545 research outputs found
High order direct Arbitrary-Lagrangian-Eulerian schemes on moving Voronoi meshes with topology changes
We present a new family of very high order accurate direct
Arbitrary-Lagrangian-Eulerian (ALE) Finite Volume (FV) and Discontinuous
Galerkin (DG) schemes for the solution of nonlinear hyperbolic PDE systems on
moving 2D Voronoi meshes that are regenerated at each time step and which
explicitly allow topology changes in time.
The Voronoi tessellations are obtained from a set of generator points that
move with the local fluid velocity. We employ an AREPO-type approach, which
rapidly rebuilds a new high quality mesh rearranging the element shapes and
neighbors in order to guarantee a robust mesh evolution even for vortex flows
and very long simulation times. The old and new Voronoi elements associated to
the same generator are connected to construct closed space--time control
volumes, whose bottom and top faces may be polygons with a different number of
sides. We also incorporate degenerate space--time sliver elements, needed to
fill the space--time holes that arise because of topology changes. The final
ALE FV-DG scheme is obtained by a redesign of the fully discrete direct ALE
schemes of Boscheri and Dumbser, extended here to moving Voronoi meshes and
space--time sliver elements. Our new numerical scheme is based on the
integration over arbitrary shaped closed space--time control volumes combined
with a fully-discrete space--time conservation formulation of the governing PDE
system. In this way the discrete solution is conservative and satisfies the GCL
by construction.
Numerical convergence studies as well as a large set of benchmarks for
hydrodynamics and magnetohydrodynamics (MHD) demonstrate the accuracy and
robustness of the proposed method. Our numerical results clearly show that the
new combination of very high order schemes with regenerated meshes with
topology changes lead to substantial improvements compared to direct ALE
methods on conforming meshes
Development of whole-heart myocardial perfusion magnetic resonance imaging
Myocardial perfusion imaging is of huge importance for the detection of
coronary artery disease (CAD), one of the leading causes of morbidity
and mortality worldwide, as it can provide non-invasive detection at the
early stages of the disease. Magnetic resonance imaging (MRI) can assess
myocardial perfusion by capturing the rst-pass perfusion (FPP) of a
gadolinium-based contrast agent (GBCA), which is now a well-established
technique and compares well with other modalities. However, current MRI
methods are restricted by their limited coverage of the left ventricle. Interest
has therefore grown in 3D volumetric \whole-heart" FPP by MRI, although
many challenges currently limit this. For this thesis, myocardial perfusion
assessment in general, and 3D whole-heart FPP in particular, were reviewed
in depth, alongside MRI techniques important for achieving 3D FPP. From
this, a 3D `stack-of-stars' (SOS) FPP sequence was developed with the aim
of addressing some current limitations. These included the breath-hold
requirement during GBCA rst-pass, long 3D shot durations corrupted by
cardiac motion, and a propensity for artefacts in FPP. Parallel imaging and
compressed sensing were investigated for accelerating whole-heart FPP, with
modi cations presented to potentially improve robustness to free-breathing.
Novel sequences were developed that were capable of individually improving
some current sequence limits, including spatial resolution and signal-to-noise
ratio, although with some sacri ces. A nal 3D SOS FPP technique was
developed and tested at stress during free-breathing examinations of CAD
patients and healthy volunteers. This enabled the rst known detection of an
inducible perfusion defect with a free-breathing, compressed sensing, 3D FPP
sequence; however, further investigation into the diagnostic performance is
required. Simulations were performed to analyse potential artefacts in 3D
FPP, as well as to examine ways towards further optimisation of 3D SOS
FPP. The nal chapter discusses some limitations of the work and proposes
opportunities for further investigation.Open Acces
An unsupervised machine-learning-based shock sensor for high-order supersonic flow solvers
We present a novel unsupervised machine-learning sock sensor based on
Gaussian Mixture Models (GMMs). The proposed GMM sensor demonstrates remarkable
accuracy in detecting shocks and is robust across diverse test cases with
significantly less parameter tuning than other options. We compare the
GMM-based sensor with state-of-the-art alternatives. All methods are integrated
into a high-order compressible discontinuous Galerkin solver, where two
stabilization approaches are coupled to the sensor to provide examples of
possible applications. The Sedov blast and double Mach reflection cases
demonstrate that our proposed sensor can enhance hybrid sub-cell
flux-differencing formulations by providing accurate information of the nodes
that require low-order blending. Besides, supersonic test cases including high
Reynolds numbers showcase the sensor performance when used to introduce
entropy-stable artificial viscosity to capture shocks, demonstrating the same
effectiveness as fine-tuned state-of-the-art sensors. The adaptive nature and
ability to function without extensive training datasets make this GMM-based
sensor suitable for complex geometries and varied flow configurations. Our
study reveals the potential of unsupervised machine-learning methods,
exemplified by this GMM sensor, to improve the robustness and efficiency of
advanced CFD codes
Aeronautical engineering: A continuing bibliography with indexes (supplement 275)
This bibliography lists 379 reports, articles, and other documents introduced into the NASA scientific and technical information system in Jan. 1991
Doctor of Philosophy
dissertationCine phase contrast (PC) magnetic resonance imaging (MRI) is a useful imaging technique that allows for the quantitative measurement of in-vivo blood velocities over the cardiac cycle. Velocity information can be used to diagnose and learn more about the mechanisms of cardio-vascular disease. Compared to other velocity measuring techniques, PC MRI provides high-resolution 2D and 3D spatial velocity information. Unfortunately, as with many other MRI techniques, PC MRI su ers from long acquisition times which places constraints on temporal and spatial resolution. This dissertation outlines the use of temporally constrained reconstruction (TCR) of radial PC data in order to signi cantly reduce the acquisition time so that higher temporal and spatial resolutions can be achieved. A golden angle-based acquisition scheme and a novel self-gating method were used in order to allow for exible selection of temporal resolution and to ameliorate the di culties associated with external electrocardiogram (ECG) gating. Finally, image reconstruction times for TCR are signi cantly reduced by implementation on a high-performance computer cluster. The TCR algorithm is executed in parallel across multiple GPUs achieving a 50 second reconstruction time for a very large cardiac perfusion data set
Aeronautical engineering: A continuing bibliography, supplement 122
This bibliography lists 303 reports, articles, and other documents introduced into the NASA scientific and technical information system in April 1980
Sparse MRI and CT Reconstruction
Sparse signal reconstruction is of the utmost importance for efficient medical imaging, conducting accurate screening for security and inspection, and for non-destructive testing. The sparsity of the signal is dictated by either feasibility, or the cost and the screening time constraints of the system. In this work, two major sparse signal reconstruction systems such as compressed sensing magnetic resonance imaging (MRI) and sparse-view computed tomography (CT) are investigated.
For medical CT, a limited number of views (sparse-view) is an option for whether reducing the amount of ionizing radiation or the screening time and the cost of the procedure. In applications such as non-destructive testing or inspection of large objects, like a cargo container, one angular view can take up to a few minutes for only one slice. On the other hand, some views can be unavailable due to the configuration of the system. A problem of data sufficiency and on how to estimate a tomographic image when the projection data are not ideally sufficient for precise reconstruction is one of two major objectives of this work. Three CT reconstruction methods are proposed: algebraic iterative reconstruction-reprojection (AIRR), sparse-view CT reconstruction based on curvelet and total variation regularization (CTV), and sparse-view CT reconstruction based on nonconvex L1-L2 regularization. The experimental results confirm a high performance based on subjective and objective quality metrics. Additionally, sparse-view neutron-photon tomography is studied based on Monte-Carlo modelling to demonstrate shape reconstruction, material discrimination and visualization based on the proposed 3D object reconstruction method and material discrimination signatures.
One of the methods for efficient acquisition of multidimensional signals is the compressed sensing (CS). A significantly low number of measurements can be obtained in different ways, and one is undersampling, that is sampling below the Shannon-Nyquist limit. Magnetic resonance imaging (MRI) suffers inherently from its slow data acquisition. The compressed sensing MRI (CSMRI) offers significant scan time reduction with advantages for patients and health care economics. In this work, three frameworks are proposed and evaluated, i.e., CSMRI based on curvelet transform and total generalized variation (CT-TGV), CSMRI using curvelet sparsity and nonlocal total variation: CS-NLTV, CSMRI that explores shearlet sparsity and nonlocal total variation: SS-NLTV. The proposed methods are evaluated experimentally and compared to the previously reported state-of-the-art methods. Results demonstrate a significant improvement of image reconstruction quality on different medical MRI datasets
Aeronautical engineering: A continuing bibliography with indexes (supplement 195)
This bibliography lists 389 reports, articles and other documents introduced into the NASA scientific and technical information system in December 1985
Recommended from our members
MR Shuffling: Accelerated Single-Scan Multi-Contrast Magnetic Resonance Imaging
Magnetic resonance imaging (MRI) is an attractive medical imaging modality as it is non-invasive and does not involve ionizing radiation. Routine clinical MRI exams obtain MR images corresponding to different soft tissue contrast by performing multiple scans. When two-dimensional (2D) imaging is used, these scans are often repeated in other scanning planes. As a result, the number of scans comprising an MRI exam leads to prohibitively long exam times as compared to other medical imaging modalities such as computed tomography. Many approaches have been designed to accelerate the MRI acquisition while maintaining diagnostic quality.One approach is to collect multiple measurements while the MRI signal is evolving due to relaxation. This enables a reduction in scan time, as fewer acquisition windows are needed to collect the same number of measurements. However, when the temporal aspect of the acquisition is left unmodeled, artifacts are likely to appear in the reconstruction. Most often, these artifacts manifest as image blurring. The effect depends on the acquisition parameters as well as the tissue relaxation itself, resulting in spatially varying blurring. The severity of the artifacts is directly related to the level of acceleration, and thus presents a tradeoff with scan time. The effect is amplified when imaging in three dimensions, severely limiting scan efficiency. Volumetric variants would be used if not for the blurring, as they are able to reconstruct images at isotropic resolution and support mutli-planar reformatting.Another established acceleration technique, called parallel imaging, takes advantage of spatially sensitive receive coil arrays to collect multiple MRI measurements in parallel. Thus, the acquisition is shortened, and the reconstruction uses the spatial sensitivity information to recover the image. More recently, methods have been developed that leverage image structure such as sparsity and low rank to reduce the required number of samples for a well-posed reconstruction. Compressed sensing and its low rank extensions use these concepts to acquire incoherent measurements below the Nyquist rate. These techniques are especially suited to MRI, as incoherent measurements can be easily achieved through pseudo-random under-sampling. As the mechanisms behind parallel imaging and compressed sensing are fundamentally different, they can be combined to achieve even higher acceleration.This dissertation proposes accelerated MRI acquisition and reconstruction techniques that account for the temporal dynamics of the MR signal. The methods build off of parallel imaging and compressed sensing to reduce scan time and flexibly model the temporal relaxation behavior. By randomly shuffling the sampling in the acquisition stage and imposing low rank constraints in the reconstruction stage, intrinsic physical parameters are modeled and their dynamics are recovered as multiple images of varying tissue contrast. Additionally, blurring artifacts are significantly reduced, as the temporal dynamics are accounted for in the reconstruction.This dissertation first introduces T2 Shuffling, a volumetric technique that reduces blurring and reconstructs multiple T2-weighted image contrasts from a single acquisition. The method is integrated into a clinical hospital environment and evaluated on patients. Next, this dissertation develops a fast and distributed reconstruction for T2 Shuffling that achieves clinically relevant processing time latency. Clinical validation results are shown comparing T2 Shuffling as a single-sequence alternative to conventional pediatric knee MRI. Based off the compelling results, a fast targeted knee MRI using T2 Shuffling is implemented, enabling same-day access to MRI at one-third the cost compared to the conventional exam. To date, over 2,400 T2 Shuffling patient scans have been performed.Continuing the theme of accelerated multi-contrast imaging, this dissertation extends the temporal signal model with T1-T2 Shuffling. Building off of T2 Shuffling, the new method additionally samples multiple points along the saturation recovery curve by varying the repetition time durations during the scan. Since the signal dynamics are governed by both T1 recovery and T2 relaxation, the reconstruction captures information about both intrinsic tissue parameters. As a result, multiple target synthetic contrast images are reconstructed, all from a single scan. Approaches for selecting the sequence parameters are provided, and the method is evaluated on in vivo brain imaging of a volunteer.Altogether, these methods comprise the theme of MR Shuffling, and may open new pathways toward fast clinical MRI
Combining Linguistic and Machine Learning Techniques for Word Alignment Improvement
Alignment of words, i.e., detection of corresponding units between two sentences that are translations of each other, has been shown to be crucial for the success of many NLP applications such as statistical machine translation (MT), construction of bilingual lexicons, word-sense disambiguation, and projection of resources between languages. With the availability of large parallel texts, statistical word alignment systems have proven to be quite successful on many language pairs. However, these systems are still faced with several challenges due to the complexity of the word alignment problem, lack of enough training data, difficulty learning statistics correctly, translation divergences, and lack of a means for incremental incorporation of linguistic knowledge.
This thesis presents two new frameworks to improve existing word alignments using supervised learning techniques. In the first framework, two rule-based approaches are introduced. The first approach, Divergence Unraveling for Statistical MT (DUSTer), specifically targets translation divergences and corrects the alignment links related to them using a set of manually-crafted, linguistically-motivated rules. In the second approach, Alignment Link Projection (ALP), the rules are generated automatically by adapting transformation-based error-driven learning to the word alignment problem. By conditioning the rules on initial alignment and linguistic properties of the words, ALP manages to categorize the errors of the initial system and correct them.
The second framework, Multi-Align, is an alignment combination framework based on classifier ensembles. The thesis presents a neural-network based implementation of Multi-Align, called NeurAlign. By treating individual alignments as classifiers, NeurAlign builds an additional model to learn how to combine the input alignments effectively.
The evaluations show that the proposed techniques yield significant improvements (up to 40% relative error reduction) over existing word alignment systems on four different language pairs, even with limited manually annotated data. Moreover, all three systems allow an easy integration of linguistic knowledge into statistical models without the need for large modifications to existing systems. Finally, the improvements are analyzed using various measures, including the impact of improved word alignments in an external application---phrase-based MT
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