MRI)

Flow Diagram for Diagnostic Information

HARP Images Analysis(Raw Strain

Measurements)

Information ExtractionDecision / Info about Patient’s

condition


Measurements Inaccuracies

Denoising the Strain Measurements



Measurements)


condition

Signal Denoising(Artifacts

Removing)

Understanding the Causes of the Errors

Method I

Method II



Measurements)


Removing)

Causes of

Errors:

1- Noise

2- Tag

Overlapping

3- Twisting

4- Tag Fading

Method - 1

Apply non-linear filter on the strain curve

Ɛin = input strain curve

Ɛfilter = output of the filter

• Each peak in Ɛfilter corresponds to a noisy point

• It computes the difference between the input strain curve and its linear approximation

Method 1 – Cont’dActual Strain Curve

Filter Result

Curve fitting - Second order Fourier series

Time Derivative

Actual strain curveSpiky Locations

Denoised strain curveCurve fittingSecond order Fourier SeriesTime Derivative

Method 2 Based on observing real

strain curves, we noticed the spikes are most likely +ve spikes

They are considered as peaks in the strain curve having

Ɛin (t ) ˃ Ɛin (t + 1)AND

Ɛin (t) > Ɛin (t – 1)

• Strain at these locations is averaged

• This process is repeated till there are no spikes remaining

Evaluation of the techniques

Method I

Method II



Measurements)


Removing)

Causes of

Errors:

1- Noise

2- Tag

Overlapping

3- Twisting

4- Tag Fading

Simulated Images

Experiments

Three experiments to evaluate the performance of the developed techniques

Simulated Tagged MR images were generated (DICOM format)

Simulation parameters: Max Contraction = 25% Tag Separation = 7mm to 8mm

Performance Evaluation: Root-Mean-Square (RMS) and Maximum error

for the strain curves and the extracted features

Experiment 1

Evaluate the techniques for different SNR levels

Noise Variance = 0.001 to 0.1 of the max intensity

SNR = 20 to 60 dB

For Systolic RateFor Diastolic RateFor Strain CurvesFor Peak Strain

Experiment 1 - Results

Experiment 2

Evaluate the technique for tags overlapping

This was done by removing the tags from

several time-frames around the max contraction

To simulate the aliasing of the tags, a tag line is

removed for 1 to 7 time-frames around end-

systole.

For Strain CurvesFor Peak StrainFor Systolic RateFor Diastolic Rate

Experiment 2 - Results

Combining noise effect and tag overlapping (Experiment 1 and 2, combined)

For Strain CurvesFor Peak StrainFor Systolic RateFor Diastolic Rate

Experiment 3

Summary and Conclusions Two methods were developed to denoise the

strain curves and accurately extract features Three experiments were performed to

evaluate the performance of the techniques Method I proved to robust to different

SNR levels Method II proved to efficiently recover

the strain curves when the overlapping tags occur

Artifacts appearing in the strain curves are more likely due to overlapping tags more than the SNR

Future Work


Images Analysis(Raw Strain

Measurements)


condition

Signal Denoising(Removing Artifacts)

Machine Learning

Thank You

mina emad azmy research assistant – signal and image processing lab

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