PET simulators

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Computational phantoms

PET data can be simulated using Monte Carlo methods, enabling development and validation of data processing algorithms. However, these software require very powerful computers, and simulations have to be tailored for each scanner.

Literature references to computation phantoms for PET

Barrett O, Carpenter TA, Clark JC, Ansorge RE, Fryer TD. Monte Carlo simulation and scatter correction of the GE advance PET scanner with SimSET and Geant4. Phys Med Biol. 2005; 50(20): 4823-4840.

Boussion N, Cinotti L, Barra V, Ryvlin P, Mauguiere F. Extraction of epileptogenic foci from PET and SPECT images by fuzzy modeling and data fusion. Neuroimage 2003; 19: 645-654.

Branco S, Jan S, Almeida P. Monte Carlo simulations in small animal PET imaging. Nucl Instr Methods Phys Res A 2007; 580: 1127-1130.

Buvat I, Castiglioni I. Monte Carlo simulations in SPET and PET. Q J Nucl Med. 2002; 46: 48-61.

Buvat I, Castiglioni I, Feuardent J, Gilardi MC. Unified description and validation of Monte Carlo simulators in PET. Phys Med Biol. 2005; 50(2): 329-346.

Cañadas M, Arce P, Mendes PR. Validation of a small-animal PET simulation using GAMOS: a GEANT4-based framework. Phys Med Biol. 2011; 56(1):273-288. doi: 10.1088/0031-9155/56/1/016.

Hirano Y (2011). Applications to Development of PET/SPECT System by Use of Geant4. In: Applications of Monte Carlo Methods in Biology, Medicine and Other Fields of Science, Prof. Charles J. Mode (Ed.), ISBN: 978-953-307-427-6, InTech.

Häggström I, Axelsson J, Schmidtlein CR, Karlsson M, Garpebring A, Johansson L, Sörensen J, Larson A. A Monte Carlo study of the dependence of early frame sampling on uncertainty and bias in pharmacokinetic parameters from dynamic PET. J Nucl Med Technol. 2015; 43: 53-60.

Jan S et al. GATE: a simulation toolkit for PET and SPECT. Phys Med Biol. 2004; 49: 4543-4561.

Kotasidis FA, Tsoumpas C, Polycarpou I, Zaidi H. A 5D computational phantom for pharmacokinetic simulation studies in dynamic emission tomography. Comput Med Imaging Graphics 2014; 38(8): 764-773.

Meikle SR, Eberl S, Fulton RR, Kassiou M, Fulham MJ. The influence of tomograph sensitivity on kinetic parameter estimation in positron emission tomography imaging studies of the rat brain. Nucl Med Biol. 2000; 27(6): 617-625.

Rousset O, Ma Y, Kamber M, Evans AC. 3D simulations of radiotracer uptake in deep nuclei of human brain. Comput Med Imaging Graphics 1993; 17(4-5): 373-379.

Rowe RW, Dai S. A pseudo-Poisson noise model for simulation of positron emission tomographic projection data. Med Phys. 1992; 19: 1113-1119.

Segars WP, Sturgeon G, Mendonca S, Grimes J, Tsui BM. 4D XCAT phantom for multimodality imaging research. Med Phys. 2010; 37(9): 4902-4915.

Strul D, Santin G, Lazaro D, Breton V, Morel C. GATE (Geant4 application for tomographic emission): a PET/SPECT general-purpose simulation platform. Nucl Phys B (Proc. Suppl.) 2003; 125: 75-79.

Vauclin S, Michel C, Buvat I, Doyeux K, Edet-Sanson A, Vera P, Gardin I, Hapdey S. Monte-Carlo simulations of clinically realistic respiratory gated 18F-FDG PET: Application to lesion detectability and volume measurements. Comput Methods Prog Biomed. 2015; 118: 84-93.

Zaidi H, Labbé C, Morel C. Implementation of an environment for Monte Carlo simulation of fully 3-D positron tomography on a high-performance parallel platform. Parallel Computing 1998; 24: 1523-1536.

Zaidi H, Schreurer AH, Morel C. An object-oriented Monte-Carlo simulator for 3D cylindrical positron tomographs. Comput Methods Prog Biomed. 1999; 58: 133-145.

Zubal IG, Harrell CR, Smith EO, Rattner Z, Gindi G, Hoffer PB. Computerized three-dimensional segmented human anatomy. Med Phys. 1994; 21(2): 299-302.

WWW links

GATE

PET-SORTEO

Simulators developed in TPC

Harri Merisaari has developed a simple simulator for testing cardiac analysis tools, especially Carimas.


Physical phantoms

The performance of PET scanners is routinely tested using static phantoms with targets of known size, filled with known radioactivity concentration. Usually these are simplistic tanks (for example uniformity phantoms) or rods/tubes (resolution measurement), but can also be detailed, such as the 3D brain phantom (Iida et al., 2013), heart phantoms (Mananga et al, 2014; Hippeläinen et al., 2017), kidney and pancreas phantoms (Woliner-van der Weg et al., 2016; Trans-Gia et al., 2016 and 2018; Adams et al., 2017), or oncology phantoms (Sunderland & Christian, 2015; Alqahtani et al., 2017).

Dynamic phantoms, with containers filled with water (representing certain organs) and pumps and tubing (representing circulation) have also been developed and used to validate the analysis tools and scanner performance (O’Doherty et al., 2017a and 2017b).

Literature references to dynamic physical phantoms for PET

Chiribiri A, Schuster A, Ishida M, Hautvast G, Zarinabad N, Morton G, Otton J, Plein S, Breeuwer M, Batchelor P, Schaeffer T, Nagel E. Perfusion phantom: an efficient and reproducible method to simulate myocardial first-pass perfusion measurements with cardiovascular magnetic resonance. Magn Reson Med. 2013; 69: 698-707.

Gabrani-Juma H, Clarkin OJ, Pourmoghaddas A, Driscoll B, Wells RG, deKemp RA, Klein R. Validation of a multimodality flow phantom and its application for assessment of dynamic SPECT and PET technologies. IEEE Trans Med Imaging 2017; 36(1): 132-141.

O’Doherty J, Sammut E, Schleyer P, Stirling J, Nazir MS, Marsden PK, Chiribiri A. Feasibility of simultaneous PET-MR perfusion using a novel cardiac perfusion phantom. Eur J Hybrid Imaging 2017; 1:4. doi: 10.1186/s41824-017-0008-9.


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Created at: 2006-02-20
Updated at: 2018-01-20
Written by: Vesa Oikonen