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Rationale and Objectives: Real-time cardiac ultrasound (US) allows monitoring the heart motion during intracardiac beating heart procedures. Our application assists pediatric atrial septal defect (ASD) closure techniques using real-time 3D US guidance and rigid instruments. ASD tracking is also an important tool for facilitating systematic clinical studies of the dynamic behavior of the intra-atrial communication. One major image processing challenge is associated with the required processing of information at high frame rate, especially given the low image quality.Materials and Methods: We present an optimization scheme for a block flow technique, which combines the probability-based velocity computation for an entire block (a 3D volume centered on the ASD) with cyclic template matching. The adapted similarity imposes constraints both locally (from frame to frame) to conserve energy, and globally (from a reference template) to minimize cumulative errors. The algorithm is optimized for fast and reliable results. For tests, we use three intra-operational 4D ultrasound sequences of clinical infant beating hearts with ASD.Results: Computing velocity at the block level with an optimized scheme, our technique tracks ASD motion at a frequency of 60 frames/s on clinical 4D datasets. Results are stable and accurate for changes in resolution and block size. In particular, we show robust real-time tracking and preliminary segmentation results of the ASD shape, size and orientation as a function of time.Conclusions: We present an optimized block flow technique for real-time tracking of ASD to assist in minimally invasive beating heart surgery. Our method proposes the standard use of references for processing repetitive data. This paper represents, to our knowledge, the first study on the dynamic morphology of ASD that takes into account the angular effect introduced by the slanted position of the intra-atrial communication with respect to the US probe. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: Automatic lung segmentation in volumetric computed tomography (CT) images has been extensively investigated, and several methods have been proposed. Most methods distinguish the lung parenchyma from the surrounding anatomy based on the difference in CT attenuation values. This leads to an irregular and inconsistent lung boundary for the regions near the mediastinum, which can cause inconsistent boundaries both across subjects and within subjects scanned at different intervals of time. Processes like lung image registration and lung atlas construction can be affected by such inconsistencies. Therefore there is a need for a more consistent lung surface near the mediastinum.Materials and Methods: This paper presents a fully automatic method for the three-dimensional smoothing of the lung boundary using information from the segmented human airway tree. First, using the segmented airway tree, we define a bounding box around the mediastinum for each lung, within which all operations are performed. We then define all generations of the airway tree distal to the right and left main stem bronchi to be part of the respective lungs and exclude all other segments. Finally, we perform a fast morphological closing with an ellipsoidal kernel to smooth the surface of the lung.Results: This method has been tested by processing the segmented lungs from eight normal datasets. The mean value of the magnitude of curvedness of the lung contours, averaged over all the datasets, is 0.13 postsmoothing, compared with 4.91 before smoothing. The accuracy of the lung contours after smoothing is assessed by comparing the automatic results to manually traced smooth lung borders by a human analyst. Averaged over all volumes, the root mean square difference between human and computer borders is 0.8691 mm after smoothing, compared with 1.3012 mm before smoothing. The mean similarity index, which is an area overlap measure based on the kappa statistic, is 0.9958 (SD 0.0032), after smoothing.Conclusions: We have described a novel scheme for smoothing the lung contour around the mediastinum. The method is based on using anatomic information from the segmented airway tree. The validation results show that there is good agreement between manual and computer results. Because there are no accepted criteria for defining the lung boundary near the mediastinum, we believe our method of defining the boundary based on the structure of the airway tree provides a good basis for three-dimensional smoothing. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: Several statistical methods have been developed for analyzing multireader, multicase (MRMC) receiver operating characteristic (ROC) studies. The objective of this article is to increase awareness of these methods and determine if their results are concordant for published datasets. Materials and Methods: Data from three previously published studies were reanalyzed using five MRMC methods. For each method the 95% confidence intervals (CIs) for the mean of the readers' ROC areas for each diagnostic test, the P value for the comparison of the diagnostic tests' mean accuracies, and the 95% CIs for the mean difference in ROC areas of the diagnostic tests were reported. Results: Important differences in P values and CIs were seen when using parametric versus nonparametric estimates of accuracy, and there were the expected differences for random-reader versus fixed-reader models. Controlling for these differences, the Dorfman-Berbaum-Metz (DBM), Obuchowski-Rockette, Beiden-Wagner-Campbell, and Song's multivariate Wilcoxon-Mann-Whitney (WMW) methods gave almost identical results for the fixed-reader model. For the random-reader model, the DBM, Obuchowski-Rockette, and Beiden-Wagner-Campbell methods yielded approximately the same inferences, but the CIs for the Beiden-Wagner-Campbell method tend to be broader. Ishwaran's hierarchical ROC sometimes yielded significance not found with other methods. Song's modification of DBM's jack-knifing algorithm sometimes led to different conclusions than the original DBM algorithm. Conclusion: In choosing and applying MRMC methods, it is important to recognize: (1) the distinction between random-reader and fixed-reader models, the uncertainties accounted for by each, and thus the level of generalizeability expected from each; (2) assumptions made by the various MRMC methods; and (3) limitations of a five- or six-reader study when the reader variability is great. [ABSTRACT FROM AUTHOR]