Browsing by Subject "Reconstruction"
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Item The Determination of An Effective Smile(2016-04-14) Ruprecht, Mark, R.; Helwig, Nathaniel, E.Item Improving Signal to Noise Ratio in Ultra High Field Magnetic Resonance Imaging(2021-03) Tavaf, NaderUltra-High Field (UHF) Magnetic Resonance Imaging (MRI) advantages, including higher image resolution, reduced acquisition time via parallel imaging, and better Signal-to-Noise Ratio (SNR) have opened new opportunities for various clinical and research projects, including Functional Magnetic Resonance Imaging (fMRI), brain connectivity mapping, and anatomical imaging. The advancement of these UHF MRI performance metrics, especially SNR, was the primary motivation of this thesis.Unaccelerated SNR depends on receive array sensitivity profile, receiver noise correlation and static magnetic field strength. Various receive array decoupling technologies, including overlap/inductive and preamplifier decoupling, were previously utilized to mitigate noise correlation. In this dissertation, I developed a novel self-decoupling principle to isolate elements of a loop-based receive array and demonstrated, via full-wave electromagnetic/circuit co-simulations validated by bench measurements, that the self-decoupling technique provides inter-element isolation on par with overlap decoupling while self-decoupling improves SNR. I then designed and constructed the first self-decoupled 32 and 64 channel receiver arrays for human brain MRI imaging at 10.5T / 447MHz. Experimental comparisons of these receive arrays with the industry’s gold-standard 7T 32 channel receiver resulted in 1.81 times and 3.53 times more average SNR using the 10.5T 32 and 64 channel receivers I built, respectively. To further improve the SNR of accelerated MRI images, I developed a novel data-driven model using a customized conditional Generative Adversarial Network (GAN) architecture for parallel MRI image reconstruction and demonstrated that, when applied to human brain images subsampled with rate of 4, the GAN model results in a Peak Signal-to-Noise Ratio (PSNR) of 37.65 compared to GeneRalized Autocalibrating Partial Parallel Acquisition (GRAPPA)’s PSNR of 33.88. In summary, the work presented in this dissertation improved the SNR available for human brain imaging and provided the experimental realization of the advantages anticipated at 10.5T MRI. The insights from this thesis inform future efforts to build self-decoupled transmit arrays and high density (i.e. 128 channel) loop-based receive arrays for human brain MRI especially at ultra-high field as well as future studies to utilize deep learning techniques for reconstruction and post-processing of parallel MRI images.Item Segmentation and Dense Keypoints Estimation of Monkeys(2021-12) Yu, HaozhengAnimal tracking and pose estimation are core topics in neuroscience. However, for monkeys, current deep learning based algorithms often fail to perform well on segmentation and dense keypoints estimation due to the lack of annotated training data. In this thesis, we address this challenge by developing transfer learning based deep learning algorithms without using fully-annotated monkey data. We develop a bootstrapping strategy to refine the pretrained segmentation model on monkey data annotated with 2D sparse landmarks. In addition, we implement a voxel-based visual hull reconstruction approach to recover the 3D monkey pose from the silhouettes. For dense keypoints estimation, we follow a similar bootstrapping strategy to refine a pretrained HRNet, which is then used to learn a dense keypoint detector by leveraging multiview consistency. Our methods outperform the baseline methods on in-the-cage and in-the-wild monkey data.Item TH-36 Full Closure Construction: Evaluation of Traffic Operations Alternatives(Minnesota Department of Transportation, 2010-01) Hourdos, John; Hong, FeiliAccording to the 2007 Urban Mobility Report, $78 billion was lost due to congestion on urban roadways. Many urban corridors around the country experience demand that is close to or greater than the available capacity. Although most agree that the transportation system has matured and that we will not build ourselves out of congestion, existing infrastructure still often requires expansion. Such expansion in an already developed system most likely does not involve new roadway construction but results in existing roadway upgrades. Such roadways normally already serve considerable demand, a fact that increases the importance of the impact to the roadway users, estimated as Road User Costs (RUCs), and raises safety concerns both for the driving public as well as for the people working on reconstruction projects. New construction methods like Full Road Closure claim to reduce RUCs as well as reduce capital costs. This project follows the first large-scale Full Closure in Minnesota in an attempt to learn from the experience and propose the most appropriate tools and methodologies for planning, staging, and executing the construction. For the latter, three traffic analysis tools are selected for estimating RUCs due to the construction project. Their effort and data requirements, as well as their accuracy is evaluated and compared to the empirical, engineering-judgment-based, method used by Mn/DOT.