Spatial Resolution Analysis of Iterative Image Ceconstruction with Separate Regularization of Real and Imaginary par

A common method of improving the conditioning in iterative image reconstruction is to include regularization in the reconstruction algorithm. One such regularization is the roughness penalty, which when used in the algorithm encourages smoother images. For complex valued images, the roughness penalty typically penalizes equally the real and imaginary parts. The desired resolution of the reconstructed image can then be evaluated using the local impulse response. A fast algorithm to calculate it was developed for the typical roughness penalty, used for matching the regularization parameter expediently to the desired resolution. For some cases its advantageous to penalize independently the real and imaginary parts. This paper proposes a fast algorithm to calculate the local impulse response for that penalty and applies it to an fMRI reconstruction problem.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85888/1/Fessler220.pd

Fast joint reconstruction of dynamic R2∗R_2^* and field maps in functional MRI.

Blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) is conventionally done by reconstructing T2 * -weighted images. However, since the images are unitless they are nonquantifiable in terms of important physiological parameters. An alternative approach is to reconstruct R2 * maps which are quantifiable and have comparable BOLD contrast as T2* -weighted images. However, conventional R2 * mapping involves long readouts and ignores relaxation during readout. Another problem with fMRI imaging is temporal drift/fluctuations in off-resonance. Conventionally, a field map is collected at the start of the fMRI study to correct for off-resonance, ignoring any temporal changes. Here, we propose a new fast regularized iterative algorithm that jointly reconstructs R2 * and field maps for all time frames in fMRI data. To accelerate the algorithm we linearize the MR signal model, enabling the use of fast regularized iterative reconstruction methods. The regularizer was designed to account for the different resolution properties of both R2 * and field maps and provide uniform spatial resolution. For fMRI data with the same temporal frame rate as data collected for T2 * -weighted imaging the resulting R2 * maps performed comparably to T2 * -weighted images in activation detection while also correcting for spatially global and local temporal changes in off-resonance.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86002/1/Fessler23.pd

Regularized Field Map Estimation in MRI

In fast magnetic resonance (MR) imaging with long readout times, such as echo-planar imaging (EPI) and spiral scans, it is important to correct for the effects of field inhomogeneity to reduce image distortion and blurring. Such corrections require an accurate field map, a map of the off-resonance frequency at each voxel. Standard field map estimation methods yield noisy field maps, particularly in image regions with low spin density. This paper describes regularized methods for field map estimation from two or more MR scans having different echo times. These methods exploit the fact that field maps are generally smooth functions. The methods use algorithms that decrease monotonically a regularized least-squares cost function, even though the problem is highly nonlinear. Results show that the proposed regularized methods significantly improve the quality of field map estimates relative to conventional unregularized methods.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85871/1/Fessler22.pd

Spectral-spatial pulse design for through-plane phase precompensatory slice selection in T 2 * -weighted functional MRI

T 2 * -weighted functional MR images suffer from signal loss artifacts caused by the magnetic susceptibility differences between air cavities and brain tissues. We propose a novel spectral-spatial pulse design that is slice-selective and capable of mitigating the signal loss. The two-dimensional spectral–spatial pulses create precompensatory phase variations that counteract through-plane dephasing, relying on the assumption that resonance frequency offset and through-plane field gradient are spatially correlated. The pulses can be precomputed before functional MRI experiments and used repeatedly for different slices in different subjects. Experiments with human subjects showed that the pulses were effective in slice selection and loss mitigation at different brain regions. Magn Reson Med 61:1137–1147, 2009. © 2009 Wiley-Liss, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62134/1/21938_ftp.pd

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

Fast and Motion Robust Dynamic R2* Reconstruction for Functional MRI.

Blood oxygen level dependent (BOLD) functional MRI (fMRI) imaging is the most common way of imaging neuronal activity in humans using MRI. The BOLD contrast is directly related to changes in vascular physiology associated with neuronal activity and can be directly linked to changes in cerebral blood volume, blood flow and metabolic rate of oxygen. Conventional BOLD imaging is done by reconstructing T2*-weighted images. T2∗-weighted images are unitless and even though they measure the magnitude of the BOLD contrast they are still nonquantifiable in terms of the vascular physiology. An alternative approach is to reconstruct R2∗ maps which are quantifiable and can be directly linked to the vascular changes during activation. However, conventional R2∗ mapping involves long readouts and generally ignores relaxation and off-resonance during readout. Since fMRI data is usually acquired over a course of several minutes, where the same image volume is collected multiple times, it is important for the time series of each pixel to only reflect changes due to neuronal activity. However, BOLD imaging suffers from temporal drift/fluctuations and subject motion which can confound the findings. Conventionally, a field map is collected at the start of the fMRI study to correct for off-resonance, ignoring any possible changes in it due to either drift or motion. Here we propose a new fast and motion robust R2∗ iterative reconstruction that jointly reconstructs initial magnetization and field maps along with the R2∗ changes, for all time frames in fMRI. To accelerate the algorithm we propose to linearize the MR signal model, enabling the use of fast regularized iterative reconstruction methods. The regularizer was designed to account for the different resolution properties of both R2∗ and field maps and provide uniform spatial resolution.Ph.D.Electrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/63702/1/volafsso_1.pd

Co-regulatory networks of human serum proteins link genetics to disease

Proteins circulating in the blood are critical for age-related disease processes; however, the serum proteome has remained largely unexplored. To this end, 4137 proteins covering most predicted extracellular proteins were measured in the serum of 5457 Icelanders over 65 years of age. Pairwise correlation between proteins as they varied across individuals revealed 27 different network modules of serum proteins, many of which were associated with cardiovascular and metabolic disease states, as well as overall survival. The protein modules were controlled by cis- and trans-acting genetic variants, which in many cases were also associated with complex disease. This revealed co-regulated groups of circulating proteins that incorporated regulatory control between tissues and demonstrated close relationships to past, current, and future disease states

Genome-wide association study identifies loci influencing concentrations of liver enzymes in plasma.

Concentrations of liver enzymes in plasma are widely used as indicators of liver disease. We carried out a genome-wide association study in 61,089 individuals, identifying 42 loci associated with concentrations of liver enzymes in plasma, of which 32 are new associations (P = 10(-8) to P = 10(-190)). We used functional genomic approaches including metabonomic profiling and gene expression analyses to identify probable candidate genes at these regions. We identified 69 candidate genes, including genes involved in biliary transport (ATP8B1 and ABCB11), glucose, carbohydrate and lipid metabolism (FADS1, FADS2, GCKR, JMJD1C, HNF1A, MLXIPL, PNPLA3, PPP1R3B, SLC2A2 and TRIB1), glycoprotein biosynthesis and cell surface glycobiology (ABO, ASGR1, FUT2, GPLD1 and ST3GAL4), inflammation and immunity (CD276, CDH6, GCKR, HNF1A, HPR, ITGA1, RORA and STAT4) and glutathione metabolism (GSTT1, GSTT2 and GGT), as well as several genes of uncertain or unknown function (including ABHD12, EFHD1, EFNA1, EPHA2, MICAL3 and ZNF827). Our results provide new insight into genetic mechanisms and pathways influencing markers of liver function