Distributed-memory large deformation diffeomorphic 3D image registration

We present a parallel distributed-memory algorithm for large deformation diffeomorphic registration of volumetric images that produces large isochoric deformations (locally volume preserving). Image registration is a key technology in medical image analysis. Our algorithm uses a partial differential equation constrained optimal control formulation. Finding the optimal deformation map requires the solution of a highly nonlinear problem that involves pseudo-differential operators, biharmonic operators, and pure advection operators both forward and back- ward in time. A key issue is the time to solution, which poses the demand for efficient optimization methods as well as an effective utilization of high performance computing resources. To address this problem we use a preconditioned, inexact, Gauss-Newton- Krylov solver. Our algorithm integrates several components: a spectral discretization in space, a semi-Lagrangian formulation in time, analytic adjoints, different regularization functionals (including volume-preserving ones), a spectral preconditioner, a highly optimized distributed Fast Fourier Transform, and a cubic interpolation scheme for the semi-Lagrangian time-stepping. We demonstrate the scalability of our algorithm on images with resolution of up to $1024^3$ on the "Maverick" and "Stampede" systems at the Texas Advanced Computing Center (TACC). The critical problem in the medical imaging application domain is strong scaling, that is, solving registration problems of a moderate size of $256^3$---a typical resolution for medical images. We are able to solve the registration problem for images of this size in less than five seconds on 64 x86 nodes of TACC's "Maverick" system.Comment: accepted for publication at SC16 in Salt Lake City, Utah, USA; November 201

An optimal factor analysis approach to improve the wavelet-based image resolution enhancement techniques

The existing wavelet-based image resolution enhancement techniques have many assumptions, such as limitation of the way to generate low-resolution images and the selection of wavelet functions, which limits their applications in different fields. This paper initially identifies the factors that effectively affect the performance of these techniques and quantitatively evaluates the impact of the existing assumptions. An approach called Optimal Factor Analysis employing the genetic algorithm is then introduced to increase the applicability and fidelity of the existing methods. Moreover, a new Figure of Merit is proposed to assist the selection of parameters and better measure the overall performance. The experimental results show that the proposed approach improves the performance of the selected image resolution enhancement methods and has potential to be extended to other methods

The Mixing and Transport Properties of the Intra Cluster Medium: a numerical study using tracers particles

We present a study of the mixing properties of the simulated intra cluster Medium, using tracers particles that are advected by the gas flow during the evolution of cosmic structures. Using a sample of seven galaxy clusters (with masses in the range of M=2-3 10^14Msol/h) simulated with a peak resolution of 25kpc/h up to the distance of two virial radii from their centers, we investigate the application of tracers to some important problems concerning the mixing of the ICM. The transport properties of the evolving ICM are studied through the analysis of pair dispersion statistics and mixing distributions. As an application, we focus on the transport of metals in the ICM. We adopt simple scenarios for the injection of metal tracers in the ICM, and find remarkable differences of metallicity profiles in relaxed and merger systems, also through the analysis of simulated emission from Doppler-shifted Fe XXIII lines.Comment: 19 pages, 24 figures, Astronomy and Astrophysics accepted; Final version after language editing and updating the bibliograph