Fiducial-free Alignment Verification Techniques for Intracranial Radiosurgery

The current process of intracranial radiosurgery treatment uses implanted titanium fiducials in the skull to assist in alignment of the patient. These fiducials add an element of physical and emotional stress to the patient, and scheduling the implantation procedures adds a delay of a few extra days before the radiosurgery procedure can begin. During the radiosurgery treatment, each proton beam is manually aligned by the therapist/physician with X-ray images and the fiducials that are visible on these images. This method of alignment can be time-intensive and requires personnel who are specifically trained in patient alignment. We propose a new method using image registration to automate this process in an effort to eliminate the need for surgical implantation of fiducials prior to treatment as well as to improve the accuracy and efficiency of alignment during treatment. Image registration is a technique used to align a moving image with respect to its known fixed image. Several methods of image registration are used for comparison: an enhanced correlation coefficient maximization algorithm, a mutual information maximization algorithm, and an extended phase correlation algorithm. Accuracy, robustness, and performance are emphasized in the comparison of these algorithms. Due to patient privacy, test images from MATLAB will be shown in this paper. This research was conducted under the clinical supervision of Dr. Andrew Wroe and Dr. Reinhard Schulte of the Loma Linda University Medical Center (LLUMC)

Instabilities in nematic liquid crystal films and droplets

The dynamics of thin films of nematic liquid crystal (NLC) are studied. Nematic liquid crystals are a type of non-Newtonian fluid with anisotropic viscous effects (due to the shape of the molecules) and elasticity effects (due to interacting electrical dipole moments). Exploiting the small aspect ratio in the geometry of interest, a fourth-order non-linear partial differential equation is used to model the free surface of the thin films. Particular attention is paid to the interplay between the bulk elasticity and the preferred orientation (boundary condition) of NLC molecules at the two interfaces: the substrate and the free surface. This work is a collection of three previously published papers and some recent unpublished work. Two main topics are covered: 1) the flow of thin films of NLC down an inclined substrate under gravity, and 2) the stability of thin NLC films on a horizontal substrate under the influence of surface tension, internal elastic effects, and fluid/solid interactions. Using a combination of analytical and computational techniques allows for a novel understanding of relevant instability mechanisms, and of their influence on transient and fully developed fluid film morphologies. While the analytical results in this thesis focus on NLC films, these results may be extended to a variety of other thin film models. Finally, a numerical code that utilizes a graphics processor unit (GPU) is presented, and the significant performance gains are discussed

Research in Applied Mathematics, Fluid Mechanics and Computer Science

This report summarizes research conducted at the Institute for Computer Applications in Science and Engineering in applied mathematics, fluid mechanics, and computer science during the period October 1, 1998 through March 31, 1999

Programmable 3D snapshot microscopy with Fourier convolutional networks

3D snapshot microscopy enables fast volumetric imaging by capturing a 3D volume in a single 2D camera image, and has found a variety of biological applications such as whole brain imaging of fast neural activity in larval zebrafish. The optimal microscope design for this optical 3D-to-2D encoding is both sample- and task-dependent, with no general solution known. Highly programmable optical elements create new possibilities for sample-specific computational optimization of microscope parameters, e.g. tuning the collection of light for a given sample structure. We perform such optimization with deep learning, using a differentiable wave-optics simulation of light propagation through a programmable microscope and a neural network to reconstruct volumes from the microscope image. We introduce a class of global kernel Fourier convolutional neural networks which can efficiently decode information from multiple depths in the volume, globally encoded across a 3D snapshot image. We show that our proposed networks succeed in large field of view volume reconstruction and microscope parameter optimization where traditional networks fail. We also show that our networks outperform the state-of-the-art learned reconstruction algorithms for lensless computational photography.Comment: Make zebrafish Types A,B,C,D more clea

Enhancing numerical modelling efficiency for electromagnetic simulation of physical layer components.

The purpose of this thesis is to present solutions to overcome several key difficulties that limit the application of numerical modelling in communication cable design and analysis. In particular, specific limiting factors are that simulations are time consuming, and the process of comparison requires skill and is poorly defined and understood. When much of the process of design consists of optimisation of performance within a well defined domain, the use of artificial intelligence techniques may reduce or remove the need for human interaction in the design process. The automation of human processes allows round-the-clock operation at a faster throughput. Achieving a speedup would permit greater exploration of the possible designs, improving understanding of the domain. This thesis presents work that relates to three facets of the efficiency of numerical modelling: minimizing simulation execution time, controlling optimization processes and quantifying comparisons of results. These topics are of interest because simulation times for most problems of interest run into tens of hours. The design process for most systems being modelled may be considered an optimisation process in so far as the design is improved based upon a comparison of the test results with a specification. Development of software to automate this process permits the improvements to continue outside working hours, and produces decisions unaffected by the psychological state of a human operator. Improved performance of simulation tools would facilitate exploration of more variations on a design, which would improve understanding of the problem domain, promoting a virtuous circle of design. The minimization of execution time was achieved through the development of a Parallel TLM Solver which did not use specialized hardware or a dedicated network. Its design was novel because it was intended to operate on a network of heterogeneous machines in a manner which was fault tolerant, and included a means to reduce vulnerability of simulated data without encryption. Optimisation processes were controlled by genetic algorithms and particle swarm optimisation which were novel applications in communication cable design. The work extended the range of cable parameters, reducing conductor diameters for twisted pair cables, and reducing optical coverage of screens for a given shielding effectiveness. Work on the comparison of results introduced ―Colour maps‖ as a way of displaying three scalar variables over a two-dimensional surface, and comparisons were quantified by extending 1D Feature Selective Validation (FSV) to two dimensions, using an ellipse shaped filter, in such a way that it could be extended to higher dimensions. In so doing, some problems with FSV were detected, and suggestions for overcoming these presented: such as the special case of zero valued DC signals. A re-description of Feature Selective Validation, using Jacobians and tensors is proposed, in order to facilitate its implementation in higher dimensional spaces

Fast computation of non-Gaussian covariance of redshift-space galaxy power spectrum multipoles

The non-Gaussian part of the covariance matrix of the galaxy power spectrum involves the connected four-point correlation in Fourier space, i.e. trispectrum. This paper introduces a fast method to compute the non-Gaussian part of the covariance matrix of the galaxy power spectrum multipoles in redshift space at tree-level standard perturbation theory. For the tree-level galaxy trispectrum, the angular integral between two wavevectors can be evaluated analytically by employing an FFTLog. The new implementation computes the non-Gaussian covariance of the power spectrum monopole, quadrupole, hexadecapole and their cross-covariance in O(10) seconds, for an effectively arbitrary number of instances of cosmological and galaxy bias parameters and redshift, without any parallelization or acceleration. It is a large advantage over conventional numerical integration. We demonstrate that the computation of the covariance at k = 0.005 - 0.4 h/Mpc gives results with 0.1 - 1% accuracy. The efficient computation of the analytic covariance can be useful for future galaxy surveys, especially utilizing multi-tracer analysis.Comment: 13 pages, 4 figures, to be submitted to Phys. Rev.