ASLPrep: a platform for processing of arterial spin labeled MRI and quantification of regional brain perfusion.

Arterial spin labeled (ASL) magnetic resonance imaging (MRI) is the primary method for noninvasively measuring regional brain perfusion in humans. We introduce ASLPrep, a suite of software pipelines that ensure the reproducible and generalizable processing of ASL MRI data.

darfix - data analysis for dark-field X-ray microscopy.

A Python package for the analysis of dark-field X-ray microscopy (DFXM) and rocking curve imaging (RCI) data is presented. DFXM is a non-destructive diffraction imaging technique that provides three-dimensional maps of lattice strain and orientation. The darfix package enables fast processing and visualization of these data, providing the user with the essential tools to extract information from the acquired images in a fast and intuitive manner. These data processing and visualization tools can be either imported as library components or accessed through a graphical user interface as an Orange add-on. In the latter case, the different analysis modules can be easily chained to define computational workflows. Operations on larger-than-memory image sets are supported through the implementation of online versions of the data processing algorithms, effectively trading performance for feasibility when the computing resources are limited. The software can automatically extract the relevant instrument angle settings from the input files' metadata. The currently available input file format is EDF and in future releases HDF5 will be incorporated.

Development of White Matter Fiber Covariance Networks Supports Executive Function in Youth.

The white matter architecture of the human brain undergoes substantial development throughout childhood and adolescence, allowing for more efficient signaling between brain regions that support executive function. Increasingly, the field understands grey matter development as a spatially and temporally coordinated mechanism that follows hierarchically organized gradients of change. While white matter development also appears asynchronous, previous studies have largely relied on anatomical atlases to characterize white matter tracts, precluding a direct assessment of how white matter structure is spatially and temporally coordinated. Here, we leveraged advances in diffusion modeling and unsupervised machine learning to delineate white matter fiber covariance networks comprised of structurally similar areas of white matter in a cross-sectional sample of 939 youth aged 8-22 years. We then evaluated associations between fiber covariance network structural properties with both age and executive function using generalized additive models. The identified fiber covariance networks aligned with the known architecture of white matter while simultaneously capturing novel spatial patterns of coordinated maturation. Fiber covariance networks showed heterochronous increases in fiber density and cross section that generally followed hierarchically organized temporal patterns of cortical development, with the greatest increases in unimodal sensorimotor networks and the most prolonged increases in superior and anterior transmodal networks. Notably, we found that executive function was associated with structural features of limbic and association networks. Taken together, this study delineates data-driven patterns of white matter network development that support cognition and align with major axes of brain maturation.

Untangling the Mechanisms of Lattice Distortions in Biogenic Crystals across Scales.

Biomineralized structures are complex functional hierarchical assemblies composed of biomineral building blocks joined together by an organic phase. The formation of individual mineral units is governed by the cellular tissue component that orchestrates the process of biomineral nucleation, growth, and morphogenesis. These processes are imprinted in the structural, compositional, and crystallographic properties of the emerging biominerals on all scales. Measurement of these properties can provide crucial information on the mechanisms that are employed by the organism to form these complex 3D architectures and to unravel principles of their functionality. Nevertheless, so far, this has only been possible at the macroscopic scale, by averaging the properties of the entire composite assembly, or at the mesoscale, by looking at extremely small parts of the entire picture. In this study, the newly developed synchrotron-based dark-field X-ray microscopy method is employed to study the link between 3D crystallographic properties of relatively large calcitic prisms in the shell of the mollusc Pinna nobilis and their local lattice properties with extremely high angular resolution down to 0.001°. Mechanistic links between variations in local lattice parameters and spacing, crystal orientation, chemical composition, and the deposition process of the entire mineral unit are unraveled.

Quantifying microscale drivers for fatigue failure via coupled synchrotron X-ray characterization and simulations.

During cyclic loading, localization of intragranular deformation due to crystallographic slip acts as a precursor for crack initiation, often at coherent twin boundaries. A suite of high-resolution synchrotron X-ray characterizations, coupled with a crystal plasticity simulation, was conducted on a polycrystalline nickel-based superalloy microstructure near a parent-twin boundary in order to understand the deformation localization behavior of this critical, 3D microstructural configuration. Dark-field X-ray microscopy was spatially linked to high energy X-ray diffraction microscopy and X-ray diffraction contrast tomography in order to quantify, with cutting-edge resolution, an intragranular misorientation and high elastic strain gradients near a twin boundary. These observations quantify the extreme sub-grain scale stress gradients present in polycrystalline microstructures, which often lead to fatigue failure.

Access to Interventional Radiology Services in Small Hospitals and Rural Communities: An ACR Membership Intercommission Survey.

PURPOSE: To evaluate the status of interventional radiology (IR) staffing, recruitment, and retention in the United States, specifically as they apply to small hospitals and rural communities. MATERIALS AND METHODS: A 22-question survey was created by an ACR intercommission workgroup and circulated via e-mail to ACR members who self-identified as a "group practice leader," "general radiologist," "interventional radiologist," or "abdominal radiologist." Contingency tables were constructed, and bivariate analyses were performed to assess overall responses and the distribution of responses among specific groups of respondents. RESULTS: A total of 1,005 e-mail recipients completed the survey. A statistically significant greater proportion of responders from rural hospitals (versus nonrural hospitals) answered that (1) their group falls short or far short of meeting demand for IR services (29.1% versus 14.3%), (2) they had difficulty recruiting IR physicians to their practice (67% versus 40.6%), and (3) they had difficulty retaining IR physicians (40% versus 29%). The most frequently reported reasons for difficulty recruiting were that IR-trained physicians "do not want to do diagnostic work" (56.2%) and "do not want to practice in a small or rural setting" (48.8%). A greater proportion of respondents from rural hospitals perceived that they had difficulty retaining IR physicians because of perceived inadequate "complexity of case mix" (67.5%) or "number of cases" (66.1%). CONCLUSION: Small hospitals and rural communities experience greater difficulty recruiting and retaining IR physicians and meeting IR service demands compared with their nonrural counterparts.

Biomineralization as a Paradigm of Directional Solidification: A Physical Model for Molluscan Shell Ultrastructural Morphogenesis.

Molluscan shells are a model system to understand the fundamental principles of mineral formation by living organisms. The diversity of unconventional mineral morphologies and 3D mineral-organic architectures that comprise these tissues, in combination with their exceptional mechanical efficiency, offers a unique platform to study the formation-structure-function relationship in a biomineralized system. However, so far, morphogenesis of these ultrastructures is poorly understood. Here, a comprehensive physical model, based on the concept of directional solidification, is developed to describe molluscan shell biomineralization. The capacity of the model to define the forces and thermodynamic constraints that guide the morphogenesis of the entire shell construct-the prismatic and nacreous ultrastructures and their transitions-and govern the evolution of the constituent mineralized assemblies on the ultrastructural and nanostructural levels is demonstrated using the shell of the bivalve Unio pictorum. Thereby, explicit tools for novel bioinspired and biomimetic bottom-up materials design are provided.

Density-driven generalized regression neural networks (DD-GRNN) for function approximation.

This paper proposes a new nonparametric regression method, based on the combination of generalized regression neural networks (GRNNs), density-dependent multiple kernel bandwidths, and regularization. The presented model is generic and substitutes the very large number of bandwidths with a much smaller number of trainable weights that control the regression model. It depends on sets of extracted data density features which reflect the density properties and distribution irregularities of the training data sets. We provide an efficient initialization scheme and a second-order algorithm to train the model, as well as an overfitting control mechanism based on Bayesian regularization. Numerical results show that the proposed network manages to reduce significantly the computational demands of having individual bandwidths, while at the same time, provides competitive function approximation accuracy in relation to existing methods.