Zahra Alizadeh Sani

In this paper, we propose a new temporal frame interpolation algorithm for frame rate up-conversion (FRUC) in echocardiography images. This algorithm employs a combination of dimension reduction techniques and image registration to increase frame rate. If the distance between two successive frames of a video be great, motion jerkiness will appear between them and visual quality of the video will decrease. Some parts of heart have a very high speed motion, and echocardiography videos, obtained by available systems can’t take enough number of frames to show them well. So, to achieve an echocardiography image set with a better visual quality, more frames are necessary between two frames at a great distance. Here, we use dimension reduction techniques to find out the number of suitable frames between two consecutive frames to show the fast motions better, but don’t take much time. We project images to a 3-dimentional space by this way. Greater difference between the frames, results greater distance between corresponding embedded points. Thus, the distance between the embedded points is a scale for the suitable number of frames, needed between two successive frames. These frames are produced with the registration techniques. On the other hand, heart doesn’t have a constant speed during a cycle, but echocardiography images are recorded with constant speed. So, frames at a greater distance show fast motions of the heart, and frames at a lower distance show slow motions of the heart. While, we put unequal number of frames between successive frames, and in this way remove temporal coordination of the image set. To solve this problem, we put efficient number of linear average of available frames, in places that the number of inserted frames in between available frames is less than maximum to obtain an equal number of frames between all successive frames.

Hypoplastic right ventricular is a rare congenital heart disease. A few cases have been reported. We presented a case with hypoplastic right ventricular and multiple associated anomaly (Ventricular septal defect, atrial septal defect and pulmonary stenosis) in whom the main concern was whether biventricular repair or Fontan type surgery would be the optimal management in this patient. Biventricular repair was done successfully using the multimodality cardiac imaging with intraoperative transesophageal echocardiography. Keywords: Heart Ventricles; Right Ventricle Hypoplasia; Hypoplastic Left Heart Syndrome

Purpose:: Identification and assessment of left ventricular (LV) global and regional wall motion (RWM) abnormalities are essential for clinical evaluation of various cardiovascular diseases. Currently, this evaluation is performed visually which is highly dependent on the training and experience of echocardiographers and thus is prone to considerable interobserver and intraobserver variability. This paper presents a new automatic method, based on nonlinear dimensionality reduction (NLDR) for global wall motion evaluation and also detection and classification of RWM abnormalities of LV wall in a three‐point scale as follows: (1) normokinesia, (2) hypokinesia, and (3) akinesia.Methods:: Isometric feature mapping (Isomap) is one of the most popular NLDR algorithms. In this paper, a modified version of Isomap algorithm, where image to image distance metric is computed using nonrigid registration, is applied on two‐dimensional (2D) echocardiography images of one cycle of heart. By this approach, nonlinear information in these images is embedded in a 2D manifold and each image is characterized by a point on the constructed manifold. This new representation visualizes the relationship between these images based on LV volume changes. Then, a new global and regional quantitative index from the resultant manifold is proposed for global wall motion estimation and also classification of RWM of LV wall in a three‐point scale. Obtained results by our method are quantitatively evaluated to those obtained visually by two experienced echocardiographers as the reference (gold standard) on 10 healthy volunteers and 14 patients.Results:: Linear regression analysis between the proposed global quantitative index and the global wall motion score index and also with LV ejection fraction obtained by reference experienced echocardiographers resulted in the correlation coefficients of 0.85 and 0.90, respectively. Comparison between the proposed automatic RWM scoring and the reference visual scoring resulted in an absolute agreement of 82% and a relative agreement of 97%.Conclusions:: The proposed diagnostic method can be used as a useful tool as well as a reference visual assessment by experienced echocardiographers for global wall motion estimation and also classification of RWM abnormalities of LV wall in a three‐point scale in clinical evaluations.

Eosinophilic Endomyocardial Fibrosis and Strongyloides stercoralis: A Case Report - Research in Cardiovascular Medicine - - Kowsar

Primary cardiac tumors are rare in all ages. Their reported prevalence ranges from 0.001 to 0.03 percent in autopsy series. 25 percent of primary cardiac tumors are considered to be malignant, the majority of which are sarcomas. On account of the late presentation of symptoms in malignant heart masses, finding locally infiltrative tumors or systemically widespread cases at initial presentation is common. We present a case of malignant heart tumor in a thirty-one-year old woman who was first examined here after the termination of pregnancy. Keywords: Malignant Tumor; Liposarcoma, Myxoid; Magnetic Resonance Imaging

One of the main causes of death the world over are cardiovascular diseases, of which coronary artery disease (CAD) is a major type. This disease occurs when the diameter narrowing of one of the left anterior descending, left circumflex, or right coronary arteries is equal to or greater than 50 percent. Angiography is the principal diagnostic modality for the stenos is of heart vessels, however, because of its complications and costs, researchers are looking for alternative methods such as data mining. This study conducts data mining algorithms on the Z-Alizadeh Sani dataset which has been collected from 303 random visitors to Tehran's Shaheed Rajaei Cardiovascular, Medical and Research Center. In this paper, the reason of effectiveness of a preprocessing algorithm on the dataset is investigated. This algorithm which has been merely introduced in our previous works, extracts three new features from the dataset. These features are then used to enrich the primary dataset in order to achieve more accurate results. Moreover, despite the fact that misclassification of diseased patients has more side effects than that of healthy ones, to the best of our knowledge cost-sensitive algorithms have yet to be used in this field. Therefore, in this paper 10-fold cross validation on cost-sensitive algorithms along with base classifiers of Naïve Bayes, Sequential Minimal Optimization (SMO), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and C4.5 were employed. As a result, the SMO algorithm has yield to very high sensitivity (97.22%) and accuracy (92.09%) rates, the likes of which have not been reported simultaneously in the existing literature.

A 26-year-old man presented with progressively worsening dyspnoea on exertion since the past 6 months. He was referred to our centre with the initial diagnosis of secundum type atrial septal defect and tricuspid regurgitation (see online appendix 1) based on a transthoracic echocardiography. On physical examination, he had a systolic murmur at the left sternal border with inspiratory augmentation. Electrocardiography showed normal sinus rhythm with right bundle branch block (figure 1). Chest x-ray was suggestive of right chamber enlargement with reduced pulmonary blood flow (figure 2). Figure 1 Figure 2 Transthoracic and transoesophageal echocardiograms revealed a normal-sized left ventricle with preserved systolic function, huge right atrium, severe right ventricular (RV) enlargement with moderate …

One of the main causes of death the world over is the family of cardiovascular diseases, of which coronary artery disease (CAD) is a major type. Angiography is the principal diagnostic modality for the stenosis of heart arteries; however, it leads to high complications and costs. The present study conducted data-mining algorithms on the Z-Alizadeh Sani dataset, so as to investigate rule based and feature based classifiers and their comparison, and the reason for the effectiveness of a preprocessing algorithm on a dataset. Misclassification of diseased patients has more side effects than that of healthy ones. To this end, this paper employs 10-fold cross-validation on cost-sensitive algorithms along with base classifiers of Naïve Bayes, Sequential Minimal Optimization (SMO), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and C4.5 and the results show that the SMO algorithm yielded very high sensitivity (97.22%) and accuracy (92.09%) rates.

The automatic detection of end-diastole and end-systole frames of echocardiography images is the first step for calculation of the ejection fraction, stroke volume and some other features related to heart motion abnormalities. In this paper, the manifold learning algorithm is applied on 2D echocardiography images to find out the relationship between the frames of one cycle of heart motion. By this approach the nonlinear embedded information in sequential images is represented in a two-dimensional manifold by the LLE algorithm and each image is depicted by a point on reconstructed manifold. There are three dense regions on the manifold which correspond to the three phases of cardiac cycle ('isovolumetric contraction', 'isovolumetric relaxation', 'reduced filling'), wherein there is no prominent change in ventricular volume. By the fact that the end-systolic and end-diastolic frames are in isovolumic phases of the cardiac cycle, the dense regions can be used to find these frames. By calculating the distance between consecutive points in the manifold, the isovolumic frames are mapped on the three minimums of the distance diagrams which were used to select the corresponding images. The minimum correlation between these images leads to detection of end-systole and end-diastole frames. The results on six healthy volunteers have been validated by an experienced echo cardiologist and depict the usefulness of the presented method.