3D Fluid Flow Estimation with Integrated Particle Reconstruction

The standard approach to densely reconstruct the motion in a volume of fluid is to inject high-contrast tracer particles and record their motion with multiple high-speed cameras. Almost all existing work processes the acquired multi-view video in two separate steps, utilizing either a pure Eulerian or pure Lagrangian approach. Eulerian methods perform a voxel-based reconstruction of particles per time step, followed by 3D motion estimation, with some form of dense matching between the precomputed voxel grids from different time steps. In this sequential procedure, the first step cannot use temporal consistency considerations to support the reconstruction, while the second step has no access to the original, high-resolution image data. Alternatively, Lagrangian methods reconstruct an explicit, sparse set of particles and track the individual particles over time. Physical constraints can only be incorporated in a post-processing step when interpolating the particle tracks to a dense motion field. We show, for the first time, how to jointly reconstruct both the individual tracer particles and a dense 3D fluid motion field from the image data, using an integrated energy minimization. Our hybrid Lagrangian/Eulerian model reconstructs individual particles, and at the same time recovers a dense 3D motion field in the entire domain. Making particles explicit greatly reduces the memory consumption and allows one to use the high-res input images for matching. Whereas the dense motion field makes it possible to include physical a-priori constraints and account for the incompressibility and viscosity of the fluid. The method exhibits greatly (~70%) improved results over our recently published baseline with two separate steps for 3D reconstruction and motion estimation. Our results with only two time steps are comparable to those of sota tracking-based methods that require much longer sequences.Comment: To appear in International Journal of Computer Vision (IJCV

Impact of Albumin on Coagulation Competence and Hemorrhage During Major Surgery:A Randomized Controlled Trial

For patients exposed to a massive blood loss during surgery, maintained coagulation competence is important. It is less obvious whether coagulation competence influences bleeding during elective surgery where patients are exposed to infusion of a crystalloid or a colloid. This randomized controlled trial evaluates whether administration of 5% human albumin (HA) or lactated Ringer solution (LR) affects coagulation competence and in turn blood loss during cystectomy due to bladder cancer. Forty patients undergoing radical cystectomy were included to receive either 5% HA (n = 20) or LR (n = 20). Nineteen patients were analyzed in the HA group and 20 patients in the lactated Ringer group. Blinded determination of the blood loss was similar in the 2 groups of patients: 1658 (800–3300) mL with the use of HA and 1472 (700–4330) mL in the lactated Ringer group (P = 0.45). Yet, by thrombelastography (TEG) evaluated coagulation competence, albumin affected clot growth (TEG-angle 69 ± 5 vs 74° ± 3°, P < 0.01) and strength (TEG-MA: 59 ± 6 vs 67 ± 6 mm, P < 0.001) more than LR. Furthermore, by multivariate linear regression analyses reduced TEG-MA was independently associated with the blood loss (P = 0.042) while administration of albumin was related to the changes in TEG-MA (P = 0.029), aPPT (P < 0.022), and INR (P < 0.033). This randomized controlled trial demonstrates that administration of HA does not affect the blood loss as compared to infusion of LR. Also the use of HA did not affect the need for blood transfusion, the incidence of postoperative complications, or the hospital in-stay. Yet, albumin decreases coagulation competence during major surgery and the blood loss is related to TEG-MA rather than to plasma coagulation variables

Divergence-Free Motion Estimation

International audienceThis paper describes an innovative approach to estimate motion from image observations of divergence-free flows. Unlike most state-of-the-art methods, which only minimize the divergence of the motion field, our approach utilizes the vorticity-velocity formalism in order to construct a motion field in the subspace of divergence free functions. A 4DVAR-like image assimilation method is used to generate an estimate of the vorticity field given image observations. Given that vorticity estimate, the motion is obtained solving the Poisson equation. Results are illustrated on synthetic image observations and compared to those obtained with state-of-the-art methods, in order to quantify the improvements brought by the presented approach. The method is then applied to ocean satellite data to demonstrate its performance on the real images

The Impact of Online Social Networks on Decision Support Systems

Previous research on this matter had already determined that many concepts are encompassed by both online social networking and decision support systems research. Due to the large number of concepts and using clustering techniques, we were able to determine four concept clusters, namely: the technical infrastructure, online communities, network analysis and knowledge management. Then, we intended to gain further knowledge on how those concepts influenced DSS related research and the contribution of each cluster to the support of the phases of decision-making process. We also wanted to perceive the interconnections among the concept clusters themselves, for which we used structural equation modeling techniques. The obtained results evidence that not only online social networks are being used as a technical infrastructure to support the three decision making phases and to support knowledge management and online communities, but also that the other clusters only regard the intelligence phase of the decision process.info:eu-repo/semantics/publishedVersio

Multiscale Weighted Ensemble Kalman Filter for Fluid Flow Estimation

International audienceThis paper proposes a novel multi-scale uid ow data as- similation approach, which integrates and complements the advantages of a Bayesian sequential assimilation technique, the Weighted Ensem- ble Kalman lter (WEnKF) [12], and an improved multiscale stochastic formulation of the Lucas-Kanade (LK) estimator. The proposed scheme enables to enforce a physically plausible dynamical consistency of the estimated motion elds along the image sequence.

Coevolved mutations reveal distinct architectures for two core proteins in the bacterial flagellar motor

Switching of bacterial flagellar rotation is caused by large domain movements of the FliG protein triggered by binding of the signal protein CheY to FliM. FliG and FliM form adjacent multi-subunit arrays within the basal body C-ring. The movements alter the interaction of the FliG C-terminal (FliGC) "torque" helix with the stator complexes. Atomic models based on the Salmonella entrovar C-ring electron microscopy reconstruction have implications for switching, but lack consensus on the relative locations of the FliG armadillo (ARM) domains (amino-terminal (FliGN), middle (FliGM) and FliGC) as well as changes during chemotaxis. The generality of the Salmonella model is challenged by the variation in motor morphology and response between species. We studied coevolved residue mutations to determine the unifying elements of switch architecture. Residue interactions, measured by their coevolution, were formalized as a network, guided by structural data. Our measurements reveal a common design with dedicated switch and motor modules. The FliM middle domain (FliMM) has extensive connectivity most simply explained by conserved intra and inter-subunit contacts. In contrast, FliG has patchy, complex architecture. Conserved structural motifs form interacting nodes in the coevolution network that wire FliMM to the FliGC C-terminal, four-helix motor module (C3-6). FliG C3-6 coevolution is organized around the torque helix, differently from other ARM domains. The nodes form separated, surface-proximal patches that are targeted by deleterious mutations as in other allosteric systems. The dominant node is formed by the EHPQ motif at the FliMMFliGM contact interface and adjacent helix residues at a central location within FliGM. The node interacts with nodes in the N-terminal FliGc α-helix triad (ARM-C) and FliGN. ARM-C, separated from C3-6 by the MFVF motif, has poor intra-network connectivity consistent with its variable orientation revealed by structural data. ARM-C could be the convertor element that provides mechanistic and species diversity.JK was supported by Medical Research Council grant U117581331. SK was supported by seed funds from Lahore University of Managment Sciences (LUMS) and the Molecular Biology Consortium

Theory and Validation of Magnetic Resonance Fluid Motion Estimation Using Intensity Flow Data

15 p.Background Motion tracking based on spatial-temporal radio-frequency signals from the pixel representation of magnetic resonance (MR) imaging of a non-stationary fluid is able to provide two dimensional vector field maps. This supports the underlying fundamentals of magnetic resonance fluid motion estimation and generates a new methodology for flow measurement that is based on registration of nuclear signals from moving hydrogen nuclei in fluid. However, there is a need to validate the computational aspect of the approach by using velocity flow field data that we will assume as the true reference information or ground truth. Methodology/Principal Findings In this study, we create flow vectors based on an ideal analytical vortex, and generate artificial signal-motion image data to verify our computational approach. The analytical and computed flow fields are compared to provide an error estimate of our methodology. The comparison shows that the fluid motion estimation approach using simulated MR data is accurate and robust enough for flow field mapping. To verify our methodology, we have tested the computational configuration on magnetic resonance images of cardiac blood and proved that the theory of magnetic resonance fluid motion estimation can be applicable practically. Conclusions/Significance The results of this work will allow us to progress further in the investigation of fluid motion prediction based on imaging modalities that do not require velocity encoding. This article describes a novel theory of motion estimation based on magnetic resonating blood, which may be directly applied to cardiac flow imaging.Kelvin Kian Loong Wong, Richard Malcolm Kelso, Stephen Grant Worthley, Prashanthan Sanders, Jagannath Mazumdar, Derek Abbot