Implementation of the compulsory universal testing scheme in Hong Kong: Mathematical simulations of a household-based pooling approach

This study aims to propose a pooling approach to simulate the compulsory universal RT-PCR test in Hong Kong and explore the feasibility of implementing the pooling method on a household basis. The mathematical model is initially verified, and then the simulation is performed under different prevalence rates and pooled sizes. The simulated population is based in Hong Kong. The simulation included 10,000,000 swab samples, with a representative distribution of populations in Hong Kong. The samples were grouped into a batch size of 20. If the entire batch is positive, then the group is further divided into an identical group size of 10 for re-testing. Different combinations of mini-group sizes were also investigated. The proposed pooling method was extended to a household basis. A representative from each household is required to perform the RT-PCR test. Results of the simulation replications, indicate a significant reduction (p < 0.001) of 83.62, 64.18, and 48.46% in the testing volume for prevalence rate 1, 3, and 5%, respectively. Combined with the household-based pooling approach, the total number of RT-PCR is 437,304, 956,133, and 1,375,795 for prevalence rates 1, 3, and 5%, respectively. The household-based pooling strategy showed efficiency when the prevalence rates in the population were low. This pooling strategy can rapidly screen people in high-risk groups for COVID-19 infections and quarantine those who test positive, even when time and resources for testing are limited

Computational spine kinematic analysis with digitised video fluoroscopy

The purpose of this thesis is set to investigate spine kinematics with Digitised Video Fluoroscopy (DVF) by developing well-defined experimental protocols, easy-to-use graphical user interface software, image professing and automated tracking procedures. Some of the developed techniques were first applied to study the spine kinematics of the subjects with vertebral fractures due to osteoporosis.  Kinematics and motion patterns of complete cycles of sagittal and lateral flexion were obtained from landmarking the DVF sequences which described segmental motions of the spine.  Compared to control subjects who did not have a fracture, fracture subjects had a more asymmetric lateral range of motion and required a longer time to complete certain phases of the motion cycle.  Five motion patterns were identified, but there was no statistical difference between the two groups in any of these patterns.  Prolonged deflection was more frequently found when the spine was moving towards the neutral position.  These suggest that segmental kinematics is useful in understanding some variances in osteoporosis subjects with vertebral fractures.  Subjects with vertebral fracture have altered spinal kinematics which could help to quantify dysfunction and response to treatment and may be an indication of instability. Landmarking from DVF sequences forms the basis of kinematic analysis.  In order to facilitate the analysis, an automated spine motion tracking algorithm for DVF sequences using particle filters was developed.  The rotation and translation parameters were estimated from the corresponding posterior distributions. The algorithm can provide results to a precision of 1 degree in rotation estimation for the calibration sequence as well as 1-2 degrees and 1-2 pixels variability in rotation and translation estimation respectively during repeated initialisation analysis on in vivo sequences from healthy human subjects. The developed techniques have demonstrated their reliability and usefulness in spine kinematic analysis.  They are provided as a platform for researchers to use and further develop.</p

Fast signal processing techniques for surface somatosensory evoked potentials measurement

tocabstractpublished_or_final_versionOrthopaedics and TraumatologyMasterMaster of Philosoph

Automated tracking in digitized videofluroscopy sequences for spine kinematics analysis

Spine kinematic analysis provides useful information to aid understanding of the segmental motion of the vertebrae. Digitized videofluoroscopy (DVF) is the existing practical modality to image spine motion for kinematic data acquisition. However, obtaining kinematic parameters from DVF sequence requires manual landmarking which is a laborious process and can be subjective and error prone.This work develops an automated spine motion tracking algorithm for DVF sequences within a Bayesian framework. By utilizing the anatomical relationships between vertebrae, a dynamic Bayesian network with a particle filter at each node is constructed. The proposed algorithm overcomes the dimensionality problem in a regular particle filter and has more efficient and robust performance. It can provide results of about 1° and 2 pixels View the MathML source variability in rotation and translation estimation, respectively, during repeated initialization analysis on sequences from simulation and in vivo healthy human subject studies

Parametric characterization of spinal motions in osteoporotic vertebral fracture at level T12 with fluoroscopy

Vertebral fractures due to osteoporosis are a common skeletal disorder affecting the mobility of the patients, although little is known about the relationship between spinal kinematics and osteoporotic fracture. The purpose of this study was to characterize the motions of the thoracolumbar spine affected by osteoporotic vertebral fracture at level T12 and compare the results with those of non-fracture osteoporosis subjects. We examined the continuous segmental kinematics of the vertebrae, and describe the segmental motion of the spine when a fracture at T12 is present.Fluoroscopy sequences of the thoracolumbar spines during sagittal and lateral flexion were collected from 16 subjects with osteoporosis of their spine (6 with vertebral fractures at T12, 10 without a fracture). Vertebrae T10–L2 in each frame of the sequences were landmarked. Kinematic parameters were calculated based on the landmarks and motion graphs were constructed. Compared to the control subjects who did not have a fracture, fracture subjects had a more asymmetric lateral range of motion (RoM) and required a longer time to complete certain phases of the motion cycle which are parameterized as lateral flexion ratio and percentage of motion cycle, respectively. Prolonged deflection was more frequently found from the fracture group. Characterizing the motions of the fractured vertebra together with its neighboring vertebrae with these kinematic parameters is useful in quantifying the dysfunction and may be a valuable aid to tracking progress of treatment

Segmentation of center brains and optic lobes in 3D confocal images of adult fruit fly brains.

Automatic alignment (registration) of 3D images of adult fruit fly brains is often influenced by the significant displacement of the relative locations of the two optic lobes (OLs) and the center brain (CB). In one of our ongoing efforts to produce a better image alignment pipeline of adult fruit fly brains, we consider separating CB and OLs and align them independently. This paper reports our automatic method to segregate CB and OLs, in particular under conditions where the signal to noise ratio (SNR) is low, the variation of the image intensity is big, and the relative displacement of OLs and CB is substantial. We design an algorithm to find a minimum-cost 3D surface in a 3D image stack to best separate an OL (of one side, either left or right) from CB. This surface is defined as an aggregation of the respective minimum-cost curves detected in each individual 2D image slice. Each curve is defined by a list of control points that best segregate OL and CB. To obtain the locations of these control points, we derive an energy function that includes an image energy term defined by local pixel intensities and two internal energy terms that constrain the curve's smoothness and length. Gradient descent method is used to optimize this energy function. To improve both the speed and robustness of the method, for each stack, the locations of optimized control points in a slice are taken as the initialization prior for the next slice. We have tested this approach on simulated and real 3D fly brain image stacks and demonstrated that this method can reasonably segregate OLs from CBs despite the aforementioned difficulties