(k,q)-Compressed Sensing for dMRI with Joint Spatial-Angular Sparsity Prior

Advanced diffusion magnetic resonance imaging (dMRI) techniques, like diffusion spectrum imaging (DSI) and high angular resolution diffusion imaging (HARDI), remain underutilized compared to diffusion tensor imaging because the scan times needed to produce accurate estimations of fiber orientation are significantly longer. To accelerate DSI and HARDI, recent methods from compressed sensing (CS) exploit a sparse underlying representation of the data in the spatial and angular domains to undersample in the respective k- and q-spaces. State-of-the-art frameworks, however, impose sparsity in the spatial and angular domains separately and involve the sum of the corresponding sparse regularizers. In contrast, we propose a unified (k,q)-CS formulation which imposes sparsity jointly in the spatial-angular domain to further increase sparsity of dMRI signals and reduce the required subsampling rate. To efficiently solve this large-scale global reconstruction problem, we introduce a novel adaptation of the FISTA algorithm that exploits dictionary separability. We show on phantom and real HARDI data that our approach achieves significantly more accurate signal reconstructions than the state of the art while sampling only 2-4% of the (k,q)-space, allowing for the potential of new levels of dMRI acceleration.Comment: To be published in the 2017 Computational Diffusion MRI Workshop of MICCA

Systemic Infinitesimal Over-dispersion on General Stochastic Graphical Models

Stochastic models of interacting populations have crucial roles in scientific fields such as epidemiology and ecology, yet the standard approach to extending an ordinary differential equation model to a Markov chain does not have sufficient flexibility in the mean-variance relationship to match data (e.g. \cite{bjornstad2001noisy}). A previous theory on time-homogeneous dynamics over a single arrow by \cite{breto2011compound} showed how gamma white noise could be used to construct certain over-dispersed Markov chains, leading to widely used models (e.g. \cite{breto2009time,he2010plug}). In this paper, we define systemic infinitesimal over-dispersion, developing theory and methodology for general time-inhomogeneous stochastic graphical models. Our approach, based on Dirichlet noise, leads to a new class of Markov models over general direct graphs. It is compatible with modern likelihood-based inference methodologies (e.g. \cite{ionides2006inference,ionides2015inference,king2008inapparent}) and therefore we can assess how well the new models fit data. We demonstrate our methodology on a widely analyzed measles dataset, adding Dirichlet noise to a classical SEIR (Susceptible-Exposed-Infected-Recovered) model. We find that the proposed methodology has higher log-likelihood than the gamma white noise approach, and the resulting parameter estimations provide new insights into the over-dispersion of this biological system.Comment: 47 page

On the momentum-dependence of K−K^{-}-nuclear potentials

The momentum dependent $K^{-}$-nucleus optical potentials are obtained based on the relativistic mean-field theory. By considering the quarks coordinates of $K^-$ meson, we introduced a momentum-dependent "form factor" to modify the coupling vertexes. The parameters in the form factors are determined by fitting the experimental $K^{-}$-nucleus scattering data. It is found that the real part of the optical potentials decrease with increasing $K^-$ momenta, however the imaginary potentials increase at first with increasing momenta up to $P_k=450\sim 550$ MeV and then decrease. By comparing the calculated $K^-$ mean free paths with those from $K^-n$/$K^-p$ scattering data, we suggested that the real potential depth is $V_0\sim 80$ MeV, and the imaginary potential parameter is $W_0\sim 65$ MeV.Comment: 9 pages, 4 figure

Dynamics of evaporative colloidal patterning

Drying suspensions often leave behind complex patterns of particulates, as might be seen in the coffee stains on a table. Here we consider the dynamics of periodic band or uniform solid film formation on a vertical plate suspended partially in a drying colloidal solution. Direct observations allow us to visualize the dynamics of the band and film deposition, and the transition in between when the colloidal concentration is varied. A minimal theory of the liquid meniscus motion along the plate reveals the dynamics of the banding and its transition to the filming as a function of the ratio of deposition and evaporation rates. We also provide a complementary multiphase model of colloids dissolved in the liquid, which couples the inhomogeneous evaporation at the evolving meniscus to the fluid and particulate flows and the transition from a dilute suspension to a porous plug. This allows us to determine the concentration dependence of the bandwidth and the deposition rate. Together, our findings allow for the control of drying-induced patterning as a function of the colloidal concentration and evaporation rate.Comment: 11 pages, 7 figures, 2 table