High-performance computing and networking as tools for accurate emission computed tomography reconstruction.
It is well known that the quantitative potential of emission computed tomography (ECT) relies on the ability to compensate for resolution, attenuation and scatter effects. Reconstruction algorithms which are able to take these effects into account are highly demanding in terms of computing resources. The reported work aimed to investigate the use of a parallel high-performance computing platform for ECT reconstruction taking into account an accurate model of the acquisition of single-photon emission tomographic (SPET) data. An iterative algorithm with an accurate model of the variable system response was ported on the MIMD (Multiple Instruction Multiple Data) parallel architecture of a 64-node Cray T3D massively parallel computer. The system was organized to make it easily accessible even from low-cost PC-based workstations through standard TCP/IP networking. A complete brain study of 30 (64x64) slices could be reconstructed from a set of 90 (64x64) projections with ten iterations of the conjugate gradients algorithm in 9 s, corresponding to an actual speed-up factor of 135. This work demonstrated the possibility of exploiting remote high-performance computing and networking resources from hospital sites by means of low-cost workstations using standard communication protocols without particular problems for routine use. The achievable speed-up factors allow the assessment of the clinical benefit of advanced reconstruction techniques which require a heavy computational burden for the compensation effects such as variable spatial resolution, scatter and attenuation. The possibility of using the same software on the same hardware platform with data acquired in different laboratories with various kinds of SPET instrumentation is appealing for software quality control and for the evaluation of the clinical impact of the reconstruction methods.
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Dipartimento di Fisiopatologia Clinica - Sezione di Medicina Nucleare, Universita' di Firenze, Italy.
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De Cristofaro
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Meldolesi
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D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging; D003205:Computing Methodologies; D003545:Cysteine / Q000031:analogs & derivatives*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008122:Local Area Networks; D015609:Organotechnetium Compounds; D019047:Phantoms, Imaging; D015899:Tomography, Emission-Computed, Single-Photon / Q000295:instrumentation / Q000379:methods*
D015609:Organotechnetium Compounds; C059659:technetium Tc 99m bicisate; D003545:Cysteine