Search

During cardiac muscle contractions, basal descent along the long axis causes the short axis in the MR image to shift, leading to errors in measurement of myocardial function. This study used magnetization vector spatial modulation of magnetization (SPAMM) to tag myocardial motions. After SPAMM image acquisition, a SPAMM image processing algorithm was used to obtain myocardial grid lines. Experience shows that specific internal parameters of the algorithm significantly affect the quality of the grid line output. In this research, grid search enhanced the quality of the grid line output by finding near–optimal combinations within the parameter set. Both geometric and physiological performance metrics were employed. The Hausdorff distance (the geometric metric) improved significantly when the parameters were optimized. Furthermore, the 2-D and 3-D wall thickness discrepancy (the physiological metrics) decreased significantly with the use of optimized parameters.

PURPOSE The diagnosis of most rotator cuff tears (RCTs) relies upon magnetic resonance (MR) imaging, but direct capture of MR images without enhanced image processing leads to poor image contrast and potential misdiagnosis. Therefore, we developed a 2-stage model for the detection and diagnosis of injury of the supraspinatus tendon. METHODS The first stage used coupled weighted histogram separation (WHS) to improve image enhancement, and the second stage extracted suspicious texture, features of both spatial and spectral domains, and sequential floating forward selection (SFFS) selected features conducive to classification of RCTs. We then tested injuries of the supraspinatus tendon using the classifier. RESULTS The extraction of features by SFFS can increase detection of supraspinatus injury by reducing the input vector by 57.78% from the enhanced input images. The receiver operating characteristic (ROC) curve indicated an azimuth (Az) value of 84.38% when SFFS selected 76 features to construct a support vector machine (SVM) classifier from the enhanced images, compared with 56.94% when all 180 features from the raw input images were used for the construction. CONCLUSIONS The performance of the classifier constructed by SFFS-selected features is superior to that using all features. These findings can serve as references to improve diagnosis and treatment of supraspinatus injuries.

Magnetic Resonance images are used to identify tears in the meniscus, a debilitating condition of the knee. In this paper, a two-staged computerized system for diagnosing meniscus tears is proposed. In the first stage, we apply an Active Contour with Level Sets model to calculate the location and shape of the meniscus area. In the second stage, 180 features, consisting of textural information in the spatial and spectral domains, were extracted from each suspected meniscus tear. Sequential floating forward selection (SFFS) was applied to select the relevant features. The feature vectors were then input to a support vector machine (SVM) classifier to detect meniscus tears. The Az value of the receiver operating characteristic (ROC) curve were used to evaluate the classification performance. Experimental results show that the dimension of the feature vector was reduced from 180 to 64 after the SFFS. Meanwhile, the SVM classifier combined with the SFFS feature selection (Az = 0.9123) outperformed the SVM clas...

During cylindrical traverse grinding processes, two types of regenerative chatter -workpiece and grinding wheel- may degrade the accuracy of the surface finish. To maintain productivity and quality, a closed-loop vibration control system should be provided for the grinding system. An algorithm for automated classification by types is essential in developing such a system. In cylindrical traverse grinding, the chatter vibration signals display unstable dynamic characteristics, which makes the task of chatter classification especially difficult. This paper introduces an approach which combines entropy techniques with morphological preprocessing to classify traverse grinding regenerative chatter by types based on the vibration spectrum. Experimental data analysis is used to demonstrate that the proposed method can effectively distinguish workpiece regenerative chatter from wheel regenerative chatter. Since both entropy function and morphological processing are computationally easy, this method is not only transparent to the understanding but also conveniently adaptable to practical system expansion and real time applications.

Background: A decline in cerebral White Matter (WM) integrity occurs across the adult lifespan, with more rapid degradation later in life. Diffusion Tensor Imaging (DTI) is a non-invasive method to evaluate the microscopic changes of WM in vivo. Linear regression models have been applied to depict the relationship between diffusion properties, such as Fractional Anisotropy (FA) and Mean Diffusivity (MD), derived from DTI and age. However, the fit of these linear regression models is unsatisfactory. Objectives: The purpose of our study was to investigate the age-related changes of WM in an elderly population. Methods: We performed statistical parametric mapping with DTI to evaluate the patterns of age-related microscopic WM changes with voxel-based analysis in the elderly population (>55 years old). A linear regression model was used to examine the associations between DTI parameters and age. Results: The fit of the linear regression model depicting the association between mean global FA, MD values (FA, MD derived from global WM), and age was better than that reported in prior studies using DTI (R2=0.4252, p

Purpose To solve the problem of the basal descent movement in quantification of the regional left ventricular (LV) myocardial wall thickness (WTh) and wall thickening (%WT) in short-axis (SA) cine MRI for effectively assessing the regional wall motion of LV myocardium. Materials and Methods LV long-axis tagged MRI and SA cine MRI were performed to calculate the longitudinal translation and circumferential WTh of LV myocardium in eight normal volunteers. The new SA end-systolic thickness (EST) data were reconstructed from the original EST data, based on the quantified longitudinal translation of LV myocardium. Results The mean %WT of six segments in the basal section after correction was significantly different from that before correction in both intra- and inter-operator experiments. The polar map also showed the significant improvement of the variability of regional %WT and lack of quantification of %WT in the most basal SA slices after correction. Conclusion The proposed technique demonstrated an important advantage to calculate the %WT in the most basal SA myocardial tissue, which was considered difficult to be achieved using cine MRI. J. Magn. Reson. Imaging 2011;33:464–473. © 2011 Wiley-Liss, Inc.

The problem of the basal descent through-plane movement in quantitative measurement of regional left ventricular (LV) function has been recognized for a long time. But, it is a lack of sophisticated analysis to overcome the limitation.

Sjogren's syndrome is a chronic inflammatory process that primarily involves the exocrine glands. Clinical manifestations range from autoimmune exocrinopathy to extraglandular involvement affecting the lungs, kidneys, blood vessels and muscles; Sjogren's syndrome can occur along with, or accompany, other autoimmune diseases. Moreover, diagnosing Sjogren's syndrome saves lives. The term support vector machine (SVM) refers to an emerging machine learning technique based on statistical learning theory which can solve classification problems using small sampling, non-linearity and high dimensions. However, both dimension reduction and parameter determination greatly influence the performance of the SVM technique. Therefore, this study develops a SVM with an immune algorithm (IA) and kernel principal component analysis (KSVMIA) model to diagnose Sjogren's syndrome effectively and efficiently. In the proposed KSVMIA model, the kernel-based principal component analysis (KPCA) technique is used to reduce the dimension of Sjogren's syndrome data; the IA is employed to determine the SVM models. Experimental results reveal that the developed model can classify Sjogren's syndrome data obtained from ultrasound in terms of efficiency and accuracy, showing that the presented KSVMIA model is a promising alternative for diagnosing Sjogren's syndrome.