Reproducibility and Clinical Validation of Tissue tracking methodology
CMR-based strain methodologies in clinical validation of tissue tracking: CMR based
techniques were first allowing the assessment of regional myocardial deformation noninvasively starting with myocardial tagging (1) which creates non-invasive markers in the
myocardium. Tag lines obscure some of the endocardial borders, reduce temporal resolution
and increase scan time. These reasons, in addition with the time consuming post-processing
have prohibited tagging from becoming a mainstream clinical application. Most postprocessing issues have been overcome by the Harmonic Phase (HARP) algorithm, using the
first harmonic peaks of the image’s k-space which has been validated intensively (2). Tagging
has been mostly used to assess circumferential strains and cardiac rotation and twist.
Alternative techniques to tagging such as DENSE (3) and SENC (4), both of which, however,
have only been used by a limited number of centers.
Speckle-Tracking Echocardiography: Both, 2D-STE and 3D-STE have been validated against
sonomicrometry and CMR tagging (Table 2) (5–8). Overall, GLS compares best to CMR
tagging, with a small bias and reasonably small limits of agreement.
CMR Feature Tracking: GLS as well as basal, mid and apical GCS showed the best intraobserver reproducibility, with a coefficient of variation (CV) ranging from 3% to 17%. GLS
performed better in the LV (CV ranging from 12% to 18%) than in the RV (CV 29% to 43%),
but clearly worse than for GCS, although they have similar absolute values. Radial strain
showed CVs ranging from 23% to 31%, with slightly better reproducibility, when measured
from short axis slices. Similar trends are observed for the inter observer variability of global
strain values (9, 10).
Morton et al. reported inter study variability (11) in 3 repeated exams in one day performed in
the morning (9:00 am) immediately after the first scan and in the afternoon (2:00 pm). Similar
findings were obtained with GCS performing best (CV: 20%) and RV GLS worst (CV: 30%).
Segmental strain values may show large variability, which may be clinically unacceptable
(CVs up to 63%). Magnetic field strength, however, does not influence reproducibility (10).
CMR-FT has been validated against the traditional CMR strain techniques (Table 4). Overall
GCS compare best to CMR tagging with HARP post-processing, e.g. (12), with high
correlation and low limits of agreement. CMR-FT has been compared to SENC for global
longitudinal function (13), with small bias and fair limits of agreement for both RV and LV.
Direct Comparison of 2D-STE and CMR-FT: Comparisons of STE with CMR-FT have
shown promising results in correlation and inter-modality variation across multiple studies
looking at strain measurements (Table 3). Global longitudinal and circumferential strain
showed the best agreement between STE and CMR-FT, except in the cases where geometry is
altered, as in hypertrophic cardiomyopathy, where circumferential strain was less comparable.
This could be attributed to different endocardial border definition in echocardiography and
CMR. Radial strain between techniques is not comparable. These inter-modality comparisons
follow the separate variability of the different components, and variability is unacceptably
large for radial strain.
References
1. Zerhouni EA, Parish DM, Rogers WJ, Yang A, Shapiro EP. Human heart: tagging with MR
imaging--a method for noninvasive assessment of myocardial motion. Radiology
1988;169:59–63.
2. Osman NF, McVeigh ER, Prince JL. Imaging heart motion using harmonic phase MRI.
IEEE Trans. Med. Imaging 2000;19:186–202.
3. Aletras AH, Ding S, Balaban RS, Wen H. DENSE: displacement encoding with stimulated
echoes in cardiac functional MRI. J. Magn. Reson. 1999;137:247–252.
4. Osman NF, Sampath S, Atalar E, Prince JL. Imaging longitudinal cardiac strain on shortaxis images using strain-encoded MRI. Magn. Reson. Med. 2001;46:324–334.
5. Amundsen BH, Helle-Valle T, Edvardsen T, et al. Noninvasive myocardial strain
measurement by speckle tracking echocardiography: Validation against sonomicrometry and
tagged magnetic resonance imaging. J. Am. Coll. Cardiol. 2006;47:789–793.
6. Bansal M, Cho GY, Chan J, Leano R, Haluska BA, Marwick TH. Feasibility and Accuracy
of Different Techniques of Two-Dimensional Speckle Based Strain and Validation With
Harmonic Phase Magnetic Resonance Imaging. J. Am. Soc. Echocardiogr. 2008;21:1318–
1325.
7. Seo Y, Ishizu T, Enomoto Y, et al. Validation of 3-dimensional speckle tracking imaging to
quantify Regional Myocardial Deformation. Circ. Cardiovasc. Imaging 2009;2:451–459.
8. Kaku K, Takeuchi M, Tsang W, et al. Age-related normal range of left ventricular strain
and torsion using three-dimensional speckle-tracking echocardiography. J. Am. Soc.
Echocardiogr. 2014;27:55–64.
9. Augustine D, Lewandowski a J, Lazdam M, et al. Global and regional left ventricular
myocardial deformation measures by magnetic resonance feature tracking in healthy
volunteers: comparison with tagging and relevance of gender. J Cardiovasc Magn Reson
2013;15:8.
10. Schuster A, Morton G, Hussain ST, et al. The intra-observer reproducibility of
cardiovascular magnetic resonance myocardial feature tracking strain assessment is
independent of field strength. Eur. J. Radiol. 2013;82:296–301.
11. Morton G, Schuster A, Jogiya R, Kutty S, Beerbaum P, Nagel E. Inter-study
reproducibility of cardiovascular magnetic resonance myocardial feature tracking. J.
Cardiovasc. Magn. Reson. 2012;14:43.
12. Hor KN, Gottliebson WM, Carson C, et al. Comparison of Magnetic Resonance Feature
Tracking for Strain Calculation With Harmonic Phase Imaging Analysis. JACC Cardiovasc.
Imaging 2010;3:144–151.
13. Ohyama Y, Ambale-Venkatesh B, Chamera E, et al. Comparison of strain measurement
from multimodality tissue tracking with strain-encoding MRI and harmonic phase MRI in
pulmonary hypertension. Int. J. Cardiol. 2015;182:342–348.
14. Altiok E, Neizel M, Tiemann S, et al. Quantitative analysis of endocardial and epicardial
left ventricular myocardial deformation-comparison of strain-encoded cardiac magnetic
resonance imaging with two-dimensional speckle-tracking echocardiography. J. Am. Soc.
Echocardiogr. 2012;25:1179–1188.
15. Kleijn SA, Brouwer WP, Aly MFA, et al. Comparison between three-dimensional
speckle-tracking echocardiography and cardiac magnetic resonance imaging for quantification
of left ventricular volumes and function. Eur. Heart J. Cardiovasc. Imaging 2012;13:834–839.
16. Helle-Valle T, Remme EW, Lyseggen E, et al. Clinical assessment of left ventricular
rotation and strain: a novel approach for quantification of function in infarcted myocardium
and its border zones. Am. J. Physiol. Heart Circ. Physiol. 2009;297:H257–67.
17. Goffinet C, Chenot F, Robert A, et al. Assessment of subendocardial vs. subepicardial left
ventricular rotation and twist using two-dimensional speckle tracking echocardiography:
comparison with tagged cardiac magnetic resonance. Eur. Heart J. 2009;30:608–617.
Table 1: Studies comparing STE to CMR techniques for assessing left ventricular deformation.
First author, year
(refrence)
Correlation
coefficient
Bias (LOA)
Endocardial segmental LS
0.53
0.8% (17.9 to -16.4)
Endocardial segmental CS
0.64
7.3% (24.7 to -10.0)
Epicardial segmental LS
0.61
-1.8% (9.7 to -13.0)
Epicardial segmental CS
0.36
0.2% (14.4 to -14.0)
0.8
10% (13.2 to 6.7)
regional rotation
0.71
0.6% (3.6 to -2.4)
regional strain
0.87
1.5% (5.7 to -2.7)
GLS
0.5
-
GRS
0.59
-
GCS
0.63
-
Endocardial twist
-
0.09°(−3.1 to 3.3)
mid-ventricular twist
-
0.78° (−3.2 to 4.8)
Epicardial twist
-
0.42°(−4.7 to 5.5)
Peak twist velocity
-
−7.9 °/s (−36.0 to 20.0)
Altiok 2012(14)
Kleijn 2012 (15)
n
Patient
characteristics
Methods compared
44
Patients referred for
ischemia
assessment
2D-STE vs. CMR-tagging
45
Healthy subjects
3D-STE vs. CMR-tagging
GCS
Helle-Valle 2009 (16)
38
Bansal 2008 (6)
30
Goffinet 2009 (17)
53
15 healthy and 23
re-perfused MI
patients
Ischemic heart
disease
43 heart failure and
10 controls
2D-STE vs. CMR-tagging
2D-STE vs. CMR-tagging
2D-STE vs. CMR-tagging
Table 2: Studies comparing CMR-FT to other CMR techniques for assessing myocardial
deformation.
First author, year (reference)
Hor 2010 (3)
n
Patient characteristics
Methods
Compared
233
Duchenne Muscular
Dystrophy
Tagging/HARP
LV-GCS (medial slice)
Moody 2014 (104)
10
Dilated
Cardiomyopathy
LV-GLS
Ohyama 2015 (23)
30
+
15
Bias (LOA)
.89
-0.4% (-3.6 to 2.9)
.77
.73
.75
.80
.70
.76
.73
.61
1.5% (-4.3 to 7.3)
2.1% (-4.1 to 8.3)
1.4% (-4.6 to 7.4)
.9% (-4.5 to 6.3)
2.5% (-4.5 to 9.5)
3.3% (-3.9 to 10.5)
2.4% (-4.6 to 9.4)
1.6% (-6.0 to 9.2)
.60
.57
.72
-2.8% (-9.6 to 4.0)
2.7 (-1.8 to 7.3)
0.4% (-5.9 to 606)
Pulmonary
Hypertension
+
Controls
LV-GCS
LV-GLS
RV-GLS
Singh, 2014 (105)
COV
Tagging/
CIMTag
whole wall
Sub epi
Mid
Sub endo
Whole wall
Sub epi
Mid
Sub endo
LV-GCS
R
Tagging-HARP
SENC
SENC
18
LV-GCS
LV-GLS
Augustine, 2013 (21)
8
10
20
Tagging (1.5T)
26
LV-GCS
LV-GRS
3.9% (-3.92 to 11.8)
3.6% (-2.99 to 10.22)
-
-
.7% (-6.0 to 4.0)
-1% (-16 to 3)
11% (-1 to 23)
.35
no
-
-3.7% (-9.6 to 2.2)
-30.5% (-84.4 to 23.4)
Tagging (3T)
Normal subjects
Tagging/HARP
LV-GCS
LV-GLS
LV-GRS
Lu, 2013 (106)
21
17
Anthracycline
Tagging/HARP
Table 3: Comparison STE and CMR-FT.
First author, year (reference)
n
Patient characteristics
R
COV
Bias (LOA)
Kempny, 2012 (88)
28
+
25
Tetralogy of Fallot
and Controls
LV-GLS
-
15.8%
-2% (-9 to 5)
LV-GCS
-
17.0%
1% (-10 to 12)
LV-GRS
-
69.0%
15% (-29 to 58)
RV-GLS
-
16.6%
-1.5% (-9 to 6.5)
-
-
-1.38% (-10.37 to 7.62)
0.77% (-4.34 to 5.88)
-10.88% (-54.47 to 32.71)
0.04% (-8.47 to 8.57)
-
14.4%
19.4%
-0.82% (-8 to 6.5)
3.1% (-9.8 to 16.1)
0.81
0.87
-
1.9% (9.9 to -6.0)
1.5% (9.7 to -6.7)
0.87
0.61
0.79
-
-6.0% (0.2 to -12.2)
-10.1% (22.5 to -42.8)
-0.7% (7.9 to -9.3)
0.68
-
0.7% (7.7 to -6.3)
0.93
-
-0.3 ms (83.3 to -83.8)
Padiyath, 2013 (103)
20
Tetralogy of Fallot and
Controls
LV-GLS
LV-GCS
LV-GRS
RV-GLS
Orwat 2014 (107)
20
+
20
Hypertrophic
Cardiomyopathy and
Controls
LV-GLS
LV-GCS
Onishi, 2015 (108)
71
Heart failure
LV-GLS
LV-GCR
Kaku, 2014 (16)
19
Normal subjects
LV-GLS
LV-GRS
LV-GCS
Makoto, 2014 (109)
Constriction and
restriction
LV-GLS
Onishi, 2013 (110)
Radial dyssynchrony
72
Dyssynchrony