Performance Analysis of Diffusion Tensor Imaging in an Academic Production Grid

Abstract

Analysis of diffusion weighted magnetic resonance images serves increasingly for non-invasive tracking of nerve fibers in the human brain, both in clinical diagnosis and basic research. Diffusion-tensor imaging (DTI) enables in-vivo research on the internal structure of the central nervous system, an estimation of the interconnection of functional areas and diagnosis of brain tumors and de-myelinating diseases. But modeling the local diffusion parameters is computationally expensive and on standard desktop computers runtimes of up to days are common. A workflow based grid implementation of the algorithm with slice-based parallelization has shown significant speedup. However, in production use, the implementation frequently delayed and even failed, discouraging the medical collaborators to take up the management of the data processing themselves. Therefore a comprehensive analysis of possible sources for errors and delays as well as their real impact in the respective infrastructure is vital to enable clinical researchers to fully exploit the benefits of the Healthgrid application. In this manuscript, we tested different implementations of the DTI analysis with respect to robustness and runtime. Based on the results, concrete application improvements as well as general suggestions for the layout and maintenance of Healthgrids are concluded.