True Spatio-Temporal Detection and Estimation for Functional Magnetic Resonance Imaging.

The development of fast imaging in magnetic resonance imaging (MRI) makes it possible for researchers in various fields to investigate functional activities of the human brain with a unique combination of high spatial and temporal resolution. A significant task in functional MRI data analysis is to develop a detection statistic for activation, showing subject’s localized brain responses to pre-specified stimuli. With rare exceptions in FMRI, these detection statistics have been derived from a measurement model under two main assumptions: spatial independence and space-time separability of background noise. One of the main goals of this thesis is to remove these assumptions which have been widely used in existing approaches. This thesis makes three main contributions:(1) a development of a detection statistic based on a spatiotemporally correlated noise model without space-time separability, (2) signal and noise modeling to implement the proposed detection statistic, (3) a development of a detection statistic that is robust to signal-to-noise ratio (SNR), Rician activation detection. For the first time in FMRI, we develop a properly formulated spatiotemporal detection statistic for activation, based on a spatiotemporally correlated noise model without space-time separability. The implementation of the developed detection statistic requires joint signal and noise modeling in three or four dimensions, which is non-trivial statistical model estimation. We complete the implementation with the parametric cepstrum, allowing dramatic reduction of computations in model fitting. These two are totally new contributions to FMRI data analysis. As byproducts, a novel test procedure for space-time separability is proposed and its asymptotic power is analyzed. The developed detection statistic and conventional statistics involving spatial smoothing by Gaussian kernel are compared through a model comparison technique and asymptotic relative efficiency. Most methods in FMRI data analysis are based on magnitude voxel time courses and their approximation by a Gaussian distribution. Since the magnitude images, in fact, obey Rician distribution and the Gaussian approximation is valid under a high SNR assumption, Gaussian modeling may perform poorly when SNR is low. In this thesis, we develop a detection statistic from a Rician distributed model, allowing a robust activation detection regardless of SNR.Ph.D.Electrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/57634/2/nohjoonk_1.pd

Statistical Sinogram Restoration in Dual-Energy CT for PET Attenuation Correction

Dual-energy (DE) X-ray computed tomography (CT) has been found useful in various applications. In medical imaging, one promising application is using low-dose DECT for attenuation correction in positron emission tomography (PET). Existing approaches to sinogram material decomposition ignore noise characteristics and are based on logarithmic transforms, producing noisy component sinogram estimates for low-dose DECT. In this paper, we propose two novel sinogram restoration methods based on statistical models: penalized weighted least square (PWLS) and penalized likelihood (PL), yielding less noisy component sinogram estimates for low-dose DECT than classical methods. The proposed methods consequently provide more precise attenuation correction of the PET emission images than do previous methods for sinogram material decomposition with DECT. We report simulations that compare the proposed techniques and existing approaches.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85900/1/Fessler11.pd

Low-Dose Dual-Energy Computed Tomography for PET Attenuation Correction with Statistical Sinogram Restoration

Dual-energy (DE) X-ray computed tomography (CT) has been proposed as an useful tool in various applications. One promising application is DECT with low radiation doses used for attenuation correction in positron emission tomography (PET). In low-dose DECT, conventional methods for sinogram decomposition have been based on logarithmic transformations and ignored noise properties, leading to very noisy component sinogram estimates. In this paper, we propose two novel sinogram restoration methods that are statistically motivated; penalized weighted least square (PWLS) and penalized likelihood (PL), producing less noisy component sinogram estimates for low-dose DECT than the conventional approaches. The restored component sinograms can improve attenuation correction, thus allowing better image quality in PET. Experiments with a digital phantom indicate that the proposed methods produce less noisy sinograms, reconstructed images, and attenuation correction factors (ACF) than the conventional one, showing promise for CT-based attenuation correction in emission tomography.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85933/1/Fessler230.pd

Empirical Tests of Asset Pricing Models with Individual Assets: Resolving the Errors-in-Variables Bias in Risk Premium Estimation

To attenuate an inherent errors-in-variables bias, portfolios are widely employed to test asset pricing models; but portfolios might mask relevant risk- or return-related features of individual stocks. We propose an instrumental variables approach that allows the use of individual stocks as test assets, yet delivers consistent estimates of ex post risk premiums. This estimator also yields well-specified tests in small samples. The market risk premium under the capital asset pricing model (CAPM) and the liquidity-adjusted CAPM, premiums on risk factors under the Fama-French three- and five-factor models, and the Hou, Xue, and Zhang (2015) four-factor model are all insignificant after controlling for asset characteristics

TRUE SPATIO-TEMPORAL DETECTION AND ESTIMATION FOR FUNCTIONAL MAGNETIC Doctoral Committee: RESONANCE IMAGING

is dedicated to my parents and Hyun Kyung. ii ACKNOWLEDGEMENTS This dissertation owes the existence to many persons. First and foremost, I am immensely grateful to my advisor, Professor Victor Solo, for his guidance, advices, en-couragement and patience. I admire his enthusiasm, insight, and attitude to science. His unique view and vast knowledge of many scientific fields showed me directions to go as a researcher and laid the cornerstone of my research. From him, I earned one invaluable maxim, ”a good researcher never gives up”, which I always keep in my mind when I struggle. Without doubt, these influences will continue on my future works and life in general. I would like to express my special thanks to my co-advisor, Professor Jeffrey A. Fessler. From the beginning of my graduate study in the University of Michigan, he has provided me invaluable advices, not only for medical imaging but also for academic life. Especially, in the last year of my graduate study, I deeply appreciat

A TRUE SPATIO-TEMPORAL TEST STATISTIC FOR ACTIVATION DETECTION IN FMRI BY PARAMETRIC CEPSTRUM

A main purpose of data analysis in functional Magnetic Resonance Imaging (fMRI) is to determine which regions of the brain are activated by pre-specified temporal stimuli. In recent work, under the assumption of known spectra, we developed a detection statistic based on a spatially and temporally correlated noise model. In this paper, we implement the developed test statistic, which includes spatial and temporal whitening operators. For the estimation of spatial and temporal correlations, we use the parametric cepstral modeling, which allows dramatic reduction of computation in the model fitting and very simple methods to obtain spatiotemporal whitening operators. Model comparison and selection are discussed as well. We apply the developed techniques to a human dataset. Index Terms — Detection statistic, spatial and temporal correlations, and parametric cepstral modeling. 1

Industry Networks and the Speed of Information Flow for their encouragement and insightful comments. I am also thankful for valuable suggestions from and discussions with Industry Networks and the Speed of Information Flow

Abstract We investigate whether an industry's position in the network of inter-industry trade affects the speed of information flow. We find that return predictability to central industries from their related (=customer and supplier) industries is substantially stronger than that to peripheral industries from their related industries. Long-short portfolios of central industries yield risk-adjusted returns of 7.0% to 7.9% per annum, which are 3.6% to 5.3% higher than those of peripheral industries. To explain this finding, we argue that investors who invest in central industries need to process more complicated information about related industries, making the prices of central industries slower to incorporate all the information. We find that sell-side analysts of central industries also face more complicated information about related industries, as their earnings forecast revisions of related industries predict their future revisions of central industries more strongly. In addition, we present evidence that our finding is not explained by existing anomalies