Regional heart motion abnormality detection: an information theoretic approach.

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.

Extraspinal findings at lumbar spine CT examinations: prevalence and clinical importance.

PURPOSE: To prospectively determine the prevalence and clinical importance of extraspinal abnormalities in adult outpatients undergoing computed tomography (CT) of the lumbar spine. MATERIALS AND METHODS: Institutional review board approval was obtained for this prospective study. Informed consent was obtained from 400 consecutive adult outpatients (mean age, 49 years; 212 male and 188 female patients) undergoing lumbar spine CT for low back pain and/or radiculopathy. Those with known malignancy were excluded. Dedicated spinal and abdominal full-field-of-view (FOV) images for each patient were reviewed by at least one neuroradiologist and two body radiologists. Extraspinal abnormalities were classified according to the CT Colonography Reporting and Data System (C-RADS). The electronic medical record of the patients with C-RADS E3 and E4 extraspinal findings were reviewed to assess how many of these findings were previously unknown, and the patients were followed up 24-36 months after the initial CT to determine their work-up and outcome. RESULTS: Extraspinal findings were present on images in 162 (40.5%) of 400 lumbar spine CT examinations; 59 (14.8%) patients had indeterminate or clinically important findings requiring clinical correlation or further evaluation. After review of the electronic medical record, the prevalence of clinically important findings was 4.3%, comprising an early-stage renal cell carcinoma and transitional cell carcinoma, chronic lymphocytic leukemia, sarcoidosis, and 13 abdominal aortic aneurysms. Excluding anatomic variants, the full FOV was required to best visualize extraspinal abnormalities in 127 (79.4%) of 160 patients. CONCLUSION: Reviewing the full-FOV images from lumbar spine CT examinations will result in the detection of a small number of substantial extraspinal pathologic findings in addition to many benign incidental findings.

Regional heart motion abnormality detection via information measures and unscented Kalman filtering.

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.

Tracking endocardial motion via multiple model filtering.

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.

Detection of left ventricular motion abnormality via information measures and bayesian filtering.

We present an original information theoretic measure of heart motion based on the Shannon's differential entropy (SDE), which allows heart wall motion abnormality detection. Based on functional images, which are subject to noise and segmentation inaccuracies, heart wall motion analysis is acknowledged as a difficult problem, and as such, incorporation of prior knowledge is crucial for improving accuracy. Given incomplete, noisy data and a dynamic model, the Kalman filter, a well-known recursive Bayesian filter, is devised in this study to the estimation of the left ventricular (LV) cavity points. However, due to similarity between the statistical information of normal and abnormal heart motions, detecting and classifying abnormality is a challenging problem, which we investigate with a global measure based on the SDE. We further derive two other possible information theoretic abnormality detection criteria, one is based on Rényi entropy and the other on Fisher information. The proposed methods analyze wall motion quantitatively by constructing distributions of the normalized radial distance estimates of the LV cavity. Using 269 x 20 segmented LV cavities of short-axis MRI obtained from 30 subjects, the experimental analysis demonstrates that the proposed SDE criterion can lead to a significant improvement over other features that are prevalent in the literature related to the LV cavity, namely, mean radial displacement and mean radial velocity.

Feasibility of magnetic resonance imaging in patients with an implantable loop recorder.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Robotic-assisted left atrial ligation for stroke reduction in chronic atrial fibrillation: a case report.

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.