Ventricular Geometry From Non-contrast Non-ECG-gated CT Scans

Rationale and Objectives

Imaging-based assessment of cardiovascular structure and function provides clinically relevant information in smokers. Non-cardiac-gated thoracic computed tomographic (CT) scanning is increasingly leveraged for clinical care and lung cancer screening. We sought to determine if more comprehensive measures of ventricular geometry could be obtained from CT using an atlas-based surface model of the heart.

Materials and Methods

Subcohorts of 24 subjects with cardiac magnetic resonance imaging (MRI) and 262 subjects with echocardiography were identified from COPDGene, a longitudinal observational study of smokers. A surface model of the heart was manually initialized, and then automatically optimized to fit the epicardium for each CT. Estimates of right and left ventricular (RV and LV) volume and free-wall curvature were then calculated and compared to structural and functional metrics obtained from MRI and echocardiograms.

Results

CT measures of RV dimension and curvature correlated with similar measures obtained using MRI. RV and LV volume obtained from CT inversely correlated with echocardiogram-based estimates of RV systolic pressure using tricuspid regurgitation jet velocity and LV ejection fraction respectively. Patients with evidence of RV or LV dysfunction on echocardiogram had larger RV and LV dimensions on CT. Logistic regression models based on demographics and ventricular measures from CT had an area under the curve of >0.7 for the prediction of elevated right ventricular systolic pressure and ventricular failure.

Conclusions

These data suggest that non-cardiac-gated, non-contrast-enhanced thoracic CT scanning may provide insight into cardiac structure and function in smokers.

Introduction

Cardiovascular disease is a major cause of morbidity in smokers, and as much as 50% of the estimated 24 million patients in the United States with chronic obstructive pulmonary disease (COPD) die of cardiovascular causes . Although echocardiography and cardiac magnetic resonance imaging (MRI) are often used to study cardiac structure and function in COPD , these are not routinely deployed in all smokers. Computed tomographic (CT) imaging of the chest is broadly used in clinical care and is increasingly used for lung cancer screening in high-risk smokers . Assessment of cardiac structure on those CT scans may help identify patients with COPD at greater risk of developing cardiac dysfunction. Rapid, noninvasive assessments of cardiac morphology and a better understanding of the functional interdependence of heart and lung may improve healthcare outcomes through early detection and initiation of treatment.

CT has been used to quantify coronary and thoracic aortic calcification , study the size of the pulmonary artery and aorta , and describe the relationship between pulmonary vascular and parenchymal disease progression . The caliber of other vessels such as the pulmonary veins has been explored as an image-based metric of volume status .

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Methods

COPDGene Study

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Cardiac MRI Cohort

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Echocardiography Cohort

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Cardiac Modeling

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Sphericity=π13(6V)23S Sphericity

(

)

where V is the volume and S is the surface area of the ventricle. The free wall was defined as the surface segments not in contact with the septum. The curvature estimate was defined as

Curvature=(κ12+κ22)2−−−−−−−√ Curvature

(

)

where κ1 κ

1 is the minimum and κ2 κ

2 is the maximum curvature of the surface .

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Statistical Analysis

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Results

Reproducibility of CT-based Measures of Ventricular Geometry

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TABLE 1

Demographics of Two Cohorts Used in This Study, Both Subsets of the COPDGene Study

Variable (A) (B) (C) Dual Enrollment MRI Cohort ECHO Cohort ( N = 11) ( N = 24) ( N = 262) Demographics Age (y) 52 ± 8 59 ± 9 65 ± 9 White race 10(91%) 14(58%) 250(95%) Male sex 11(100%) 16(67%) 126(48%) BMI 29 ± 9 29 ± 8 28 ± 7 BSA (m 2 ) 2.06 ± 0.25 1.99 ± 0.22 1.93 ± .28 6-minute walk distance (ft) 1105 ± 552 1069 ± 209 1173 ± 365 Lung function FEV 1 % predicted 70 ± 22 57 ± 27 47 ± 23 FEV 1 -to-FVC ratio 0.68 ± 0.17 0.52 ± 0.16 0.46 ± 0.17 TLCpp

(Race adjusted, L) 76 ± 20 102 ± 14 106 ± 19 ( N = 257) MRI LVEF (%) 64 ± 8 RVEF (%) 58 ± 10 RVESVI 24 ± 9 RVMI 13 ± 4 ECHO RV minor axis 3.24 ± 0.54 ( N = 238) RVSP 36.0 ± 13.22 LVEF (%) 64.1 ± 7.7

BMI, body mass index; BSA, estimated body surface area; COPDGene, Genetic Epidemiology of COPD; FEV 1 , forced expiratory volume in 1 second; FVC, forced vital capacity; LVEF, left ventricular ejection fraction; MRI, magnetic resonance imaging; RVEF, right ventricular ejection fraction; RVESVI, right ventricular end systolic volume index; RVMI, right ventricular mass index; TLCpp, total lung capacity, percent predicted.

Data were presented as n (%) and mean (±standard deviation).

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Validation of CT-based Ventricular Geometry as Compared to MRI Measures

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TABLE 2

Correlation Coefficient ( R ) Between CT-derived Cardiac Volume Measurements and Cardiac MRI-based Volume and Mass Estimates in 24 Subjects

Cardiac MRI-derived Metrics CT-derived Metrics RV ED Volume MR RV ED Mass MR RV_EPI Volume CT 0.65 (.0006) 0.60 (.002) RV_ENDO Volume CT 0.61 (.001) 0.57 (.004) LV ED volume MR LV ED mass MR LV_EPI volume CT 0.61 (.001) 0.56 (.004) LV_ENDO volume CT 0.60 (.002) 0.51 (.01) LV ED length MR LV Long Axis CT 0.49 (.02) LV mid ED curvature MR LV Free Wall curvature CT 0.70 (.0001) LV ED radius ratio MR LV sphericity CT 0.51 (.01) RV/LV ED volume ratio MR RV/LV volume ratio CT 0.46 (.02)

CT, computed tomographic; ED, end diastolic; LV, left ventricular; MID, middle of ventricle; MRI, magnetic resonance imaging; RV, right ventricular.

Spearman correlation coefficients, with accompanying P values.

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Association Between CT-based Ventricular Geometry and Echocardiographic-based Ventricular Structure and Function

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$Figure 1, The right ventricle ( blue ) and left ventricle ( red ) in three subjects with a cardiac model fitted to the surface of the heart using volumetric non-contrast computed tomographic scan. A subject with Gold 0 disease and with a right ventricular-to-left ventricular (RV-to-LV) ratio of 0.5, RV systolic pressure (RVSP) of 20 and no evidence of ventricular dysfunction (a) , subject with Gold 3 disease and RV/LV ratio of 0.8, RV dilation, elevated RVSP of 47 (b) , and subject with Gold 2 disease with LV dilation and ejection fraction of 35% (c) .$

TABLE 3

Correlation of CT-derived Right Ventricular Geometry With Echocardiogram-derived Geometric and Functional Measures (First Three Columns) and the Difference in Those Metrics Between Patients Characterized as Having Abnormal Versus Patients Having Normal RV Function

CT-derived Measures of Right Ventricle Echocardiogram-derived Measures RV Minor Axis ECHO RVSP Decreased RV Function Normal RV Function_P_ ( N = 238) ( N = 194) ( N = 10) ( N = 120) RV Long Axis CT R = 0.36,R = 0.28, 7.3[6.1–7.5] 6.9[6.4–7.3] .44P < .0001P < .0001 RV short Axis CT R = 0.44,R = 0.27, 4.3[4.0–4.7] 4.0[3.6–4.3] .03P < .0001P = .0002 RV_EPI volume CT R = 0.46,R = 0.3, 106[87–117] 85[66–103] .04P < .0001P < .0001 RV_ENDO volume CT R = 0.45R = 0.28 67[54–79] 57[42–67] .06P < .0001P = .0001 RV surface area CT R = 0.49,R = 0.24, 169[142–194] 148[130–169] .05P < .0001P = .0006

CT, computed tomographic; RA, right atrium; RV, right ventricle; RVSP, estimated right ventricular systolic pressure by echocardiogram.

Values shown are Spearman correlation coefficients ( R ) and P values associated with Spearman correlation. Values shown for subgroups with decreased and normal RV function are shown as median [interquartile range].

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TABLE 4

Correlation of CT-derived Left Ventricular Geometry With Echocardiogram-derived Functional Measures (First Three Columns) and the Difference in Those Metrics Between Patients Characterized as Having Abnormal Versus Patients Having Normal Left Ventricular Ejection Fraction

CT Measures of Left Ventricle ECHO-derived Measures LVEF Reduced EF Normal EF_P_ Value ( N = 98) ( N = 18) ( N = 162) LV Long Axis CT R = −0.20, P = .05 8.54[7.69–9.21] 8.05[7.42–8.62] .08 LV_EPI volume CT R = −0.22, P = .03 207.1[167.5–240.1] 173.1[143.8–199.2] .01 LV_ENDO volume CT R = −0.23, P = .03 118.0[89.0–141.1] 97.1[78.4–111.6] .02 LV surface area CT R = −0.21, P = .03 202.7[164.1–223.7] 176.6[153.1–197.0] .03 LV volume/surface Area CT R = −0.18, P = .08 1.03[0.98–1.07] 0.97[0.92–1.02] .009 LV Free-Wall Curvature CT R = 0.20, P = .04 0.018[0.0169–0.0195] 0.0192[0.18-0.0205] .03

EF, ejection fraction; LV, left ventricle.

Values shown are correlation coefficients ( R ) and P values associated with Spearman correlation. Values shown for subgroups with decreased and normal ejection fraction are shown as median [interquartile range].

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TABLE 5

Logistic Regression Model Performance Based on AUC (Area Under the Curve) for Models Predicting RVSP >40, Reduced RV Function, Reduced LV Ejection Fraction, and History of CHF

Model Variables RVSP >40 RVF Reduced LVEF_N_ = 194N = 130N = 180 (64/194) (10/130) (18/180) AUC AUC Model Variables AUC RVV 0.67 0.70 LVV 0.68 RVV+Age+Gender 0.75 0.75 LVV+BMI 0.71 RVV+Age+Gender+RVV*Gender 0.77

LV, left ventricle; LVEF, left ventricular ejection fraction; LVV, left ventricle volume; RV, right ventricle; RVF, evidence of RV failure on echocardiogram; RVSP, estimated right ventricular systolic pressures by echocardiogram; RVV, right ventricle volume.

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Discussion

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$Figure 2, Model discrimination performance as measured by the area under the receiver operating characteristic curve, shown for four logistic regression models constructed using a stepwise forward selection method for markers of cardiac dysfunction in the cohort with available echocardiograms. A model of ability to predict right ventricular (RV) systolic pressure ≥40 (a) , RV failure as marked on the echocardiogram (b) , and left ventricular failure defined by ejection fraction <55% (c) .$

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Appendix

Supplementary Data

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Table S1

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Table S2

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Appendix S1

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Ventricular Geometry From Non-contrast Non-ECG-gated CT Scans

Rationale and Objectives

Materials and Methods

Results

Conclusions

Introduction

Methods

COPDGene Study

Cardiac MRI Cohort

Echocardiography Cohort

Cardiac Modeling

Statistical Analysis

Results

Reproducibility of CT-based Measures of Ventricular Geometry

Validation of CT-based Ventricular Geometry as Compared to MRI Measures

Association Between CT-based Ventricular Geometry and Echocardiographic-based Ventricular Structure and Function

Discussion

Appendix

Supplementary Data

References

Further Reading

3D Printing of Preoperative Simulation Models of a Splenic Artery Aneurysm

A Clinically Meaningful Interpretation of the Prospective Investigation of Pulmonary Embolism Diagnosis (PIOPED) Scintigraphic Data

A Tribute to Mark Richard Robbin, MD