Nanoparticles in the bloodstream are subjected to complex fluid forces as they move through the curves and branches of healthy or tumor vasculature. While nanoparticles are known to preferentially accumulate in angiogenic vessels, little is known about the flow conditions in these vessels and how these conditions may influence localization. Here, we report a methodology which combines confocal imaging of nanoparticle-injected transgenic zebrafish embryos, 3D modeling of the vasculature, particle mapping, and computational fluid dynamics, to quantitatively assess the effects of fluid forces on nanoparticle distribution in vivo. Six-fold lower accumulation was found in zebrafish arteries compared to the lower velocity veins. Nanoparticle localization varied inversely with shear stress. Highest accumulation was present in regions of disturbed flow found at branch points and curvatures in the vasculature. To further investigate cell-particle association under flow, human endothelial cells were exposed to nanoparticles under hemodynamic conditions typically found in human vessels. Physiological adaptations of endothelial cells to 20 hours of flow enhanced nanoparticle accumulation in regions of disturbed flow. Overall our results suggest that fluid shear stress magnitude, flow disturbances, and flow-induced changes in endothelial physiology modulate nanoparticle localization in angiogenic vessels.

A method to improve time resolution in 3D contrast-enhanced magnetic resonance angiography (CE-MRA) is proposed. A temporal basis based on prior knowledge of the contrast flow dynamics is applied to a sequence of image reconstructions.In CE-MRA a contrast agent (gadolinium) is injected into a peripheral vein and MR data is acquired as the agent arrives in the arteries and then the veins of the region of clinical interest. The acquisition extends over several minutes. Information is effectively measured in 3D k-space (spatial frequency space) one line at-atime. That line may be along a Cartesian grid line in k-space, a radial line or a spiral trajectory. A complete acquisition comprises many such lines but in order to improve temporal resolution, reconstructions are made from only partial sets of k-space data. By imposing a basis for the temporal changes, based on prior expectation of the smoothness of the changes in contrast concentration with time, it is demonstrated that a significant reduction in artifacts caused by the under-sampling of k-space can be achieved. The basis is formed from a set of gamma variate functions. Results are presented for a simulated set of 2D spiral-sampled CE-MRA data.

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Abstract-A new method for visualisation and segmentation of vessel structures in 3D magnetic resonance angiography (MRA) images is presented. This method uses a simple statistical model of the information stored along parallel rays within the data set to derive a 2D projection image. Although similar to the maximum image projection (MIP) method, the new method uses a single parameter to achieve a higher contrast-to-noise ratio at a modest computational cost. The same idea is employed to provide a means of segmenting a 3D data set in order to derive a region of support for the purpose of reconstructing image sequences with high temporal resolution.

There is a strong motivation to reduce the amount of acquired data necessary to reconstruct clinically useful MR images, since less data means faster acquisition sequences, less time for the patient to remain motionless in the scanner and better time resolution for observing temporal changes within the body. We recently introduced an improvement in image quality for reconstructing parallel MR images by incorporating a data ordering step with compressed sensing (CS) in an algorithm named 'PECS'. 1 That method requires a prior estimate of the image to be available. We are extending the algorithm to explore ways of utilising the data ordering step without requiring a prior estimate. The method presented here first reconstructs an initial image x1 by compressed sensing (with sparsity enhanced by SVD), then derives a data ordering from x1, R 1 , which ranks the voxels of x1 according to their value. A second reconstruction is then performed which incorporates minimisation of the first norm of the estimate after ordering by R 1 , resulting in a new reconstruction x2. Preliminary results are encouraging.