EMrecon: An Expectation Maximization Based Image Reconstruction Framework for Emission Tomography Data
1European Institute for Molecular Imaging (EIMI), University of Münster, Germany
2Institute for Applied Mathematics: Analysis and Numerics, University of Münster, Germany
3Siemens Healthineers, Erlangen, Germany
Open Source Reconstruction Framework for Emission Tomography Data
The EMrecon (Expectation Maximization RECONstruction) Project was started in 2006 as an open reconstruction software for the quadHIDAC wirechamber small animal PET scanner. It was lead by Thomas Kösters with Klaus Schäfers and Frank Wübbeling. After Thomas left for NYU/Siemens Healthineers, project leadership in Münster was taken over by Dirk Mannweiler. It was funded by the SFB MoBil, the European Institute for Molecular Imaging, and the applied math institute of the University of Münster.
The program was built upon a fast OSEM parallel reconstruction algorithm and included corrections for physical effects, e.g. attenuation, scatter, randoms and motion, as well as support for resolution recovery and list mode. The framework allowed us to test in practice extended reconstruction algorithms, like TV (Total Variation) and other nonlinear regularization schemes.
The code was then extended to a broad range of different scanner geometries, among these are Siemens Biograph Sensation 16, mCT, mMR and Inveon small animal PET, and the MEDISO nanoScan in list mode. External scanner definitions, e.g. imported from the GATE simulation toolbox, can be used to adapt the software to general crystal based designs.
The basic reconstruction algorithm is EM (Expectation Maximization) or OSEM (Ordered Subset Expectation Maximization). Variational penalty terms can be included. Extensions allow to estimate motion (via gating, data driven or with external signals) and to correct for this motion in the reconstruction process (model-based approach). For all modalities, listmode and sinogram/michelogram input is supported. EMrecon is implemented for parallelization on Shared Memory-Systems (multicore).
Newer developments include TOF (time of flight), angular meshing and fully quantitative reconstruction for the mCT.
If interested, please contact email@example.com . Note that some of modules are closed source due to non disclosure agreements, so the public version may not contain all scanners or algorithms.
Due to insufficient resources, the code is currently not being actively developed and not available.
Publications (up to 2009)
T. Kösters, K. P. Schäfers, and F. Wübbeling: EMrecon: An Expectation Maximization Based Image Reconstruction Framework for Emission Tomography Data. In NSS/MIC Conference Record, IEEE, 2011, pp. 4365-4368.
T. Kösters: Derivation and Analysis of Scatter Correction Algorithms for Quantitative Positron Emission Tomography. In Ph.D. Thesis, Universität Münster, 2010.
M. Fieseler, T. Kösters, F. Gigengack, H. Braun, H. H. Quick, K. P. Schäfers and X. Jiang: Motion Correction in PET-MRI: A Human Torso Phantom Study. In NSS/MIC Conference Record, IEEE, 2011, pp. 3586-3588.
A. Konik, T. Kösters, M. T. Madsen, and J. J. Sunderland: Evaluation of Attenuation and Scatter Correction Requirements as a Function of Object Size in Small Animal PET Imaging. In Transactions on Nuclear Science, IEEE, 2011, vol. 58, no. 5, pp. 2308-2314.
J. Müller, C. Brune, F. Wübbeling, A. Sawatzky, T. Kösters, K. P. Schäfers and M. Burger: Reconstruction of Short Time PET Scans Using Bregman Iterations. In NSS/MIC Conference Record, IEEE, 2011, pp. 2383-2385.
M. Dawood, T. Kösters, M. Fieseler, F. Büther, X. Jiang, F. Wübbeling and K. P. Schäfers: Motion Correction in Respiratory Gated Cardiac PET/CT a Using Multi-Scale Optical Flow. In Proceedings of the MICCAI 2008, LNCS, vol. 5242, 2008, pp. 155-162.
A. Sawatzky, C. Brune, F. Wübbeling, T. Kösters, K. P. Schäfers, and M. Burger: Accurate EM-TV Algorithm in PET With Low SNR. In NSS/MIC Conference Record, IEEE, 2008, pp. 5133-5137.
M. Benning, T. Kösters, F. Wübbeling, K. P. Schäfers, and M. Burger: A Nonlinear Variational Method for Improved Quantification of Myocardial Blood Flow Using Dynamic H2O15 PET. In NSS/MIC Conference Record, IEEE, 2008, pp. 4472-4477.
M. Schellmann, S. Gorlatch, D. Meiländer, T. Kösters, K. P. Schäfers, F. Wübbeling, and M. Burger: Parallel Medical Image Reconstruction: From Graphics Processors to Grids. In Parallel Computing Technologies, ser. Lecture Notes in Computer Science, V. Malyshkin, Ed. Springer Berlin / Heidelberg, 2009, vol. 5698, pp. 457-473.