Accelerating magnetic induction tomography‐based imaging through heterogeneous parallel computing

Magnetic Induction Tomography (MIT) is a non‐invasive imaging technique, which has applications in both industrial and clinical settings. In essence, it is capable of reconstructing the electromagnetic parameters of an object from measurements made on its surface. With the exploitation of parallelism, it is possible to achieve high quality inexpensive MIT images for biomedical applications on clinically relevant time scales. In this paper we investigate the performance of different parallel implementations of the forward eddy current problem, which is the main computational component of the inverse problem through which measured voltages are converted into images. We show that a heterogeneous parallel method that exploits multiple CPUs and GPUs can provide a high level of parallel scaling, leading to considerably improved runtimes. We also show how multiple GPUs can be used in conjunction with deal.II, a widely‐used open source finite element library

In-situ synchrotron X-ray imaging and tomography studies of the evolution of solidification microstructures under pulse electromagnetic fields

This research studies the dynamic evolution of dendritic structures and intermetallic phases of four Al based alloys during the solidification under pulse electromagnetic fields (PMFs). An advanced PMF solidification device was upgraded, built, commissioned for the research. The alloys used were Al-15Cu, Al-35Cu, Al-15Ni and Al-5Cu-1.5Fe-1Si. Systematic in-situ and real-time observation and studies were carried out at the TOMCAT beamline of Swiss Light Source, I13-2 beamline of Diamond Light Source and ID19 beamline of European Synchrotron Radiation Facility in the duration of this project. Synchrotron X-ray radiography and tomography were used primarily to observe and study the influence of PMFs on the nucleation and growth of primary dendritic structures and intermetallic phases under different magnetic flux and solidification conditions for the four alloys. More than 20 TB images and tomography datasets have been obtained throughout this research. Much effort and time was spent on segmenting, visualising and analysing these huge datasets using the Hull University supercomputer cluster, Viper, and the software, Avizo, ImageJ (Fiji), etc to explore and extract new insights and new science from those datasets. In particular, the skeletonisation function available from Avizo was customised and used to quantify the complex 3D microstructures and interconnected networks of different phases for the alloys. The important new findings of the research are:(1) Fragmentation of primary Al dendrites in the Al-15%Cu alloy was found when the magnetic flux of PMF applied is above 0.75 T; similarly, the fragmentation of Al3Ni intermetallic phases in the Al-15%Ni alloy was also observed when the magnetic flux of PMF applied is above 0.8 T. The clear and real-time observation of the fragmentation events in both dendritic and intermetallic phases provide unambiguous evidence to demonstrate that PMFs play a dominant role in structure fragmentation and multiplication, which is one important mechanism for structure (grain) refinement.(2) PMFs also produces pinch pressure gradient inside the semi-solid melt. Due to the different magnetic anisotropic properties between the liquid and solid phases, shear stresses due to the pinch pressure gradient may be produced. In the case of Al-15%Ni alloy, shear stresses of up to 30 MPa is created, which is sufficient to fracture Al3Ni phases. For the first time, such fragmentation mechanism for the Al3Ni phases in the Al-15%Ni alloy was revealed in this research.(3) The transition (or change of growth modes) of Al columnar dendrites to seaweed type dendrites in Al-15Cu alloy; and the facet growth to dendritic growth of the Al3Ni phases in the Al-15%Ni alloy were also observed in real-time when the magnetic flux is in the range of 0.75~0.8 T. Again, such dynamic changes in structure growth under PMFs are due to the enhanced melt flow caused by the applied fields.(4) In-situ tomography observation of PMF processing of the Al-5Cu-1.5Fe-1Si alloy also shows the effect of PMF on the refinement of the Chinese script type Fe intermetallic phases. In addition, the true 3D morphologies of three different types of Fe intermetallic phases in this alloy were clarified, again for the first time, in this research

Virtual clinical trials in medical imaging: a review

The accelerating complexity and variety of medical imaging devices and methods have outpaced the ability to evaluate and optimize their design and clinical use. This is a significant and increasing challenge for both scientific investigations and clinical applications. Evaluations would ideally be done using clinical imaging trials. These experiments, however, are often not practical due to ethical limitations, expense, time requirements, or lack of ground truth. Virtual clinical trials (VCTs) (also known as in silico imaging trials or virtual imaging trials) offer an alternative means to efficiently evaluate medical imaging technologies virtually. They do so by simulating the patients, imaging systems, and interpreters. The field of VCTs has been constantly advanced over the past decades in multiple areas. We summarize the major developments and current status of the field of VCTs in medical imaging. We review the core components of a VCT: computational phantoms, simulators of different imaging modalities, and interpretation models. We also highlight some of the applications of VCTs across various imaging modalities

NASA SBIR abstracts of 1991 phase 1 projects

The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included

Accelerating Radiation Dose Calculation with High Performance Computing and Machine Learning for Large-scale Radiotherapy Treatment Planning

Radiation therapy is powered by modern techniques in precise planning and executionof radiation delivery, which are being rapidly improved to maximize its benefit to cancerpatients. In the last decade, radiotherapy experienced the introduction of advanced methodsfor automatic beam orientation optimization, real-time tumor tracking, daily planadaptation, and many others, which improve the radiation delivery precision, planning easeand reproducibility, and treatment efficacy. However, such advanced paradigms necessitatethe calculation of orders of magnitude more causal dose deposition data, increasing the timerequirement of all pre-planning dose calculation. Principles of high-performance computingand machine learning were applied to address the insufficient speeds of widely-used dosecalculation algorithms to facilitate translation of these advanced treatment paradigms intoclinical practice.To accelerate CT-guided X-ray therapies, Collapsed-Cone Convolution-Superposition(CCCS), a state-of-the-art analytical dose calculation algorithm, was accelerated through itsnovel implementation on highly parallelized GPUs. This context-based GPU-CCCS approachtakes advantage of X-ray dose deposition compactness to parallelize calculation acrosshundreds of beamlets, reducing hardware-specific overheads, and enabling acceleration bytwo to three orders of magnitude compared to existing GPU-based beamlet-by-beamletapproaches. Near-linear increases in acceleration are achieved with a distributed, multi-GPUimplementation of context-based GPU-CCCS.Dose calculation for MR-guided treatment is complicated by electron return effects(EREs), exhibited by ionizing electrons in the strong magnetic field of the MRI scanner. EREsnecessitate the use of much slower Monte Carlo (MC) dose calculation, limiting the clinicalapplication of advanced treatment paradigms due to time restrictions. An automaticallydistributed framework for very-large-scale MC dose calculation was developed, grantinglinear scaling of dose calculation speed with the number of utilized computational cores. Itwas then harnessed to efficiently generate a large dataset of paired high- and low-noise MCdoses in a 1.5 tesla magnetic field, which were used to train a novel deep convolutionalneural network (CNN), DeepMC, to predict low-noise dose from faster high-noise MC-simulation. DeepMC enables 38-fold acceleration of MR-guided X-ray beamlet dosecalculation, while remaining synergistic with existing MC acceleration techniques to achievemultiplicative speed improvements.This work redefines the expectation of X-ray dose calculation speed, making it possibleto apply new highly-beneficial treatment paradigms to standard clinical practice for the firsttime