Compressive sensing based Q-space resampling for handling fast bulk motion in hardi acquisitions

Diffusion-weighted (DW) MRI has become a widely adopted imaging modality to reveal the underlying brain connectivity. Long acquisition times and/or non-cooperative patients increase the chances of motion-related artifacts. Whereas slow bulk motion results in inter-gradient misalignment which can be handled via retrospective motion correction algorithms, fast bulk motion usually affects data during the application of a single diffusion gradient causing signal dropout artifacts. Common practices opt to discard gradients bearing signal attenuation due to the difficulty of their retrospective correction, with the disadvantage to lose full gradients for further processing. Nonetheless, such attenuation might only affect limited number of slices within a gradient volume. Q-space resampling has recently been proposed to recover corrupted slices while saving gradients for subsequent reconstruction. However, few corrupted gradients are implicitly assumed which might not hold in case of scanning unsedated infants or patients in pain. In this paper, we propose to adopt recent advances in compressive sensing based reconstruction of the diffusion orientation distribution functions (ODF) with under sampled measurements to resample corrupted slices. We make use of Simple Harmonic Oscillator based Reconstruction and Estimation (SHORE) basis functions which can analytically model ODF from arbitrary sampled signals. We demonstrate the impact of the proposed resampling strategy compared to state-of-art resampling and gradient exclusion on simulated intra-gradient motion as well as samples from real DWI data

Model selection for spatiotemporal modeling of early childhood sub-cortical development

Spatiotemporal shape models capture the dynamics of shape change over time and are an essential tool for monitoring and measuring anatomical growth or degeneration. In this paper we evaluate non-parametric shape regression on the challenging problem of modeling early childhood sub-cortical development starting from birth. Due to the flexibility of the model, it can be challenging to choose parameters which lead to a good model fit yet does not overfit. We systematically test a variety of parameter settings to evaluate model fit as well as the sensitivity of the method to specific parameters, and we explore the impact of missing data on model estimation

Multi-Object Analysis of Volume, Pose, and Shape Using Statistical Discrimination

One goal of statistical shape analysis is the discrimination between two populations of objects. Whereas traditional shape analysis was mostly concerned with studying single objects, analysis of multi-object complexes presents new challenges related to alignment and relative object pose. In this paper, we present a methodology for discriminant analysis of sets multiple shapes. Shapes are represented by sampled medial manifolds including normals to the boundary. Non-Euclidean metrics that describe geodesic distance between sets of sampled representations are used for shape alignment and discrimination. Our choice of discriminant method is the distance weighted discriminant (DWD) because of its generalization ability in high dimensional, low sample size settings. Using an unbiased, soft discrimination score we can associate a statistical hypothesis test with the discrimination results. Furthermore, localization and nature significant differences between populations can be visualized via the average best discriminating axis

Automated voxel-wise brain DTI analysis of fitness and aging

Diffusion Tensor Imaging (DTI) has become a widely used MR modality to investigate white matter integrity in the brain. This paper presents the application of an automated method for voxel-wise group comparisons of DTI images in a study of fitness and aging. The automated processing method consists of 3 steps: 1) preprocessing including image format converting, image quality control, eddy-current and motion artifact correction, skull stripping and tensor image estimation, 2) study-specific unbiased DTI atlas computation via diffeomorphic fluid-based and demons deformable registration and 3) voxel-wise statistical analysis via heterogeneous linear regression and a wild bootstrap technique for correcting for multiple comparisons. Our results show that this fully automated method is suitable for voxel-wise group DTI analysis. Furthermore, in older adults, the results suggest a strong link between reduced fractional anisotropy (FA) values, fitness and aging

Automatic 4-D Registration in Dynamic MR Renography Based on Over-complete Dyadic Wavelet and Fourier Transforms

Dynamic contrast-enhanced 4-D MR renography has the potential for broad clinical applications, but suffers from respiratory motion that limits analysis and interpretation. Since each examination yields at least over 10-20 serial 3-D images of the abdomen, manual registration is prohibitively labor-intensive. Besides in-plane motion and translation, out-of-plane motion and rotation are observed in the image series. In this paper, a novel robust and automated technique for removing out-of-plane translation and rotation with sub-voxel accuracy in 4-D dynamic MR images is presented. The method was evaluated on simulated motion data derived directly from a clinical patient's data. The method was also tested on 24 clinical patient kidney data sets. Registration results were compared with a mutual information method, in which differences between manually co-registered time-intensity curves and tested time-intensity curves were compared. Evaluation results showed that our method agreed well with these ground truth data

Market Impact and Trading Protocols of Hidden Orders in Stock Markets

We empirically study the market impact of trading orders. We are specically interested in large trading orders that are executed incrementally, which we call hidden orders. These are reconstructed based on information about market member codes using data from the Spanish Stock Market and the London Stock Exchange. We nd that market impact is strongly concave, approximately increasing as the square root of order size. Furthermore, as a given order is executed, the impact grows in time according to a power-law; after the order is nished, it reverts to a level of about 0:5We empirically study the market impact of trading orders. We are specically interested in large trading orders that are executed incrementally, which we call hidden orders. These are reconstructed based on information about market member codes using data from the Spanish Stock Market and the London Stock Exchange. We nd that market impact is strongly concave, approximately increasing as the square root of order size. Furthermore, as a given order is executed, the impact grows in time according to a power-law; after the order is nished, it reverts to a level of about 0:5Refereed Working Papers / of international relevanc

A novel framework for the local extraction of extra-axial cerebrospinal fluid from MR brain images

The quantification of cerebrospinal fluid (CSF) in the human brain has shown to play an important role in early postnatal brain developmental. Extr a-axial fluid (EA-CSF), which is characterized by the CSF in the subarachnoid space, is promising in the early detection of children at risk for neurodevelopmental disorders. Currently, though, there is no tool to extract local EA-CSF measurements in a way that is suitable for localized analysis. In this paper, we propose a novel framework for the localized, cortical surface based analysis of EA-CSF. In our proposed processing, we combine probabilistic brain tissue segmentation, cortical surface reconstruction as well as streamline based local EA-CSF quantification. For streamline computation, we employ the vector field generated by solving a Laplacian partial differential equation (PDE) between the cortical surface and the outer CSF hull. To achieve sub-voxel accuracy while minimizing numerical errors, fourth-order Runge-Kutta (RK4) integration was used to generate the streamlines. Finally, the local EA-CSF is computed by integrating the CSF probability along the generated streamlines. The proposed local EA-CSF extraction tool was used to study the early postnatal brain development in typically developing infants. The results show that the proposed localized EA-CSF extraction pipeline can produce statistically significant regions that are not observed in previous global approach

Market impact and trading profile of large trading orders in stock markets

We empirically study the market impact of trading orders. We are specifically interested in large trading orders that are executed incrementally, which we call hidden orders. These are reconstructed based on information about market member codes using data from the Spanish Stock Market and the London Stock Exchange. We find that market impact is strongly concave, approximately increasing as the square root of order size. Furthermore, as a given order is executed, the impact grows in time according to a power-law; after the order is finished, it reverts to a level of about 0.5-0.7 of its value at its peak. We observe that hidden orders are executed at a rate that more or less matches trading in the overall market, except for small deviations at the beginning and end of the order.Comment: 9 pages, 7 figure

Contaminant biotransport by Pacific salmon to Lake Michigan tributaries

The Great Lakes are ideal systems for evaluating the synergistic components of environmental change, such as exotic species introductions and legacy pollutants. Introduced Pacific Salmon (Oncorhynchus spp.) represent an intersection of these drivers because they are non-native species of economic importance that bioaccumulate contaminants during the open water phase of their life cycle. Furthermore, Pacific salmon can deliver a significant pulse of contaminated tissue to tributaries during spawning and subsequent death. Thus, salmon represent a key pathway by which contaminants accumulated in Lake Michigan are transported inland to tributaries that otherwise lack point source pollution. Our research has revealed that salmon exhibit basin-specific persistent organic pollutant (POP) and mercury (Hg) concentrations reflecting pollutant inputs from both current and historic sources. Overall, Lake Michigan salmon were more contaminated with POPs and Hg than conspecifics from Lakes Huron or Superior. Consequently, Lake Michigan salmon pose a higher risk and magnitude of contaminant biotransport and transfer. Resident stream fish (e.g., brook trout) sampled from salmon spawning reaches had higher pollutant concentrations than fish sampled from upstream reaches lacking salmon, but the extent of fish contamination varied among lake basins and streams. In general, Lake Michigan tributaries were the most impacted, suggesting a direct relationship between the extent of salmon-derived contaminant inputs and resident fish contaminant levels. Within and among lake basins, contaminant biotransport by salmon is context dependent and likely reflects a suite of ecological characteristics such as species identity and trophic position, dynamics of the salmon run, watershed land-use, and instream geomorphology such as sediment size. We suggest that future management of salmon-mediated contaminant biotransport to stream communities in the Great Lakes basin should consider biological, chemical, and physical factors that constitute the environmental context