Richard Taschereau1, Arion F Chatziioannou. 1. Department of Molecular and Medical Pharmacology, Crump Institute for Molecular Imaging, The David Geffen School of Medicine at UCLA, Los Angeles, CA, USA. rtaschereau@mednet.ucla.edu
Abstract
PURPOSE: We report here on a technique to implement high-resolution objects with voxels having variable dimensions (compressed) for the reduction of memory and central processing unit (CPU) requirements in Monte Carlo simulations. The technique, which was implemented in GATE, the GEANT4 application for positron emission tomography/single photon emission computed tomography (PET/SPECT) imaging simulations, was developed in response to our need for realistic high-resolution phantoms for dosimetry calculations. PROCEDURES: A compression algorithm similar to run-length encoding for one-dimensional data streams, was used to fuse together adjacent voxels with identical physical properties. The algorithm was verified by conducting dosimetric calculations and imaging experiments on compressed and uncompressed phantoms. RESULTS: Depending on the initial phantom size and composition, compression ratios of up to 99.9% were achieved allowing memory and CPU reductions of up to 85% and 70%, respectively. The output of the simulations was consistent with respect to the goals for each type of simulation performed (dosimetry and imaging). CONCLUSIONS: The implementation of compressed voxels in GATE allows for significant memory and CPU reduction and is suitable for dosimetry as well as for imaging experiments.
PURPOSE: We report here on a technique to implement high-resolution objects with voxels having variable dimensions (compressed) for the reduction of memory and central processing unit (CPU) requirements in Monte Carlo simulations. The technique, which was implemented in GATE, the GEANT4 application for positron emission tomography/single photon emission computed tomography (PET/SPECT) imaging simulations, was developed in response to our need for realistic high-resolution phantoms for dosimetry calculations. PROCEDURES: A compression algorithm similar to run-length encoding for one-dimensional data streams, was used to fuse together adjacent voxels with identical physical properties. The algorithm was verified by conducting dosimetric calculations and imaging experiments on compressed and uncompressed phantoms. RESULTS: Depending on the initial phantom size and composition, compression ratios of up to 99.9% were achieved allowing memory and CPU reductions of up to 85% and 70%, respectively. The output of the simulations was consistent with respect to the goals for each type of simulation performed (dosimetry and imaging). CONCLUSIONS: The implementation of compressed voxels in GATE allows for significant memory and CPU reduction and is suitable for dosimetry as well as for imaging experiments.
Authors: S Jan; G Santin; D Strul; S Staelens; K Assié; D Autret; S Avner; R Barbier; M Bardiès; P M Bloomfield; D Brasse; V Breton; P Bruyndonckx; I Buvat; A F Chatziioannou; Y Choi; Y H Chung; C Comtat; D Donnarieix; L Ferrer; S J Glick; C J Groiselle; D Guez; P F Honore; S Kerhoas-Cavata; A S Kirov; V Kohli; M Koole; M Krieguer; D J van der Laan; F Lamare; G Largeron; C Lartizien; D Lazaro; M C Maas; L Maigne; F Mayet; F Melot; C Merheb; E Pennacchio; J Perez; U Pietrzyk; F R Rannou; M Rey; D R Schaart; C R Schmidtlein; L Simon; T Y Song; J M Vieira; D Visvikis; R Van de Walle; E Wieërs; C Morel Journal: Phys Med Biol Date: 2004-10-07 Impact factor: 3.609
Authors: Matthew D Belley; Chu Wang; Giao Nguyen; Rathnayaka Gunasingha; Nelson J Chao; Benny J Chen; Mark W Dewhirst; Terry T Yoshizumi Journal: Med Phys Date: 2014-03 Impact factor: 4.071