Papers by Darren Thompson
Lecture Notes in Computer Science, 2016
Journal of Synchrotron Radiation, 2015
Results are presented of a recent experiment at the Imaging and Medical beamline of the Australia... more Results are presented of a recent experiment at the Imaging and Medical beamline of the Australian Synchrotron intended to contribute to the implementation of low-dose high-sensitivity three-dimensional mammographic phase-contrast imaging, initially at synchrotrons and subsequently in hospitals and medical imaging clinics. The effect of such imaging parameters as X-ray energy, source size, detector resolution, sample-to-detector distance, scanning and data processing strategies in the case of propagation-based phase-contrast computed tomography (CT) have been tested, quantified, evaluated and optimized using a plastic phantom simulating relevant breast-tissue characteristics. Analysis of the data collected using a Hamamatsu CMOS Flat Panel Sensor, with a pixel size of 100 µm, revealed the presence of propagation-based phase contrast and demonstrated significant improvement of the quality of phase-contrast CT imaging compared with conventional (absorption-based) CT, at medically acceptable radiation doses.
Advances in Computational Methods for X-Ray Optics II, 2011
A software system has been developed for high-performance Computed Tomography (CT) reconstruction... more A software system has been developed for high-performance Computed Tomography (CT) reconstruction, simulation and other X-ray image processing tasks utilizing remote computer clusters optionally equipped with multiple Graphics Processing Units (GPUs). The system has a streamlined Graphical User Interface for interaction with the cluster. Apart from extensive functionality related to X-ray CT in plane-wave and cone-beam forms, the software includes
Journal of Physics: Conference Series, 2013
ABSTRACT A dedicated micro-CT beamline is planned for the Australian Synchrotron which will exten... more ABSTRACT A dedicated micro-CT beamline is planned for the Australian Synchrotron which will extend the synchrotron's imaging and tomography capability down to the smaller scale, incorporating phase-contrast and absorption-contrast, and an additional focussing-based mode for high-resolution. The beamline will use multi-layer mirror monochromators for enhanced flux, and will focus particularly on dynamic and high throughput studies in both monochromatic and pink-beam mode. Together with the existing Imaging and Medical beamline, this beamline will produce numerous large datasets of 10 GB or more, providing a significant data-processing challenge. The Remote-CT project addresses this by combining the "MASSIVE" supercomputing GPU cluster with XLI / X-TRACT software, developed at CSIRO. This software has extensive functionality for both processing and simulation of absorption and phase-contrast tomography data and has now been modified for parallel operation on a GPU cluster to take maximum advantage of the speed-up this enables.
Since a theoretical calculation of delay is very complex and direct observation of delay on the r... more Since a theoretical calculation of delay is very complex and direct observation of delay on the road is complicated by uncontrollable variations, it was decided to use a method whereby the events on the road are reproduced in the laboratory by means of some machine which simulates behaviour of traffic …
Computed Tomography (CT) reconstruction is a computationally and data-intensive process applied a... more Computed Tomography (CT) reconstruction is a computationally and data-intensive process applied across many fields of scientific endeavor, including medical and materials science, as a noninvasive imaging technique. A typical CT dataset obtained with a CCD-based X-ray detector, such as that at the Australian Synchrotron with 4K×4K pixels captured over multiple-view angles, is in the order of 128GB. The reconstructed output volume is in the order 256GB. CT data sizes increase at 1.5 times the number of pixels in the detector, while the data-processing load generally increases as the square of the number of pixels, hence data storage, management and throughput capabilities become paramount. From a computational perspective, CT reconstruction is particularly well suited to mass parallelisation whereby the problem can be decomposed into many smaller independent parts. We have achieved significant performance gains by adapting our XLI software algorithms to a two-level parallelisation scheme, utilising multiple CPU cores and multiple GPUs on a single machine. In turn, where data sizes become prohibitively large to be processed on a single machine, we have developed an integrated CT reconstruction software system that is able to scale up and be deployed onto large GPU-enabled HPC clusters. We present here the results of reconstructing large CT datasets using our XLI software on both the CSIRO GPU cluster and the new MASSIVE-1 cluster located at the Australian Synchrotron. Both of these clusters provide high-end compute nodes with multiple GPUs coupled by high-speed interconnect and IO capabilities which combine to allow rapid CT reconstruction. Provided in this paper are examples of the application of the developed tools to the reconstruction of large CT datasets collected both at synchrotrons and with laboratory-based CT scanners.
2012 IEEE 8th International Conference on E-Science, 2012
ABSTRACT Computed Tomography (CT) is a non-destructive imaging technique widely used across many ... more ABSTRACT Computed Tomography (CT) is a non-destructive imaging technique widely used across many scientific, industrial and medical fields. It is both computationally and data intensive, and therefore can benefit from infrastructure in the “supercomputing” domain for research purposes, such as Synchrotron science. Our group within CSIRO has been actively developing X-ray tomography and image processing software and systems for HPC clusters. We have also leveraged the use of GPU's (Graphical Processing Units) for several codes enabling speedups by an order of magnitude or more over CPU-only implementations. A key goal of our systems is to enable our targeted “end users”, researchers, easy access to the tools, computational resources and data via familiar interfaces and client applications such that specialized HPC expertise and support is generally not required in order to initiate and control data processing, analysis and visualzation workflows. We have strived to enable the use of HPC facilities in an interactive fashion, similar to the familiar Windows desktop environment, in contrast to the traditional batch-job oriented environment that is still the norm at most HPC installations. Several collaborations have been formed, and we currently have our systems deployed on two clusters within CSIRO, Australia. A major installation at the Australian Synchrotron (MASSIVE GPU cluster) where the system has been integrated with the Imaging and Medical Beamline (IMBL) detector to provide rapid on-demand CT-reconstruction and visualization capabilities to researchers whilst on-site and remotely. A smaller-scale installation has also been deployed on a mini-cluster at the Shanghai Synchrotron Radiation Facility (SSRF) in China. All clusters run the Windows HPC Server 2008 R2 operating system. The two large clusters running our software, MASSIVE and CSIRO Bragg are currently configured as “hybrid clusters” in which individual nodes can b- dual-booted between Linux and Windows as demand requires. We have also recently explored the adaptation of our CT-reconstruction code to Cloud infrastructure, and have constructed a working “proof-of-concept” system for the Microsoft Azure Cloud. However, at this stage several challenges remain to be met in order to make it a truly viable alternative to our HPC cluster solution. Recently, CSIRO was successful in its proposal to develop eResearch tools for the Australian Government funded NeCTAR Research Cloud. As part of this project our group will be contributing CT and imaging processing components.
Advances in experimental medicine and biology, 2015
This chapter describes a novel way of carrying out image analysis, reconstruction and processing ... more This chapter describes a novel way of carrying out image analysis, reconstruction and processing tasks using cloud based service provided on the Australian National eResearch Collaboration Tools and Resources (NeCTAR) infrastructure. The toolbox allows users free access to a wide range of useful blocks of functionalities (imaging functions) that can be connected together in workflows allowing creation of even more complex algorithms that can be re-run on different data sets, shared with others or additionally adjusted. The functions given are in the area of cellular imaging, advanced X-ray image analysis, computed tomography and 3D medical imaging and visualisation. The service is currently available on the website www.cloudimaging.net.au .
International Journal of Computational Science and Engineering, 2013
ABSTRACT As the size and complexity of scientific problems and datasets grow, scientists from a b... more ABSTRACT As the size and complexity of scientific problems and datasets grow, scientists from a broad range of discipline areas are relying more and more on computational methods and simulations to help solve their problems. This paper presents a summary of heterogeneous algorithms and applications that have been developed by a large research organization (CSIRO) for solving practical and challenging science problems faster than is possible with conventional multi-core CPUs alone. The problem domains discussed include biological image analysis, computed tomography reconstruction, marine biogeochemical models, fluid dynamics, and bioinformatics. The algorithms utilize GPUs and multi-core CPUs on a scale ranging from single workstation installations through to large GPU clusters. Results demonstrate that large GPU clusters can be used to accelerate a variety of practical science applications, and justify the significant financial investment and interest being placed into such systems.
Frontiers in neuroinformatics, 2014
The Multi-modal Australian ScienceS Imaging and Visualization Environment (MASSIVE) is a national... more The Multi-modal Australian ScienceS Imaging and Visualization Environment (MASSIVE) is a national imaging and visualization facility established by Monash University, the Australian Synchrotron, the Commonwealth Scientific Industrial Research Organization (CSIRO), and the Victorian Partnership for Advanced Computing (VPAC), with funding from the National Computational Infrastructure and the Victorian Government. The MASSIVE facility provides hardware, software, and expertise to drive research in the biomedical sciences, particularly advanced brain imaging research using synchrotron x-ray and infrared imaging, functional and structural magnetic resonance imaging (MRI), x-ray computer tomography (CT), electron microscopy and optical microscopy. The development of MASSIVE has been based on best practice in system integration methodologies, frameworks, and architectures. The facility has: (i) integrated multiple different neuroimaging analysis software components, (ii) enabled cross-pla...
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Papers by Darren Thompson