Accuracy of the NDI Wave Speech Research System

Purpose: This work provides a quantitative assessment of the positional tracking accuracy of the NDI Wave Speech Research System. Method: Three experiments were completed: (a) static rigid-body tracking across different locations in the electromagnetic field volume, (b) dynamic rigid-body tracking across different locations within the electromagnetic field volume, and (c) human jaw-movement tracking during speech. Rigid-body experiments were completed for 4 different instrumentation settings, permuting 2 electromagnetic field volume sizes with and without automated reference sensor processing. Results: Within the anthropometrically pertinent near field (\u3c 200 mm) of the NDI Wave field generator, at the 300-mm3 volume setting, 88% of dynamic positional errors were \u3c 0.5 mm and 98% were \u3c 1.0 mm. Extreme tracking errors (\u3e 2 mm) occurred within the near field for \u3c 1% of position samples. For human jaw-movement tracking, 95% of position samples had \u3c 0.5 mm errors for 9 out of 10 subjects. Conclusions: Static tracking accuracy is modestly superior to dynamic tracking accuracy. Dynamic tracking accuracy is best for the 300-mm3 field setting in the 200-mm near field. The use of automated head correction has no deleterious effect on tracking. Tracking errors for jaw movements during speech are typically \u3c 0.5 mm

Computational fluid dynamics applied to gas-liquid contactors.

In this paper a `hierarchy of models¿ is discussed to study the fluid dynamic behaviour of gas-liquid bubble columns. This `hierarchy of models¿ consists of a Eulerian-Eulerian two fluid model, a Eulerian-Lagrangian discrete bubble model and a Volume Tracking or Marker Particle model. These models will be briefly reviewed and their advantages and disadvantages will be highlighted. In addition, a mixed Eulerian-Lagrangian model and a volume tracking model, both developed at Twente University, will be discussed. Some selected results obtained with these models will be presented with emphasis on the results obtained with the volume tracking model. Finally, a brief discussion on advanced experimental techniques, which reflect the recent progress in experimental fluid dynamics, will be presente

Numerical Simulation of Bubble Coalescence using a Volume of Fluid (VOF) Model

This paper presents a Volume Tracking model developed with the specific objective of studying the time – dependent behavior of multiple, “large” gas bubbles rising in an initially quiescent liquid. The model, based on the Volume – Of – Fluid concept, employs an advanced interface tracking scheme known as Youngs’ VOF to advance the gas – liquid interface through the Eulerian mesh. Additionally, the model solves the incompressible Navier – Stokes equations to obtain the flow field. Results obtained for four different cases will be discussed: the formation and rise of a skirted bubble and of a spherical cap bubble, the coalescence of two identical gas bubbles and the behavior of two gas bubbles emanating from two adjacent orifices. It could be concluded that the Volume Tracking model is able to track the motion of a gas – liquid interface, subject to appreciable changes in its topology, embedded in a flow field with significant vorticity

EchoFusion: Tracking and Reconstruction of Objects in 4D Freehand Ultrasound Imaging without External Trackers

Ultrasound (US) is the most widely used fetal imaging technique. However, US images have limited capture range, and suffer from view dependent artefacts such as acoustic shadows. Compounding of overlapping 3D US acquisitions into a high-resolution volume can extend the field of view and remove image artefacts, which is useful for retrospective analysis including population based studies. However, such volume reconstructions require information about relative transformations between probe positions from which the individual volumes were acquired. In prenatal US scans, the fetus can move independently from the mother, making external trackers such as electromagnetic or optical tracking unable to track the motion between probe position and the moving fetus. We provide a novel methodology for image-based tracking and volume reconstruction by combining recent advances in deep learning and simultaneous localisation and mapping (SLAM). Tracking semantics are established through the use of a Residual 3D U-Net and the output is fed to the SLAM algorithm. As a proof of concept, experiments are conducted on US volumes taken from a whole body fetal phantom, and from the heads of real fetuses. For the fetal head segmentation, we also introduce a novel weak annotation approach to minimise the required manual effort for ground truth annotation. We evaluate our method qualitatively, and quantitatively with respect to tissue discrimination accuracy and tracking robustness.Comment: MICCAI Workshop on Perinatal, Preterm and Paediatric Image analysis (PIPPI), 201

Individual differences in human path integration abilities correlate with gray matter volume in retrosplenial cortex, hippocampus, and medial prefrontal cortex

Humans differ in their individual navigational abilities. These individual differences may exist in part because successful navigation relies on several disparate abilities, which rely on different brain structures. One such navigational capability is path integration, the updating of position and orientation, in which navigators track distances, directions, and locations in space during movement. Although structural differences related to landmark-based navigation have been examined, gray matter volume related to path integration ability has not yet been tested. Here, we examined individual differences in two path integration paradigms: (1) a location tracking task and (2) a task tracking translational and rotational self-motion. Using voxel-based morphometry, we related differences in performance in these path integration tasks to variation in brain morphology in 26 healthy young adults. Performance in the location tracking task positively correlated with individual differences in gray matter volume in three areas critical for path integration: the hippocampus, the retrosplenial cortex, and the medial prefrontal cortex. These regions are consistent with the path integration system known from computational and animal models and provide novel evidence that morphological variability in retrosplenial and medial prefrontal cortices underlies individual differences in human path integration ability. The results for tracking rotational self-motion-but not translation or location-demonstrated that cerebellum gray matter volume correlated with individual performance. Our findings also suggest that these three aspects of path integration are largely independent. Together, the results of this study provide a link between individual abilities and the functional correlates, computational models, and animal models of path integration

Biplane Fluoroscopy for Hindfoot Motion Analysis during Gait: A Model-based Evaluation

The purpose of this study was to quantify the accuracy and precision of a biplane fluoroscopy system for model-based tracking of in vivo hindfoot motion during over-ground gait. Gait was simulated by manually manipulating a cadaver foot specimen through a biplane fluoroscopy system attached to a walkway. Three 1.6-mm diameter steel beads were implanted into the specimen to provide marker-based tracking measurements for comparison to model-based tracking. A CT scan was acquired to define a gold standard of implanted bead positions and to create 3D models for model-based tracking. Static and dynamic trials manipulating the specimen through the capture volume were performed. Marker-based tracking error was calculated relative to the gold standard implanted bead positions. The bias, precision, and root-mean-squared (RMS) error of model-based tracking was calculated relative to the marker-based measurements. The overall RMS error of the model-based tracking method averaged 0.43 ± 0.22 mm and 0.66 ± 0.43° for static and 0.59 ± 0.10 mm and 0.71 ± 0.12° for dynamic trials. The model-based tracking approach represents a non-invasive technique for accurately measuring dynamic hindfoot joint motion during in vivo, weight bearing conditions. The model-based tracking method is recommended for application on the basis of the study results