Endocardial Contour Detection Research Articles

Abstract 17867: Inward Displacement: A Novel CMR Technique for Quantification of Myocardial Wall Motion in Patients With Myocardial Infarction

Introduction: Inward displacement (InD) is a novel parameter which quantifies myocardial wall motion (WM) by measuring the distance travelled by endocardial tracked points towards their corresponding center of the left ventricle in systole, calculated from 3 LAX views obtained during CMR. Hypothesis: InD using CMR is capable of obtaining global quantifiable measurement of WM abnormalities in patients with myocardial infarction (MI) as defined by Late Gadolinium Enhancement (LGE). Methods: We compared patients with history of MI documented by LGE CMR.Studies were acquired in a 1.5 T Siemens Aera (Siemens, Erlangen Germany) scanner with an 18-channel body array coil; Multi-planar ECG-gated cine CMR was obtained using a balanced steady-state free precession readout and LGE images were acquired using an IR prepped sequence 10 min after Gadolinium contrast injection. Offline analysis using automated endocardial LV contour detection was conducted for each of the LAX series and manual corrections were performed as required. InD was measured in mm and expressed as a percentage. Values were compared between MI patients and normal subjects, and analyzed using a Medis Suite workstation (Medis Medical Imaging BV, Leiden, The Netherlands). Results: We analyzed 646 segments in 38 patients of which 9 had past history of myocardial infarction. Global InD for healthy subjects was 28% vs 19% SEM 0.97, as compared to patients with MI (p:< 0.00001). Conclusions: InD is capable of quantification of abnormal motility in subjects with myocardial infarction, therefore producing an objective measure of LV segmental WM abnormalities, further studies are warranted.

Effects of contrast administration on cardiac MRI volumetric, flow and pulse wave velocity quantification using manual and software-based analysis.

Objective:The aim of the current study was to determine the effects of gadolinium contrast agent on right (RV) and left ventricular (LV) volumetric, aortic flow and pulse wave velocity (PWV) quantification using manual, semi-automatic and fully automatic analysis techniques.Methods:61 participants free from known cardiovascular disease were recruited. Cardiac MR was performed on a 3 T scanner. A balanced steady-state free precession stack was acquired of the ventricles with phase contrast imaging of the aorta performed pre- and post-administration of 10 ml 0.5 mmol ml−1 gadoterate meglumine. The images were analysed manually, and using a semi-automated and a fully automated technique.Results:54 completed the study. Gadolinium-based contrast administration significantly increase the signal-to-noise ratio (pre: 830 ± 398 vs post: 1028 ± 540, p = 0.003) with no significant change in contrast-to-noise ratio (pre: 583 ± 302 vs post: 559 ± 346, p = 0.54). On LV analysis, post-contrast analysis yielded significantly higher end systolic volume (54 ± 20 vs 57 ± 18 ml, p = 0.04), and lower ejection fraction (59 ± 9 vs 57 ± 8%, p = 0.023). On RV analysis, gadolinium contrast resulted in no significant differences. Similar results were seen using the semi-automated and fully-automated techniques but with a larger magnitude of difference. Conversely, using both manual and software analysis aortic flow and PWV quantification proved robust to the effects of contrast agent producing only small non-significant differences.Conclusion:Gadolinium contrast administration significantly alters LV endocardial contour detection with this effect amplified when using semi-automated analysis techniques. In comparison, RV and PWV analysis is robust to these effects.Advances in knowledge:Contrast administration alters LV quantification but not flow analysis. However, these differences are small.

Automatic myocardium segmentation of LGE MRI by deformable models with prior shape data

Background Previously a myocardial tissue classification algorithm has been developed to locate and quantify infarct in a given myocardial region-of-interest specified on late gadolinium enhancement (LGE) MR images [1]. To complete the automation requires an endocardial and epicardial contour detection algorithm to replace the current practice of manual contouring that is time-consuming and subject to intraand inter-observer variability. Challenges include: 1) the intensity inhomogeneity of both the healthy and infarct myocardium; 2) the existence of an infarct on a given slice is not known a priori; 3) a subendocardial infarct region’s boundary can be easily mistaken for the endocardial contour due to the proximity and strength of the edge (gradient); and 4) incorporating prior anatomical information (e.g., cine steady-state free precession (SSFP) MRI) while allowing for possible motion between separate studies.

Time Efficiency and Diagnostic Accuracy of New Automated Myocardial Perfusion Analysis Software in 320-Row CT Cardiac Imaging

ObjectiveWe aimed to evaluate the time efficiency and diagnostic accuracy of automated myocardial computed tomography perfusion (CTP) image analysis software.Materials and Methods320-row CTP was performed in 30 patients, and analyses were conducted independently by three different blinded readers by the use of two recent software releases (version 4.6 and novel version 4.71GR001, Toshiba, Tokyo, Japan). Analysis times were compared, and automated epi- and endocardial contour detection was subjectively rated in five categories (excellent, good, fair, poor and very poor). As semi-quantitative perfusion parameters, myocardial attenuation and transmural perfusion ratio (TPR) were calculated for each myocardial segment and agreement was tested by using the intraclass correlation coefficient (ICC). Conventional coronary angiography served as reference standard.ResultsThe analysis time was significantly reduced with the novel automated software version as compared with the former release (Reader 1: 43:08 ± 11:39 min vs. 09:47 ± 04:51 min, Reader 2: 42:07 ± 06:44 min vs. 09:42 ± 02:50 min and Reader 3: 21:38 ± 3:44 min vs. 07:34 ± 02:12 min; p < 0.001 for all). Epi- and endocardial contour detection for the novel software was rated to be significantly better (p < 0.001) than with the former software. ICCs demonstrated strong agreement (≥ 0.75) for myocardial attenuation in 93% and for TPR in 82%. Diagnostic accuracy for the two software versions was not significantly different (p = 0.169) as compared with conventional coronary angiography.ConclusionThe novel automated CTP analysis software offers enhanced time efficiency with an improvement by a factor of about four, while maintaining diagnostic accuracy.

LV Challenge LKEB Contribution: Fully Automated Myocardial Contour Detection

In this paper a contour detection method is described and evaluated on the evaluation data sets of the Cardiac MR Left Ventricle Segmentation Challenge as part of MICCAI 2009’s 3D Segmentation Challenge for Clinical Applications. The proposed method, using 2D AAM and 3D ASM, performs a fully automated detection of the myocardial contours, not requiring any user interaction. The algorithm’s performance is reported using the metrics provided by the LV Challenge organization. Endocardial contour detection was classified as successful in 86% of the images and epicardial contours in 94%. The average perpendicular distance (APD) of the successful contours was 2.28 mm and 2.29 mm for the endo- and epicardial contours, respectively.

Comparison of Measures of Left Ventricular Function from Electrocardiographically Gated 82Rb PET with Contrast-Enhanced CT Ventriculography: A Hybrid PET/CT Analysis

Because of the ultrashort tracer half-life and high positron energy of (82)Rb, PET images acquired with this tracer are noisier and of lower resolution than those obtained with other PET tracers. The validity of electrocardiographic gating using (82)Rb for assessment of left ventricular (LV) function is not well established. To support feasibility, we compared functional parameters from gated (82)Rb PET with simultaneous high-resolution contrast-enhanced CT ventriculography, obtained as a byproduct a CT coronary angiography during hybrid cardiac PET/CT. A total of 24 patients underwent PET/CT, consisting of rest and dipyridamole (82)Rb perfusion studies and contrast-enhanced CT angiography, using a 64-slice scanner, for the workup of coronary artery disease. From gated PET images, LV ejection fraction (EF), end-diastolic volume (EDV), and end-systolic volume (ESV) were calculated using 2 commercial products. For functional CT analysis, commercial software using endocardial contour detection was applied. Inter- and intraobserver agreement was good for all methods. On CT, EF was 66% +/- 13%, ESV was 41 +/- 29 mL, and EDV was 115 +/- 36 mL. On PET, EF during dipyridamole was 56% +/- 15% and 52% +/- 15% using the 2 commercial products (P < 0.05 vs. CT), ESV was 36 +/- 28 and 47 +/- 35 mL (P = not significant vs. CT), and EDV was 75 +/- 30 and 91 +/- 33 mL (P < 0.05 vs. CT). Correlations with CT were 0.85 and 0.87 for EF using commercial software, 0.76 and 0.88 for ESV, and 0.60 and 0.68 for EDV (P < 0.01 for all). Bland-Altman analysis confirmed systematic underestimation of EF and EDV by PET versus CT but did not show a significant deviation from linearity. Global LV function can be measured reproducibly from gated (82)Rb PET, using different available software products. However, underestimation of EF by (82)Rb PET, compared with CT ventriculography, is present, which is a result of underestimation of EDV from count-poor ED frames. This underestimation needs to be considered for clinical interpretation of (82)Rb PET.

Addition of the Long-Axis Information to Short-Axis Contours Reduces Interstudy Variability of Left-Ventricular Analysis in Cardiac Magnetic Resonance Studies

To reduce interstudy variability using long-axis information for correcting short-axis (SA) contours at basal and apical level for left-ventricular analysis by magnetic resonance imaging. A total of 20 patients with documented heart failure and 20 volunteers underwent magnetic resonance imaging examination twice for measuring endocardial end-diastolic volume, endocardial end-systolic volume, mass, and ejection fraction. The boundary of the left ventricle, the mitral valve plane, and apex were marked manually on the 2- and 4-chamber long-axis images. Automatic epicardial and endocardial contour detection was performed on the SA images using the intersection of the outlines from the long axis as starting positions. The same observer compared the interstudy variability of this method with analysis that was based on the SA images only. The interstudy variability decreased when information from the long axis was included; for end-systolic volume, 9.6% versus 4.7% (P = 0.00014); for end-diastolic volume, 4.9% versus 2.5% (P = 0.0011); for mass, 7.4% versus 5.0% (P = 0.11); and for ejection fraction 12.2% versus 5.6% (P = 0.0017), respectively. Identification of the mitral valve plane and apex on long-axis images to limit the extent of volume at the base and the apex of the heart reduces interstudy variability for left-ventricular functional assessment.

Computer-aided diagnosis via model-based shape analysis: Automated classification of wall motion abnormalities in echocardiograms 1

Shape analysis of endocardial contour sequences from echocardiograms can provide classification of wall motion abnormalities (WMA). We previously reported on active appearance motion models (AAMM) for automated detection of endocardial contours in sequences of echocardiograms. The shape analysis of AAMM renders eigenvariations of shape/motion, including typical normal and pathologic endocardial contraction patterns. A set of stress echocardiograms (single-beat four-chamber and two-chamber sequences with expert-verified endocardial contours) of 129 infarct patients was split randomly into training (n = 65) and testing (n = 64) sets. AAMMs were generated from the training set and AAMM shape coefficients (ASCs) were extracted for all sequences and statistically related to regional/global visual wall motion scoring (VWMS) and volumetric parameters. Linear regression showed clear correlations between ASCs and VWMS. Discriminant analysis showed good prediction by ASCs of both segmental (74% correctness) and global WMA (90% correctness). Volumetric parameters correlated poorly to regional VWMS. 1) ASCs show promising accuracy for automated WMA classification. 2) VWMS and endocardial border motion are closely related; with accurate automated border detection, automated WMA classification should be feasible. 3) ASC shape analysis allows contour set evaluation by direct comparison to clinical parameters.

Noninvasive mapping of left ventricular electromechanical asynchrony by three-dimensional echocardiography and semi-automatic contour detection

A system for analyzing left ventricular (LV) electromechanical asynchrony based on transesophageal 3-dimensional echocardiography (3-DE) and semi-automatic endocardial contour detection is described. Eighteen consecutive patients underwent 3-DE. Using TomTec 4DLV software, a 3-dimensional endocardial surface was reconstructed throughout the cardiac cycle. Matlab software generated color-coded polar maps, displaying regional LV displacement and its timing. At the segmental level, Bland-Altmann assessment showed intraobserver variability of LV displacement of 0.1 +/- 3.0 mm and timing of -5.6 +/- 160 ms (bias +/- 2 SD) for all segments and -1.6 +/- 94 ms for the nonapical segments. The combination of 3-DE and semi-automatic contour detection is feasible and provides unique information for assessing regional LV endocardial displacement and electromechanical asynchrony.

Active Appearance–Motion Models for fully automated endocardial contour detection in time sequences of echocardiograms

Active Appearance Models (AAM) are suitable for segmenting 2D images, but for image sequences, time-continuous results are desired. Active Appearance–Motion Models (AAMM) model shape and appearance of the heart over the full cardiac cycle. Single-beat sequences are phase-normalized into stacks of 16 2D images. In a training set, corresponding shape points on the endocard are defined for each image based on expert drawn contours. Shape (2D) and intensity vectors are derived similar to AAM. Intensities are normalized nonlinearly to handle ultrasound-specific problems. For all time frames, shape vectors are simply concatenated as well as intensity vectors. Principal Component Analysis (PCA) extracts appearance eigenvariations over the cycle, capturing typical motion patterns. AAMMs perform segmentation on complete sequences by minimizing model-to-target differences, adjusting AAMM eigenvariation coefficients using gradient descent minimization. This results in time-continuous segmentation. The method was trained and tested on echocardiographic four-chamber sequences of 129 unselected patients, split randomly into a training set ( n=65) and a test set ( n=64). In all sequences, an independent expert manually drew endocardial contours. On the test set, fully automated AAMM performed well in 97% of cases (average distance 3.3 mm, below human interobserver variabilities).

Artificial neural networks and spatial temporal contour linking for automated endocardial contour detection on echocardiograms: a novel approach to determine left ventricular contractile function

This study investigated the use of artificial neural networks (ANN) for image segmentation and spatial temporal contour linking for the detection of endocardial contours on echocardiographic images. Using a backpropagation network, the system was trained with 279 sample regions obtained from eight training images to segment images into either tissue or blood pool region. The ANN system was then applied to parasternal short axis images of 38 patients. Spatial temporal contour linking was performed on the segmented images to extract endocardial boarders. Left ventricular areas (end-systolic and end-diastolic) determined with the automated system were calculated and compared to results obtained by manual contour tracing performed by two independent investigators. In addition, ejection fractions (EF) were derived using the area-length method and compared with radionuclide ventriculography. Image quality was classified as good in 12 (32%), moderate in 13 (34%) and poor in 13 (34%) patients. The ANN system provided estimates of end-diastolic and end-systolic areas in 36 (89%) of echocardiograms, which correlated well with those obtained by manual tracing (R = 0.99, SEE = 1.44). A good agreement was also found for the comparison of EF between the ANN system and Tc-radionuclide ventriculography (RNV, R = 0.93, SEE = 6.36). The ANN system also performed well in the subset of patients with poor image quality. Endocardial contour detection using artificial neural networks and spatial temporal contour linking allows accurate calculations of ventricular areas from transthoracic echocardiograms and performs well even in images with poor quality. This system could greatly enhance the feasibility, accuracy and reproducibility of calculating cardiac areas to derive left ventricular volumes and ejection fractions.

Evaluation of a semiautomatic contour detection approach in sequences of short-axis two-dimensional echocardiographic images

Quantitative analysis of echocardiographic sequences has been limited by time-consuming and strenuous manual tracing approaches. To circumvent these disadvantages, we have developed the EchoCardiographic Measurement System (ECHO-CMS). ECHO-CMS employs the robust, model-based Minimum Cost Contour Tracking technique for semiautomatic detection of left ventricular (LV) endocardial contours in sequences of consecutive echocardiographic images. An evaluation study was carried out on 20 short-axis patient studies (10 transesophageal and 10 transthoracic studies), each consisting of 16 consecutive images covering approximately one cardiac cycle. The LV endocardial contours in the 320 images were analyzed both by manual tracing and semiautomatically. In addition, interobserver and intraobserver variabilities were determined for both techniques in two patients (32 images). Manual editing was required in only 57 (18%) of all 320 contours detected. Average processing time per patient for manual tracing was 25 minutes (of which 18 1/2 minutes was for drawing and corrections) and for semiautomatic tracing it was only 5 1/2 minutes (of which just 1 1/2 minutes was for corrections). Regression analysis showed excellent correspondence between manual and semiautomatic tracing: semiautomatic = 1.01 manual + 5.58%; r = 0.989; standard error of the estimate = 11.9% (n = 320 contours). Interobserver and intraobserver variabilities were smaller for semiautomatic than for manual tracing, although not in all cases statistically significant. In conclusion, semiautomatic LV short-axis endocardial contour detection by ECHO-CMS provides contours that are highly similar to those drawn by an expert; it is five to 10 times faster than manual tracing and reduces intraobserver and interobserver variabilities. This demonstrates that ECHO-CMS is a useful tool for clinical echocardiographic research studies.