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Novel adaptive reconstruction schemes for accelerated myocardial perfusion magnetic resonance imagingLingala, Sajan Goud 01 December 2013 (has links)
Coronary artery disease (CAD) is one of the leading causes of death in the world. In the United States alone, it is estimated that approximately every 25 seconds, a new CAD event will occur, and approximately every minute, someone will die of one. The detection of CAD during in its early stages is very critical to reduce the mortality rates. Magnetic resonance imaging of myocardial perfusion (MR-MPI) has been receiving significant attention over the last decade due to its ability to provide a unique view of the microcirculation blood flow in the myocardial tissue through the coronary vascular network. The ability of MR-MPI to detect changes in microcirculation during early stages of ischemic events makes it a useful tool in identifying myocardial tissues that are alive but at the risk of dying. However this technique is not yet fully established clinically due to fundamental limitations imposed by the MRI device physics. The limitations of current MRI schemes often make it challenging to simultaneously achieve high spatio-temporal resolution, sufficient spatial coverage, and good image quality in myocardial perfusion MRI. Furthermore, the acquisitions are typically set up to acquire images during breath holding. This often results in motion artifacts due to improper breath hold patterns.
This dissertation deals with developing novel image reconstruction methods in conjunction with non-Cartesian sampling for the reconstruction of dynamic MRI data from highly accelerated / under-sampled Fourier measurements. The reconstruction methods are based on adaptive signal models to represent the dynamic data using few model coefficients. Three novel adaptive reconstruction methods are developed and validated: (a) low rank and sparsity based modeling, (b) blind compressed sensing, and (c) motion compensated compressed sensing. The developed methods are applicable to a wide range of dynamic imaging problems. In the context of MR-MPI, this dissertation show feasibilities that the developed methods can enable free breathing myocardial perfusion MRI acquisitions with high spatio-temporal resolutions ( < 2mm x 2mm, 1 heart beat) and slice coverage (upto 8 slices).
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A probabilistic framework and algorithms for modeling and analyzing multi-instance dataBehmardi, Behrouz 28 November 2012 (has links)
Multi-instance data, in which each object (e.g., a document) is a collection of instances
(e.g., word), are widespread in machine learning, signal processing, computer vision,
bioinformatic, music, and social sciences. Existing probabilistic models, e.g., latent
Dirichlet allocation (LDA), probabilistic latent semantic indexing (pLSI), and discrete
component analysis (DCA), have been developed for modeling and analyzing multiinstance
data. Such models introduce a generative process for multi-instance data which
includes a low dimensional latent structure. While such models offer a great freedom
in capturing the natural structure in the data, their inference may present challenges.
For example, the sensitivity in choosing the hyper-parameters in such models, requires
careful inference (e.g., through cross-validation) which results in large computational
complexity. The inference for fully Bayesian models which contain no hyper-parameters
often involves slowly converging sampling methods. In this work, we develop approaches
for addressing such challenges and further enhancing the utility of such models.
This dissertation demonstrates a unified convex framework for probabilistic modeling
of multi-instance data. The three main aspects of the proposed framework are as follows.
First, joint regularization is incorporated into multiple density estimation to simultaneously
learn the structure of the distribution space and infer each distribution. Second,
a novel confidence constraints framework is used to facilitate a tuning-free approach to
control the amount of regularization required for the joint multiple density estimation
with theoretical guarantees on correct structure recovery. Third, we formulate the problem
using a convex framework and propose efficient optimization algorithms to solve
it.
This work addresses the unique challenges associated with both discrete and continuous
domains. In the discrete domain we propose a confidence-constrained rank minimization
(CRM) to recover the exact number of topics in topic models with theoretical
guarantees on recovery probability and mean squared error of the estimation. We provide
a computationally efficient optimization algorithm for the problem to further the
applicability of the proposed framework to large real world datasets. In the continuous
domain, we propose to use the maximum entropy (MaxEnt) framework for multi-instance
datasets. In this approach, bags of instances are represented as distributions using the
principle of MaxEnt. We learn basis functions which span the space of distributions for
jointly regularized density estimation. The basis functions are analogous to topics in a
topic model.
We validate the efficiency of the proposed framework in the discrete and continuous
domains by extensive set of experiments on synthetic datasets as well as on real world
image and text datasets and compare the results with state-of-the-art algorithms. / Graduation date: 2013
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Komprimované snímání v perfuzním zobrazování pomocí magnetické rezonance / Compressed sensing in magnetic resonance perfusion imaging.Mangová, Marie January 2014 (has links)
Magnetic resonance perfusion imaging is a today's very promising method for medicine diagnosis. This thesis deals with a sparse representation of signals, low-rank matrix recovery and compressed sensing, which allows overcoming present physical limitations of magnetic resonance perfusion imaging. Several models for reconstruction of measured perfusion data is introduced and numerical methods for their software implementation, which is an important part of the thesis, is mentioned. Proposed models are verified on simulated and real perfusion data from magnetic resonance.
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