Characterization of hepatic fatty acids using magnetic resonance spectroscopy for the assessment of treatment response to metformin in an eNOS mouse model of metabolic nonalcoholic fatty liver disease/nonalcoholic steatohepatitis.

Nonalcoholic fatty liver disease (NAFLD) is the leading cause of chronic liver disease worldwide. Liver biopsy remains the gold standard for diagnosis and staging of disease. There is a clinical need for noninvasive diagnostic tools for risk stratification, follow-up, and monitoring treatment response that are currently lacking, as well as preclinical models that recapitulate the etiology of the human condition.

Rapid data-driven model reduction of nonlinear dynamical systems including chemical reaction networks using ℓ-regularization.

Large-scale nonlinear dynamical systems, such as models of atmospheric hydrodynamics, chemical reaction networks, and electronic circuits, often involve thousands or more interacting components. In order to identify key components in the complex dynamical system as well as to accelerate simulations, model reduction is often desirable. In this work, we develop a new data-driven method utilizing ℓ-regularization for model reduction of nonlinear dynamical systems, which involves minimal parameterization and has polynomial-time complexity, allowing it to easily handle large-scale systems with as many as thousands of components in a matter of minutes.

Multi-parametric liver tissue characterization using MR fingerprinting: Simultaneous T , T , T *, and fat fraction mapping.

Purpose: Quantitative T , T , T *, and fat fraction (FF) maps are promising imaging biomarkers for the assessment of liver disease, however these are usually acquired in sequential scans. Here we propose an extended MR fingerprinting (MRF) framework enabling simultaneous liver T , T , T *, and FF mapping from a single ~14 s breath-hold scan.

Methods: A gradient echo (GRE) liver MRF sequence with nine readouts per TR, low flip angles (5-15°), varying magnetisation preparation and golden angle radial trajectory is acquired at 1.

Intrahepatic fatty acids composition as a biomarker of NAFLD progression from steatosis to NASH by using H-MRS.

Non-alcoholic fatty liver disease (NAFLD) is the most common liver disease in the world and it is becoming one of the most frequent cause of liver transplantation. Unfortunately, the only available method that can reliably determine the stage of this disease is liver biopsy, however, it is invasive and risky for patients. The purpose of this study is to investigate changes in the intracellular composition of the liver fatty acids during the progression of the NAFLD in a mouse model fed with Western diet, with the aim of identify non-invasive biomarkers of NAFLD progression based in H-MRS.

Noise estimation for the velocity in MRI phase-contrast.

The purpose of this study is to estimate the precision or statistical variability of the velocity measurements computed from MRI phase-contrast. From the analytical probability density function (PDF) of the phase in the signal we obtain the PDF of the velocity by means of an auto-convolution. This PDF allows the estimation of the precision of the velocity, important for the correct interpretation of the many parameters that are based on it.

Transferable Kinetic Monte Carlo Models with Thousands of Reactions Learned from Molecular Dynamics Simulations.

Molecular dynamics (MD) simulation of complex chemistry typically involves thousands of atoms propagating over millions of time steps, generating a wealth of data. Traditionally these data are used to calculate some aggregate properties of the system and then discarded, but we propose that these data can be reused to study related chemical systems. Using approximate chemical kinetic models and methods from statistical learning, we study hydrocarbon chemistries under extreme thermodynamic conditions.

Learning reduced kinetic Monte Carlo models of complex chemistry from molecular dynamics.

We propose a novel statistical learning framework for automatically and efficiently building reduced kinetic Monte Carlo (KMC) models of large-scale elementary reaction networks from data generated by a single or few molecular dynamics simulations (MD). Existing approaches for identifying species and reactions from molecular dynamics typically use bond length and duration criteria, where bond duration is a fixed parameter motivated by an understanding of bond vibrational frequencies. In contrast, we show that for highly reactive systems, bond duration should be a model parameter that is chosen to maximize the predictive power of the resulting statistical model.

3D axial and circumferential wall shear stress from 4D flow MRI data using a finite element method and a laplacian approach.

Purpose: To decompose the 3D wall shear stress (WSS) vector field into its axial (WSS ) and circumferential (WSS ) components using a Laplacian finite element approach.

Methods: We validated our method with in silico experiments involving different geometries and a modified Poiseuille flow. We computed 3D maps of the WSS, WSS , and WSS using 4D flow MRI data obtained from 10 volunteers and 10 patients with bicuspid aortic valve (BAV).

Single-photon sampling architecture for solid-state imaging sensors.

Advances in solid-state technology have enabled the development of silicon photomultiplier sensor arrays capable of sensing individual photons. Combined with high-frequency time-to-digital converters (TDCs), this technology opens up the prospect of sensors capable of recording with high accuracy both the time and location of each detected photon. Such a capability could lead to significant improvements in imaging accuracy, especially for applications operating with low photon fluxes such as light detection and ranging and positron-emission tomography.

Application of the fractional Fourier transform to image reconstruction in MRI.

The classic paradigm for MRI requires a homogeneous B(0) field in combination with linear encoding gradients. Distortions are produced when the B(0) is not homogeneous, and several postprocessing techniques have been developed to correct them. Field homogeneity is difficult to achieve, particularly for short-bore magnets and higher B(0) fields.

TRIO a technique for reconstruction using intensity order: application to undersampled MRI.

Long acquisition times are still a limitation for many applications of magnetic resonance imaging (MRI), specially in 3-D and dynamic imaging. Several undersampling reconstruction techniques have been proposed to overcome this problem. These techniques are based on acquiring less samples than specified by the Nyquist criterion and estimating the nonacquired data by using some sort of prior information.