Abstract
Clinical diagnosis environments often require the availability of processed data in real-time, unfortunately, reconstruction times are prohibitive on conventional computers, neither the adoption of expensive parallel computers seems to be a viable solution. Here, we focus on development of mathematical software on high performance architectures for Total Variation based regularization reconstruction of 3D SPECT images. The software exploits the low-cost of Beowulf parallel architectures.
The activity has been developed within the Project: “Inverse Problems in Medical Imaging” partially supported by MURST, grant number MM01111258
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Keywords
- Single Photon Emission Compute Tomography
- Mean Square Error
- Point Spread Function
- Single Photon Emission Compute Tomography Image
- Projection Angle
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References
D. Baldini, P. Clavini and A. R. Formiconi, ‘Image reconstruction with conjugate gradient algorithm and compensation of the variable system response for an annular SPECT system’, Phys. Medica, vol 14, pp. 159–173, 1998.
P. Boccacci, P. Bonetto, P. Calvini and A. R. Formiconi, ‘A simple model for the efficient correction of collimator blur in 3D SPECT imaging’, Inverse Problems, vol. 15, pp. 907–930, 1999.
T. Chan, G. Golub and P. Mulet, ‘A primal-dual method for total variation-based image reconstruction’, UCLA CAM Report n. CAM-95-43, 1995.
P. Charbonnier, L. Blanc-Ferlaud, G. Aubert, M. Barlaud, ‘Deterministic edge-preserving regularization in computed imaging’, IEEE Trans, on Image Processing, vol. 6, pp. 298–311.
E. Giusti, Minimal surfaces and functions of bounded variation, Birkhauser, Boston, 1984.
S. Z. Li, ‘On discontinuity-adaptive smoothness priors in computer vision”, IEEE Transactions on Pattern Analysis and machine Intelligence, vol. 17, pp. 576–586, 1995.
A. Passeri, A. R. Formiconi, M. T. De Cristofaro, A. Pupi and U. Meldolesi,’ High performance computing and networking as tools, for accurate emission computed tomography reconstruction’, Europ. Journal of Nuclear Medicine, vol. 24, n. 4, pp. 390–397, 1997.
L. Rudin, S. Osher, and E. Fatemi, ‘NonLinear total variation based noise removal algorithms’, Physica D, 1992, 60, pp. 259–268.
C. R. Vogel and M. E. Oman, ‘Iterative methods for total variation denoising’, SIAM J. Sei. Statist. Computation, vol. 17, pp. 227–238, 1996.
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Antonelli, L., Carracciuolo, L., Ceccarelli, M., D’Amore, L., Murli, A. (2002). Total Variation Regularization for Edge Preserving 3D SPECT Imaging in High Performance Computing Environments. In: Sloot, P.M.A., Hoekstra, A.G., Tan, C.J.K., Dongarra, J.J. (eds) Computational Science — ICCS 2002. ICCS 2002. Lecture Notes in Computer Science, vol 2330. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46080-2_18
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DOI: https://doi.org/10.1007/3-540-46080-2_18
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