Arterial endothelium experience physical stress associated with blood flow and play a central role in maintaining vascular integrity and homeostasis in response to hemodynamic forces. Blood flow within vessels is generally laminar and streamlined. However, abrupt changes in the vessel geometry due to branching, sharp turns or stenosis can disturb the laminar blood flow, causing secondary flows in the form of vortices. Such disturbed flow patterns activate pro-inflammatory phenotypes in endothelial cells, damaging the endothelial layer and can lead to atherosclerosis and thrombosis. Here, we report a microfluidic system with integrated ridge-shaped obstacles for generating controllable disturbed flow patterns. This system is used to study the effect of disturbed flow on the cytoskeleton remodeling and nuclear shape and size of cultured human aortic endothelial cells. Our results demonstrate that the generated disturbed flow changes the orientation angle of actin stress fibers and reduces the nuclear size while increases the nuclear circularity.

Physiological motion, such as cardiac and respiratory motion, during Magnetic Resonance (MR) image acquisition can cause image artifacts. Motion correction techniques have been proposed to compensate for these types of motion during thoracic scans, relying on accurate motion estimation from undersampled motion-resolved reconstruction. A particular interest and challenge lie in the derivation of reliable non-rigid motion fields from the undersampled motion-resolved data. Motion estimation is usually formulated in image space via diffusion, parametric-spline, or optical flow methods. However, image-based registration can be impaired by remaining aliasing artifacts due to the undersampled motion-resolved reconstruction. In this work, we describe a formalism to perform non-rigid registration directly in the sampled Fourier space, i.e. k-space. We propose a deep-learning based approach to perform fast and accurate non-rigid registration from the undersampled k-space data. The basic working principle originates from the Local All-Pass (LAP) technique, a recently introduced optical flow-based registration. The proposed LAPNet is compared against traditional and deep learning image-based registrations and tested on fully-sampled and highly-accelerated (with two undersampling strategies) 3D respiratory motion-resolved MR images in a cohort of 40 patients with suspected liver or lung metastases and 25 healthy subjects. The proposed LAPNet provided consistent and superior performance to image-based approaches throughout different sampling trajectories and acceleration factors.

The plenoptic function is a powerful tool to analyze the properties of multi-view image data sets. In particular, the understanding of the spectral properties of the plenoptic function is essential in many computer vision applications, including image-based rendering. In this paper, we derive for the first time an exact closed-form expression of the plenoptic spectrum of a slanted plane with finite width and use this expression as the elementary building block to derive the plenoptic spectrum of more sophisticated scenes. This is achieved by approximating the geometry of the scene with a set of slanted planes and evaluating the closed-form expression for each plane in the set. We then use this closed-form expression to revisit uniform plenoptic sampling. In this context, we derive a new Nyquist rate for the plenoptic sampling of a slanted plane and a new reconstruction filter. Through numerical simulations, on both real and synthetic scenes, we show that the new filter outperforms alternative existing filters.

Reinforcing a latex balloon with nylon stockings leads to a high pressure self-sufficient pump, which is used for studying the mechanobiology of aortic cells and hydrodynamic capturing of large human monocytes.