Strain Comparisons from SSFP and FGRE using MTT

Author: Patrick Young5/3/15

Principle Investigator: Dr. Joao Lima

Early Methods Heart is a fascinating organ -> Self-sustaining! How do we investigate its functions?

1. stethoscope + sphygmometer – heart beats/HR -> later for measuring BP2. But it’s hard to visualize the heart’s performance. How we quantify the functions of the heart?

ECHO – Doppler ultrasound -> EF ECG – measure electrical stimulation 3. Modern Technology – medical imaging: MRI – greater spatial resolution/localization of the whole organ

Cardiac Cycle Wiggers diagram, showing coordination of pressure and volume in the cycle.

Figure 5: http://en.wikipedia.org/wiki/Wiggers_diagram#/media/File:Wiggers_Diagram.svg

Parameter of Interest: Strain Strain is vector made up of multiple axis.

1-D Strain 2-D strain 3-D Strain

Figure 8: http://commons.wikimedia.org/wiki/File:2D_geometric_strain.pngFigure 9: http://homepages.engineering.auckland.ac.nz/~pkel015/Sol idMechanicsBooks/Part_II/07_3DElasticity/07_3DElasticity_02_3D_StressStrain.pdf

Myocardial Strain The 3 proponents of strain in the heart correspond to global longitudinal, global

circumferential, and radial.

Longitudinal Strain (Ell)

Radial Strain (Err)

Circumferential strain (Ecc)

Figure 10: Ola Gjesdal and Thor Edvardsen (2011). Tissue Doppler in Ischemic Heart Disease, Establishing Better Standards of Care in Doppler Echocardiography, Computed Tomography and Nuclear Cardiology, Dr. Richard M. Fleming (Ed.)

EF vs. Strain Two powerful parameters but there are limitations. 50% of

cases of heart failure patients present preserved EF.

Supposedly inversely related (greater HF, lower LVEF). (Bhartia, 2006).

Heart “healthiness” - how well and consistent the heart beats?Contractive quality!Global circumferential strain (GCS) and global longitudinal strain (GLS) are promising risk factors to predict cardiac diseases in a subclinical setting.

Physics of MRI

Figure 11: http://s.hswstatic.com/gif/mri-steps.jpg

Goal of K-space

Figure 12: http://rad.usuhs.mil/rad/minipath/mr_opmsk/minipath.html

Pulse Sequences MRI Pulse Sequence – a programmed set of changing magnetic gradients. These gradients are used to reconstruct spatially resolved images

– after k-space applied (Bernstein, 2004).

Different pulse sequences allows tissues to be imaged in various ways. Multiple Classifications based on different parameters. Different combinations affect tissue contrast and spatial resolution– our focus: echo gradients

SSFP vs. FGRE FGRE combines low-flip angle radio-frequency excitation

of the NMR signal + rapid repetition of the gradient echo seq.- longitudinal magnetization is used (T1 time) – short TE

SSFP + transverse magnetizations (T2 time) from the overlap of multi-order spin echoes/and stimulated echoes– shorter TE and TR times

Image run by SSFP, has greater color contrast between different tissues than FGRE. Has become the new standard for MRI sequences.

SSFP vs. FGRE Tracking

Template Matching Algorithm

Figure 13: Template matching algorithm in MTT software

MESA5 MESA – (Multi-Ethnic Study of Atherosclerosis) a clinical

research study involving 6000+ men and women from six communities in the U.S.

MESA5 – the follow-up study of 3000 participants in 2010 from the baseline study MESA1 in 2000; FGRE was only available in MESA1. In MESA5, images were taken both from SSFP and FGRE for 600 studies. Can MESA5 data be translated back to MESA1?

Objective: Find a technical systematic error between SSFP and FGRE in order to translate longitudinal strain values for further MESA studies.

MethodologyDouble blinded experiment - 50 studies in MESA5 chosen at

random – each have SSFP/FGRE- Software: MTT chosen to collect strain values. - Drew contour lines for epicardium and endocardium from diastole to systole for each chamber of heart. - Data placed into excel.

Run statistical calculations1. Correlation – linear regression plot -> SSFP (x-axis) vs. FGRE (y-axis)2. Agreement – Bland-Altman plot (difference: SSFP – FGRE)

Right Ventricle (RV)

SSFP FGRE

Mean -28.236 -23.7152

Std 7.088408378 7.128367695

Difference average: -5.661Standard Deviation: 6.66195% Limits of Agreement: [-18.716, 7.393]Pearson’s r coefficient: 0.546

-45 -40 -35 -30 -25 -20 -15 -10 -5 0

-50

-40

-30

-20

-10

0

f(x) = 0.653101893550083 x − 3.52847773796476R² = 0.29842112938431

ssfp1 vs fgre1Linear (ssfp1 vs fgre1)

Longitudinal Strain - SSFP vs. FGRE For RV

-20

-10

100

Diff

eren

ce o

f RV-

Sm

ax (S

SFP

1) a

nd R

V-S

max

(FG

RE

1)

-40 -30 -20 -10Mean of RV-Smax (SSFP1) and RV-Smax (FGRE1)

observed average agreement 95% limits of agreement

y=0 is line of perfect average agreement

Left Ventricle (LV)

SSFP FGRE

mean -20.5396 -12.839

std 5.06052468 5.331827519

Difference average: -7.701Standard Deviation: 4.4895% Limits of Agreement: [-16.481, 1.079]Pearson’s r coefficient: 0.629

-35 -30 -25 -20 -15 -10 -5 0

-35

-30

-25

-20

-15

-10

-5

0

f(x) = 0.663242596929306 x + 0.783737643889168R² = 0.396263191551035

Longitudinal Strain - SSFP vs. FGRE 4CH for LV

SSFP1 VS FGRE1 4CH

Linear (SSFP1 VS FGRE1 4CH)

-20

-15

-10

-50

Diff

eren

ce o

f LV

-4C

h S

max

(SS

FP1)

and

LV

- 4C

h S

max

(FG

RE

1)

-25 -20 -15 -10 -5Mean of LV -4Ch (SSFP1) and LV - 4Ch (FGRE1)

observed average agreement 95% limits of agreement

y=0 is line of perfect average agreement

Right Atrium (RA)

SSFP FGRE

mean 54.8208 37.2142

std 37.35547736 31.15177422

Difference average: 17.607Standard Deviation: 15.48095% Limits of Agreement: [-12.733, 47.946]Pearson’s r coefficient: 0.793

0 20 40 60 80 100 120 140 160 180 2000

50

100

150

200

f(x) = 0.761859935418588 x − 4.55157114759534R² = 0.834628632242995

Longitudinal Strain - SSFP vs. FGRE for RA

ssfp1 vs fgre1Linear (ssfp1 vs fgre1)

-20

2040

6080

0

Diff

eren

ce o

f RA

-Sm

ax (S

SFP

1) a

nd R

A-S

max

(FG

RE

1)

0 50 100 150 200Mean of RA-Smax (SSFP1) and RA-Smax (FGRE1)

observed average agreement 95% limits of agreement

y=0 is line of perfect average agreement

Left Atrium (LA)

SSFP FGRE

mean 32.1634 25.6654

std 18.43386066 14.85691802

Difference average: 6.498Standard Deviation: 9.85595% Limit of Agreement: [-12.818, 25.814]Pearson’s r coefficient: 0.768

0 20 40 60 80 1000

20

40

60

80

100

f(x) = 0.681864920023589 x + 3.7343058313133R² = 0.715767281436593

Longitudinal Strain - SSFP1 vs FGRE1 4CH for LA

SSFP vs FGRE

Linear (SSFP vs FGRE)

-20

2040

0D

iffer

ence

of L

A -4

Ch

Sm

ax (S

SFP

1) a

nd L

A - 4

Ch

Sm

ax (F

GR

E1)

0 20 40 60 80Mean of LA -4Ch (SSFP1) and LA - 4Ch (FGRE1)

observed average agreement 95% limits of agreement

y=0 is line of perfect average agreement

Discussion/Conclusion RV and LV are not well correlated; much variance in the values. RA

and LA show a stronger correlation. Correlation Atria > Ventricles.

However, not all are in agreement. RV>LV; LA>RA; But, for RV & LA, agreement is based on the difference in strain valuesPossibility for error in LV? Longer the endocardium-> the greater the probability to generate an error!

However, in all chambers of the heart – using MTT to analyze SSFP images, the absolute values of strain are consistently greater than FGRE.FGRE does a worse job of tracking endocardium than epicardium.SSFP: comparable in both.

The End!

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Medical Imaging: MRI Why MRI? It gives us the ability to look at strain of the heart with our eyes.

How? MRI (magnetic resonance imaging): an imaging technique in radiology used to visualize tissue/organs. Based on NMR - strong magnetic fields influence spin on protons.

Protons return to their original spin state (relaxation). – T1/T2 times. Very useful for diagnosing diseased tissue such as tumors because protons in different tissues return to their equilibrium state at different rates (different T1 times).

Fast Gradient Echo (FGRE) FGRE is based on the another subset of gradient echo sequences FLASH (fast

imaging using low angle shot. Introduction of FGRE reduced the MRI measuring times without loss of image

quality. Also, based on the flip angle (<90) – varies between 10-80 degrees. Basic FLASH

that used larger tip angles give more T1 weighting to the image. Smaller tip angle give more T2 or T2* weighting to the image.

Generally FGRE combines low-flip angle radio-frequency excitation of the NMR signal with rapid repetition of the gradient echo seq. Repetition time >> T1 time of the protons.

(1) Low-flip angle excitation (creates unused longitudinal magnetization for the next immediate excitation) + (2) gradient echo = allows rapid repetition of the sequence interval

It speeds up the image acquisition. FLASH eliminates the waiting periods of T1 saturation

Steady – State Free Precession (SSFP) Classified under Gradient Echo Pulse Sequences – uses steady states of

magnetizations. SSFP is based on a low flip angle gradient-echo sequence with a short repetition

time – an improvement of FLASH (spoiled gradient-echo seq). Difference? FLASH uses steady state of longitudinal magnetization only

SSFP seq includes transverse magnetizations from the overlap of multi-order spin echoes/and stimulated echoes. This is done by refocusing the phase-encoding gradient in each repetition interval in order to keep the gradient moment constant (keep it steady!)

bSSFP (balanced SSFP) seq use a phase of zero by refocusing all gradients. Steady state is achieved by radiofrequency (RF) irradiation and relaxation

behaviors of spins. Factors include flip angle of RF pulse (<90), repetition time (TR) of pulse repeat, relaxation time constants T1 and T2, and gradient moments.

When seeing an image run by SSFP seq, there is greater color contrast between different tissues than FGRE. Has become the new standard for MRI sequences.

Analysis Method: MTT MTT (multimodality tissue tracking) is a software that utilizes tissue patterns obtained from cine CMR images

and automatically tracks them frame to frame by an automated pattern matching technique. Measures regional myocardial function for all chambers from cine imaging of different pulse sequences,

including HARP. MTT utilizes a pixel-to-pixel matching technique by defining angle-independent motion vectors from multiple tracking points to find voxels in successive frames (Hell-Valle et. al).

Must draw the endocardial and epicardial borders at a reference frame, usually the ventricular end-diastolic frame (1st frame of the clip).

Software then propagates the borders across the cardiac cycle automatically using a template matching algorithm (Ohyama, 2015). It then records a specific pixel pattern in each 10 x 10 square area in the reference frame – this pattern is recognized in the next frame; so it tries to maximize similarity evaluated by cross-correlation between the squares. This procedure is repeated for all pixels for each frame of the image to track the borders through whole cardiac cycle.

Then, the systolic segmental strains are calculated from inner, mid, and outer wall.