Anisotropic Mean Flow Enhancement and Anomalous Transport of Finite-Size Spherical Particles in Turbulent Flows.

We investigate the influence of dispersed solid spherical particles on the largest scales of the turbulent Arnold-Beltrami-Childress (ABC) flow. The ABC flow is an ideal instance of a complex flow: it does not have solid boundaries, but possesses an inhomogeneous and three-dimensional mean shear. By tuning the parameters of the suspension, we show that particles modulate the largest scales of the flow toward an anisotropic, quasi-two-dimensional and more energetic state. In this regime, particles move along quasistraight trajectories and exhibit anomalous transport.

Investigating the influence of haemodynamic stimuli on intracranial aneurysm inception.

We propose a novel method to reconstruct the hypothetical geometry of the healthy vasculature prior to intracranial aneurysm (IA) formation: a Frenet frame is calculated along the skeletonization of the arterial geometry; upstream and downstream boundaries of the aneurysmal segment are expressed in terms of the local Frenet frame basis vectors; the hypothetical healthy geometry is then reconstructed by propagating a closed curve along the skeleton using the local Frenet frames so that the upstream boundary is smoothly morphed into the downstream boundary. This methodology takes into account the tortuosity of the arterial vasculature and requires minimal user subjectivity. The method is applied to 22 clinical cases depicting IAs. Computational fluid dynamic simulations of the vasculature without IA are performed and the haemodynamic stimuli in the location of IA formation are examined. We observe that locally elevated wall shear stress (WSS) and gradient oscillatory number (GON) are highly correlated (20/22 for WSS and 19/22 for GON) with regions susceptible to sidewall IA formation whilst haemodynamic indices associated with the oscillation of the WSS vectors have much lower correlations.

Aneurysm identification by analysis of the blood-vessel skeleton.

At least 1% of the general population have an aneurysm (or possibly more) in their cerebral blood vessels. If an aneurysm ruptures, it kills the patient in up to 60% of cases. In order to choose the optimal treatment, clinicians have to monitor the development of the aneurysm in time. Nowadays, aneurysms are typically identified manually, which means that the monitoring is often imprecise since the identification is observer dependent. As a result, the number of misdiagnosed cases may be large. This paper proposes a fast semi-automatic method for the identification of aneurysms which is based on the analysis of the skeleton of blood vessels. Provided that the skeleton is accurate, the results achieved by our method have been deemed acceptable by expert clinicians.

@neurIST: infrastructure for advanced disease management through integration of heterogeneous data, computing, and complex processing services.

The increasing volume of data describing human disease processes and the growing complexity of understanding, managing, and sharing such data presents a huge challenge for clinicians and medical researchers. This paper presents the @neurIST system, which provides an infrastructure for biomedical research while aiding clinical care, by bringing together heterogeneous data and complex processing and computing services. Although @neurIST targets the investigation and treatment of cerebral aneurysms, the system's architecture is generic enough that it could be adapted to the treatment of other diseases. Innovations in @neurIST include confining the patient data pertaining to aneurysms inside a single environment that offers clinicians the tools to analyze and interpret patient data and make use of knowledge-based guidance in planning their treatment. Medical researchers gain access to a critical mass of aneurysm related data due to the system's ability to federate distributed information sources. A semantically mediated grid infrastructure ensures that both clinicians and researchers are able to seamlessly access and work on data that is distributed across multiple sites in a secure way in addition to providing computing resources on demand for performing computationally intensive simulations for treatment planning and research.

New aspects and approaches in pre-operative planning of hip reconstruction: a computer simulation.

BACKGROUND: All computer-aided surgery technologies assume that the surgeon knows the best position for the implant components. However, there is indirect evidence that simple anatomical information may not be sufficient for the surgeon to decide size and position of the implant in a repeatable manner. METHOD: In the present study we estimated the variability in choosing the size and position of the components of a cementless total hip replacement (THR), using template-on-radiograph as well as computed tomography (CT)-based computer-aided planning. In addition, using a computer model, we assessed the sensitivity to such variability of implant fitting, location of the joint centre, skeletal range of motion, and resting length of major hip muscles. Using templates, surgeons selected the size with variability up to 2.5 mm for the stem and up to 4 mm for the socket. A similar variability was also observed when the CT-based planning program was used. RESULTS: No major differences were found between surgeons. The standard deviation over repeated planning sessions of the selected position for each component, using the CT-based planning software, was found to be 3.9 mm and 8.9 degrees . CONCLUSION: On the basis of the computer simulation, this variability did not affect the selected biomechanical parameters in a drastic way, although some differences were observed, especially in the lever arm of the hip muscles.