Left Ventricular Segmentation Research Articles

Imagej's Contribution to Left Ventricular Segmentation in Myocardial Perfusion Imaging

Imagej's Contribution to Left Ventricular Segmentation in Myocardial Perfusion Imaging

Quantification of mitral regurgitation in patients with hypertrophic cardiomyopathy using aortic and pulmonary flow data: impacts of left ventricular outflow tract obstruction and different left ventricular segmentation methods

BackgroundCardiovascular magnetic resonance (CMR) imaging in patients with hypertrophic cardiomyopathy (HCM) enables the assessment of not only left ventricular (LV) hypertrophy and scarring but also the severity of mitral regurgitation. CMR assessment of mitral regurgitation is primarily based on the difference between LV stroke volume (LVSV) and aortic forward flow (Ao) measured using the phase-contrast (PC) technique. However, LV outflow tract (LVOT) obstruction causing turbulent, non-laminar flow in the ascending aorta may impact the accuracy of aortic flow quantification, leading to false conclusions regarding mitral regurgitation severity. Thus, we decided to quantify mitral regurgitation in patients with HCM using Ao or, alternatively, main pulmonary artery forward flow (MPA) for mitral regurgitation volume (MRvol) calculations.MethodsThe analysis included 143 prospectively recruited subjects with HCM and 15 controls. MRvol was calculated as the difference between LVSV computed with either the inclusion (LVSVincl) or exclusion (LVSVexcl) of papillary muscles and trabeculations from the blood pool and either Ao (MRvolAoi or MRvolAoe) or MPA (MRvolMPAi or MRvolMPAe). The presence or absence of LVOT obstruction was determined based on Doppler echocardiography findings.ResultsMRvolAoi was higher than MRvolMPAi in HCM patients with LVOT obstruction [47.0 ml, interquartile range (IQR) = 31.5–60.0 vs. 35.5 ml, IQR = 26.0–51.0; p < 0.0001] but not in non-obstructive HCM patients (23.0 ml, IQR = 16.0–32.0 vs. 24.0 ml, IQR = 15.3–32.0; p = 0.26) or controls (18.0 ml, IQR = 14.3–21.8 vs. 20.0 ml, IQR = 14.3–22.0; p = 0.89). In contrast to controls and HCM patients without LVOT obstruction, in HCM patients with LVOT obstruction, aortic flow-based MRvol (MRvolAoi) was higher than pulmonary-based findings (MRvolMPAi) (bias = 9.5 ml; limits of agreement: −11.7–30.7 with a difference of 47 ml in the extreme case). The differences between aortic-based and pulmonary-based MRvol values calculated using LVSVexcl mirrored those derived using LVSVincl. However, MRvol values calculated using LVSVexcl were lower in all the groups analyzed (HCM with LVOT obstruction, HCM without LVOT obstruction, and controls) and with all methods of MRvol quantification used (p ≤ 0.0001 for all comparisons).ConclusionsIn HCM patients, LVOT obstruction significantly affects the estimation of aortic flow, leading to its underestimation and, consequently, to higher MRvol values than those obtained with MPA-based MRvol calculations.

“Same-Patient Processing” for multiple cardiac SPECT studies. 1. Improving LV segmentation accuracy

ObjectivesThis paper describes a novel approach (same-patient processing, or SPP) aimed at improving left ventricular segmentation accuracy in patients with multiple SPECT studies, and evaluates its performance compared to conventional processing in a large population of 962 patients undergoing rest and stress electrocardiography-gated SPECT MPI, for a total of 5,772 image datasets (6 per patient). MethodsEach dataset was independently processed using a standard algorithm, and a shape quality control score (SQC) was produced for every segmentation. Datasets with a SQC score higher than a specific threshold, suggesting algorithmic failure, were automatically reprocessed with the SPP-modified algorithm, which incorporates knowledge of the segmentation mask location in the other datasets belonging to the same patient. Experienced operators blinded as to whether datasets had been processed based on the standard or SPP approach assessed segmentation success/failure for each dataset. ResultsThe SPP approach reduced segmentation failures from 219/5772 (3.8%) to 42/5772 (0.7%) overall, with particular improvements in attenuation corrected (AC) datasets with high extra-cardiac activity (from 100/962 (10.4%) to 12/962 (1.4%) for rest AC, and from 41/962 (4.3%) to 9/962 (0.9%) for stress AC). The number of patients who had at least one of their 6 datasets affected by segmentation failure decreased from 141/962 (14.7%) to 14/962 (1.7%) using the SPP approach. ConclusionWhenever multiple image datasets for the same patient exist and need to be processed, it is possible to deal with the images as a group rather than individually. The same-patient processing approach can be implemented automatically, and may substantially reduce the need for manual reprocessing due to cardiac segmentation failure.

Detection and diagnosis of dilated cardiomyopathy and hypertrophic cardiomyopathy using image processing techniques

Major heart diseases like heart muscle damage and valvular problems are diagnosed using echocardiogram. Since the echocardiogram is an image or sequence of images with less information the cardiologist spends more time to predict or to make decision. Automating the detection and diagnosis of dilated cardiomyopathy (DCM) and hypertrophic cardiomyopathy (HCM) is a key enabling technology in computer aided diagnosis systems. In this paper, a system is proposed to automatically detect and diagnose dilated cardiomyopathy (DCM) and hypertrophic cardiomyopathy (HCM). This system performs denoising, enhancement, before left ventricular segmentation is carried out in the individual frames. Using the segmented left ventricle, the LV parameters like volume and ejection fraction (EF) are calculated and also the end-diastolic LV is extracted. The PCA and DCT features are obtained from the extracted end-diastolic LV and the classifiers BPNN, SVM and combined K-NN are used to classify the normal hearts, hearts affected with DCM and hearts affected with HCM. The PCA feature with BPNN classifier gives a highest overall accuracy of 92.04% in classifying normal and abnormal hearts. Experiments over 60 echocardiogram videos expose that the proposed system can be effectively utilized to detect and diagnose DCM and HCM.

Simultaneous extraction of endocardial and epicardial contours of the left ventricle by distance regularized level sets.

Segmentation of the cardiac left ventricle (LV) is still an open problem and is challenging due to the poor contrast between tissues around the epicardium and image artifacts. To extract the endocardium and epicardium of the cardiac left ventricle accurately, the authors propose a two-layer level set approach for segmentation of the LV from cardiac magnetic resonance short-axis images. In the proposed method, the endocardium and epicardium are represented by two specified level contours of a level set function. Segmentation of the LV is formulated as a problem of optimizing the level set function such that these two level contours best fit the epicardium and endocardium, subject to a distance regularization (DR) term to preserve a smoothly varying distance between them. The DR term introduces a desirable interaction between the two level contours of a single level set function, which contributes to preserve the anatomical geometry of the epicardium and endocardium of the LV. In addition, the proposed method has an intrinsic ability to deal with intensity inhomogeneity in MR images, which is a common image artifact in MRI. Their method is quantitatively validated by experiments on the datasets for the MICCAI 2009 grand challenge on left ventricular segmentation and the MICCAI 2013 challenge workshop on segmentation: algorithms, theory and applications (SATA). To overcome discontinuity of 2D segmentation results at some adjacent slices for a few cases, the authors extend distance regularized two-layer level set to 3D to refine the segmentation results. The corresponding metrics for their method are better than the methods in the MICCAI 2009 challenge. Their method was ranked at the first place in terms of Hausdorff distance and the second place in terms of Dice similarity coefficient in the MICCAI 2013 challenge. Experimental results demonstrate the advantages of their method in terms of segmentation accuracy and consistency with the heart anatomy.

Reproducibility and observer variability of tissue phase mapping for the quantification of regional myocardial velocities.

To systematically investigate the reproducibility of global and segmental left ventricular (LV) velocities derived from tissue phase mapping (TPM). Breath held and ECG synchronized TPM data (spatial/temporal resolution = 2 × 2mm(2)/20.8ms) were acquired in 18 healthy volunteers. To analyze scan-rescan variability, TPM was repeated in all subjects during a second visit separated by 16 ± 5 days. Data analysis included LV segmentation, and quantification of global and regional (AHA 16-segment modal) metrics of LV function [velocity-time curves, systolic and diastolic peak and time-to-peak (TTP) velocities] for radial (Vr), long-axis (Vz) and circumferential (VΦ) LV velocities. Mean velocity time curves in basal, mid-ventricular, and apical locations showed highly similar LV motion patterns for all three velocity components (Vr, VΦ, Vz) for scan and rescan. No significant differences for both systolic and diastolic peak and TTP myocardial velocities were observed. Segmental analysis revealed similar regional peak Vr and Vz during both systole and diastole except for three LV segments (p = 0.045, p = 0.033, and p = 0.009). Excellent (p < 0.001) correlations between scans and rescan for peak Vr (R(2) = 0.92), peak Vz (R(2) = 0.90), radial TTP (R(2) = 0.91) and long-axis TTP (R(2) = 0.88) confirmed good agreement. Bland-Altman analysis demonstrated excellent intra-observer and good inter-observer analysis agreement but increased variability for long axis peak velocities. TPM based analysis of global and regional myocardial velocities can be performed with good reproducibility. Robustness of regional quantification of long-axis velocities was limited but spatial velocity distributions across the LV could reliably be replicated.

Histopathological Correlates of Global and Segmental Left Ventricular Systolic Dysfunction in Experimental Chronic Chagas Cardiomyopathy.

BackgroundChronic Chagas cardiomyopathy in humans is characterized by segmental left ventricular wall motion abnormalities (WMA), mainly in the early stages of disease. This study aimed at investigating the detection of WMA and its correlation with the underlying histopathological changes in a chronic Chagas cardiomyopathy model in hamsters.Methods and ResultsFemale Syrian hamsters (n=34) infected with 3.5×104 or 105 blood trypomastigote Trypanosoma cruzi (Y strain) forms and an uninfected control group (n=7) were investigated. After 6 or 10 months after the infection, the animals were submitted to in vivo evaluation of global and segmental left ventricular systolic function by echocardiography, followed by euthanasia and histological analysis for quantitative assessment of fibrosis and inflammation with tissue sampling in locations coinciding with the left ventricular wall segmentation employed at the in vivo echocardiographic evaluation. Ten of the 34 infected animals (29%) showed reduced left ventricular ejection fraction (<73%). Left ventricular ejection fraction was more negatively correlated with the intensity of inflammation (r=−0.63; P<0.0001) than with the extent of fibrosis (r=−0.36; P=0.036). Among the 24 animals with preserved left ventricular ejection fraction (82.9±5.5%), 8 (33%) showed segmental WMA predominating in the apical, inferior, and posterolateral segments. The segments exhibiting WMA, in comparison to those with normal wall motion, showed a greater extent of fibrosis (9.3±5.7% and 7±6.3%, P<0.0001) and an even greater intensity of inflammation (218.0±111.6 and 124.5±84.8 nuclei/mm², P<0.0001).ConclusionsIsolated WMA with preserved global systolic left ventricular function is frequently found in Syrian hamsters with experimental chronic Chagas cardiomyopathy whose underlying histopathological features are mainly inflammatory.

Standardized Evaluation System for Left Ventricular Segmentation Algorithms in 3D Echocardiography.

Real-time 3D Echocardiography (RT3DE) has been proven to be an accurate tool for left ventricular (LV) volume assessment. However, identification of the LV endocardium remains a challenging task, mainly because of the low tissue/blood contrast of the images combined with typical artifacts. Several semi and fully automatic algorithms have been proposed for segmenting the endocardium in RT3DE data in order to extract relevant clinical indices, but a systematic and fair comparison between such methods has so far been impossible due to the lack of a publicly available common database. Here, we introduce a standardized evaluation framework to reliably evaluate and compare the performance of the algorithms developed to segment the LV border in RT3DE. A database consisting of 45 multivendor cardiac ultrasound recordings acquired at different centers with corresponding reference measurements from three experts are made available. The algorithms from nine research groups were quantitatively evaluated and compared using the proposed online platform. The results showed that the best methods produce promising results with respect to the experts' measurements for the extraction of clinical indices, and that they offer good segmentation precision in terms of mean distance error in the context of the experts' variability range. The platform remains open for new submissions.

Sparse group composition for robust left ventricular epicardium segmentation

Left ventricular (LV) epicardium segmentation in cardiac magnetic resonance images (MRIs) is still a challenging task, where the a-priori knowledge like those that incorporate the heart shape model is usually used to derive reasonable segmentation results. In this paper, we propose a sparse group composition (SGC) approach to model multiple shapes simultaneously, which extends conventional sparsity-based single shape prior modeling to incorporate a-priori spatial constraint information among multiple shapes on-the-fly. Multiple interrelated shapes (shapes of epi- and endo-cardium of myocardium in the case of LV epicardium segmentation) are regarded as a group, and sparse linear composition of training groups is computed to approximate the input group. A framework of iterative procedure of refinement based on SGC and segmentation based on deformation model is utilized for LV epicardium segmentation, in which an improved shape-constraint gradient Chan-Vese model (GCV) acted as deformation model. Compared with the standard sparsity-based single shape prior modeling, the refinement procedure has strong robust for relative gross and not much sparse errors in the input shape and the initial epicardium location can be estimated without complicated landmark detection due to modeling spatial constraint information among multiple shapes effectively. Proposed method was validated on 45 cardiac cine-MR clinical datasets and the results were compared with expert contours. The average perpendicular distance (APD) error of contours is 1.50±0.29mm, and the dice metric (DM) is 0.96±0.01. Compared to the state-of-the-art methods, our proposed approach appealed competitive segmentation performance and improved robustness.

IJARCCE - Computer and Communication Engineering

This paper completely describes the fully automatic segmentation system for left ventricular myocardium segmentation from CT images by using active contour model. The system follows a coarse-to-fine strategy by first restricting the left ventricle and then damaging the myocardial surfaces of the left ventricle for segmentation. Particularly, the blood surface of a CT image is removed and illustrated as a triangular surface. Then, the ventricle is localized as a salient element on these surface victimization geometric and anatomical characteristics. After that, the heart muscle surfaces square measure initialized from the localization result and evolved by applying forces from the image intensities with a constraint supported the initial heart muscle surface locations.

Three-dimensional left ventricular segmentation from magnetic resonance imaging for patient-specific modelling purposes.

To propose a nearly automated left ventricular (LV) three-dimensional (3D) surface segmentation procedure, based on active shape modelling (ASM) and built on a database of 3D echocardiographic (3DE) LV surfaces, for cardiac magnetic resonance (CMR) images, and to test its accuracy for LV volumes computation compared with 'gold standard' manual tracings and discs-summation method. The ASM was created based on segmented LV surfaces (4D LV analysis, Tomtec) from 3DE datasets of 205 patients. Then, it was applied to the cardiac magnetic resonance imaging short-axis (SAX) images stack of 12 consecutive patients. After proper realignment using two- and four-chambers CMR long-axis views both as reference and for initializing LV apex and base (six points in total), the ASM was iteratively and automatically updated to match the information of all the SAX planes contemporaneously, resulting in an endocardial LV 3D mesh from which volume was directly derived. The same CMR images were analysed by an experienced cardiologist to derive end-diastolic and end-systolic volumes. Linear correlation and Bland-Altman analyses were applied vs. the manual 'gold standard'. Active shape modelling results showed high correlations with manual values both for LV volumes (r(2) > 0.98) and ejection fraction (EF) (r(2) > 0.90), non-significant biases and narrow limits of agreement. The proposed method resulted in accurate detection of 3D LV endocardial surfaces, which lead to fast and reliable measurements of LV volumes and EF when compared with manual tracing of CMR SAX images. The segmented 3D mesh, including a realistic LV apex and base, could constitute a novel starting point for more realistic patient-specific finite element modelling.

Challenge on Endocardial Three-dimensional Ultrasound Segmentation (CETUS)

Real-time 3D echocardiography has already been shown to be an accurate tool for left ventricular (LV) volume assessment. However, LV border identification remains a challenging task, mainly because of the low contrast of the images combined with drop-out artifacts. Many (semi-)automatic algorithms have been proposed to segment the LV border, but a systematic and fair comparison between such methods has so far been impossible due to a lack of publicly available common database. The aim of this MICCAI challenge was to gather researchers around the field of LV segmentation in 3D cardiac ultrasound by providing a common database to compare algorithms directly. The proposed platform will allow a consistent evaluation and ranking of the current state-of-the-art segmentation solutions and will contribute to a faster clinical translation of groundbreaking technical advances. The purpose of this paper is to describe the technical aspects of the generation of the database, give an overview of the ranking strategy and the outline of the challenge itself.

Semi-automated left ventricular segmentation based on a guide point model approach for 3D cine DENSE cardiovascular magnetic resonance.

BackgroundThe most time consuming and limiting step in three dimensional (3D) cine displacement encoding with stimulated echoes (DENSE) MR image analysis is the demarcation of the left ventricle (LV) from its surrounding anatomical structures. The aim of this study is to implement a semi-automated segmentation algorithm for 3D cine DENSE CMR using a guide point model approach.MethodsA 3D mathematical model is fitted to guide points which were interactively placed along the LV borders at a single time frame. An algorithm is presented to robustly propagate LV epicardial and endocardial surfaces of the model using the displacement information encoded in the phase images of DENSE data. The accuracy, precision and efficiency of the algorithm are tested.ResultsThe model-defined contours show good accuracy when compared to the corresponding manually defined contours as similarity coefficients Dice and Jaccard consist of values above 0.7, while false positive and false negative measures show low percentage values. This is based on a measure of segmentation error on intra- and inter-observer spatial overlap variability. The segmentation algorithm offers a 10-fold reduction in the time required to identify LV epicardial and endocardial borders for a single 3D DENSE data set.ConclusionA semi-automated segmentation method has been developed for 3D cine DENSE CMR. The algorithm allows for contouring of the first cardiac frame where blood-myocardium contrast is almost nonexistent and reduces the time required to segment a 3D DENSE data set significantly.

A collaborative resource to build consensus for automated left ventricular segmentation of cardiac MR images

A collaborative framework was initiated to establish a community resource of ground truth segmentations from cardiac MRI. Multi-site, multi-vendor cardiac MRI datasets comprising 95 patients (73 men, 22 women; mean age 62.73±11.24years) with coronary artery disease and prior myocardial infarction, were randomly selected from data made available by the Cardiac Atlas Project (Fonseca et al., 2011). Three semi- and two fully-automated raters segmented the left ventricular myocardium from short-axis cardiac MR images as part of a challenge introduced at the STACOM 2011 MICCAI workshop (Suinesiaputra et al., 2012). Consensus myocardium images were generated based on the Expectation-Maximization principle implemented by the STAPLE algorithm (Warfield et al., 2004). The mean sensitivity, specificity, positive predictive and negative predictive values ranged between 0.63 and 0.85, 0.60 and 0.98, 0.56 and 0.94, and 0.83 and 0.92, respectively, against the STAPLE consensus. Spatial and temporal agreement varied in different amounts for each rater. STAPLE produced high quality consensus images if the region of interest was limited to the area of discrepancy between raters. To maintain the quality of the consensus, an objective measure based on the candidate automated rater performance distribution is proposed. The consensus segmentation based on a combination of manual and automated raters were more consistent than any particular rater, even those with manual input. The consensus is expected to improve with the addition of new automated contributions. This resource is open for future contributions, and is available as a test bed for the evaluation of new segmentation algorithms, through the Cardiac Atlas Project (www.cardiacatlas.org).

Rest myocardial perfusion disturbance is related to inflammation but not to fibrosis inexperimental chronic chagas cardiomyopathy

Purpose: Myocardial perfusion disturbance is a common finding in human chronic Chagas cardiomyopathy (CC), but its underlying tissue changes and pathophysiologic mechanisms are unclear. We aimed at investigating the histologic changes related to the myocardial perfusion changes detected in an experimental model of CC in hamsters. Methods: Female Syrian hamsters (n=34) were infected with 35.000 or 100.000 trypomastigotes forms of T. cruzi Y-strain and were submitted to in vivo imaging 6 to 10 months afterwards. High-resolution rest Sestamibi-Tc-99m-SPECT imaging was used for detection of myocardial perfusion defects (MPD), defined as segments with uptake <50% of the maximum pixel uptake value on polar maps analysis. The segmental and global left ventricular (LV) function was assessed by using 2D-Echocardiogram. Ex vivo histologic analysis included quantitation of fibrosis (picrosirius red) and inflammatory infiltrate. A 13-segment model of LV segmentation was used for all imaging analyses and histologic topographic correlations. Results: MPD were found in 17 (50%) of the infected animals. No transmural scar was detected in segments with MPD. The subgroup of animals with MPD, in comparison to animals without MPD, showed: 1) reduced LV ejection fraction (65±21 and 81±9%, respectively, p<0.01); 2) larger inflammatory infiltrate (172.8±89 and 111.5±54 nuclei/mm2; p=0.04); 3) higher segmental wall motion score (1.44±0.5 and 1.04±0.05%; p=0.003); 4) but similar extent of fibrosis (8.4±3.9; 6.6±3.2%; p=0.06). There was a significant association between regional MPD and abnormal wall motion (p<0,001). The segments with MPD (n=57) as compared to the segments with normal perfusion (n=487) showed larger extent of inflammatory infiltrate (175.8±145.0 and 111.6±69.2 nuclei/mm2, respectively; p=0.001), higher segmental wall motion score (1.65±0.6 and 1.1±0.4; p<0.0001) and comparable extent of fibrosis (10.5±18.6 and 7.4±6.2%; p=0.37). Conclusions: Rest MPD in experimental CC is topographically correlated to inflammatory changes that are intense enough to be associated to wall motion impairment. The fact that these derangements are not due to fibrosis opens the possibility that therapeutic interventions aimed at reducing the degree of myocardial inflammation and/or hypoperfusion may favorably impact on the natural history of this model of experimental CC.

Left ventricular segmentation from MRI datasets with edge modelling conditional random fields.

BackgroundThis paper considers automatic segmentation of the left cardiac ventricle in short axis magnetic resonance images. Various aspects, such as the presence of papillary muscles near the endocardium border, makes simple threshold based segmentation difficult.MethodsThe endo- and epicardium are modelled as two series of radii which are inter-related using features describing shape and motion. Image features are derived from edge information from human annotated images. The features are combined within a discriminatively trained Conditional Random Field (CRF). Loopy belief propagation is used to infer segmentations when an unsegmented video sequence is given. Powell’s method is applied to find CRF parameters by minimizing the difference between ground truth annotations and the inferred contours. We also describe how the endocardium centre points are calculated from a single human-provided centre point in the first frame, through minimization of frame alignment error.ResultsWe present and analyse the results of segmentation. The algorithm exhibits robustness against inclusion of the papillary muscles by integrating shape and motion information. Possible future improvements are identified.ConclusionsThe presented model integrates shape and motion information to segment the inner and outer contours in the presence of papillary muscles. On the Sunnybrook dataset we find an average Dice metric of 0.91±0.02 and 0.93±0.02 for the inner and outer segmentations, respectively. Particularly problematic are patients with hypertrophy where the blood pool disappears from view at end-systole.

1030Role of cardiac magnetic resonance and echocardiography prior to cardiac resynchronization therapy

Purpose: Cardiac resynchronization therapy is an established method for treatment of patients with heart failure. Optimal left ventricular lead implantation guided by novel imaging techniques appears to be a promising method to increase the response to this therapy. Methods: 29 patients scheduled for cardiac resynchronization therapy underwent detailed ECHO and cardiac magnetic resonance studies prior to CRT device implantation. The aim of the imaging studies was to determine an optimal pacing site for the left ventricular lead placement. The off-line analysis was performed by two experienced readers in a blinded fashion. CMR was performed on 3T system (Siemens). Cine, late gadolinium enhancement and myocardial tagging sequences were acquired. Image software Segment (Sweden) was used for left ventricular segmentation. Left ventricular wall motion and myocardial scar analysis were performed in all 17 segments . Tagging sequences were analysed with inTag (Universite de Lyon) in short axis slices at basal, mid ventricular and apical levels of the left ventricle. The care was taken to ensure correct alignment of these slices to achieve the same segmentation as with echocardiography. Standard echocardiographic parasternal and apical views were stored and analysed off-line. Left ventricular radial, longitudinal and circumferential strains were assessed using commercially available software (EchoPac 6.1). The cut-off value for regional longitudinal strain was – 4.5% to discriminate between transmural (>50%) and nontransmural (<50%) myocardial scar (Roes S.D., Mollema S.A., Bax J.J., Am J Cardiol 2009;104;312-317). Results: Wall motion analysis was possible in all 493 segments in CMR and in 472 (96%) in echocardiography studies. Mean ejection fraction of the left ventricle was 29 ± 5 % when assessed by echocardiography and 23 ± 9 % in CMR studies (P = 0.0005). The average LV EDVi was 99 ± 21 ml/m2 (ECHO) and 115 ± 32 ml/m2 (CMR) (P = 0.003). Scar transmurality was significantly overestimated by ECHO using the longitudinal strain cut-off -4.5% for discrimination between transmural and nontransmural scar – 2.8 times. The dyssynchrony analysis targeted to define the latest activated LV segment showed significant agreement between the two methods. The exact segments agreement was detected in 19 cases (66%). In 6 cases (21%) targeted segment was ± 1 neighbour segment. Discordance between the latest activated segment was detected in 4 patiens (14 %). Conclusions: We have demonstrated that CMR has a potential to improve diagnostic preoperative assessment in patients with severe heart failure and might be a useful tool for LV dyssynchrony assessment. CMR is an excellent method for scar assessment and is a useful tool for LV strain analysis.

Validation of an algorithm for left ventricular segmentation in 150 patients shows potential for further development towards fully automatic segmentation

Background Automatic segmentation of the left ventricle (LV) is desirable to assess the cardiac parameters end-diastolic volume (EDV), end-systolic volume (ESV), ejection fraction (EF) and left ventricular mass (LVM) since manual segmentation is time consuming and observer dependent. A physiologically correct segmentation of the left ventricle requires careful consideration of the long axis displacement and the LV outflow tract which makes the myocardium non-circumferential in the basal slices. To detect the long axis displacement a constraint could be used to keep the LVM fairly constant over the cardiac cycle. However, in order to use this constraint, the error of the segmentation has to be low regarding both endocardial and epicardial borders in the non-basal part of the LV. Therefore, the purpose of this study was to improve and validate an automatic algorithm for LV segmentation in the non-basal part of the LV, as a first step towards fully automatic segmentation.

Aligning Coronary Anatomy and Myocardial Perfusion Territories

Background— Appropriate clinical decisions concerning diagnosis and treatment of coronary artery disease rely on correct integration of data on coronary anatomy and myocardial perfusion. The purpose of this article is to introduce a new left ventricular segmentation model for improved alignment of coronary arterial segments and myocardial perfusion territories, designed for the CORE320 study. Methods and Results— CORE320 is a prospective, multicenter study with a primary objective to evaluate the diagnostic accuracy of 320-row detector computed tomography (CT) to detect coronary artery luminal stenosis and corresponding myocardial perfusion deficits in patients with suspected coronary artery disease compared with the gold standard of conventional coronary angiography and single-photon emission CT myocardial perfusion imaging. We describe a 19-coronary segment and 13-myocardial territory alignment model, its application in both standard and CT image data sets, and the adjudication process of the initial cohort of patients recruited for the CORE320 study. Adjudication committees reviewed the images of the first 101 gold standard and 107 CT data sets. On the basis of the presented model and rules, all cases for adjudication were correctly identified. During image review, 6 (5.9%) gold standard and 9 (8.4%) CT data sets needed further realignment not triggered by the algorithm. Conclusions— We present a vascular territory distribution model developed for the CORE320 multicenter study, which accounts for variability in coronary anatomy and potential myocardial perfusion territory overlap. Clinical Trial Registration— URL: http://www.clinicaltrials.gov . Unique identifier: NCT00934037.

Left ventricular myocardium segmentation on delayed phase of multi-detector row computed tomography

Advanced ischemic heart disease is usually accompanied by left ventricular (LV) myocardial volume loss and an abnormal enhancing pattern on delayed phase of multi-detector row computed tomography (MDCT). To assist radiologists and physicians in estimating the LV myocardial volume on delayed phase, this paper proposes an adaptive segmentation method for contouring the myocardial region in the delayed-phase MDCT and for computing the volume. The proposed method uses an anisotropic diffusion filter as a preprocessing procedure to enhance contrast and reduce specks in MDCT imaging. This work picks the middle of mid-ventricular level image slices as the lead slice. The proposed method develops two contouring modes to sketch the myocardium contour on the lead slice. By establishing the obtained contours as the initial contours, the region-growing method is employed to identify the contour of the myocardial region for each slice. The convex-hull finding algorithm is then used to refine the extracted contour. Finally, the width properties of the myocardial region and the morphological operators are used to obtain the entire LV myocardial volume. Twenty-seven healthy patients who had no symptoms of ischemic heart disease are examined to evaluate the performance of the proposed method. Compared with manual contours delineated by two experienced experts, the contouring results using computer simulation reveal that the proposed method reliably identifies contours similar to those obtained using manual sketching. The proposed method provides robust contouring for the LV myocardium on delayed-phase MDCT. The potential role of this technique may substantially reduce the time required to sketch manually a precise contour with high stability.