Using the GRID to improve the computation speed of electrical impedance tomography (EIT) reconstruction algorithms

• Published in: Physiological measurement Vol. 26; no. 2; p. S209
• Main Authors: Fritschy, J, Horesh, L, Holder, D S, Bayford, R H
• Format: Journal Article
• Language: English
• Published: England 01-04-2005
• Subjects: Algorithms; Animals; Body Constitution - physiology; Computing Methodologies; Electric Impedance; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Plethysmography, Impedance - instrumentation; Plethysmography, Impedance - methods; Reproducibility of Results; Sensitivity and Specificity; Tomography - instrumentation; Tomography - methods
• ISSN: 0967-3334
• DOI: 10.1088/0967-3334/26/2/020

In our group at University College London, we have been developing electrical impedance tomography (EIT) of brain function. We have attempted to improve image quality by the use of realistic anatomical meshes and, more recently, non-linear reconstruction methods. Reconstruction with linear methods, with pre-processing, may take up to a few minutes per image for even detailed meshes. However, iterative non-linear reconstruction methods require much more computational resources, and reconstruction with detailed meshes was taking far too long for clinical use. We present a solution to this timing bottleneck, using the resources of the GRID, the development of coordinated computing resources over the internet that are not subject to centralized control using standard, open, general-purpose protocols and are transparent to the user. Optimization was performed by splitting reconstruction of image series into individual jobs of one image each; no parallelization was attempted. Using the GRID middleware 'Condor' and a cluster of 920 nodes, reconstruction of EIT images of the human head with a non-linear algorithm was speeded up by 25-40 times compared to serial processing of each image. This distributed method is of direct practical value in applications such as EIT of epileptic seizures where hundreds of images are collected over the few minutes of a seizure and will be of value to clinical data collection with similar requirements. In the future, the same resources could be employed for the more ambitious task of parallelized code.