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. We have characterized the progression of NAFLD in eNOS-/- mice fed a high fat diet (HFD) using noninvasive Dixon-based magnetic resonance imaging and single voxel STEAM spectroscopy-based protocols to measure liver fat fraction at 3 T. After 8 weeks of diet intervention, eNOS-/- mice exhibited significant accumulation of intra-abdominal and liver fat compared with control mice. Liver fat fraction measured by 1 H-MRS in vivo showed a good correlation with the NAFLD activity score measured by histology. Treatment of HFD-fed NOS3-/- mice with metformin showed significantly reduced liver fat fraction and altered hepatic lipidomic profile compared with untreated mice. Our results show the potential of in vivo liver MRI and 1 H-MRS to noninvasively diagnose and stage the progression of NAFLD and to monitor treatment response in an eNOS-/- murine model that represents the classic NAFLD phenotype associated with metabolic syndrome.

Multi-parametric liver tissue characterization using MR fingerprinting: Simultaneous T1 , T2 , T2 *, and fat fraction mapping.

PURPOSE: Quantitative T1 , T2 , T2 *, 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 T1 , T2 , T2 *, 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 1.5T to encode T1 , T2 , T2 *, and FF simultaneously. The nine-echo time-series are reconstructed using a low-rank tensor constrained reconstruction and used to fit T2 *, B0 and to separate the water and fat signals. Water- and fat-specific T1 , T2, and M0 are obtained through dictionary matching, whereas FF estimation is extracted from the M0 maps. The framework was evaluated in a standardized T1 /T2 phantom, a water-fat phantom, and 12 subjects in comparison to reference methods. Preliminary clinical feasibility is shown in four patients. RESULTS: The proposed water T1 , water T2 , T2 *, and FF maps in phantoms showed high coefficients of determination (r2 > 0.97) relative to reference methods. Measured liver MRF values in vivo (mean ± SD) for T1 , T2 , T2 *, and FF were 671 ± 60 ms, 43.2 ± 6.8 ms, 29 ± 6.6 ms, and 3.2 ± 2.6% with biases of 92 ms, -7.1 ms, -1.4 ms, and 0.63% when compared to conventional methods. CONCLUSION: A nine-echo liver MRF sequence allows for quantitative multi-parametric liver tissue characterization in a single breath-hold scan of ~14 s. Future work will aim to validate the proposed approach in patients with liver disease.

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. We show that for high Signal-to-Noise Ratio (SNR) voxels, the distribution is well approximated by a Gaussian distribution. On the other hand, this is not true for lower SNR voxels, where the distribution adopts a form in between the Gaussian and the uniform distributions. This was confirmed empirically. Also, knowing the PDF on a coil by coil basis it is possible to combine the data from multiple coils in an optimal way. We showed that the optimal combination reduces the resulting global variability of the velocity, in comparison with the commonly used Weighted Mean or with a SENSE reconstruction with R = 1.

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. We discover that a single MD simulation can contain sufficient information about reactions and rates to predict the dynamics of related yet different chemical systems using kinetic Monte Carlo (KMC) simulation. Our learned KMC models identify thousands of reactions and run 4 orders of magnitude faster than MD. The transferability of these models suggests that we can viably reuse data from existing MD simulations to accelerate future simulation studies and reduce the number of new MD simulations required.

Intrahepatic fatty acids composition as a biomarker of NAFLD progression from steatosis to NASH by using 1H-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 1H-MRS. Our results showed that the intracellular liver fatty acid composition changes as NAFLD progresses from simple steatosis to steatohepatitis (NASH). Using principal component analysis with a clustering method, it was possible to identify the three most relevant clinical groups: normal, steatosis and NASH by using 1H-MRS. These results showed a good agreement with the results obtained by GC-MS and histology. Our results suggest that it would be possible to detect the progression of simple steatosis to NASH using 1H-MRS, that has the potential to be used routinely in clinical application for screening high-risk patients.

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 (WSSA ) and circumferential (WSSC ) 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, WSSA , and WSSC using 4D flow MRI data obtained from 10 volunteers and 10 patients with bicuspid aortic valve (BAV). We compared our method with the centerline method. The mean value, standard deviation, root mean-squared error, and Wilcoxon signed rank test are reported. RESULTS: We obtained an error <0.05% processing analytical geometries. We found good agreement between our method and the modified Poiseuille flow for the WSS, WSSA , and WSSC . We found statistically significance differences between our method and a 3D centerline method. In BAV patients, we found a 220% significant increase in the WSSC in the ascending aorta with respect to volunteers. CONCLUSION: We developed a novel methodology to decompose the WSS vector in WSSA and WSSC in 3D domains, using 4D flow MRI data. Our method provides a more robust quantification of WSSA and WSSC in comparison with other reported methods. Magn Reson Med 79:2816-2823, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

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. We demonstrate our method on a high temperature, high pressure system of reacting liquid methane, and show that the learned KMC model is able to extrapolate more than an order of magnitude in time for key molecules. Additionally, our KMC model of elementary reactions enables us to isolate the most important set of reactions governing the behavior of key molecules found in the MD simulation. We develop a new data-driven algorithm to reduce the chemical reaction network which can be solved either as an integer program or efficiently using L1 regularization, and compare our results with simple count-based reduction. For our liquid methane system, we discover that rare reactions do not play a significant role in the system, and find that less than 7% of the approximately 2000 reactions observed from molecular dynamics are necessary to reproduce the molecular concentration over time of methane. The framework described in this work paves the way towards a genomic approach to studying complex chemical systems, where expensive MD simulation data can be reused to contribute to an increasingly large and accurate genome of elementary reactions and rates.

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. The demands placed on on-chip readout circuitry impose stringent trade-offs between fill factor and spatiotemporal resolution, causing many contemporary designs to severely underuse the technology's full potential. Concentrating on the low photon flux setting, this paper leverages results from group testing and proposes an architecture for a highly efficient readout of pixels using only a small number of TDCs. We provide optimized design instances for various sensor parameters and compute explicit upper and lower bounds on the number of TDCs required to uniquely decode a given maximum number of simultaneous photon arrivals. To illustrate the strength of the proposed architecture, we note a typical digitization of a 60 × 60 photodiode sensor using only 142 TDCs. The design guarantees registration and unique recovery of up to four simultaneous photon arrivals using a fast decoding algorithm. By contrast, a cross-strip design requires 120 TDCs and cannot uniquely decode any simultaneous photon arrivals. Among other realistic simulations of scintillation events in clinical positron-emission tomography, the above design is shown to recover the spatiotemporal location of 99.98% of all detected photons.

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. Nonlinear magnetic components can also arise from concomitant fields, particularly in low-field imaging, or intentionally used for nonlinear encoding. In any of these situations, the second-order component is key, because it constitutes the first step to approximate higher-order fields. We propose to use the fractional Fourier transform for analyzing and reconstructing the object's magnetization under the presence of quadratic fields. The fractional fourier transform provides a precise theoretical framework for this. We show how it can be used for reconstruction and for gaining a better understanding of the quadratic field-induced distortions, including examples of reconstruction for simulated and in vivo data. The obtained images have improved quality compared with standard Fourier reconstructions. The fractional fourier transform opens a new paradigm for understanding the MR signal generated by an object under a quadratic main field or nonlinear encoding.

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. Most of these reconstruction methods use prior information based on estimations of the pixel intensities of the images and therefore they are prone to introduce spatial or temporal blurring. Instead of using the pixel intensities, we propose to use information that allows us to sort the pixels of an image from darkest to brightest. The set of order relations which sort the pixels of an image has been called intensity order. The intensity order of an image can be estimated from low-resolution images, adjacent slices in volumetric acquisitions, temporal correlation in dynamic sequences or from prior reconstructions. Our technique for reconstruction using intensity order (TRIO) consists of looking for an image that satisfies the intensity order and minimizes the discrepancy between the acquired and reconstructed data. Results show that TRIO can effectively reconstruct 2-D-cine cardiac MR images (under-sampling factor of 4), estimating correctly the temporal evolution of the objects. Furthermore, TRIO is used as a second stage reconstruction after reconstructing with other techniques, keyhole, sliding window and k-t BLAST, to estimate the order information. In all cases the images are improved by TRIO.