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High-Performance Iterative Electron Tomography Reconstruction with Long-Object Compensation using Graphics Processing Units (GPUs)

Wei Xu, Fang Xu, Mel Jones, Bettina Keszthelyi, John Sedat, David Agard, Klaus Mueller
Center for Visual Computing, Computer Science Department, Stony Brook University, Stony Brook, NY 11794-4400, United States
Journal of Structural Biology, Volume 171, Issue 2, August 2010, Pages 142-153 (04 April 2010)

@article{xu2010high,

   title={High-performance iterative electron tomography reconstruction with long-object compensation using graphics processing units (GPUs)},

   author={Xu, W. and Xu, F. and Jones, M. and Keszthelyi, B. and Sedat, J. and Agard, D. and Mueller, K.},

   journal={Journal of structural biology},

   volume={171},

   number={2},

   pages={142–153},

   issn={1047-8477},

   year={2010},

   publisher={Elsevier}

}

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Iterative reconstruction algorithms pose tremendous computational challenges for 3D Electron Tomography (ET). Similar to X-ray Computed Tomography (CT), graphics processing units (GPUs) offer an affordable platform to meet these demands. In this paper, we outline a CT reconstruction approach for ET that is optimized for the special demands and application setting of ET. It exploits the fact that ET is typically cast as a parallel-beam configuration, which allows the design of an efficient data management scheme, using a holistic sinogram-based representation. Our method produces speedups of about an order of magnitude over a previously proposed GPU-based ET implementation, on similar hardware, and completes an iterative 3D reconstruction of practical problem size within minutes. We also describe a novel GPU-amenable approach that effectively compensates for reconstruction errors resulting from the TEM data acquisition on (long) samples which extend the width of the parallel TEM beam. We show that the vignetting artifacts typically arising at the periphery of non-compensated ET reconstructions are completely eliminated when our method is employed.
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