Cardiac indices estimation is of great importance during identification and diagnosis of cardiac disease in clinical routine. However, estimation of multitype cardiac indices with consistently reliable and high accuracy is still a great challenge due to the high variability of cardiac structures and the complexity of temporal dynamics in cardiac MR sequences. While efforts have been devoted into cardiac volumes estimation through feature engineering followed by a independent regression model, these methods suffer from the vulnerable feature representation and incompatible regression model. In this paper, we propose a semi-automated method for multitype cardiac indices estimation. After the manual labeling of two landmarks for ROI cropping, an integrated deep neural network Indices-Net is designed to jointly learn the representation and regression models. It comprises two tightly-coupled networks, such as a deep convolution autoencoder for cardiac image representation, and a multiple output convolution neural network for indices regression. Joint learning of the two networks effectively enhances the expressiveness of image representation with respect to cardiac indices, and the compatibility between image representation and indices regression, thus leading to accurate and reliable estimations for all the cardiac indices. When applied with five-fold cross validation on MR images of 145 subjects, Indices-Net achieves consistently low estimation error for LV wall thicknesses (1.44 ± 0.71 mm) and areas of cavity and myocardium (204 ± 133 mm). It outperforms, with significant error reductions, segmentation method (55.1% and 17.4%), and two-phase direct volume-only methods (12.7% and 14.6%) for wall thicknesses and areas, respectively. These advantages endow the proposed method a great potential in clinical cardiac function assessment.
Abstract. Accurate estimation of ventricular volumes plays an essential role in clinical diagnosis of cardiac diseases. Existing methods either rely on segmentation or are restricted to direct estimation of the left ventricle. In this paper, we propose a novel method for direct and joint volume estimation of bi-ventricles, i.e., the left and right ventricles, without segmentation and user inputs. Based on the cardiac image representation by multiple and complementary features, we adopt regression forests to jointly estimate the two volumes. Our method is validated on a dataset of 56 subjects with a total of 3360 MR images which shows that our method can achieve a high correlation coefficient of around 0.9 with manual segmentation obtained by human experts. With our proposed method, the most daily-used estimation of cardiac function, e.g., ejection fraction, can be conducted in a much more efficient, accurate and convenient way.
Automating the detection and localization of segmental (regional) left ventricle (LV) abnormalities in magnetic resonance imaging (MRI) has recently sparked an impressive research effort, with promising performances and a breadth of techniques. However, despite such an effort, the problem is still acknowledged to be challenging, with much room for improvements in regard to accuracy. Furthermore, most of the existing techniques are labor intensive, requiring delineations of the endo- and/or epi-cardial boundaries in all frames of a cardiac sequence. The purpose of this study is to investigate a real-time machine-learning approach which uses some image features that can be easily computed, but that nevertheless correlate well with the segmental cardiac function. Starting from a minimum user input in only one frame in a subject dataset, we build for all the regional segments and all subsequent frames a set of statistical MRI features based on a measure of similarity between distributions. We demonstrate that, over a cardiac cycle, the statistical features are related to the proportion of blood within each segment. Therefore, they can characterize segmental contraction without the need for delineating the LV boundaries in all the frames. We first seek the optimal direction along which the proposed image features are most descriptive via a linear discriminant analysis. Then, using the results as inputs to a linear support vector machine classifier, we obtain an abnormality assessment of each of the standard cardiac segments in real-time. We report a comprehensive experimental evaluation of the proposed algorithm over 928 cardiac segments obtained from 58 subjects. Compared against ground-truth evaluations by experienced radiologists, the proposed algorithm performed competitively, with an overall classification accuracy of 86.09% and a kappa measure of 0.73.
screened for study eligibility. Inclusion criteria were the following:Background-Transmural scar occupying left ventricular (LV) pacing regions has been associated with reduced response to cardiac resynchronization therapy (CRT). However, spatial influences of lead tip delivery relative to scar at both pacing sites remain poorly explored. This study evaluated scar distribution relative to LV and right ventricular (RV) lead tip placement through coregistration of late gadolinium enhancement MRI and cardiac computed tomographic (CT) findings. Influences on CRT response were assessed by serial echocardiography. Methods and Results-Sixty patients receiving CRT underwent preimplant late gadolinium enhancement MRI, postimplant cardiac CT, and serial echocardiography. Blinded segmental evaluations of mechanical delay, percentage scar burden, and lead tip location were performed. Response to CRT was defined as a reduction in LV end-systolic volume ≥15% at 6 months. The mean age and LV ejection fraction were 64±9 years and 25±7%, respectively. Mean scar volume was higher among CRT nonresponders for both the LV (23±23% versus 8±14% [P=0.01]) and RV pacing regions (40±32% versus 24±30% [P=0.04]). Significant pacing region scar was identified in 13% of LV pacing regions and 37% of RV pacing regions. Absence of scar in both regions was associated with an 81% response rate compared with 55%, 25%, and 0%, respectively, when the RV, LV, or both pacing regions contained scar. LV pacing region dyssynchrony was not predictive of response. Conclusions-Myocardial scar occupying the LV pacing region is associated with nonresponse to CRT. Scar occupying the RV pacing region is encountered at higher frequency and seems to provide a more intermediate influence on CRT response. (Circ Cardiovasc Imaging. 2013;6:542-550.)
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