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Mina Emad Azmy Research Assistant – Signal and Image Processing Lab.

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Presentation on theme: "Mina Emad Azmy Research Assistant – Signal and Image Processing Lab."— Presentation transcript:

1 Mina Emad Azmy Research Assistant – Signal and Image Processing Lab

2  Coronary artery disease is the leading cause of death  Early diagnosis can help in preventing heart attacks by providing better diagnosis and treatment

3  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.

4  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)

5  Automatic 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

6 Peak Strain Systolic Rate Diastolic Rate Objective: To extract diagnostic information from the strain curves HARP Images Analysis (Raw Strain Measurements) HARP Images Analysis (Raw Strain Measurements) Images Acquisition (Cardiac MRI) Images Acquisition (Cardiac MRI)

7 HARP Images Analysis (Raw Strain Measurements) HARP Images Analysis (Raw Strain Measurements) Information Extraction Decision / Info about Patient’s condition Images Acquisition (Cardiac MRI) Images Acquisition (Cardiac MRI)

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9 Images Acquisition (Cardiac MRI) Images Acquisition (Cardiac MRI) HARP Images Analysis (Raw Strain Measurements) HARP Images Analysis (Raw Strain Measurements) Information Extraction Decision / Info about Patient’s condition Signal Denoising (Artifacts Removing) Signal Denoising (Artifacts Removing)

10 Images Acquisition (Cardiac MRI) Images Acquisition (Cardiac MRI) HARP Images Analysis (Raw Strain Measurements) HARP Images Analysis (Raw Strain Measurements) Signal Denoising (Artifacts Removing) Signal Denoising (Artifacts Removing)

11  Apply non-linear filter on the strain curve Ɛ in = input strain curve Ɛ filter = output of the filter Each peak in Ɛ filter corresponds to a noisy point It computes the difference between the input strain curve and its linear approximation

12 Actual Strain Curve Filter Result Curve fitting - Second order Fourier series Time Derivative

13 Actual strain curve Spiky Locations Denoised strain curve Curve fitting Second order Fourier Series Time Derivative  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 Ɛ in (t ) ˃ Ɛ in (t + 1) AND Ɛ in (t) > Ɛ in (t – 1) Strain at these locations is averaged This process is repeated till there are no spikes remaining

14 Images Acquisition (Cardiac MRI) Images Acquisition (Cardiac MRI) HARP Images Analysis (Raw Strain Measurements) HARP Images Analysis (Raw Strain Measurements) Signal Denoising (Artifacts Removing) Signal Denoising (Artifacts Removing) Simulated Images

15  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

16  Evaluate the techniques for different SNR levels  Noise Variance = to 0.1 of the max intensity  SNR = 20 to 60 dB

17 For Systolic RateFor Diastolic RateFor Strain CurvesFor Peak Strain

18  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.

19 For Strain CurvesFor Peak StrainFor Systolic RateFor Diastolic Rate

20  Combining noise effect and tag overlapping (Experiment 1 and 2, combined) For Strain CurvesFor Peak StrainFor Systolic RateFor Diastolic Rate

21  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

22 Images Acquisition (Cardiac MRI) Images Acquisition (Cardiac MRI) Images Analysis (Raw Strain Measurements) Images Analysis (Raw Strain Measurements) Information Extraction Decision / Info about Patient’s condition Signal Denoising (Removing Artifacts) Signal Denoising (Removing Artifacts) Machine Learning

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