Right Ventricular Ejection Fraction with Cardiac Magnetic Resonance using a Wall Motion Score
Réal Lebeau, MD1, Maude Pagé, MD1, Karim Serri, MD1, Maxime Pichette, MD, MSc1, Maria Di
Lorenzo, MD1, Claude Sauvé, MD1, Alain Vinet, PhD2, Frédéric Poulin, MD, MSc1
1 Division of Cardiology, Department of Medicine, Hôpital du Sacré-Cœur de Montréal,
Université de Montréal, Montréal, Canada
2 Department of Pharmacology and Physiology, Hôpital du Sacré-Cœur de Montréal Research
Center, Université de Montréal, Montréal, Canada
Short title: RV wall motion for RVEF in CMR
Word count: 3800
Corresponding author
Dr. Frédéric Poulin, Hôpital du Sacré-Coeur de Montréal,
5400 Gouin Blvd W., Montreal, Quebec, Canada, H4J 1C5
Telephone: (514) 338-2222; Fax: (514) 338-2381
Email: [email protected]
ABSTRACT
Purpose:
Volumetric method in cardiac magnetic resonance (CMR), the reference standard for right
ventricular ejection fraction (RVEF), requires expertise due to the complex RV geometry and
anatomical landmarks. The aim of our retrospective study was to describe a new method to
evaluate RVEF based on the wall motion score index (WMSI) in CMR.
Methods:
Visual assessment of wall motion was performed using an 8-segment model (normokinesia=1,
hypokinesia=2, akinesia=3). Correlation between the WMSI (WMS/8) with the reference CMR
volumetric-RVEF was analysed. A regression equation was derived to convert the WMSI into
RVEF. Accuracy of CMR WMSI-derived RVEF compared to volumetric-RVEF was evaluated using
Bland-Altman analysis.
Results:
In the 112 patients using the volumetric CMR method, the mean RVEF was 48±14%. Fifty-nine
patients had normal RV kinetics (WMSI = 1) which corresponded to a volumetric-RVEF of 56%
(SD 7%; range from 43 to 76%). The WMSI showed a very strong correlation with the CMR
volumetric-RVEF (r=-0.85). A regression equation was created: RVEF = 80 – 22 X WMSI. Overall,
the WMSI-RVEF resulted in good agreement with CMR (mean bias 3%). We describe a second
method to derive RVEF based on segmental kinetics and attributing a value of 7% to
normokinetic, 4% to hypokinetic and 2% to akinetic segments with equivalent correlation and
accuracy. In addition, using a WMSI cut-off of ≥ 1.5 was highly accurate (92%) to predict a
reference RVEF of ˂ 45%, an important prognostic indicator in CMR.
Conclusion:
Our results suggest that using the WMS in CMR (8-segment) to estimate RVEF is accurate and
correlates well to the volumetric method. A wall motion score index ≥ 1.5 is optimal to
categorize patients in the higher-risk subset of CMR-RVEF ˂ 45%.
KEYWORDS
Right ventricle, ejection fraction, cardiac magnetic resonance, wall motion score
INTRODUCTION
The technological refinement of imaging modalities and emergence of newer techniques(1, 2)
for the evaluation of the right ventricle (RV) have contributed to reinforce its important
prognostic role in cardiovascular diseases. Free of geometric assumptions(3), cardiac magnetic
resonance (CMR) is the most accurate method to measure RV ejection fraction (RVEF) which
translates into several validated implications in clinical decision-making. (4-11)
The CMR evaluation of RVEF done by volumetric analysis using short and long axis views (3) is
robust but is time-consuming and requires careful attention to details in the endocardial
tracings. For the left ventricular ejection fraction, the wall motion score (WMS) method is a
simpler alternative that has been well validated using echocardiography (12) and CMR.(13)
The aim of this retrospective study is to develop a RV wall motion score index (WMSI) in CMR to
calculate the RVEF and correlate it to the volumetric RVEF.
MATERIAL AND METHODS
Study population
From March 2016 to March 2019, 112 randomly selected patients referred for CMR were
enrolled in the study. Patients with poor diagnostic quality of CMR images and insufficient
endocardial definition to allow RV kinetic evaluation were excluded. The study was approved
by our institution Research Ethics Board and all participants provided written informed consent.
CMR acquisition technique
All evaluations were performed using a 1.5-T CMR scanner (Magnetom Avanto, Siemens,
Erlangen, Germany). Eighteen-channel anterior and posterior phased-array coils were used for
signal acquisition. Balanced steady-state free precession (bSSFP) cine CMR images were
acquired over a single breath-hold using the following imaging parameters: repetition time (TR)
< 4 ms; echo time (TE) 1.5 ms; flip angle 60; slice thickness 8–10 mm; matrix 192 x 256; field of
view 300–400 mm; and temporal resolution 30-40 ms. The following cine views were acquired:
LV 2-chamber, 4-chamber, 3-chamber, biventricular short axis (including 9–12 contiguous
ventricular slices), and RV outflow tract (RVOT).
CMR RV assessment
Volumetric RVEF
The entire short-axis stack was analysed offline with a computer analysis system (Circle
Cardiovascular Imaging, Calgary, Canada) dedicated to CMR for the measurement of RVEF by
the volumetric method, by tracing the endocardial outline at end-systole and end-diastole, as
recommended.(14)
WMS RVEF
Three standard short axis views were analysed from the short axis stack: basal (mitral level),
mid-ventricular (papillary muscle) and apical, as in the echocardiographic WMSI method. From
the nine slices covering RV and LV base to apex, we selected the most representative slice of RV
base, mid and apex. Visual semi quantitative assessment of regional wall motion and thickening
for WMSI was performed by an experienced cardiologist (RL) in a blinded fashion using an 8-
segment model (Figure 1). At the basal and mid-ventricular levels, the RV was divided into 3
segments and at the apical level it was divided into two segments. Each segment was graded
according to the following score: normal= 1, hypokinesia= 2, and akinesia= 3. The global WMS
was obtained by adding the score for each segment and the WMSI was calculated by dividing
the WMS by 8 (Figure 1).
Intraobserver and interobserver variability
Twenty randomly selected studies were reanalysed by the same operator several months after
the initial analysis. A second experienced observer (MP), also blinded to previously obtained
data, analysed the same loops for the assessment of interobserver variability of the CMR-WMS.
Statistical analysis
Categorical variables are expressed in frequency and percentages. Normality of distribution of
continuous variables are assessed with the Shapiro-Wilk test. Continuous variables are
described as mean ± standard deviation (SD) if the distribution was normal, otherwise as
median and interquartile range (IQR) (25th-75th). CMR-WMSI and CMR volumetric RVEF were
compared by linear regression analysis and Bland-Altman analysis. Correlation was assessed by
the Pearson correlation coefficient. A regression equation was derived to convert the WMSI
into RVEF using the volumetric CMR method as the reference standard.
A simplified formula to easily derive RVEF from the segmental kinetic data was conceived. The
new formula consisted of attributing an individual ejection fraction (%) to each RV segment
based on the WMS and adding all 8 segmental EF into a global RVEF. The simplified formula
was:
Simplified segmental EF method = WNormokinesia NN + WHypokinesia NH + WAkinesia NA
In which W is the segmental EF and Nx is the number of segments of each class. The weights
(W) of the simplified segmental EF method were determined by least square optimisation of
the concordance with CMR-RVEF, which were afterward rounded to the nearest integer. Using
Bland-Altman analysis, we evaluated systematic bias (using mean differences between
methods), SD of inter-method difference, and precision (range within which are 95% of values
of differences between methods, i.e. ±1.96 SD of differences between methods).
Based on a RVEF cut-off of ˂ 45%, which is supported in the literature as a prognostically
important value (4, 6-10, 15), the classification performance of the WMSI (discrimination
between normal, CMR RVEF ≥ 45% and abnormal CMR RVEF< 45%) was assessed by the positive
predictive value.
The intra- and inter-observer variability were evaluated with the intraclass correlation
coefficient. All statistical analyses were conducted using SPSS, version 25 (SPSS Inc., Chicago IL).
RESULTS
Diagnostic quality data were obtained in 112 subjects (mean age 56, range from 19 to 82 years,
38% female) with a mean volumetric CMR-RVEF of 48±14% (IQR 43-57%; range from 5 to 76%).
Participants were referred for assessment of non-ischemic (57%; ˃15 different diagnoses) or
ischemic (21%) cardiomyopathy, valvular diseases (8%), and other diagnoses (14%).
We observed a very strong linear correlation between CMR-WMSI and the volumetric CMR-
RVEF (r=-0.85) (Figure 2). The regression equation to derive RVEF based on the WMSI is:
WMS-derived RVEF = 80 – 22 x WMSI (Figure 3)
The mean RVEF obtained by the regression equation was 51% (SD 12%; range from 16 to 58%).
The RVEF corresponding to each WMS and WMSI according to the regression equation are
shown in Table 1. In our cohort, 8/8 normal segments (WMSI =1) correspond to a RVEF of 58%,
8/8 hypokinetic segments (WMSI = 2) to a RVEF of 36% and 8/8 akinetic segments (WMSI = of
3) to a RVEF of 14%.
Among our 112 patients, 59 patients had normal CMR RV wall motion (WMSI = 1). In those
patients, the corresponding volumetric-RVEF was 56% (SD 7%; range from 43 to 76%).
Analysis of systematic bias
Bland-Altman analysis showed good agreement between the WMSI-RVEF and volumetric CMR-
RVEF (mean RVEF bias = -3%) (Figure 4 and Table 2).
Analysis of precision
The SDs of the distribution of inter-method differences between the WMSI-RVEF and
volumetric CMR were acceptable. The SD was ± 7.5% (± 16% of the median CMR-RVEF).
Consequently, the 95% confidence interval of inter-method difference (±1.96 SD i.e. precision)
was 29.4% (Figure 4 and Table 2).
Optimal pathologic threshold in WMSI
The optimal threshold in WMSI to discriminate normal from pathologic patients (volumetric
CMR RVEF < 45%) was 1.5 (which corresponds to 2 akinetic or 4 hypokinetic segments in the 8-
segment model). It has a sensibility of 77%, a specificity of 98% and a positive predictive value
of 92%.
Simplified segmental 7-4-2% RVEF method (Score 7-4-2)
Based on the results of the regression analysis, we calculated a simpler method to derive RVEF
by attributing an individual EF of 7% to each normokinetic segment (normal WMS-RVEF =
58%/8 segments ≈7%/ segment); of 4% to hypokinetic segments (hypokinetic WMS-RVEF =
36%/8 segments≈4%/segment), and of 2% to akinetic segments (akinetic WMS-RVEF = 14%/8
segments ≈2%/ segment). Segmental EFs are then added to obtain global RVEF (Figure 5).
RVEF by Score 7-4-2= 7 NN + 4 NH +2 NA
In which Nbx is the number of segments of each class. This simplified method also correlated
well with the CMR-RVEF (r=0.85) with similar precision (mean difference between methods 1.3
± 7.4%) than the WMSI-derived RVEF. (not shown)
Reproducibility
Analysis of intra- and inter-observer variability for the WMS demonstrated good agreement
between observations (intra-observer intraclass correlation coefficient=0.93; inter-observer
intraclass correlation coefficient=0.85).
DISCUSSION
In this study of patients with a broad range of RVEF and cardiac pathologies, the assessment of
RVEF in CMR using a simple 8-segment WMS in short-axis, similar to the echocardiographic
WMS for LVEF, resulted in accurate and precise estimation of RVEF compared to the reference
standard. This is a novel application of the WMS that allows a quicker method to provide a
reliable estimation of RVEF in CMR. The 7-4-2 score simplifies the calculation of the RVEF based
on the regional kinetic and has a very strong correlation to the CMR volumetric RVEF.
Because of the complex shape of the RV and its wall trabeculations, it may require extensive
manual adjustment of the endocardial contouring in both diastole and systole when using the
volumetric method in CMR. Studies have shown that the reproducibility of the manual RV
contouring by short-axis cine-CMR is not optimal, especially in the infundibulum and tricuspid
areas (basal tomographic plane).(16-18) While CMR-volumetric quantification is facilitated by
automatically generated contours, they have to be carefully reviewed by an expert. As opposed
to the multiple and complex segmentation for RV CMR analysis found in the literature (16, 19)
the WMS proposed herein using short-axis views is a simpler reproducible method with
unambiguous anatomic landmarks.
Absence of Wall Motion Abnormality
In our study, the absence of wall motion abnormality (WMA) (WMS = 8), the most common
finding, corresponded to a certain range of volumetric-RVEF (56%±7%; from 43 to 76%). This is
not surprising since the normal range of CMR volumetric-RVEF and the lower limits vary widely
in studies of normal subjects.
In the large Framingham Heart Study adult cohort (N=1336, 64±9 years, 43% men) free of
prevalent cardiovascular and pulmonary disease and with normal LV systolic function, mean
CMR-RVEF was 68 ± 6% in women and 64 ± 7% in men. The lower limits of normal were 57 and
52%, respectively.(20) In a second large UK biobank of 804 healthy participants of Caucasian
ethnicity (59±7 years, 46% men), mean CMR-RVEF was 58 ± 6% in women and 54 ± 6% in men.
The lower limits of normal were 47 and 45%, respectively.(21) The multiethnic study of
atherosclerosis (MESA), a large study of RV morphology in participants without cardiovascular
disease (N=4123, 62±10 years, 48% men), mean CMR-RVEF was 62 ± 11% in women and 72 ±
13% in men. The lower limits of normal were 40 and 47%, respectively.(22) Thus, in our
derivation cohort, the range of volumetric CMR RVEF obtained when the WMSI = 1 is similar to
what has been previously published.
Clinically Relevant WMS Threshold
While the determination of a clinically significant threshold in CMR WMS was beyond the scope
of our study, it has been demonstrated that CMR-RVEF ≤ 45% carries adverse implications in a
variety of cardiovascular diseases.(4, 6-10, 15) In our cohort, CMR-RVEF ≤ 45% was equivalent
to a WMS of ≥ 1.5 corresponding to a validated prognostic indicator with a good positive
predictive value (92%).
Wall Motion Score in CMR
Studies of RV wall motion assessment in CMR are scarce. In 65 healthy individuals, Quick et al.
have noticed a surprisingly high incidence of non-pathological wall motion disorders (91%),
especially dyskinesia, involving 2 or more segments in 60% of patients.(23) These WMA were
visible in the horizontal long axis and transverse planes but rarely depicted in the short-axis
plane. These findings are also concordant with the report by Sievers et al. of regional WMA
present in 27/29 healthy subjects. (24) The extent of these abnormalities was small and located
mostly around the moderator band. In our cohort, the 57 patients with normal CMR-RVEF
(≥50%) had no (n=34, 60%) or minimal WMA (1 WMA in 13, 23%; 2 WMA in 7, 12%; and 3 WMA
in 3 patients, 5%). The basal or mid- anterior segments accounted for 69% of the WMA
observed (data not shown). Our lower incidence of WMA in healthy patients is probably related
to the fact that our method uses only short-axis views to assess RVEF.
We have previously demonstrated the correlation between the RV WMS by echocardiography
and radionuclide angiography RVEF. (25) This study is the first to demonstrate a similar
correlation between the standard wall motion grading (normal, hypokinesia, akinesia) in short-
axis CMR views, comparable to the one used in echocardiography, and the volumetric-CMR
RVEF.
Limitations
This is a small derivation cohort with biases inherent to its retrospective nature. A validation in
a different and larger patient population would reinforce the findings, including the proposed
threshold of WMS ≥ 1.5. While we acknowledge that estimation of the WMA can be subjective,
our interobserver variability was good. This model of WMS is not designed for the evaluation of
hyperkinetic states or aneurysmal and dyskinetic segments. The incidence of aneurysmal and
dyskinetic segments in our cohort was insufficient to develop a distinct WMS. Thus, this
method might not apply to diseases such as arrhythmogenic RV cardiomyopathy. Finally, this is
not surprising in an unselected cohort that more than 50% of patients had no WMA. The
significant range of reference RVEF associated with visually unimpaired segmental RV kinetic
(WMS=1) depicts the concept of variability in the so-called normal RVEF (important load
dependency).
CONCLUSIONS
Our results suggest that using the WMS in CMR (8-segment model) to estimate RVEF is accurate
and correlates strongly with the volumetric method. A WMS index ≥ 1.5 is optimal to
categorize patients in the higher-risk subset of CMR-RVEF ˂ 45%. While these results require
further validation, the intuitive RV segmentation in short-axis, easy kinetic grading (normal,
hypokinesia, akinesia), and good reproducibility argue for a wider application of this method.
Declarations
ACKNOWLEDGEMENTS: We gratefully acknowledge Sylvie Loranger for secretarial assistance and Dr Reginald Nadeau for the careful assistance with the preparation and review of the manuscript.
FUNDING: None.
CONFLICTS OF INTEREST: None.
ETHICAL STANDARDSThe study was approved by our institution Research Ethics Board and all participants provided written informed consent.
REFERENCES
1. Surkova E, Muraru D, Iliceto S, Badano LP. The use of multimodality cardiovascular
imaging to assess right ventricular size and function. Int J Cardiol. 2016;214:54-69.
2. Sciaccaluga C, D'Ascenzi F, Mandoli GE, Rizzo L, Sisti N, Carrucola C, et al. Traditional and
Novel Imaging of Right Ventricular Function in Patients with Heart Failure and Reduced Ejection
Fraction. Curr Heart Fail Rep. 2020;17(2):28-33.
3. Kramer CM, Barkhausen J, Flamm SD, Kim RJ, Nagel E, Society for Cardiovascular
Magnetic Resonance Board of Trustees Task Force on Standardized P. Standardized
cardiovascular magnetic resonance (CMR) protocols 2013 update. J Cardiovasc Magn Reson.
2013;15:91.
4. Larose E, Ganz P, Reynolds HG, Dorbala S, Di Carli MF, Brown KA, et al. Right ventricular
dysfunction assessed by cardiovascular magnetic resonance imaging predicts poor prognosis
late after myocardial infarction. J Am Coll Cardiol. 2007;49(8):855-62.
5. Di Bella G, Siciliano V, Aquaro GD, De Marchi D, Rovai D, Carerj S, et al. Right ventricular
dysfunction: an independent and incremental predictor of cardiac deaths late after acute
myocardial infarction. Int J Cardiovasc Imaging. 2015;31(2):379-87.
6. Lella LK, Sales VL, Goldsmith Y, Chan J, Iskandir M, Gulkarov I, et al. Reduced Right
Ventricular Function Predicts Long-Term Cardiac Re-Hospitalization after Cardiac Surgery. PLoS
One. 2015;10(7):e0132808.
7. Miszalski-Jamka T, Klimeczek P, Tomala M, Krupinski M, Zawadowski G, Noelting J, et al.
Extent of RV dysfunction and myocardial infarction assessed by CMR are independent outcome
predictors early after STEMI treated with primary angioplasty. JACC Cardiovasc Imaging.
2010;3(12):1237-46.
8. Pouleur AC, Rousseau MF, Ahn SA, Amzulescu M, Demeure F, de Meester C, et al. Right
Ventricular Systolic Dysfunction Assessed by Cardiac Magnetic Resonance Is a Strong Predictor
of Cardiovascular Death After Coronary Bypass Grafting. Ann Thorac Surg. 2016;101(6):2176-84.
9. Gulati A, Ismail TF, Jabbour A, Alpendurada F, Guha K, Ismail NA, et al. The prevalence
and prognostic significance of right ventricular systolic dysfunction in nonischemic dilated
cardiomyopathy. Circulation. 2013;128(15):1623-33.
10. Pueschner A, Chattranukulchai P, Heitner JF, Shah DJ, Hayes B, Rehwald W, et al. The
Prevalence, Correlates, and Impact on Cardiac Mortality of Right Ventricular Dysfunction in
Nonischemic Cardiomyopathy. JACC Cardiovasc Imaging. 2017;10(10 Pt B):1225-36.
11. Fine NM, Chen L, Bastiansen PM, Frantz RP, Pellikka PA, Oh JK, et al. Outcome prediction
by quantitative right ventricular function assessment in 575 subjects evaluated for pulmonary
hypertension. Circ Cardiovasc Imaging. 2013;6(5):711-21.
12. Lebeau R, Di Lorenzo M, Amyot R, Veilleux M, Lemieux R, Sauve C. A new tool for
estimating left ventricular ejection fraction derived from wall motion score index. Can J Cardiol.
2003;19(4):397-404.
13. Lebeau R, Serri K, Morice MC, Hovasse T, Unterseeh T, Piechaud JF, et al. Assessment of
left ventricular ejection fraction using the wall motion score index in cardiac magnetic
resonance imaging. Arch Cardiovasc Dis. 2012;105(2):91-8.
14. Schulz-Menger J, Bluemke DA, Bremerich J, Flamm SD, Fogel MA, Friedrich MG, et al.
Standardized image interpretation and post-processing in cardiovascular magnetic resonance -
2020 update : Society for Cardiovascular Magnetic Resonance (SCMR): Board of Trustees Task
Force on Standardized Post-Processing. J Cardiovasc Magn Reson. 2020;22(1):19.
15. van de Veerdonk MC, Kind T, Marcus JT, Mauritz GJ, Heymans MW, Bogaard HJ, et al.
Progressive right ventricular dysfunction in patients with pulmonary arterial hypertension
responding to therapy. J Am Coll Cardiol. 2011;58(24):2511-9.
16. Bonnemains L, Mandry D, Marie PY, Micard E, Chen B, Vuissoz PA. Assessment of right
ventricle volumes and function by cardiac MRI: quantification of the regional and global
interobserver variability. Magn Reson Med. 2012;67(6):1740-6.
17. Clarke CJ, Gurka MJ, Norton PT, Kramer CM, Hoyer AW. Assessment of the accuracy and
reproducibility of RV volume measurements by CMR in congenital heart disease. JACC
Cardiovasc Imaging. 2012;5(1):28-37.
18. James SH, Wald R, Wintersperger BJ, Jimenez-Juan L, Deva D, Crean AM, et al. Accuracy
of right and left ventricular functional assessment by short-axis vs axial cine steady-state free-
precession magnetic resonance imaging: intrapatient correlation with main pulmonary artery
and ascending aorta phase-contrast flow measurements. Can Assoc Radiol J. 2013;64(3):213-9.
19. D'Errico L, Lamacie MM, Jimenez Juan L, Deva D, Wald RM, Ley S, et al. Effects of slice
orientation on reproducibility of sequential assessment of right ventricular volumes and
ejection fraction: short-axis vs transverse SSFP cine cardiovascular magnetic resonance. J
Cardiovasc Magn Reson. 2016;18(1):60.
20. Foppa M, Arora G, Gona P, Ashrafi A, Salton CJ, Yeon SB, et al. Right Ventricular Volumes
and Systolic Function by Cardiac Magnetic Resonance and the Impact of Sex, Age, and Obesity
in a Longitudinally Followed Cohort Free of Pulmonary and Cardiovascular Disease: The
Framingham Heart Study. Circ Cardiovasc Imaging. 2016;9(3):e003810.
21. Petersen SE, Aung N, Sanghvi MM, Zemrak F, Fung K, Paiva JM, et al. Reference ranges
for cardiac structure and function using cardiovascular magnetic resonance (CMR) in Caucasians
from the UK Biobank population cohort. J Cardiovasc Magn Reson. 2017;19(1):18.
22. Kawut SM, Lima JA, Barr RG, Chahal H, Jain A, Tandri H, et al. Sex and race differences in
right ventricular structure and function: the multi-ethnic study of atherosclerosis-right ventricle
study. Circulation. 2011;123(22):2542-51.
23. Quick S, Speiser U, Kury K, Schoen S, Ibrahim K, Strasser R. Evaluation and classification
of right ventricular wall motion abnormalities in healthy subjects by 3-tesla cardiovascular
magnetic resonance imaging. Neth Heart J. 2015;23(1):64-9.
24. Sievers B, Addo M, Franken U, Trappe HJ. Right ventricular wall motion abnormalities
found in healthy subjects by cardiovascular magnetic resonance imaging and characterized with
a new segmental model. J Cardiovasc Magn Reson. 2004;6(3):601-8.
25. Lebeau R, Di Lorenzo M, Sauve C, Villemaire JM, Veilleux M, Lemieux R, et al. Two-
dimensional echocardiography estimation of right ventricular ejection fraction by wall motion
score index. Can J Cardiol. 2004;20(2):169-76.
FIGURE LEGENDS
Figure 1: RV segmentation used in the wall motion score.
Legend: The RV short-axis views (A-C, A=basal level; B=mid-ventricular level; C=apical level) are
divided in 8 segments (D).
Figure 2. Linear regression analysis between the RV wall motion score index vs. CMR
volumetric-RVEF.
Legend: RV, right ventricle; CMR, cardiac magnetic resonance; RVEF, right ventricular ejection
fraction.
Figure 3. Linear regression analysis between the RV WMSI-RVEF wall motion score index vs.
CMR volumetric-RVEF.
Legend: RV, right ventricle; WMSI, wall motion score index; CMR, cardiac magnetic resonance;
RVEF, right ventricular ejection fraction.
Figure 4. Comparison between volumetric RVEF vs. WMSI-derived RVEF in CMR
Legend: RVEF, right ventricular ejection fraction; CMR, cardiac magnetic resonance; WMSI, wall
motion score index, SD, standard deviation.
Figure 5. Simplified segmental 7-4-2% RVEF method. Calculation tool in a patient with wall
motion abnormalities in the right coronary artery territory.
Legend: Global RVEF is obtained by multiplying the number of segments by the segmental EF
based on the wall motion, as depicted. In this patient, CMR-volumetric RVEF was 44%.
Table 1. The conversion of CMR WMS and WMSI into CMR-volumetric RVEF by the regression model.
WMS WMSI RVEF WMS WMSI RVEF
8 1 58 16 2 36
9 1,1 55 17 2,1 33
10 1,3 53 18 2,3 31
11 1,4 50 19 2,4 28
12 1,5 47 20 2,5 25
13 1,6 44 21 2,6 22
14 1,8 42 22 2,8 20
15 1,9 39 23 2,9 17
16 2 36 24 3 14
RVEF: right ventricular ejection fraction by volumetric cardiac magnetic resonance; WMSI: wall motion score index. Regression equation for the 112 patients was: CMR RVEF = 80 – 22 x WMSI.
Table 2. Comparison between volumetric RVEF vs. WMSI-derived RVEF in CMR
Mean difference between methods ± SD, %
Precision, %
CMR volumetric RVEF vs. WMSI-derived RVEF
-3.0 ± 7.5 29.4
RVEF, right ventricular ejection fraction; CMR, cardiac magnetic resonance; WMSI, wall motion score index
Figure 1: RV segmentation used in the wall motion score.
Figure 2. Linear regression analysis between the RV wall motion score index vs. CMR
volumetric-RVEF.
Figure 3. Linear regression analysis between the RV WMSI-RVEF wall motion score index vs.
CMR volumetric-RVEF.
Figure 4. Comparison between volumetric RVEF vs. WMSI-derived RVEF in CMR
Figure 5. Simplified segmental 7-4-2% RVEF method. Calculation tool in a patient with wall
motion abnormalities in the right coronary artery territory.