985 resultados para space planning


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The authors compared radial steady-state free precession (SSFP) coronary magnetic resonance (MR) angiography, cartesian k-space sampling SSFP coronary MR angiography, and gradient-echo coronary MR angiography in 16 healthy adults and four pilot study patients. Standard gradient-echo MR imaging with a T2 preparatory pulse and cartesian k-space sampling was the reference technique. Image quality was compared by using subjective motion artifact level and objective contrast-to-noise ratio and vessel sharpness. Radial SSFP, compared with cartesian SSFP and gradient-echo MR angiography, resulted in reduced motion artifacts and superior vessel sharpness. Cartesian SSFP resulted in increased motion artifacts (P <.05). Contrast-to-noise ratio with radial SSFP was lower than that with cartesian SSFP and similar to that with the reference technique. Radial SSFP coronary MR angiography appears preferable because of improved definition of vessel borders.

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We prove a characterization of the support of the law of the solution for a stochastic wave equation with two-dimensional space variable, driven by a noise white in time and correlated in space. The result is a consequence of an approximation theorem, in the convergence of probability, for equations obtained by smoothing the random noise. For some particular classes of coefficients, approximation in the Lp-norm for p¿1 is also proved.

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Agency Performance Plan, Iowa Public Television

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This is volume 1 of the final report on the Comprehensive Plan for the City of Boone. Contained in this report are reproductions of the important maps and charts prepared in connection with this study.

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This is volume 2 of the final report on the Comprehensive Plan for the City of Boone. Contained in this report are reproductions of the important maps and charts prepared in connection with this study.

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This is volume 3 of the final report on the Comprehensive Plan for the City of Boone. Contained in this report are reproductions of the important maps and charts prepared in connection with this study.

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In this paper, we present the segmentation of the headand neck lymph node regions using a new active contourbased atlas registration model. We propose to segment thelymph node regions without directly including them in theatlas registration process; instead, they are segmentedusing the dense deformation field computed from theregistration of the atlas structures with distinctboundaries. This approach results in robust and accuratesegmentation of the lymph node regions even in thepresence of significant anatomical variations between theatlas-image and the patient's image to be segmented. Wealso present a quantitative evaluation of lymph noderegions segmentation using various statistical as well asgeometrical metrics: sensitivity, specificity, dicesimilarity coefficient and Hausdorff distance. Acomparison of the proposed method with two other state ofthe art methods is presented. The robustness of theproposed method to the atlas selection, in segmenting thelymph node regions, is also evaluated.

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For radiotherapy treatment planning of retinoblastoma inchildhood, Computed Tomography (CT) represents thestandard method for tumor volume delineation, despitesome inherent limitations. CT scan is very useful inproviding information on physical density for dosecalculation and morphological volumetric information butpresents a low sensitivity in assessing the tumorviability. On the other hand, 3D ultrasound (US) allows ahigh accurate definition of the tumor volume thanks toits high spatial resolution but it is not currentlyintegrated in the treatment planning but used only fordiagnosis and follow-up. Our ultimate goal is anautomatic segmentation of gross tumor volume (GTV) in the3D US, the segmentation of the organs at risk (OAR) inthe CT and the registration of both. In this paper, wepresent some preliminary results in this direction. Wepresent 3D active contour-based segmentation of the eyeball and the lens in CT images; the presented approachincorporates the prior knowledge of the anatomy by usinga 3D geometrical eye model. The automated segmentationresults are validated by comparing with manualsegmentations. Then, for the fusion of 3D CT and USimages, we present two approaches: (i) landmark-basedtransformation, and (ii) object-based transformation thatmakes use of eye ball contour information on CT and USimages.