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ABSTRACT: Tracking regional heart motion and detecting the corresponding abnormalities play an essential role in the diagnosis of cardiovascular diseases. Based on functional images, which are subject to noise and segmentation/registration inaccuracies, regional heart motion analysis is acknowledged as a difficult problem and, therefore, incorporation of prior knowledge is desirable to enhance accuracy. Given noisy data and a nonlinear dynamic model to describe myocardial motion, an unscented Kalman smoother is proposed in this study to estimate the myocardial points. Due to the similarity between the statistical information of normal and abnormal heart motions, detecting and classifying abnormality is a challenging problem. We use the Shannon's differential entropy of the distributions of potential classifier features to detect and locate regional heart motion abnormality. A naive Bayes classifier algorithm is constructed from the Shannon's differential entropy of different features to automatically detect abnormal functional regions of the myocardium. Using 174 segmented short-axis magnetic resonance cines obtained from 58 subjects (21 normal and 37 abnormal), the proposed method is quantitatively evaluated by comparison with ground truth classifications by radiologists over 928 myocardial segments. The proposed method performed significantly better than other recent methods, and yielded an accuracy of 86.5% (base), 89.4% (mid-cavity) and 84.5% (apex). The overall classification accuracy was 87.1%. Furthermore, standard kappa statistic comparisons between the proposed method and visual wall motion scoring by radiologists showed that the proposed algorithm can yield a kappa measure of 0.73.
Medical image analysis 01/2013; · 3.09 Impact Factor
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ABSTRACT: Tracking heart motion plays an essential role in the diagnosis of cardiovascular diseases. As such, accurate characterization of dynamic behavior of the left ventricle (LV) is essential in order to enhance the performance of motion estimation. However, a single Markovian model is not sufficient due to the substantial variability in typical heart motion. Moreover, dynamics of an abnormal heart could be very different from that of a normal heart. This study introduces a tracking approach based on multiple models, each matched to a different phase of the LV motion. First, the algorithm adopts a graph cut distribution matching method to tackle the problem of segmenting LV cavity from cardiac MR images, which is acknowledged as a difficult problem because of low contrast and photometric similarities between the heart wall and papillary muscles within the LV cavity. Second, interacting multiple model (IMM), an effective estimation algorithm for Markovian switching system, is devised subsequent to the segmentations to yield state estimates of the endocardial boundary points. The IMM also yields the model probability indicating the model that most closely matches the LV motion. The proposed method is evaluated quantitatively by comparison with independent manual segmentations over 2280 images acquired from 20 subjects, which demonstrated competitive results in comparisons with related recent methods.
IEEE transactions on bio-medical engineering 08/2010; 57(8):2001-10. · 2.15 Impact Factor
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The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2010, San Francisco, CA, USA, 13-18 June 2010; 01/2010
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IEEE Transactions on Information Technology in Biomedicine. 01/2010; 14:1106-1113.
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ABSTRACT: This study investigates regional heart motion abnormality detection using various classifier features with Shannon's Differential Entropy (SDE). Rather than relying on elementary measurements or a fixed set of moments, the SDE measures global distribution information and, as such, has more discriminative power in classifying distributions. Based on functional images, which are subject to noise and segmentation inaccuracies, heart wall motion analysis is acknowledged as a difficult problem and, therefore, incorporation of prior knowledge is desirable to enhance the accuracy. Given noisy data and nonlinear dynamic model to describe the myocardial motion, unscented Kalman filter, a recursive nonlinear Bayesian filter, is devised in this study so as to estimate LV cavity points. Subsequently, a naive Bayes classifier algorithm is constructed from the SDEs of different features in order to automatically detect abnormal functional regions of the myocardium. Using 90 x 20 segmented LV cavities of short-axis magnetic resonance images obtained from 30 subjects, the experimental analysis carried over 480 myocardial segments demonstrates that the proposed method perform significantly better than other recent methods, and can lead to a promising diagnostic support tool to assist clinicians.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 01/2010; 13(Pt 1):409-17.
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IEEE Trans. Med. Imaging. 01/2009; 28:1902-1913.
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Computer Vision - ACCV 2009, 9th Asian Conference on Computer Vision, Xi'an, China, September 23-27, 2009, Revised Selected Papers, Part III; 01/2009
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International Journal of Computer Vision. 01/2009; 85:115-132.
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ABSTRACT: The implantable loop recorder (ILR) is a useful tool in the diagnosis of syncope. Our understanding of their functional and safety profile in interfering environments such as magnetic resonance imaging (MRI) becomes increasingly important as they become more prevalent.
We report four patients with an ILR who underwent MRI. The ILR memory was cleared before MRI and no changes were made to programmed settings. Device interrogation took place immediately after the scan. Patients were surveyed for device movement and heating, in addition to cardiopulmonary symptoms after their MRI.
Following MRI scanning, all patients were asymptomatic and no device movement or heating was observed. In addition, the functionality of the device remained unaffected. Artifacts mimicking arrhythmias were seen in all ILR patients regardless of the type of MRI scan.
MRI scanning of ILR patients can be performed without harm to patient or device, but artifacts that could be mistaken for a tachyarrhythmia are seen frequently.
Pacing and Clinical Electrophysiology 04/2008; 31(3):333-7. · 1.35 Impact Factor
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2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008), 24-26 June 2008, Anchorage, Alaska, USA; 01/2008
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ABSTRACT: Patients with atrial fibrillation are at significant risk for sustaining a thromboembolic stroke. More than 90% of thromboemboli form in the left atrial appendage. Ligation of the left atrial appendage to reduce the risk of stroke is often performed in connection with other cardiac surgical procedures. As a stand-alone procedure, however, left atrial ligation has generally been deemed too invasive and has gained little support as an alternative therapeutic option. We report a case of port-access robotic-assisted left atrial ligation as a stand-alone procedure in a patient with chronic atrial fibrillation in whom anticoagulation was a contraindication. To our knowledge, this is the first reported case of stand-alone robotic-assisted left atrial ligation in the literature.
Heart Surgery Forum 02/2006; 9(1):E533-5; discussion E535. · 0.63 Impact Factor
