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Assessment of Left Ventricular Parameters Using 16-MDCT and New Software for Endocardial and Epicardial Border Delineation

¹ All authors: Department of Diagnostic and Interventional Radiology, University Hospital Essen, Hufelandstrasse 55, 45122 Essen, Germany.

Received May 1, 2004; accepted after revision August 10, 2004.

 
Address correspondence to J. Barkhausen (joerg.barkhausen{at}uni-essen.de).

Abstract

Top Abstract Introduction Subjects and Methods Results Discussion References

 
OBJECTIVE. The purpose of our study was to quantify left ventricular function and mass derived from retrospectively ECG-gated 16-MDCT coronary angiography data sets using a new analysis software based on automatic contour detection in comparison to corresponding standard of reference measurements acquired with MRI.

SUBJECTS AND METHODS. Multiplanar reformations in the short-axis orientation were calculated from axial contrast-enhanced CT images in 18 patients (men, 15; women, three; age range, 38–70 years; mean, 57.4 ± 10.2 [SD] years) who were referred for CT coronary angiography. End-diastolic volume (EDV), end-systolic volume (ESV), ejection fraction (EF), and left ventricular mass (LVM) were analyzed with a recently developed imaging software using an automated contour detection algorithm of left ventricular endo- and epicardial contours and by manual tracing. The data were compared with similar measurements on MRI as the standard of reference.

RESULTS. EDV, ESV, EF, and LVM derived from an automated contour detection algorithm were not statistically significantly different from manual tracing (CT_auto vs CT_manual: EDV = 137.1 ± 45.7 mL vs 134.2 ± 39.9 mL, ESV = 58.8 ± 34.2 mL vs 58.1 ± 30.1 mL, EF = 59.2% ± 13.7% vs 58.1% ± 12.0%, LVM = 130.9 ± 29.1 g vs 133.7 ± 33.2 g; p > 0.05). However, EDV (118.7 ± 43.6 mL), ESV (50.1 ± 33.5 mL), and LVM (142.8 ± 38.4 g) as calculated on MR data sets were statistically significantly different from those calculated on CT (p < 0.05), whereas MRI-based EF (59.9% ± 14.4%) did not differ statistically significantly from those based on both CT algorithms (p > 0.05).

CONCLUSION. Automatic and manual analysis of data acquired during CT coronary angiography using a 16-MDCT scanner allows a reliable assessment of left ventricular ejection fraction and a rough estimation of left ventricular volumes and mass.

Introduction

Top Abstract Introduction Subjects and Methods Results Discussion References

 
Left ventricular volumes and function are predictive markers of a variety of cardiovascular diseases. Patients with both coronary artery disease and depressed left ventricular function are at high risk for sudden death, and left ventricular hypertrophy is associated with a significant excess of cardiovascular mortality and morbidity independent of the presence of coronary artery disease or arterial hypertension [1, 2]. Therefore, a precise quantitative and qualitative assessment of left ventricular function and mass is indispensable.

In clinical practice, echocardiography has been established as the method of choice for determination of ventricular volumes and mass because of its wide availability and relatively short examination times. Considerable drawbacks inherent to echocardiography are operator dependence and rather poor contrast between blood and myocardium. Because of its multiplanar cross-sectional nature coupled with high spatial and temporal resolution and the different signal intensities between blood and myocardium, MRI has evolved as the standard of reference for quantification of left ventricular function and mass [3–9].

During the past decade, CT scanners with four parallel slices and a gantry rotation time of 500 msec have been introduced clinically. This technique allows for the first time a reliable noninvasive visualization of the coronary artery lumen [10, 11]. Recently published studies have shown that a new generation of MDCT scanners, equipped with more and thinner detector rows, allows reliable noninvasive detection of obstructive coronary artery disease [12] and dysfunctional bypass grafts [13]. Analysis of multiplanar reformations of such retrospectively ECG-gated CT coronary angiography data sets also permits the assessment of left ventricular parameters [14, 15].

In this study, using new CT analysis software, we intraindividually compared fully automatically and manually determined left ventricular function and mass measurements derived from retrospectively ECG-gated 16-MDCT coronary angiography examinations with those obtained by MRI.

Subjects and Methods

Top Abstract Introduction Subjects and Methods Results Discussion References

 
The study protocol was approved by the institutional review board, and written informed consent was obtained from all study participants. Eighteen consecutive patients (15 men, three women; age range, 38–70 years; mean, 57.4 ± 10.2 [SD] years; men: age range, 38–70 years; mean, 57.6 ± 11.1 years; women: age range, 53–60 years; mean, 56.5 ± 4.9 years) who were referred for CT angiography of the coronary arteries because of known (n = 7) or suspected (n = 11) coronary artery disease were included in the study. The age between both groups was not significantly different (Mann-Whitney U test, p > 0.05). Only patients with sinus rhythm were included in the study. Patients with renal insufficiency, hyperthyroidism, anamnestic allergy to iodine contrast media, claustrophobia, and metallic implants were excluded from the study. In all patients an additional cardiac MR examination was performed within 48 hr after the CT examination.

CT
CT examinations were performed on a 16-MDCT scanner (Somatom Sensation 16, Siemens Medical Solutions) with a gantry rotation time of 420 msec (collimation, 0.75 mm; table feed, 1.5 mm per rotation; reconstruction increment, 0.5 mm). All CT scans were obtained in the craniocaudal direction. Image acquisition was performed in inspiratory breath-hold. To familiarize the patient with the protocol, the examination, including breath-holding, was practiced beforehand. Betablockers (Brevibloc [esmolol], Baxter) were injected IV in patients with heart rates exceeding 65 beats per minute.

One hundred twenty milliliters of iodinated contrast agent (Xenetix [iobitridol], Guerbet; 300 mg I/mL) was continuously injected into the right antecubital vein via an 18-gauge catheter with an infusion rate of 3.5 mL/sec. To assure maximum contrast material concentration in the coronary arteries, a circular region of interest (ROI) was placed in the ascending aorta. As soon as the signal intensity in the ROI reached a threshold of 120 HU, the patient was instructed to maintain an inspiratory breath-hold, and data acquisition was started. The data set covered the entire heart from base to apex as planned on an unenhanced localizer scan.

Two separate data sets were reconstructed in end-systole and end-diastole, respectively. Endsystole was defined as maximum contraction and end-diastole as maximum dilation of the left ventricle. End-diastolic and end-systolic reconstruction windows were selected on the basis of axial images reconstructed at mid ventricular level in 5% steps throughout the entire RR interval. End-diastolic and end-systolic phases were identified visually on those images showing the largest and smallest left ventricular cavity areas, respectively [16].

After reconstruction, CT raw data were transferred to a PC-based workstation (Wizard, Siemens). Multiplanar reformations in the short-axis orientation (slice thickness, 8 mm; no interslice gap) were calculated from the axial images. The end-diastolic volume (EDV), end-systolic volume (ESV), ejection fraction (EF), and left ventricular mass (LVM) of the reformatted images were analyzed using an automated left ventricular endo- and epicardial contour detection algorithm (CT Mass, MEDIS) and in a separate analysis by manual tracing. The most basal section was defined as the section in which the left ventricular myocardium extended over at least 50% of the circumference on the end-diastolic and end-systolic images. The first slice with a visible lumen was defined as the left ventricular apex.

MRI
MRI was performed on a 1.5-T whole-body scanner (Magnetom Sonata, Siemens) using contiguous segmented cine steady-state free-precession sequences (TR/TE, 3/1.5; flip angle, 60°). All data were collected in inspiratory breath-hold. Slice thickness was 8 mm, and the entire left ventricle was covered without interslice gaps. The true temporal resolution was 40 msec. The phased-array torso coil (2 coil elements) placed anteriorly on the patient and the table-integrated spine coil (2 coil elements) were used for signal reception. EDV, ESV, EF, and LVM were analyzed using commercially available software (Argus, Siemens) on a standard postprocessing workstation (Leonardo, Siemens). The most basal section was defined according to the previously described criteria.

Data Analysis
All CT and MR images were analyzed by an experienced radiologist. EDV, ESV, EF, and LVM were expressed as mean values ± SD. The left ventricular parameters of the CT examinations assessed by automated contour detection algorithm and by manual tracing were compared mutually and with MR data. To detect differences between automated and manually calculated data, we performed Wilcoxon's signed rank test, in which a p value equal to or less than 0.05 was considered statistically significant. CT and MR data were compared using the Bland-Altman approach.

Results

Top Abstract Introduction Subjects and Methods Results Discussion References

 
Of 18 patients referred for CT coronary angiography, one had to be excluded from further analysis because of arrhythmias that occurred during data acquisition. This patient did not undergo the MRI examination. In the remaining 17 patients, CT and MRI examinations were successfully accomplished without any complications. The mean heart rate was 58 ± 4 beats per minute during the CT examinations. During MRI, the mean heart rate was 65 ± 8 beats per minute. Five patients had ß-blockers in their standard medication, and in four patients ß-blockers had to be injected IV before CT. CT and MR image quality was adequate in all patients. The mean duration of analysis using the CT automated contour detection algorithm was 1 min 04 sec ± 21 sec and 3 min 27 sec ± 46 sec for manual tracing, respectively. The mean duration of analysis of MR data was 4 min 01 sec ± 37 sec.

End-Diastolic Volume
The mean EDV measured on MRI was 118.7 ± 43.6 mL (range, 65–239 mL). CT values derived from automated contour detection (CT_auto, 137.1 ± 45.7 mL; range, 75–254 mL) and from manual tracing (CT_manual, 134.2 ± 39.9 mL; range, 78–237 mL) were significantly higher than with MRI (MRI vs CT_auto, p < 0.05; mean difference, –20.3 ± 15.7 mL; MRI vs CT_manual, p < 0.05; mean difference, –17.2 ± 13.1 mL). EDV derived from both CT algorithms was not significantly different (CT_auto vs CT_manual, p > 0.05; mean difference, 3.1 ± 7.1 mL) (Figs. 1, 2A, 2B, and 2C).

View larger version (11K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 1. —Left ventricular end-diastolic volume assessed using MRI and CT. CTAUTO = automatic contour detection; CTMAN = manual contour tracing.

View larger version (14K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 2A. —Bland-Altman plots of end-diastolic volume. Bland-Altman plots were assessed using MRI versus CTauto (automated contour detection, A), MRI versus CTmanual (manual tracing, B), and CTauto versus CTmanual (C).

View larger version (13K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 2B. —Bland-Altman plots of end-diastolic volume. Bland-Altman plots were assessed using MRI versus CTauto (automated contour detection, A), MRI versus CTmanual (manual tracing, B), and CTauto versus CTmanual (C.)

View larger version (14K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 2C. —Bland-Altman plots of end-diastolic volume. Bland-Altman plots were assessed using MRI versus CTauto (automated contour detection, A), MRI versus CTmanual (manual tracing, B), and CTauto versus CTmanual (C).

End-Systolic Volume
The mean ESV calculated from the MR data sets was 50.1 ± 33.5 mL (range, 16–143 mL). ESV values derived from both CT algorithms (CT_auto, 58.8 ± 34.2 mL; range, 11–129 mL; CT_manual, 58.1 ± 30.1 mL; range, 21–130 mL) were significantly higher than with MRI (MRI vs CT_auto, p < 0.05; mean difference, –9.2 ± 13.9 mL; MRI vs CT_manual, p < 0.05; mean difference, –9.1 ± 10.9 mL). ESV values from the CT examination assessed by automated and manual measurements were not significantly different (CT_auto vs CT_manual, p > 0.05; mean difference, 0.1 ± 7.1 mL) (Figs. 3, 4A, 4B, and 4C).

View larger version (11K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 3. —Left ventricular end-systolic volume assessed using MRI and CT. CTAUTO = automatic contour detection; CTMAN = manual contour tracing.

View larger version (14K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 4A. —Bland-Altman plots of end-systolic volume. Bland-Altman plots were assessed using MRI versus CTauto (automated contour detection, A), MRI versus CTmanual (manual tracing, B), and CTauto versus CTmanual (C).

View larger version (14K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 4B. —Bland-Altman plots of end-systolic volume. Bland-Altman plots were assessed using MRI versus CTauto (automated contour detection, A), MRI versus CTmanual (manual tracing, B), and CTauto versus CTmanual (C).

View larger version (14K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 4C. —Bland-Altman plots of end-systolic volume. Bland-Altman plots were assessed using MRI versus CTauto (automated contour detection, A), MRI versus CTmanual (manual tracing, B), and CTauto versus CTmanual (C).

Ejection Fraction
EF assessed on MRI (59.9% ± 14.4%; range, 18–76%) was slightly higher but not significantly different from CT values derived by automated contour detection (59.2% ± 13.7%; range, 31–85%) and manual tracing (58.1% ± 11.9%; range, 30–73%; MRI vs CT_auto, p > 0.05; mean difference, 1.0% ± 9.0%; MRI vs CT_manual, p > 0.05; mean difference, 2.6% ± 7.3%). EF derived from both CT algorithms was not significantly different (CT_auto vs CT_manual, p > 0.05; mean difference, 1.5% ± 4.4%) (Figs. 5, 6A, 6B, and 6C).

View larger version (10K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 5. —Ejection fraction assessed using MRI and CT. CTAUTO = automatic contour detection; CTMAN = manual contour tracing.

View larger version (14K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 6A. —Bland-Altman plots of ejection fraction. Bland-Altman plots were assessed using MRI versus CTauto (automated contour detection, A), MRI versus CTmanual (manual tracing, B), and CTauto versus CTmanual (C).

View larger version (14K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 6B. —Bland-Altman plots of ejection fraction. Bland-Altman plots were assessed using MRI versus CTauto (automated contour detection, A), MRI versus CTmanual (manual tracing, B), and CTauto versus CTmanual (C).

View larger version (13K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 6C. —Bland-Altman plots of ejection fraction. Bland-Altman plots were assessed using MRI versus CTauto (automated contour detection, A), MRI versus CTmanual (manual tracing, B), and CTauto versus CTmanual (C).

Left Ventricular Mass
The highest mean LVM was assessed using MRI (142.7 ± 38.4 g; range, 87–224 g). These data were significantly different from CT values derived by automated contour detection (130.9 ± 29.1 g; range, 89–175 g) and manual tracing (133.7 ± 33.2 g; range, 92–198 g; MRI vs CT_auto, p < 0.05; mean difference, 11.7 ± 15.9 g; MRI vs CT_manual, p < 0.05; mean difference, 8.3 ± 12.4 g). LVM measurements derived from both CT algorithms were not significantly different (CT_auto vs CT_manual, p > 0.05; mean difference, –3.3 ± 7.0 g) (Figs. 7, 8A, 8B, and 8C).

View larger version (12K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 7. —Left ventricular mass assessed using MRI and CT. CTAUTO = automatic contour detection; CTMAN = manual contour tracing.

View larger version (16K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 8A. —Bland-Altman plots of left ventricular mass. Bland-Altman plots were assessed using MRI versus CTauto (automated contour detection, A), MRI versus CTmanual (manual tracing, B), and CTauto versus CTmanual (C).

View larger version (15K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 8B. —Bland-Altman plots of left ventricular mass. Bland-Altman plots were assessed using MRI versus CTauto (automated contour detection, A), MRI versus CTmanual (manual tracing, B), and CTauto versus CTmanual (C).

View larger version (15K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 8C. —Bland-Altman plots of left ventricular mass. Bland-Altman plots were assessed using MRI versus CTauto (automated contour detection, A), MRI versus CTmanual (manual tracing, B), and CTauto versus CTmanual (C).

Examples of end-diastolic and end-systolic MR and CT images and automatic detected and manually traced endo- and epicardial contours are displayed in Figures 9A and 9B.

View larger version (61K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 9A. —Examples of end-diastolic and end-systolic MR and CT images. End-diastolic MR (top row) and CT images and automatically detected (center row) and manually traced (bottom row) endo- and epicardial contours. Only each second short-axis slice is displayed. See next page for end-systolic images.

View larger version (69K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 9B. —Examples of end-diastolic and end-systolic MR and CT images. End-systolic MR (top row) and CT images and automatically detected (center row) and manually traced (bottom row) endo- and epicardial contours. Only each second short-axis slice is displayed. See previous page for end-diastolic images.

Discussion

Top Abstract Introduction Subjects and Methods Results Discussion References

 
In this study we evaluated the ability of the newest generation of CT scanners to assess left ventricular parameters. This study carries four major findings we believe are important: First, EDV and ESV values calculated on MR data sets are statistically significantly lower compared with automated and manual measurements derived from 16-MDCT examinations. Second, LVM measurements on MR data sets resulted in statistically significantly higher values compared with automated and manual measurements derived from 16-MDCT examinations. Third, EF measurements are not statistically significantly different among all techniques. Fourth, values from the automated contour detection algorithm using the new software CT Mass are not significantly different compared with manual tracing.

Cine MRI has been established as the most accurate clinical method for assessing ventricular volumes [17–19] and mass [20, 21]. In particular, steady-state free-precession cine MRI is the technique of choice because of its excellent contrast between the blood-filled cavities and the surrounding myocardium. In this setting, automatic segmentation provides accurate volumetric data [8].

In contrast to conventional angiographic volumetric analysis, the cross-sectional nature of MRI makes it independent of geometric assumptions. In addition, noninvasiveness, the lack of ionizing radiation, and the excellent soft-tissue contrast without IV contrast material injection render MRI highly attractive for patients with various cardiac diseases with compromised left ventricular function. In contrast to 3D echocardiography, MRI is operator-independent and permits a far better distinction between myocardium and the ventricular cavity. However, cardiac MRI is still limited with regard to restricted scanner availability, relatively high costs, and generally long examination times.

Several studies have shown the ability of electron beam CT and MDCT to assess left ventricular function and mass by multiplanar reformation algorithms using short-axis images [14, 15, 22]. Because of the retrospective gating used for cardiac MDCT, all data for the analysis of left ventricular parameters can be calculated from a standard CT coronary angiography data set without any additional radiation exposure.

Because of the introduction of the new generation of MDCT scanners that allow the reliable detection of significant coronary stenoses and calcified plaque, the number of cardiac CT investigations has increased. With regard to time constraints in clinical practice, an automatic postprocessing tool allowing fast and reliable assessment of left ventricular volumes and masses is a prerequisite for a more widespread use of left ventricular measurements based on CT data sets.

The results of our study show that automatic contour detection is feasible for CT data sets and results in fast and reliable measurements without significant differences compared with manual contour tracing. However, a drawback of CT measurements in general is the overestimation of EDV and ESV compared with MRI as the standard of reference. Regarding ESV, this overestimation might be explained by the relatively low temporal resolution, even of the latest 16-MDCT scanners, of about 210 msec compared with cine MRI (40 msec) and the inability to acquire the maximum systolic contraction. For this reason, an underestimation of the EDV might be plausible. However, in our study, CT measurements overestimated EDV in the same dimension as ESV. One reason might be that the two techniques do not display identical slices, and variations of the most basal slice thickness may result in a difference of up to 27 mL, as determined in our study.

Left ventricular EF estimated on CT with both manual and fully automated contour determination was almost identical to measurements using MRI, indicating a reliable estimation of the global left ventricular function using the former technique. Using the CT Mass software, automated analysis of left ventricular parameters was significantly quicker than manual drawing, indicating a potential improvement in workflow and data analysis.

The data provided in this study need to be interpreted critically. First, only a small group of patients were examined; the data need to be validated by larger patient cohorts. Furthermore, the influence of ß-blocker administration before CT has not been assessed. For functional analysis it is crucial that no medication influencing the patient's heart rate or myocardial contractility be applied. Therefore, the administration of ß-blockers in some patients before CT is not only a limitation of this study but also a general limitation of the method.

However, the major advantage of 16-MDCT regarding estimation of left ventricular parameters is the shorter examination time per patient. However, as a result of lower temporal resolution, the need of nephrotoxic contrast material, and substantial radiation doses up to 13 mSv delivered during a single CT scan [23], CT can hardly be considered a method of choice for the determination of left ventricular parameters in clinical practice. Nevertheless these parameters are most likely to be seen as additional information in patients undergoing CT coronary angiography.

In conclusion, fully automated analysis of data acquired during CT coronary angiography using a 16-MDCT scanner allows fast and reliable assessment of left ventricular EF and a rough estimation of left ventricular volumes and mass.

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