Accelerated, physics-inspired inference of skeletal muscle microstructure from diffusion-weighted MRI.

Muscle health is a critical component of overall health and quality of life. However, current measures of skeletal muscle health take limited account of microstructural variations within muscle, which play a crucial role in mediating muscle function. To address this, we present a physics-inspired, machine learning-based framework for the non-invasive estimation of microstructural organization in skeletal muscle from diffusion-weighted MRI (dMRI) in an uncertainty-aware manner. To reduce the computational expense associated with direct numerical simulations of dMRI physics, a polynomial meta-model is developed that accurately represents the input/output relationships of a high-fidelity numerical model. This meta-model is used to develop a Gaussian process (GP) model that provides voxel-wise estimates and confidence intervals of microstructure organization in skeletal muscle. Given noise-free data, the GP model accurately estimates microstructural parameters. In the presence of noise, the diameter, intracellular diffusion coefficient, and membrane permeability are accurately estimated with narrow confidence intervals, while volume fraction and extracellular diffusion coefficient are poorly estimated and exhibit wide confidence intervals. A reduced-acquisition GP model, consisting of one-third the diffusion-encoding measurements, is shown to predict parameters with similar accuracy to the original model. The fiber diameter and volume fraction estimated by the reduced GP model is validated via histology, with both parameters accurately estimated, demonstrating the capability of the proposed framework as a promising non-invasive tool for assessing skeletal muscle health and function.

Harmonic viscoelastic response of 3D histology-informed white matter model.

White matter (WM) consists of bundles of long axons embedded in a glial matrix, which lead to anisotropic mechanical properties of brain tissue, and this complicates direct numerical simulations of WM viscoelastic response. The detailed axonal geometry contains scales that range from axonal diameter (microscale) to many diameters (mesoscale) imposing an additional challenge to numerical simulations. Here we describe the development of a 3D homogenization model for the central nervous system (CNS) that accounts for the anisotropy introduced by the axon/neuroglia composite, the axonal trace curvature, and the tissue dynamic response in the frequency domain. Homogenized models that allow the incorporation of all the above factors are important for accurately simulating the tissue's mechanical behavior, and this in turn is essential in interpreting non-invasive elastography measurements. Geometric and material parameters affect the material properties and thus the response of the brain tissue. More complex, orthotropic, or anisotropic material properties are to be considered as necessitated by the 3D tissue structure. An assembly of micro-scale 3D representative elemental volumes (REVs) is constructed, leading to an integrated mesoscale WM finite element model. Assemblies of microscopic REVs, with orientations based on geometrical reconstructions driven by confocal microscopy data are employed to form the elements of the WM model. Each REV carries local material properties based on a finite element model of biphasic (axon-glial matrix) unidirectional composite. The viscoelastic response of the microscopic REVs is extracted based on geometric information and fiber volume fractions calculated from the relative distance between the local elements and global axonal trace. The response of the WM tissue model is homogenized by averaging the shear moduli over the total volume (thus deriving effective properties) under realistic external loading conditions. Under harmonic shear loading, it is proven that that the effective transverse shear moduli are higher than the axial moduli when the axon moduli are higher than the glial. Methodologically, the process of using micro-scale 3D REVs to describe more complex axon geometries avoids the partition process in traditional composite finite element methods (based on partition of finite element grids) and constitutes a robust algorithm to automatically build a WM model based on available axonal trace information. Analytically, the model provides unmatched simulation flexibility and computational power as the position, orientation, and the magnitude of each tissue building block is calculated using real tissue data, as are the training and testing processes at each level of the multiscale WM tissue.

Sensitivity analysis of effective transverse shear viscoelastic and diffusional properties of myelinated white matter.

Motivated by the need to interpret the results from a combined use of in vivo brain Magnetic Resonance Elastography (MRE) and Diffusion Tensor Imaging (DTI), we developed a computational framework to study the sensitivity of single-frequency MRE and DTI metrics to white matter microstructure and cell-level mechanical and diffusional properties. White matter was modeled as a triphasic unidirectional composite, consisting of parallel cylindrical inclusions (axons) surrounded by sheaths (myelin), and embedded in a matrix (glial cells plus extracellular matrix). Only 2D mechanics and diffusion in the transverse plane (perpendicular to the axon direction) was considered, and homogenized (effective) properties were derived for a periodic domain containing a single axon. The numerical solutions of the MRE problem were performed with ABAQUS and by employing a sophisticated boundary-conforming grid generation scheme. Based on the linear viscoelastic response to harmonic shear excitation and steady-state diffusion in the transverse plane, a systematic sensitivity analysis of MRE metrics (effective transverse shear storage and loss moduli) and DTI metric (effective radial diffusivity) was performed for a wide range of microstructural and intrinsic (phase-based) physical properties. The microstructural properties considered were fiber volume fraction, and the myelin sheath/axon diameter ratio. The MRE and DTI metrics are very sensitive to the fiber volume fraction, and the intrinsic viscoelastic moduli of the glial phase. The MRE metrics are nonlinear functions of the fiber volume fraction, but the effective diffusion coefficient varies linearly with it. Finally, the transverse metrics of both MRE and DTI are insensitive to the axon diameter in steady state. Our results are consistent with the limited anisotropic MRE and co-registered DTI measurements, mainly in the corpus callosum, available in the literature. We conclude that isotropic MRE and DTI constitutive models are good approximations for myelinated white matter in the transverse plane. The unidirectional composite model presented here is used for the first time to model harmonic shear stress under MRE-relevant frequency on the cell level. This model can be extended to 3D in order to inform the solution of the inverse problem in MRE, establish the biological basis of MRE metrics, and integrate MRE/DTI with other modalities towards increasing the specificity of neuroimaging.

Lattice Boltzmann method for simulation of diffusion magnetic resonance imaging physics in multiphase tissue models.

We report an implementation of the lattice Boltzmann method (LBM) to integrate the Bloch-Torrey equation, which describes the evolution of the transverse magnetization vector and the fate of the signal of diffusion magnetic resonance imaging (dMRI). Motivated by the need to interpret dMRI experiments in biological tissues, and to offset the small time-step limitation of classical LBM, a hybrid LBM scheme is introduced and implemented to solve the Bloch-Torrey equation. A membrane boundary condition is presented which is able to accurately represent the effects of thin curvilinear membranes typically found in biological tissues. As implemented, the hybrid LBM scheme accommodates piece-wise uniform transport, dMRI parameters, periodic and mirroring outer boundary conditions, and finite membrane permeabilities on non-boundary-conforming inner boundaries. By comparing with analytical solutions of limiting cases, we demonstrate that the hybrid LBM scheme is more accurate than the classical LBM scheme. The proposed explicit LBM scheme maintains second-order spatial accuracy, stability, and first-order temporal accuracy for a wide range of parameters. The parallel implementation of the hybrid LBM code in a multi-CPU computer system, as well as on GPUs, is straightforward and efficient. Along with offering certain advantages over finite element or Monte Carlo schemes, the proposed hybrid LBM constitutes a flexible scheme that can by easily adapted to model more complex interfacial conditions and physics in heterogeneous multiphase tissue models and to accommodate sophisticated dMRI sequences.

Global sensitivity analysis of skeletal muscle dMRI metrics: Effects of microstructural and pulse parameters.

PURPOSE: Estimating microstructural parameters of skeletal muscle from diffusion MRI (dMRI) signal requires understanding the relative importance of both microstructural and dMRI sequence parameters on the signal. This study seeks to determine the sensitivity of dMRI signal to variations in microstructural and dMRI sequence parameters, as well as assess the effect of noise on sensitivity. METHODS: Using a cylindrical myocyte model of skeletal muscle, numerical solutions of the Bloch-Torrey equation were used to calculate global sensitivity indices of dMRI metrics (FA, RD, MD, λ1 , λ2 , λ3 ) for wide ranges of microstructural and dMRI sequence parameters. The microstructural parameters were: myocyte diameter, volume fraction, membrane permeability, intra- and extracellular diffusion coefficients, and intra- and extracellular T2 times. Two separate pulse sequences were examined, a PGSE and a generalized diffusion-weighted sequence that accommodates a larger range of diffusion times. The effect of noise and signal averaging on the sensitivity of the dMRI metrics was examined by adding synthetic noise to the simulated signal. RESULTS: Among the examined parameters, the intracellular diffusion coefficient has the strongest effect, and myocyte diameter is more influential than permeability for FA and RD. The sensitivity indices do not vary significantly between the two pulse sequences. Also, noise strongly affects the sensitivity of the dMRI signal to microstructural variations. CONCLUSIONS: With the identification of key microstructural features that affect dMRI measurements, the reported sensitivity results can help interpret dMRI measurements of skeletal muscle in terms of the underlying microstructure and further develop parsimonious dMRI models of skeletal muscle.

Immobilized RGD concentration and proteolytic degradation synergistically enhance vascular sprouting within hydrogel scaffolds of varying modulus.

Insufficient vascularization limits the volume and complexity of engineered tissue. The formation of new blood vessels (neovascularization) is regulated by a complex interplay of cellular interactions with biochemical and biophysical signals provided by the extracellular matrix (ECM) necessitating the development of biomaterial approaches that enable systematic modulation in matrix properties. To address this need poly(ethylene) glycol-based hydrogel scaffolds were engineered with a range of decoupled and combined variations in integrin-binding peptide (RGD) ligand concentration, elastic modulus and proteolytic degradation rate using free-radical polymerization chemistry. The modularity of this system enabled a full factorial experimental design to simultaneously investigate the individual and interaction effects of these matrix cues on vascular sprout formation in 3 D culture. Enhancements in scaffold proteolytic degradation rate promoted significant increases in vascular sprout length and junction number while increases in modulus significantly and negatively impacted vascular sprouting. We also observed that individual variations in immobilized RGD concentration did not significantly impact 3 D vascular sprouting. Our findings revealed a previously unidentified and optimized combination whereby increases in both immobilized RGD concentration and proteolytic degradation rate resulted in significant and synergistic enhancements in 3 D vascular spouting. The above-mentioned findings would have been challenging to uncover using one-factor-at-time experimental analyses.

Comparison of two-compartment exchange and continuum models of dMRI in skeletal muscle.

Clinical diffusion MRI (dMRI) is sensitive to micrometer scale spin displacements, but the image resolution is ∼mm, so the biophysical interpretation of the signal relies on establishing appropriate subvoxel tissue models. A class of two-compartment exchange models originally proposed by Kärger have been used successfully in neural tissue dMRI. Their use to interpret the signal in skeletal muscle dMRI is challenging because myocyte diameters are comparable to the root-mean-square of spin displacement and their membrane permeability is high. A continuum tissue model consisting of the Bloch-Torrey equation integrated by a hybrid lattice Boltzmann scheme is used for comparison. The validity domain of a classical two-compartment tissue model is probed by comparing it with the prediction of the continuum model for a 2D unidirectional composite continuum model of myocytes embedded in a uniform matrix. This domain is described in terms of two dimensionless parameters inspired by mass transfer phenomena, the Fourier (F) and Biot (B) numbers. The two-compartment model is valid when [Formula: see text] and [Formula: see text], or when [Formula: see text] and [Formula: see text]. The model becomes less appropriate for muscle dMRI as the cell diameter and volume fraction increase, with the primary source of error associated with modeling diffusion in the extracellular matrix.

Observation of direction-dependent mechanical properties in the human brain with multi-excitation MR elastography.

Magnetic resonance elastography (MRE) has shown promise in noninvasively capturing changes in mechanical properties of the human brain caused by neurodegenerative conditions. MRE involves vibrating the brain to generate shear waves, imaging those waves with MRI, and solving an inverse problem to determine mechanical properties. Despite the known anisotropic nature of brain tissue, the inverse problem in brain MRE is based on an isotropic mechanical model. In this study, distinct wave patterns are generated in the brain through the use of multiple excitation directions in order to characterize the potential impact of anisotropic tissue mechanics on isotropic inversion methods. Isotropic inversions of two unique displacement fields result in mechanical property maps that vary locally in areas of highly aligned white matter. Investigation of the corpus callosum, corona radiata, and superior longitudinal fasciculus, three highly ordered white matter tracts, revealed differences in estimated properties between excitations of up to 33%. Using diffusion tensor imaging to identify dominant fiber orientation of bundles, relationships between estimated isotropic properties and shear asymmetry are revealed. This study has implications for future isotropic and anisotropic MRE studies of white matter tracts in the human brain.

3D multislab, multishot acquisition for fast, whole-brain MR elastography with high signal-to-noise efficiency.

PURPOSE: To develop an acquisition scheme for generating MR elastography (MRE) displacement data with whole-brain coverage, high spatial resolution, and adequate signal-to-noise ratio (SNR) in a short scan time. THEORY AND METHODS: A 3D multislab, multishot acquisition for whole-brain MRE with 2.0 mm isotropic spatial resolution is proposed. The multislab approach allowed for the use of short repetition time to achieve very high SNR efficiency. High SNR efficiency allowed for a reduced acquisition time of only 6 min while the minimum SNR needed for inversion was maintained. RESULTS: The mechanical property maps estimated from whole-brain displacement data with nonlinear inversion (NLI) demonstrated excellent agreement with neuroanatomical features, including the cerebellum and brainstem. A comparison with an equivalent 2D acquisition illustrated the improvement in SNR efficiency of the 3D multislab acquisition. The flexibility afforded by the high SNR efficiency allowed for higher resolution with a 1.6 mm isotropic voxel size, which generated higher estimates of brainstem stiffness compared with the 2.0 mm isotropic acquisition. CONCLUSION: The acquisition presented allows for the capture of whole-brain MRE displacement data in a short scan time, and may be used to generate local mechanical property estimates of neuroanatomical features throughout the brain.

Including spatial information in nonlinear inversion MR elastography using soft prior regularization.

Tissue displacements required for mechanical property reconstruction in magnetic resonance elastography (MRE) are acquired in a magnetic resonance imaging (MRI) scanner, therefore, anatomical information is available from other imaging sequences. Despite its availability, few attempts to incorporate prior spatial information in the MRE reconstruction process have been reported. This paper implements and evaluates soft prior regularization (SPR), through which homogeneity in predefined spatial regions is enforced by a penalty term in a nonlinear inversion strategy. Phantom experiments and simulations show that when predefined regions are spatially accurate, recovered property values are stable for SPR weighting factors spanning several orders of magnitude, whereas inaccurate segmentation results in bias in the reconstructed properties that can be mitigated through proper choice of regularization weighting. The method was evaluated in vivo by estimating viscoelastic mechanical properties of frontal lobe gray and white matter for five repeated scans of a healthy volunteer. Segmentations of each tissue type were generated using automated software, and statistically significant differences between frontal lobe gray and white matter were found for both the storage modulus and loss modulus . Provided homogeneous property assumptions are reasonable, SPR produces accurate quantitative property estimates for tissue structures which are finer than the resolution currently achievable with fully distributed MRE.

Local mechanical properties of white matter structures in the human brain.

The noninvasive measurement of the mechanical properties of brain tissue using magnetic resonance elastography (MRE) has emerged as a promising method for investigating neurological disorders. To date, brain MRE investigations have been limited to reporting global mechanical properties, though quantification of the stiffness of specific structures in the white matter architecture may be valuable in assessing the localized effects of disease. This paper reports the mechanical properties of the corpus callosum and corona radiata measured in healthy volunteers using MRE and atlas-based segmentation. Both structures were found to be significantly stiffer than overall white matter, with the corpus callosum exhibiting greater stiffness and less viscous damping than the corona radiata. Reliability of both local and global measures was assessed through repeated experiments, and the coefficient of variation for each measure was less than 10%. Mechanical properties within the corpus callosum and corona radiata demonstrated correlations with measures from diffusion tensor imaging pertaining to axonal microstructure.

Magnetic resonance elastography of the brain using multishot spiral readouts with self-navigated motion correction.

Magnetic resonance elastography (MRE) has been introduced in clinical practice as a possible surrogate for mechanical palpation, but its application to study the human brain in vivo has been limited by low spatial resolution and the complexity of the inverse problem associated with biomechanical property estimation. Here, we report significant improvements in brain MRE data acquisition by reporting images with high spatial resolution and signal-to-noise ratio as quantified by octahedral shear strain metrics. Specifically, we have developed a sequence for brain MRE based on multishot, variable-density spiral imaging, and three-dimensional displacement acquisition and implemented a correction scheme for any resulting phase errors. A Rayleigh damped model of brain tissue mechanics was adopted to represent the parenchyma and was integrated via a finite element-based iterative inversion algorithm. A multiresolution phantom study demonstrates the need for obtaining high-resolution MRE data when estimating focal mechanical properties. Measurements on three healthy volunteers demonstrate satisfactory resolution of gray and white matter, and mechanical heterogeneities correspond well with white matter histoarchitecture. Together, these advances enable MRE scans that result in high-fidelity, spatially resolved estimates of in vivo brain tissue mechanical properties, improving upon lower resolution MRE brain studies that only report volume averaged stiffness values.

Micromechanical properties of hydrogels measured with MEMS resonant sensors.

Hydrogels have gained wide usage in a range of biomedical applications because of their biocompatibility and the ability to finely tune their properties, including viscoelasticity. The use of hydrogels on the microscale is increasingly important for the development of drug delivery techniques and cellular microenvironments, though the ability to accurately characterize their micromechanical properties is limited. Here we demonstrate the use of microelectromechanical systems (MEMS) resonant sensors to estimate the properties of poly(ethylene glycol) diacrylate (PEGDA) microstructures over a range of concentrations. These microstructures are integrated on the sensors by deposition using electrohydrodynamic jet printing. Estimated properties agree well with independent measurements made using indentation with atomic force microscopy.

Effect of off-frequency sampling in magnetic resonance elastography.

In magnetic resonance elastography (MRE), shear waves at a certain frequency are encoded through bipolar gradients that switch polarity at a controlled encoding frequency and are offset in time to capture wave propagation using a controlled sampling frequency. In brain MRE, there is a possibility that the mechanical actuation frequency is different from the vibration frequency, leading to a mismatch with encoding and sampling frequencies. This mismatch can occur in brain MRE from causes both extrinsic and intrinsic to the brain, such as scanner bed vibrations or active damping in the head. The purpose of this work was to investigate how frequency mismatch can affect MRE shear stiffness measurements. Experiments were performed on a dual-medium agarose gel phantom, and the results were compared with numerical simulations to quantify these effects. It is known that off-frequency encoding alone results in a scaling of wave amplitude, and it is shown here that off-frequency sampling can result in two main effects: (1) errors in the overall shear stiffness estimate of the material on the global scale and (2) local variations appearing as stiffer and softer structures in the material. For small differences in frequency, it was found that measured global stiffness of the brain could theoretically vary by up to 12.5% relative to actual stiffness with local variations of up to 3.7% of the mean stiffness. It was demonstrated that performing MRE experiments at a frequency other than that of tissue vibration can lead to artifacts in the MRE stiffness images, and this mismatch could explain some of the large-scale scatter of stiffness data or lack of repeatability reported in the brain MRE literature.

The effects of a higher protein intake during energy restriction on changes in body composition and physical function in older women.

BACKGROUND: The purpose of this double-blind randomized clinical trial was to compare the relative effectiveness of a higher protein and conventional carbohydrate intake during weight loss on body composition and physical function in older women. METHODS: Thirty-one overweight or obese, postmenopausal women (mean ± SD: age 65.2 ± 4.6 years, body mass index 33.7 ± 4.9 kg/m(2)) were prescribed a reduced calorie diet (1,400 kcal/day; 15%, 65%, 30% energy from protein, carbohydrate, and fat, respectively) and randomly assigned to 2 × 25 g/day whey protein (PRO n = 15) or maltodextrin (CARB n = 16) supplementation for 6 months. Lean soft tissue (LST) via dual-energy X-ray absorptiometry; thigh muscle, subcutaneous adipose tissue (SAT), and intermuscular adipose tissue with magnetic resonance imaging; knee strength with isokinetic dynamometry; balance and physical function with a battery of performance tests. RESULTS: PRO lost more weight than CARB (-8.0% ± 6.2%, -4.1% ± 3.6%, p = .059; respectively). Changes in LST, %LST, and strength, balance, or physical performance measures did not differ between groups (all p > .05). Weight to leg LST ratio improved more in PRO versus CARB (-4.6 ± 3.6%, -1.8 ± 2.6%, p = .03). PRO lost 4.2% more muscle (p = .01), 10.9% more SAT (p = .02), and 8.2% more intermuscular adipose tissue (p = .03) than CARB. Relative to thigh volume changes, PRO gained 5.8% more muscle (p = .049) and lost 3.8% greater SAT (p = .06) than CARB. Weight to leg LST ratio (r(2) = .189, p = .02) and SAT (r(2) = .163, p = .04) predicted improved up and go, relative muscle (r(2) = .238, p = .01) and SAT (r(2) = .165, p = .04) predicted improved transfer test, and %LST predicted improved balance (r(2) = .179, p = .04). CONCLUSIONS: A higher protein intake during caloric restriction maintains muscle relative to weight lost, which in turn enhances physical function in older women.

Intravoxel partially coherent motion technique: characterization of the anisotropy of skeletal muscle microvasculature.

PURPOSE: To propose a reformulation of the intravoxel incoherent motion (IVIM) technique exploiting the low b-value diffusion-weighted imaging regime that can characterize microcirculation of tissues perfused with partially coherent blood flow. MATERIALS AND METHODS: The new methodology, termed intravoxel partially coherent motion (IVPCM) technique, is suitable for probing microcirculation in tissues with ordered microvasculature, such as skeletal muscle. We employ a subvoxel model utilizing a randomly oriented bundle of straight vessels whose orientation statistics are characterized by a Fisher axial distribution with concentration parameter K quantifying the anisotropy of the distribution (K = 0 indicates isotropic capillary orientation). The methodology is first validated with a proof-of-principle phantom experiment and is then applied to analyze the microvasculature of human calf muscle at rest. RESULTS: The microcirculatory part of the diffusion-weighted signal at b < 200 s/mm(2) is anisotropic. The variation of the diffusion-weighted signal with b-value exhibits stronger deviation from the expected monoexponential decay when the diffusion encoding gradient is applied parallel to the mean myofiber direction in the calf muscle of three healthy volunteers. The application of the model to data from the medial gastrocnemius and the soleus of the three volunteers gives results within the expected range for the mean microvascular volume fraction, the mean microflow velocity, and the parameter K. CONCLUSION: The proposed methodology has the capability of characterizing the anisotropy of the capillary network in vivo in a manner analogous to the capability of high b-value diffusion to characterize the anisotropy of muscle fibers.

K-space and image-space combination for motion-induced phase-error correction in self-navigated multicoil multishot DWI.

Motion during diffusion encodings leads to different phase errors in different shots of multishot diffusion-weighted acquisitions. Phase error incoherence among shots results in undesired signal cancellation when data from all shots are combined. Motion-induced phase error correction for multishot diffusion-weighted imaging (DWI) has been studied extensively and there exist multiple phase error correction algorithms. A commonly used correction method is the direct phase subtraction (DPS). DPS, however, can suffer from incomplete phase error correction due to the aliasing of the phase errors in the high spatial resolution phases. Furthermore, improper sampling density compensation is also a possible issue of DPS. Recently, motion-induced phase error correction was incorporated in the conjugate gradient (CG) image reconstruction procedure to get a nonlinear phase correction method that is also applicable to parallel DWI. Although the CG method overcomes the issues of DPS, its computational requirement is high. Further, CG restricts to sensitivity encoding (SENSE) for parallel reconstruction. In this paper, a new time-efficient and flexible k-space and image-space combination (KICT) algorithm for rigid body motion-induced phase error correction is introduced. KICT estimates the motion-induced phase errors in image space using the self-navigated capability of the variable density spiral trajectory. The correction is then performed in k -space. The algorithm is shown to overcome the problem of aliased phase errors. Further, the algorithm preserves the phase of the imaging object and receiver coils in the corrected k -space data, which is important for parallel imaging applications. After phase error correction, any parallel reconstruction method can be used. The KICT algorithm is tested with both simulated and in vivo data with both multishot single-coil and multishot multicoil acquisitions. We show that KICT correction results in diffusion-weighted images with higher signal-to-noise ratio (SNR) and fractional anisotropy (FA) maps with better resolved fiber tracts as compared to DPS. In peripheral-gated acquisitions, KICT is comparable to the CG method.

Myofiber ellipticity as an explanation for transverse asymmetry of skeletal muscle diffusion MRI in vivo signal.

Due to its unique non-invasive microstructure probing capabilities, diffusion tensor imaging (DTI) constitutes a valuable tool in the study of fiber orientation in skeletal muscles. By implementing a DTI sequence with judiciously chosen directional encoding to quantify in vivo the microarchitectural properties in the calf muscles of three healthy volunteers at rest, we report that the secondary eigenvalue is significantly higher than the tertiary eigenvalue, a phenomenon corroborated by prior DTI findings. Toward a physics-based explanation of this phenomenon, we propose a composite medium model that accounts for water diffusion in the space within the muscle fiber and the extracellular space. The muscle fibers are abstracted as cylinders of infinite length with an elliptical cross section, the latter closely approximating microstructural features well documented in prior histological studies of excised muscle. The range of values of fiber ellipticity predicted by our model agrees with these studies, and the spatial orientation of the cross-sectional ellipses is consistent with local muscle strain fields and the putative direction of lateral transmission of stress between fibers in certain regions in three antigravity muscles (Tibialis Anterior, Soleus, and Gastrocnemius), as well as independent measurements of deformation in active calf muscles. As a metric, fiber cross-sectional ellipticity may be useful for quantifying morphological changes in skeletal muscle fibers with aging, hypertrophy, or sarcopenia.

High-resolution diffusion tensor imaging of the human pons with a reduced field-of-view, multishot, variable-density, spiral acquisition at 3 T.

Diffusion tensor imaging of localized anatomic regions, such as brainstem, cervical spinal cord, and optic nerve, is challenging because of the existence of significant susceptibility differences, severe physiologic motion in the surrounding tissues, and the need for high spatial resolution to resolve the underlying complex neuroarchitecture. The aim of the methodology presented here is to achieve high-resolution diffusion tensor imaging in localized regions of the central nervous system that is motion insensitive and immune to susceptibility while acquiring a set of two-dimensional images with more than six diffusion encoding directions within a reasonable total scan time. We accomplish this aim by implementing self-navigated, multishot, variable-density, spiral encoding with outer volume suppression. We establish scan protocols for achieving equal signal-to-noise ratio at 1.2 mm and 0.8 mm in-plane resolution for reduced field-of-view diffusion tensor imaging of the brainstem. In vivo application of the technique on the human pons of three subjects shows a clear delineation of the multiple local neural tracts. By comparing scans acquired with varying in-plane resolution but with constant signal-to-noise ratio, we demonstrate that increasing the resolution and reducing the partial volume effect result in higher fractional anisotropy values for the corticospinal tracts.

Science and technology for water purification in the coming decades.

One of the most pervasive problems afflicting people throughout the world is inadequate access to clean water and sanitation. Problems with water are expected to grow worse in the coming decades, with water scarcity occurring globally, even in regions currently considered water-rich. Addressing these problems calls out for a tremendous amount of research to be conducted to identify robust new methods of purifying water at lower cost and with less energy, while at the same time minimizing the use of chemicals and impact on the environment. Here we highlight some of the science and technology being developed to improve the disinfection and decontamination of water, as well as efforts to increase water supplies through the safe re-use of wastewater and efficient desalination of sea and brackish water.