Deformable regions of interest with multiple points for tissue tracking in echocardiography.

By tracking echocardiography images more accurately and stably, we can better assess myocardial functions. In this paper, we propose a new tracking method with deformable Regions of Interest (ROIs) aiming at rational pattern matching. For this purpose we defined multiple tracking points for an ROI and regarded these points as nodes in the Meshfree Method to interpolate displacement fields. To avoid unreasonable distortion of the ROI caused by noise and perturbation in echo images, we introduced a stabilization technique based on a nonlinear strain energy function. Examples showed that the combination of our new tracking method and stabilization technique provides competitive and stable tracking.

Quantitative evaluation of diagnostic information around the contours in ultrasound images.

PURPOSE: To develop a new contour extraction method for identifying abnormal tissue. METHODS: We combined two techniques: logarithmic K distribution of a scattering model (method 1) and regional discrimination using the characteristics of local ultrasound images (method 2) into an integrated method (method 3) that provides accurate contours, which are essential for quantitizing border information. RESULTS: The diagnostic tissue information around the border of an image can be characterized by its shape and texture statistics. The degrees of circularity and irregularity and the depth-width ratio were calculated for the extracted contours of breast tumors. In addition, gradients, separability, and variance between the two regions along the contour and the area and variance of the internal echoes, were calculated as indices of diagnostic criteria of breast tumors. The quantitized indices were able to discriminate among cysts, fibroadenomas, and cancer. CONCLUSION: In many ultrasound images of breast tumors, the combined techniques, the variance ratio of the logarithmic K distribution to the logarithmic Rayleigh distribution and the multilevel technique with local image information can effectively extract abnormal tissue contours.

Echocardiographic image tracker with a speckle adaptive noise reduction filter for the automatic measurement of the left atrial volume curve.

AIMS: Since tracking accuracy in left atrial (LA) images decreases due to low image quality around the LA in the apical view, a practical tracking method for LA images has not yet been proposed. The aim of this study was to assess an accurate and high-speed LA volume tracking (LAVT) method for the automatic measurement of LA volume (LAV) curves. METHODS AND RESULTS: We used three approved protocols in this study: (i) LAV curves were measured by LAVT on computer-simulated images; (ii) in 20 healthy volunteers, we assessed the feasibility and accuracy of this method compared with expert's measurements; and (iii) echocardiography and multi-detector row computed tomography (MDCT) imaging were performed on the same day in 20 patients with suspected coronary artery disease. On computer-simulated images, mean absolute percentage LAVT error in one cardiac cycle was 3% in filtered images and 16% in original images. In 20 healthy volunteers, there are strong correlations between LAVT and the expert's LA measurements (LA maximum volume; R = 0.93, P < 0.001). In 400 LA images with 20 patients, an excellent correlation was obtained between LAVs using echocardiography and MDCT (R = 0.98, P < 0.001), with a small bias (-14% of the mean) and narrow limits of agreement (+15% of the mean). The mean time required for the LAVT analysis was 1.8 min, for the MDCT analysis was 35.8 min, and for the manual echocardiographic analysis was 14.0 min. CONCLUSION: This LAVT method is fast, valid, accurate, and reproducible for determining LAV in both simulated images and the clinical setting.