[Show abstract][Hide abstract]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.
Full-text · Article · Jan 2013 · Medical image analysis
[Show abstract][Hide abstract]ABSTRACT: Example methods and apparatus to process left-ventricle cardiac images are disclosed. A disclosed example method includes identifying a first landmark point in a first cardiac image, identifying a first centroid of a left ventricle depicted in the first cardiac image, and performing a Cartesian-to-polar transformation to form a first rectangular representation of the left ventricle depicted in the first cardiac image based on the first landmark point and the first centroid.
[Show abstract][Hide abstract]ABSTRACT: The Cardiac Ejection Fraction (EF) is an essential criterion in cardiovascular disease prognosis. In clinical routine, EF is often computed from manually or automatically segmenting the Left Ventricle (LV) in End-dyastole and Endsystole frames, which is prohibitively time consuming and needs user interactions. In this paper, we propose a method to minimize user effort and estimate the EF directly from image statistics via machine-learning techniques, without the need for comprehensive segmentations of all the MRI images in a subject dataset. From a user-provided segmentation of a single image, we build a statistic based on the Bhattacharyya coefficient of similarity between image distributions for each of the images in a subject dataset (200 images). We demonstrate that these statistical features are non-linearly related to the LV cavity areas and therefore can be used to estimate the EF. We used Principal Component Analysis (PCA) to reduce the dimensionality of the features and areas. Then, an Artificial Neural Network (ANN) was used to predict the LV cavity areas from the dimension-reduced features. The EF is finally estimated from the obtained areas.
Full-text · Article · May 2012 · Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging
[Show abstract][Hide abstract]ABSTRACT: Image tracking as described herein can include: segmenting a first image into regions; determining an overlap of intensity distributions in the regions of the first image; and segmenting a second image into regions such that an overlap of intensity distributions in the regions of the second image is substantially similar to the overlap of intensity distributions in the regions of the first image. In certain embodiments, images can depict a heart at different points in time and the tracked regions can be the left ventricle cavity and the myocardium. In such embodiments, segmenting the second image can include generating first and second curves that track the endocardium and epicardium boundaries, and the curves can be generated by minimizing functions containing a coefficient based on the determined overlap of intensity distributions in the regions of the first image.
[Show abstract][Hide abstract]ABSTRACT: To prospectively determine the prevalence and clinical importance of extraspinal abnormalities in adult outpatients undergoing computed tomography (CT) of the lumbar spine.
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.
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.
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.
[Show abstract][Hide abstract]ABSTRACT: Obliterating the left atrial appendage from systemic circulation in patients with atrial fibrillation has been proposed to reduce thromboembolic events. The goal of this study was to assess the effectiveness of a circular method of epicardial surgical ligation in obliterating the left atrial appendage and maintaining sustained exclusion.
Patients with permanent atrial fibrillation and an indication for elective cardiac surgery were enrolled. All patients underwent preoperative cardiac gated computerized tomography (CT) and transesophageal echocardiography (TEE). During the cardiac procedure circular ligation of the appendage was performed.
Twelve patients, mean (SD) age 65 (12) years completed the study. Intraoperative TEE demonstrated all patients (12/12) had complete postligation occlusion of the left atrial appendage. At three-month follow-up, cardiac gated CT demonstrated that 75% (9/12) of the patients had communication of contrast dye from the left atrial appendage to body of left atrium. Left atrial appendage orifice area and volume were reduced from mean (SD) (5.5 cm(2) [1.8] to 0.5 cm(2) [0.4] p = 0.002) and (14.0 cm(3) [8.3] to 2.7 cm(3) [1.3] p = .005) postligation, respectively. No clinically significant thromboembolic events were reported.
Epicardial suture ligation of the left atrial appendage resulted in successful intra-operative exclusion on TEE; however, a significant portion of patient's demonstrated communication of contrast on CT. This is suggestive of incomplete long-term exclusion. The clinical significance of reduction in left atrial appendage orifice area and volume with a persistent communication requires further study.
No preview · Article · Mar 2012 · Journal of Cardiac Surgery
[Show abstract][Hide abstract]ABSTRACT: Certain embodiments of the present technology provide systems, methods and computer instructions for computer aided analysis of images. In certain embodiments, for example, such a method includes: isolating a motion area in an image; segmenting the image; utilizing a support vector machine to identify a region of interest in the image; utilizing a graph-cut algorithm to refine the region of interest; and verifying the region of interest. In certain embodiments, for example, such a method further includes: aligning a set of images and/or outputting a set of aligned images sequentially. In certain embodiments, the systems, methods and computer instructions disclosed herein can be used to aid analysis of cardiac images, for example. In certain embodiments, the systems, methods and computer instructions disclosed herein can be used to aid analysis of four dimensional images, for example.
[Show abstract][Hide abstract]ABSTRACT: This study investigates fast detection of the left ventricle (LV) endo- and epicardium boundaries in a cardiac magnetic resonance (MR) sequence following the optimization of two original discrete cost functions, each containing global intensity and geometry constraints based on the Bhattacharyya similarity. The cost functions and the corresponding max-flow optimization built upon an original bound of the Bhattacharyya measure yield competitive results in nearly real-time. Within each frame, the algorithm seeks the LV cavity and myocardium regions consistent with subject-specific model distributions learned from the first frame in the sequence. Based on global rather than pixel-wise information, the proposed formulation relaxes the need of a large training set and optimization with respect to geometric transformations. Different from related active contour methods, it does not require a large number of iterative updates of the segmentation and the corresponding computationally onerous kernel density estimates (KDEs). The algorithm requires very few iterations and KDEs to converge. Furthermore, the proposed bound can be used for several other applications and, therefore, can lead to segmentation algorithms which share the flexibility of active contours and computational advantages of max-flow optimization. Quantitative evaluations over 2280 images acquired from 20 subjects demonstrated that the results correlate well with independent manual segmentations by an expert. Moreover, comparisons with a related recent active contour method showed that the proposed framework brings significant improvements in regard to accuracy and computational efficiency.
Full-text · Article · May 2011 · Medical image analysis
[Show abstract][Hide abstract]ABSTRACT: Example methods, apparatus and articles of manufacture to track endocardial motion are disclosed. A disclosed example method includes segmenting a plurality of cardiac images of a left ventricle to form respective ones of a plurality of segmented images, updating a plurality of models based on the plurality of segmented images to form respective ones of a plurality of motion estimates for the left ventricle, computing a plurality of probabilities for respective ones of the plurality of models, and computing a weighted sum of the plurality of motion estimates based on the plurality of probabilities, the weighted sum representing a predicted motion of the left ventricle.
[Show abstract][Hide abstract]ABSTRACT: Example methods, apparatus and articles of manufacture to process cardiac images to detect heart motion abnormalities are disclosed. A disclosed example method includes adapting a state of a state-space model based on a plurality of cardiac images to characterize motion of a heart, computing an information-theoretic metric from the state of the state-space model, and comparing the information-theoretic metric to a threshold to determine whether the motion of the heart is abnormal.
[Show abstract][Hide abstract]ABSTRACT: This study investigates a novel method of tracking Left Ventricle (LV)
curve in Magnetic Resonance (MR) sequences. The method focuses on energy
minimization by level-set curve boundary evolution. The level-set
framework allows introducing knowledge of the field prior on the
solution. The segmentation in each particular time relies not only on
the current image but also the segmented image from previous phase.
Field prior is defined based on the experimental fact that the mean
logarithm of intensity inside endo and epi-cardium is approximately
constant during a cardiac cycle. The solution is obtained by evolving
two curves following the Euler-Lagrange minimization of a functional
containing a field constraint. The functional measures the consistency
of the field prior over a cardiac sequence. Our preliminary results show
that the obtained segmentations are very well correlated with those
manually obtained by experts. Furthermore, we observed that the proposed
field prior speeds up curve evolution significantly and reduces the
Full-text · Article · Mar 2011 · Proceedings of SPIE - The International Society for Optical Engineering
[Show abstract][Hide abstract]ABSTRACT: Early and accurate detection of Left Ventricle (LV) regional wall motion abnormalities significantly helps in the diagnosis and followup of cardiovascular diseases. We present a regional myocardial abnormality detection framework based on image statistics. The proposed framework requires a minimal user interaction, only to specify initial delineation and anatomical landmarks on the first frame. Then, approximations of regional myocardial segments in subsequent frames were systematically obtained by superimposing the initial delineation on the rest of the frames. The proposed method exploits the Bhattacharyya coefficient to measure the similarity between the image distribution within each segment approximation and the distribution of the corresponding user-provided segment. Linear Discriminate Analysis (LDA) is applied to find the optimal direction along which the projected features are the most descriptive. Then a Linear Support Vector Machine (SVM) classifier is employed for each of the regional myocardial segments to automatically detect abnormally contracting regions of the myocardium. Based on a clinical dataset of 30 subjects, the evaluation demonstrates that the proposed method can be used as a promising diagnostic support tool to assist clinicians.
[Show abstract][Hide abstract]ABSTRACT: Factor VII has been utilized to treat post-operative bleeding after cardiac surgery refractory to other intervention. We report the case of a patient who developed intractable bleeding after a severe protamine reaction following emergency repair of type A aortic dissection and was successfully treated with factor VII.
No preview · Article · Jan 2011 · Innovations Technology and Techniques in Cardiothoracic and Vascular Surgery
[Show abstract][Hide abstract]ABSTRACT: Robotics-assisted endoscopic atraumatic coronary artery bypass has been shown to be effective in reducing surgical morbidity and length of hospital stay. Unfortunately, the criteria for selecting eligible patients for this procedure are still primitive. This has motivated the use of preoperative computed tomography scans to establish patient eligibility. The objective of this study is to establish which image measurements can be correlated to procedure success.
A retrospective study was performed in 144 patients who underwent robotics-assisted coronary bypass surgery. After an initial set of 55 patients, preoperative computed tomography scans of the other patients were used to obtain patient specific measurements: the lateral distance between the midline of the sternum to the left anterior descending coronary artery and its depth from the skin surface, anteroposterior diameter of the thoracic cavity, and the transverse diameter of the thoracic cavity. The procedures were rated as successful if completed in a minimally invasive manner. Different combinations of the variables were evaluated and correlated with success.
A strong correlation was found between success rate and the ratio of the lateral distance to the transverse diameter in the female patients only (0.532, P = 0.006). A ratio of less than 0.20 significantly increased the occurrence of conversion during this procedure in female cases.
The lateral distance of the left anterior descending coronary artery from the midline divided by the transverse thoracic width of a female patient shows a significant correlation with procedure success. No significant correlations were found for male patients.
No preview · Article · Sep 2010 · Innovations Technology and Techniques in Cardiothoracic and Vascular Surgery
[Show abstract][Hide abstract]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.
[Show abstract][Hide abstract]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.
Full-text · Article · Aug 2010 · IEEE transactions on bio-medical engineering
[Show abstract][Hide abstract]ABSTRACT: 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.
Full-text · Article · Jul 2010 · IEEE transactions on information technology in biomedicine: a publication of the IEEE Engineering in Medicine and Biology Society
[Show abstract][Hide abstract]ABSTRACT: This study investigates an efficient algorithm for image segmentation with a global constraint based on the Bhattacharyya measure. The problem consists of finding a region consistent with an image distribution learned a priori. We derive an original upper bound of the Bhattacharyya measure by introducing an auxiliary labeling. From this upper bound, we reformulate the problem as an optimization of an auxiliary function by graph cuts. Then, we demonstrate that the proposed procedure converges and give a statistical interpretation of the upper bound. The algorithm requires very few iterations to converge, and finds nearly global optima. Quantitative evaluations and comparisons with state-of-the-art methods on the Microsoft GrabCut segmentation database demonstrated that the proposed algorithm brings improvements in regard to segmentation accuracy, computational efficiency, and optimality. We further demonstrate the flexibility of the algorithm in object tracking.
[Show abstract][Hide abstract]ABSTRACT: We propose to embed overlap priors in variational tracking of the left ventricle (LV) in cardiac magnetic resonance (MR) sequences. The method consists of evolving two curves toward the LV endo- and epicardium boundaries. We derive the curve evolution equations by minimizing two functionals each containing an original overlap prior constraint. The latter measures the conformity of the overlap between the nonparametric (kernel-based) intensity distributions within the three target regions--LV cavity, myocardium and background-to a prior learned from a given segmentation of the first frame. The Bhattacharyya coefficient is used as an overlap measure. Different from existing intensity-driven constraints, the proposed priors do not assume implicitly that the overlap between the intensity distributions within different regions has to be minimal. This prevents both the papillary muscles from being included erroneously in the myocardium and the curves from spilling into the background. Although neither geometric training nor preprocessing were used, quantitative evaluation of the similarities between automatic and independent manual segmentations showed that the proposed method yields a competitive score in comparison with existing methods. This allows more flexibility in clinical use because our solution is based only on the current intensity data, and consequently, the results are not bounded to the characteristics, variability, and mathematical description of a finite training set. We also demonstrate experimentally that the overlap measures are approximately constant over a cardiac sequence, which allows to learn the overlap priors from a single frame.
Full-text · Article · Dec 2009 · IEEE Transactions on Medical Imaging
[Show abstract][Hide abstract]ABSTRACT: PURPOSE/AIM
The purpose of this exhibit is: To review the indications and evolution of robotic-assisted minimally-invasive cardiac surgery To review the role pre-operative imaging plays in the planning of these procedures To inform radiologists of the unique procedural considerations and the key findings that need to reported that impact these procedures.
1. Historical evolution/indications for robot-assisted cardiac surgery. 2. Preoperative imaging considerations: intraoperative patient positioning and importance of re-creating similar positioning at preoperative CT, location of surgical port placement, importance of location of internal thoracic and coronary arteries, relationship between chest wall and heart/ pericardium. Anatomic variants and preexisting pathology that will affect the surgical approach. 3. Case examples 4. Future issues
1. Preoperative imaging is vital to planning and execution of robot-assisted minimally-invasive cardiac surgery 2. Simulation of intraoperative patient positioning at preoperative CT essential for accurate measurement and prediction of location to key anatomic structures mobilized and accessed at surgery. 3. Preexisting thoracic pathology may significantly impact the feasibility of robot-assisted minimally-invasive cardiac surgery and alter decision-making regarding the need for thoracotomy.