In grating-based x-ray phase sensitive imaging, dark-field contrast refers to the extinction of the interference fringes due to small-angle scattering. For configurations where the sample is placed before the beamsplitter grating, the dark-field contrast has been quantified with theoretical wave propagation models. Yet when the grating is placed before the sample, the dark-field contrast has only been modeled in the geometric optics regime. Here we attempt to quantify the dark-field effect in the grating-before-sample geometry with first-principle wave calculations and understand the associated particle-size selectivity. We obtain an expression for the dark-field effect in terms of the sample material’s complex refractive index, which can be verified experimentally without fitting parameters. A dark-field computed tomography experiment shows that the particle-size selectivity can be used to differentiate materials of identical x-ray absorption.

We describe an x-ray differential phase contrast imaging method based on two-dimensional transmission gratings that are directly resolved by an x-ray camera. X-ray refraction and diffraction in the sample lead to variations of the positions and amplitudes of the grating fringes on the camera. These effects can be quantified through spatial harmonic analysis. The use of 2D gratings allows differential phase contrast in several directions to be obtained from a single image. When compared to previous grating-based interferometry methods, this approach obviates the need for multiple exposures and separate measurements for different directions, and thereby accelerates imaging speed.

Wearable sensor technology continues to advance and provide significant opportunities for improving personalized healthcare. In recent years, advances in flexible electronics, smart materials and low-power computing and networking have reduced barriers to technology accessibility, integration and cost, unleashing the potential for ubiquitous monitoring. This paper discusses recent advances in wearable sensors and systems that monitor movement, physiology and environment, with a focus on applications for Parkinson's disease, stroke, and head and neck injuries.

Exercise provides a robust physiological stimulus that evokes cross-talk among multiple tissues that when repeated regularly (i.e., training) improves physiological capacity, benefits numerous organ systems, and decreases the risk for premature mortality. However, a gap remains in identifying the detailed molecular signals induced by exercise that benefits health and prevents disease. The Molecular Transducers of Physical Activity Consortium (MoTrPAC) was established to address this gap and generate a molecular map of exercise. Preclinical and clinical studies will examine the systemic effects of endurance and resistance exercise across a range of ages and fitness levels by molecular probing of multiple tissues before and after acute and chronic exercise. From this multi-omic and bioinformatic analysis, a molecular map of exercise will be established. Altogether, MoTrPAC will provide a public database that is expected to enhance our understanding of the health benefits of exercise and to provide insight into how physical activity mitigates disease.

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MRI of the heart with magnetization tagging provides a potentially useful new way to assess cardiac mechanical function, through revealing the local motion of otherwise indistinguishable portions of the heart wall. Although still an evolving area, tagged cardiac MRI is already able to provide novel quantitative information on cardiac function. Exploiting this potential requires developing tailored methods for both imaging and image analysis. In this article, we review some of the progress that has been made in developing imaging methods for tagged cardiac MRI.

Diffusion-weighted MRI studies generally lose signal intensity to physiological motion, which can adversely affect quantification/diagnosis. Averaging over multiple repetitions, often used to improve image quality, does not eliminate the signal loss. In this article, PCATMIP, a combined principal component analysis and temporal maximum intensity projection approach, is developed to address this problem. Data are first acquired for a fixed number of repetitions. Assuming that physiological fluctuations of image intensities locally are likely temporally correlated unlike random noise, a local moving boxcar in the spatial domain is used to reconstruct low-noise images by considering the most relevant principal components in the temporal domain. Subsequently, a temporal maximum intensity projection yields a high signal-intensity image. Numerical and experimental studies were performed for validation and to determine optimal parameters for increasing signal intensity and minimizing noise. Subsequently, a combined principal component analysis and temporal maximum intensity projection approach was used to analyze diffusion-weighted porcine liver MRI scans. In these scans, the variability of apparent diffusion coefficient values among repeated measurements was reduced by 59% relative to averaging, and there was an increase in the signal intensity with higher intensity differences observed at higher b-values. In summary, a combined principal component analysis and temporal maximum intensity projection approach is a postprocessing approach that corrects for bulk motion-induced signal loss and improves apparent diffusion coefficient measurement reproducibility. Magn Reson Med 65:1611-1619, 2011. V C 2010 Wiley-Liss, Inc.Key words: principal component analysis; diffusion; temporal MIP; low SNR Diffusion-weighted MRI (DWI) studies generally lose signal intensity (SI) to physiological motion, which can adversely affect quantification and diagnosis. Averaging over multiple repetitions, often used to improve image quality, does not eliminate the signal loss. In DWI, physiological movement of anatomical features can be corrected for with nonrigid-body registration software packages (1-3). However, bulk motion also causes phase shifts and phase dispersion in image voxels, which never repeat precisely, leading to fluctuating intensity losses (4-6). The intensity fluctuations are smoothed out when averaging approaches are used; hence, although an increased signal-to-noise ratio is achieved, the local SI is dampened. This gets trickier at higher b-values where the motion sensitivity of the pulse sequence is particularly high, resulting in erroneous diffusivity values and potentially incorrect clinical diagnosis.Principal component analysis (PCA) is a mathematical approach that helps reduce the dimensionality of the data by expressing it as a linear combination of its basis vectors (7). The first principal component accounts for a large percentage of the variability in the data, and each succeeding component accounts for as much of t...