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You searched for +publisher:"Vanderbilt University" +contributor:("David S. Smith"). Showing records 1 – 2 of 2 total matches.

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Vanderbilt University

1. Ianni, Julianna Denise. Trajectory Optimization and Machine Learning Radiofrequency Pulses for Enhanced Magnetic Resonance Imaging.

Degree: PhD, Biomedical Engineering, 2017, Vanderbilt University

High field magnetic resonance imaging (MRI) offers several advantages over imaging at low field strengths, namely increased spectral resolution, better contrast due to longer T1 relaxation, higher signal to noise ratio (SNR), and better parallel imaging performance. However, many imaging techniques require strong flip angle uniformity and fast readouts, which are susceptible to trajectory errors. Optimization and machine learning methods are introduced to reduce image artifacts and decrease RF inhomogeneities in high field acquisitions. This is accomplished by employing algorithms that 1) exploit redundancies inherent in parallel imaging and 2) exploit redundant information in multi-subject data to learn characteristic relationships between RF and image parameters. First, an algorithm to reduce trajectory errors – Trajectory Auto-Corrected image Reconstruction (TrACR) – is presented. TrACR was evaluated with in vivo 7 Tesla (7T) brain data from non-Cartesian acquisitions. TrACR reconstructions reduced blurring and streaking artifacts and bear similar quality to images reconstructed using trajectory measurements. Second, an extension of TrACR is presented for echo planar imaging acquisitions to reduce trajectory and phase errors. EPI-TrACR is validated in vivo at 7T, at multiple acceleration and multishot factors, and in a time series, and consistently reduces image artifacts. Finally, to improve transmit field uniformity, a method is introduced for predicting tailored RF shims. RF-shim Prediction by Iteratively Projected Ridge Regression (PIPRR) was validated in simulation for single-slice shimming for 100 phantom human heads. PIPPR-predicted shims reduced profile inhomogeneity and maintained comparable specific absorption rate (SAR) efficiency and homogeneity to that of directly designed shims. PIPRR predictions for a new patient require just milliseconds, reducing compute time for RF shimming by orders of magnitude. Advisors/Committee Members: E. Brian Welch (committee member), David S. Smith (committee member), Bennett A. Landman (committee member), Adam W. Anderson (committee member), William A. Grissom (chair).

Subjects/Keywords: MRI; optimization; image reconstruction; machine learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Ianni, J. D. (2017). Trajectory Optimization and Machine Learning Radiofrequency Pulses for Enhanced Magnetic Resonance Imaging. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://etd.library.vanderbilt.edu/available/etd-11162017-130346/ ;

Chicago Manual of Style (16th Edition):

Ianni, Julianna Denise. “Trajectory Optimization and Machine Learning Radiofrequency Pulses for Enhanced Magnetic Resonance Imaging.” 2017. Doctoral Dissertation, Vanderbilt University. Accessed October 20, 2019. http://etd.library.vanderbilt.edu/available/etd-11162017-130346/ ;.

MLA Handbook (7th Edition):

Ianni, Julianna Denise. “Trajectory Optimization and Machine Learning Radiofrequency Pulses for Enhanced Magnetic Resonance Imaging.” 2017. Web. 20 Oct 2019.

Vancouver:

Ianni JD. Trajectory Optimization and Machine Learning Radiofrequency Pulses for Enhanced Magnetic Resonance Imaging. [Internet] [Doctoral dissertation]. Vanderbilt University; 2017. [cited 2019 Oct 20]. Available from: http://etd.library.vanderbilt.edu/available/etd-11162017-130346/ ;.

Council of Science Editors:

Ianni JD. Trajectory Optimization and Machine Learning Radiofrequency Pulses for Enhanced Magnetic Resonance Imaging. [Doctoral Dissertation]. Vanderbilt University; 2017. Available from: http://etd.library.vanderbilt.edu/available/etd-11162017-130346/ ;


Vanderbilt University

2. Huang, Longxiu. Dynamical Sampling and its Applications.

Degree: PhD, Mathematics, 2019, Vanderbilt University

Dynamical sampling is a new area in sampling theory that deals with signals that evolve over time under the action of a linear operator. There are lots of studies on various aspects of the dynamical sampling problem. However, they all focus on uniform discrete time-sets mathcal{T} subset ℕ. In our first paper, we concentrate on the case mathcal{T} = [0, L]. The goal of the present work is to study the frame property of the systems {Atg : g in𝓖, t in [0, L]}. To this end, we also characterize the completeness and Besselness properties of these systems. In our second paper, we consider dynamical sampling when the samples are corrupted by additive noises. The purpose of the second paper is to analyze the performance of the basic dynamical sampling algorithms in the finite dimensional case and study the impact of additive noise. The algorithms are implemented and tested on synthetic and real data sets, and denoising techniques are integrated to mitigate the effect of the noise. We also develop theoretical and numerical results that validate the algorithm for recovering the driving operators, which are defined via a real symmetric convolution. Advisors/Committee Members: Alexander M. Powell (committee member), Larry L. Schumaker (committee member), David S. Smith (committee member), Douglas P. Hardin (committee member), Akram Aldroubi (chair).

Subjects/Keywords: Cadzow denoising algorithm; numerical linear algebra; continuous frames; dynamical sampling

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Huang, L. (2019). Dynamical Sampling and its Applications. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://etd.library.vanderbilt.edu/available/etd-03192019-114134/ ;

Chicago Manual of Style (16th Edition):

Huang, Longxiu. “Dynamical Sampling and its Applications.” 2019. Doctoral Dissertation, Vanderbilt University. Accessed October 20, 2019. http://etd.library.vanderbilt.edu/available/etd-03192019-114134/ ;.

MLA Handbook (7th Edition):

Huang, Longxiu. “Dynamical Sampling and its Applications.” 2019. Web. 20 Oct 2019.

Vancouver:

Huang L. Dynamical Sampling and its Applications. [Internet] [Doctoral dissertation]. Vanderbilt University; 2019. [cited 2019 Oct 20]. Available from: http://etd.library.vanderbilt.edu/available/etd-03192019-114134/ ;.

Council of Science Editors:

Huang L. Dynamical Sampling and its Applications. [Doctoral Dissertation]. Vanderbilt University; 2019. Available from: http://etd.library.vanderbilt.edu/available/etd-03192019-114134/ ;

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