Surface Projection Method for Visualizing Volumetric Data

The goal of this project was to explore, develop, and implement additional visualization methods for volumetric data within MindSeer. This paper discusses the implementation of one such visualization method, the surface projection method, and compares it to other existing methods

Robust semi-automated path extraction for visualising stenosis of the coronary arteries

Computed tomography angiography (CTA) is useful for diagnosing and planning treatment of heart disease. However, contrast agent in surrounding structures (such as the aorta and left ventricle) makes 3-D visualisation of the coronary arteries difficult. This paper presents a composite method employing segmentation and volume rendering to overcome this issue. A key contribution is a novel Fast Marching minimal path cost function for vessel centreline extraction. The resultant centreline is used to compute a measure of vessel lumen, which indicates the degree of stenosis (narrowing of a vessel). Two volume visualisation techniques are presented which utilise the segmented arteries and lumen measure. The system is evaluated and demonstrated using synthetic and clinically obtained datasets

Using high resolution displays for high resolution cardiac data

The ability to perform fast, accurate, high resolution visualization is fundamental to improving our understanding of anatomical data. As the volumes of data increase from improvements in scanning technology, the methods applied to rendering and visualization must evolve. In this paper we address the interactive display of data from high resolution MRI scanning of a rabbit heart and subsequent histological imaging. We describe a visualization environment involving a tiled LCD panel display wall and associated software which provide an interactive and intuitive user interface. The oView software is an OpenGL application which is written for the VRJuggler environment. This environment abstracts displays and devices away from the application itself, aiding portability between different systems, from desktop PCs to multi-tiled display walls. Portability between display walls has been demonstrated through its use on walls at both Leeds and Oxford Universities. We discuss important factors to be considered for interactive 2D display of large 3D datasets, including the use of intuitive input devices and level of detail aspects

Immersive Visualization for Enhanced Computational Fluid Dynamics Analysis

Modern biomedical computer simulations produce spatiotemporal results that are often viewed at a single point in time on standard 2D displays. An immersive visualization environment (IVE) with 3D stereoscopic capability can mitigate some shortcomings of 2D displays via improved depth cues and active movement to further appreciate the spatial localization of imaging data with temporal computational fluid dynamics (CFD) results. We present a semi-automatic workflow for the import, processing, rendering, and stereoscopic visualization of high resolution, patient-specific imaging data, and CFD results in an IVE. Versatility of the workflow is highlighted with current clinical sequelae known to be influenced by adverse hemodynamics to illustrate potential clinical utility

Holographic laser Doppler imaging of pulsatile blood flow

We report on wide-field imaging of pulsatile motion induced by blood flow using heterodyne holographic interferometry on the thumb of a healthy volunteer, in real-time. Optical Doppler images were measured with green laser light by a frequency-shifted Mach-Zehnder interferometer in off-axis configuration. The recorded optical signal was linked to local instantaneous out-of-plane motion of the skin at velocities of a few hundreds of microns per second, and compared to blood pulse monitored by plethysmoraphy during an occlusion-reperfusion experiment.Comment: 5 pages, 5 figure

PROTEOMIC ANALYSIS OF TWO DIFFERENT STATES OF NAEGLERIA FOWLERI

Naegleria fowleri are free-living ameboflagellates found in soil and freshwater habitats throughout the world that cause a fatal disease in humans called Primary Amoebic Meningoencephalitis (PAM). Mechanisms of host resistance or susceptibility to infection have not been fully elucidated, and possible treatment methods are still sub optimal. The disease is diagnosed using specific laboratory tests available in only a few laboratories in the United States. Because of the rarity of infection and difficulty in initial detection, more than often PAM is misdiagnosed. Therefore, it is very important to find causative marker for early detection of an infection. The purpose of this study is to create a proteomic signature map using two-dimensional gel electrophoresis (2-D gel) and recommend a subset of proteins that may be directly linked to the pathogenic state of N. fowleri. Replicates of 2-D Gels were created for both strains of N. fowleri and the proteomic templates from these gels were compared with each other. Scatter Plots were generated measuring the density of protein spots from 2-D gels being analyzed for each study. For each strains of N. fowleri, the 2-D gels from each study were compared within and compared between the two studies for reproducibility in data. The resulting correlation values for all of the Scatter Plots were greater or equal to 0.90. Finally, the representative proteomic template for axenically grown N. fowleri and mouse passaged N. fowleri were compared and the correlation value of 0.60 was observed. This confirmed our theory that these two strains or states of N. fowleri have very different protein expressions, and we were able to identify a subset of proteins, both over expressed and newly synthesized, that may be linked to the highly pathogenic state of N. fowleri

Dynamic Volume Rendering of Functional Medical Data on Dissimilar Hardware Platforms

In the last 30 years, medical imaging has become one of the most used diagnostic tools in the medical profession. Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) technologies have become widely adopted because of their ability to capture the human body in a non-invasive manner. A volumetric dataset is a series of orthogonal 2D slices captured at a regular interval, typically along the axis of the body from the head to the feet. Volume rendering is a computer graphics technique that allows volumetric data to be visualized and manipulated as a single 3D object. Iso-surface rendering, image splatting, shear warp, texture slicing, and raycasting are volume rendering methods, each with associated advantages and disadvantages. Raycasting is widely regarded as the highest quality renderer of these methods. Originally, CT and MRI hardware was limited to providing a single 3D scan of the human body. The technology has improved to allow a set of scans capable of capturing anatomical movements like a beating heart. The capturing of anatomical data over time is referred to as functional imaging. Functional MRI (fMRI) is used to capture changes in the human body over time. While fMRI’s can be used to capture any anatomical data over time, one of the more common uses of fMRI is to capture brain activity. The fMRI scanning process is typically broken up into a time consuming high resolution anatomical scan and a series of quick low resolution scans capturing activity. The low resolution activity data is mapped onto the high resolution anatomical data to show changes over time. Academic research has advanced volume rendering and specifically fMRI volume rendering. Unfortunately, academic research is typically a one-off solution to a singular medical case or set of data, causing any advances to be problem specific as opposed to a general capability. Additionally, academic volume renderers are often designed to work on a specific device and operating system under controlled conditions. This prevents volume rendering from being used across the ever expanding number of different computing devices, such as desktops, laptops, immersive virtual reality systems, and mobile computers like phones or tablets. This research will investigate the feasibility of creating a generic software capability to perform real-time 4D volume rendering, via raycasting, on desktop, mobile, and immersive virtual reality platforms. Implementing a GPU-based 4D volume raycasting method for mobile devices will harness the power of the increasing number of mobile computational devices being used by medical professionals. Developing support for immersive virtual reality can enhance medical professionals’ interpretation of 3D physiology with the additional depth information provided by stereoscopic 3D. The results of this research will help expand the use of 4D volume rendering beyond the traditional desktop computer in the medical field. Developing the same 4D volume rendering capabilities across dissimilar platforms has many challenges. Each platform relies on their own coding languages, libraries, and hardware support. There are tradeoffs between using languages and libraries native to each platform and using a generic cross-platform system, such as a game engine. Native libraries will generally be more efficient during application run-time, but they require different coding implementations for each platform. The decision was made to use platform native languages and libraries in this research, whenever practical, in an attempt to achieve the best possible frame rates. 4D volume raycasting provides unique challenges independent of the platform. Specifically, fMRI data loading, volume animation, and multiple volume rendering. Additionally, real-time raycasting has never been successfully performed on a mobile device. Previous research relied on less computationally expensive methods, such as orthogonal texture slicing, to achieve real-time frame rates. These challenges will be addressed as the contributions of this research. The first contribution was exploring the feasibility of generic functional data input across desktop, mobile, and immersive virtual reality. To visualize 4D fMRI data it was necessary to build in the capability to read Neuroimaging Informatics Technology Initiative (NIfTI) files. The NIfTI format was designed to overcome limitations of 3D file formats like DICOM and store functional imagery with a single high-resolution anatomical scan and a set of low-resolution anatomical scans. Allowing input of the NIfTI binary data required creating custom C++ routines, as no object oriented APIs freely available for use existed. The NIfTI input code was built using C++ and the C++ Standard Library to be both light weight and cross-platform. Multi-volume rendering is another challenge of fMRI data visualization and a contribution of this work. fMRI data is typically broken into a single high-resolution anatomical volume and a series of low-resolution volumes that capture anatomical changes. Visualizing two volumes at the same time is known as multi-volume visualization. Therefore, the ability to correctly align and scale the volumes relative to each other was necessary. It was also necessary to develop a compositing method to combine data from both volumes into a single cohesive representation. Three prototype applications were built for the different platforms to test the feasibility of 4D volume raycasting. One each for desktop, mobile, and virtual reality. Although the backend implementations were required to be different between the three platforms, the raycasting functionality and features were identical. Therefore, the same fMRI dataset resulted in the same 3D visualization independent of the platform itself. Each platform uses the same NIfTI data loader and provides support for dataset coloring and windowing (tissue density manipulation). The fMRI data can be viewed changing over time by either animation through the time steps, like a movie, or using an interface slider to “scrub” through the different time steps of the data. The prototype applications data load times and frame rates were tested to determine if they achieved the real-time interaction goal. Real-time interaction was defined by achieving 10 frames per second (fps) or better, based on the work of Miller [1]. The desktop version was evaluated on a 2013 MacBook Pro running OS X 10.12 with a 2.6 GHz Intel Core i7 processor, 16 GB of RAM, and a NVIDIA GeForce GT 750M graphics card. The immersive application was tested in the C6 CAVE™, a 96 graphics node computer cluster comprised of NVIDIA Quadro 6000 graphics cards running Red Hat Enterprise Linux. The mobile application was evaluated on a 2016 9.7” iPad Pro running iOS 9.3.4. The iPad had a 64-bit Apple A9X dual core processor with 2 GB of built in memory. Two different fMRI brain activity datasets with different voxel resolutions were used as test datasets. Datasets were tested using both the 3D structural data, the 4D functional data, and a combination of the two. Frame rates for the desktop implementation were consistently above 10 fps, indicating that real-time 4D volume raycasting is possible on desktop hardware. The mobile and virtual reality platforms were able to perform real-time 3D volume raycasting consistently. This is a marked improvement for 3D mobile volume raycasting that was previously only able to achieve under one frame per second [2]. Both VR and mobile platforms were able to raycast the 4D only data at real-time frame rates, but did not consistently meet 10 fps when rendering both the 3D structural and 4D functional data simultaneously. However, 7 frames per second was the lowest frame rate recorded, indicating that hardware advances will allow consistent real-time raycasting of 4D fMRI data in the near future

Proof of concept of a workflow methodology for the creation of basic canine head anatomy veterinary education tool using augmented reality

Neuroanatomy can be challenging to both teach and learn within the undergraduate veterinary medicine and surgery curriculum. Traditional techniques have been used for many years, but there has now been a progression to move towards alternative digital models and interactive 3D models to engage the learner. However, digital innovations in the curriculum have typically involved the medical curriculum rather than the veterinary curriculum. Therefore, we aimed to create a simple workflow methodology to highlight the simplicity there is in creating a mobile augmented reality application of basic canine head anatomy. Using canine CT and MRI scans and widely available software programs, we demonstrate how to create an interactive model of head anatomy. This was applied to augmented reality for a popular Android mobile device to demonstrate the user-friendly interface. Here we present the processes, challenges and resolutions for the creation of a highly accurate, data based anatomical model that could potentially be used in the veterinary curriculum. This proof of concept study provides an excellent framework for the creation of augmented reality training products for veterinary education. The lack of similar resources within this field provides the ideal platform to extend this into other areas of veterinary education and beyond