Simpleware (United Kingdom)

PurposeImage-based computational fluid dynamics (CFD) is widely used to predict intracranial aneurysm wall shear stress (WSS), particularly with the goal of improving rupture risk assessment. Nevertheless, concern has been expressed over the variability of predicted WSS and inconsistent associations with rupture. Previous challenges, and studies from individual groups, have focused on individual aspects of the image-based CFD pipeline. The aim of this Challenge was to quantify the total variability of the whole pipeline.Methods3D rotational angiography image volumes of five middle cerebral artery aneurysms were provided to participants, who were free to choose their segmentation methods, boundary conditions, and CFD solver and settings. Participants were asked to fill out a questionnaire about their solution strategies and experience with aneurysm CFD, and provide surface distributions of WSS magnitude, from which we objectively derived a variety of hemodynamic parameters.ResultsA total of 28 datasets were submitted, from 26 teams with varying levels of self-assessed experience. Wide variability of segmentations, CFD model extents, and inflow rates resulted in interquartile ranges of sac average WSS up to 56%, which reduced to < 30% after normalizing by parent artery WSS. Sac-maximum WSS and low shear area were more variable, while rank-ordering of cases by low or high shear showed only modest consensus among teams. Experience was not a significant predictor of variability.ConclusionsWide variability exists in the prediction of intracranial aneurysm WSS. While segmentation and CFD solver techniques may be difficult to standardize across groups, our findings suggest that some of the variability in image-based CFD could be reduced by establishing guidelines for model extents, inflow rates, and blood properties, and by encouraging the reporting of normalized hemodynamic parameters.

Brain swelling is a serious condition associated with an accumulation of fluid inside the brain that can be caused by trauma, stroke, infection, or tumors. It increases the pressure inside the skull and reduces blood and oxygen supply. To relieve the intracranial pressure, neurosurgeons remove part of the skull and allow the swollen brain to bulge outward, a procedure known as decompressive craniectomy. Decompressive craniectomy has been preformed for more than a century; yet, its effects on the swollen brain remain poorly understood. Here we characterize the deformation, strain, and stretch in bulging brains using the nonlinear field theories of mechanics. Our study shows that even small swelling volumes of 28 to 56 ml induce maximum principal strains in excess of 30%. For radially outward-pointing axons, we observe maximal normal stretches of 1.3 deep inside the bulge and maximal tangential stretches of 1.3 around the craniectomy edge. While the stretch magnitude varies with opening site and swelling region, our study suggests that the locations of maximum stretch are universally shared amongst all bulging brains. Our model has the potential to inform neurosurgeons and rationalize the shape and position of the skull opening, with the ultimate goal to reduce brain damage and improve the structural and functional outcomes of decompressive craniectomy in trauma patients.

An inherent element of research and applications in photonics is a beam of light. In magnonics, which is the magnetic counterpart of photonics, where spin waves are used instead of electromagnetic waves to transmit and process information, the lack of a beam source limits exploration. Here, we present an approach enabling generation of narrow spin wave beams in thin homogeneous nanosized ferromagnetic films by microwave current. We show that the desired beam-type behavior can be achieved with the aid of a properly designed coplanar waveguide transducer generating a nonuniform microwave magnetic field. We test this idea using micromagnetic simulations, confirming numerically that the resulting spin wave beams propagate over distances of several micrometers. The proposed approach requires neither inhomogeneity of the ferromagnetic film nor nonuniformity of the biasing magnetic field. It can be generalized to different magnetization configurations and yield multiple spin wave beams of different width at the same frequency.

Computer simulation of biological systems for in silico validation has the potential of increasing the efficiency of pharmaceutical research and development by expanding the number of parameters tested virtually. Then only the most interesting subset of these has to be probed in vivo. By focusing on variables with the greatest influence on clinical end points, valuable drug targets can be advanced more quickly. A large number of methods have been developed to rebuild a three-dimensional (3D) model of a liver, mostly to prepare a liver surgery. These models are often not accurate and most of the them don't take into account the fluidics inside the vessels. The aim of this work is to provide an accurate computational multi-compartement model of the healthy and the pathological liver with their network of blood vessels (vasculature) using a finite-element-modeling software. Computed tomography (CT) slices, in DICOM format, from two different patients were used to provide the datasets of transverse images for the modeling. Each dataset of images was segmented in order to extract the liver’s shape and define the vein and artery networks. On CT images, the contrast between the liver and the nearby organs (background) is very low because all these structures are a similar density. Thus, we used semi-automatic tools to determine liver contours. Manual segmentation was used as a last resort. Then, strong filtering (bilateral filter) and confidence-connected-region-growing algorithm were applied to rebuild from each - healthy and pathological - liver a multicompartment model including parenchyma, arteries and veins. The precision of the obtained vasculature model allowed anatomical classification of hepatic segments and the quantification of their volumes. Although our study demonstrated the difficulties in use of CT images for computational modeling of the liver, it also confirmed that semi-automatic tools can be used to develop anatomically accurate models of hepatic vasculature.