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Recent literature has reported increasing interest in using contrast agents for ultrasound imaging, in the form of shelled gas microbubbles, for innovative advanced purposes such as noninvasive targeted imaging and drug delivery. Effectiveness of such agents is time-dependent and is determined by microbubble dissolution behavior, a complex phenomenon whose knowledge is still limited. In the present study, we monitored the microbubbles of an experimental phospholipid-shelled perfluorobutane contrast agent through time-scheduled size distribution measurements. The diameter-time curve we obtained for shelled perfluorobutane microbubbles showed a rapid diameter increment up to about 1.4 times the initial value, followed by a slow decrement towards bubble disappearance. This behavior is qualitatively similar to the one theoretically predicted by Kabalnov's model for unshelled bubbles, with an extended lifetime due to shell effect. Kabalnov's model, devised for spontaneous dissolution of unshelled microbubbles, was consequently modified in order to get a proper prediction of experimental results also in the case of encapsulated bubbles. A theoretical diameter-time curve was then derived from this new model and fitted to our experimental data points, to estimate microbubble surface tension and to determine the value of an empirical parameter accounting for the shell effect. The proposed model has the potential to predict the dissolution behavior of all kinds of microbubble contrast agents for ultrasound imaging and the adopted experimental methodology represents a new and simple way to estimate microbubble surface tension, essential also for predicting microbubble oscillation performance. [ABSTRACT FROM PUBLISHER]

An adaptive initialization method was developed to produce fully automatic processing frameworks based on graph-cut and gradient flow active contour algorithms. This method was applied to abdominal Computed Tomography (CT) images for segmentation of liver tissue and hepatic tumors. Twenty-five anonymized datasets were randomly collected from several radiology centres without specific request on acquisition parameter settings nor patient clinical situation as inclusion criteria. Resulting automatic segmentations of liver tissue and tumors were compared to their reference standard delineations manually performed by a specialist. Segmentation accuracy has been assessed through the following evaluation framework: dice similarity coefficient (DSC), false negative ratio (FNR), false positive ratio (FPR) and processing time. Regarding liver surfaces, graph-cuts achieved a DSC of 95.49% ( FPR=2.35% and FNR=5.10%), while active contours reached a DSC of 96.17% (FPR=3.35% and FNR=3.87%). The analyzed datasets presented 52 tumors: graph-cut algorithm detected 48 tumors with a DSC of 88.65%, while active contour algorithm detected only 44 tumors with a DSC of 87.10%. In addition, in terms of time performances, less time was requested for graph-cut algorithm with respect to active contour one. The implemented initialization method allows fully automatic segmentation leading to superior overall performances of graph-cut algorithm in terms of accuracy and processing time. The initialization method here presented resulted suitable and reliable for two different segmentation techniques and could be further extended. [ABSTRACT FROM PUBLISHER]

Rationale and Objectives: The aim of this study was to identify the optimal parameter configuration of a new algorithm for fully automatic segmentation of hepatic vessels, evaluating its accuracy in view of its use in a computer system for three-dimensional (3D) planning of liver surgery. Materials and Methods: A phantom reproduction of a human liver with vessels up to the fourth subsegment order, corresponding to a minimum diameter of 0.2 mm, was realized through stereolithography, exploiting a 3D model derived from a real human computed tomographic data set. Algorithm parameter configuration was experimentally optimized, and the maximum achievable segmentation accuracy was quantified for both single two-dimensional slices and 3D reconstruction of the vessel network, through an analytic comparison of the automatic segmentation performed on contrast-enhanced computed tomographic phantom images with actual model features. Results: The optimal algorithm configuration resulted in a vessel detection sensitivity of 100% for vessels > 1 mm in diameter, 50% in the range 0.5 to 1 mm, and 14% in the range 0.2 to 0.5 mm. An average area overlap of 94.9% was obtained between automatically and manually segmented vessel sections, with an average difference of 0.06 mm(2). The average values of corresponding false-positive and false-negative ratios were 7.7% and 2.3%, respectively. Conclusions: A robust and accurate algorithm for automatic extraction of the hepatic vessel tree from contrast-enhanced computed tomographic volume images was proposed and experimentally assessed on a liver model, showing unprecedented sensitivity in vessel delineation. This automatic segmentation algorithm is promising for supporting liver surgery planning and for guiding intraoperative resections. [ABSTRACT FROM AUTHOR]