Effect of scatter coincidences, partial volume, positron range and non-colinearity on the quantification of FDOPA Patlak analysis

Document Type: Original Article

Authors

1 GCA Imagen Molecular (IDIS), Fundación IDICHUS, Santiago de Compostela, Galicia, Spain

2 MRC Clinical Sciences Centre, Imperial College London, United Kingdom

3 Unitat Biofísica, Departament de Ciències Fisiològiques I, Universitat de Barcelona, Spain and CIBER en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Spain

4 CIBER en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Spain and Servei de Medicina Nuclear, Hospital Clínic de Barcelona, Spain

Abstract

Introduction: The key characteristics of positron emission tomography (PET) are its quantitative capability and its sensitivity, which allow the in vivo imaging of biochemical interactions with small amounts of tracer concentrations. Therefore, accurate quantification is important. However, it can be sensitive to several physical factors. The aim of this investigation is the assessment of the effect of physical effects, such as: scatter coincidences, partial volume, positron range and non-colinearity on the quantification of FDOPA uptake using PET. Methods: The SimSET Monte Carlo package was employed to simulate acquisitions of the PET/CT Siemens Biograph scanner. The study was performed with a numerical brain model obtained from the CT scan of a commercial striatal phantom. Theoretical pharmacokinetic values were simulated. The simulations were carried out with and without scatter, positron range and non-colinearity effects. The OSEM algorithm from STIR library was used to reconstruct the PET data. Different correction strategies were employed in order to evaluate the effects caused by the different type of degradation on results obtained with Patlak analysis. Results: The FDOPA uptake of Patlak plot increased from 70.4% of the theoretical value to 80.4%, if scatter was perfectly corrected, and it increased to 99% of the theoretical value when the partial volume correction was employed, as well. No significant improvement was found for positron range and non-colinearity effects when the partial volume correction was employed. Conclusions: The results show that the compensation for scatter and partial volume degradations increases accuracy in the uptake calculation.

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Main Subjects


Poyot T, Condé F, Grégoire MC, Frouin V, Coulon C, Fuseau C, Hinnen F, Dollé F, Hantraye P, Bottlaender M. Anatomic and biochemical correlates of the dopamine transporter ligand 11C-PE2I in normal and parkinsonian primates: comparison with 6-[18F]fluoro-L-dopa. J Cereb Blood Flow Metab. 2001 Jul;21(7):782-92.

Brooks DJ, Ibanez V, Sawle GV, Quinn N, Lees AJ, Mathias CJ, Bannister R, Marsden CD, Frackowiak RS. Differing patterns of striatal 18F-dopa uptake in Parkinson's disease, multiple system atrophy, and progressive supranuclear palsy. Ann Neurol. 1990 Oct;28(4):547-55.

Brooks DJ, Salmon EP, Mathias CJ, Quinn N, Leenders KL, Bannister R, Marsden CD, Frackowiak RS. The relationship between locomotor disability, autonomic dysfunction, and the integrity of the striatal dopaminergic system in patients with multiple system atrophy, pure autonomic failure, and Parkinson's disease, studied with PET. Brain. 1990 Oct;113 ( Pt 5):1539-52.

Wahl L, Nahmias C. Modeling of fluorine-18-6-fluoro-L-Dopa in humans. J Nucl Med. 1996 Mar;37(3):432-7.

Matsubara K, Watabe H, Kumakura Y, Hayashi T, Endres CJ, Minato K, Iida H. Sensitivity of kinetic macro parameters to changes in dopamine synthesis, storage, and metabolism: a simulation study for [¹⁸F]FDOPA PET by a model with detailed dopamine pathway. Synapse. 2011 Aug;65(8):751-62.

Patlak CS, Blasberg RG, Fenstermacher JD. Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. J Cereb Blood Flow Metab. 1983 Mar;3(1):1-7.

Badawi RD, Miller MP, Bailey DL, Marsden PK. Randoms variance reduction in 3D PET. Phys Med Biol. 1999 Apr;44(4):941-54.

Kinahan PE, Townsend DW, Beyer T, Sashin D. Attenuation correction for a combined 3D PET/CT scanner. Med Phys. 1998 Oct;25(10):2046-53.

van Velden FH, Kloet RW, van Berckel BN, Lammertsma AA, Boellaard R. Accuracy of 3-dimensional reconstruction algorithms for the high-resolution research tomograph. J Nucl Med. 2009 Jan;50(1):72-80.

Verhaeghe J, Reader AJ. AB-OSEM reconstruction for improved Patlak kinetic parameter estimation: a simulation study. Phys Med Biol. 2010 Nov 21;55(22):6739-57.

van Velden FH, Kloet RW, van Berckel BN, Wolfensberger SP, Lammertsma AA, Boellaard R. Comparison of 3D-OP-OSEM and 3D-FBP reconstruction algorithms for High-Resolution Research Tomograph studies: effects of randoms estimation methods. Phys Med Biol. 2008 Jun 21;53(12):3217-30.

Cheng JC, Rahmim A, Blinder S, Camborde ML, Raywood K, Sossi V. A scatter-corrected list-mode reconstruction and a practical scatter/random approximation technique for dynamic PET imaging. Phys Med Biol. 2007 Apr 21;52(8):2089-106.

Planeta-Wilson B, Yan J, Mulnix T, Carson RE. Quantitative Accuracy of HRRT List-mode Reconstructions: Effect of Low Statistics. IEEE Nucl Sci Symp Conf Rec (1997). 2008 Oct 1;2008:5121-5124.

Dai X, Chen Z, Tian J. Performance evaluation of kinetic parameter estimation methods in dynamic FDG-PET studies. Nucl Med Commun. 2011 Jan;32(1):4-16.

Le Pogam A, Hatt M, Descourt P, Boussion N, Tsoumpas C, Turkheimer FE, Prunier-Aesch C, Baulieu JL, Guilloteau D, Visvikis D. Evaluation of a 3D local multiresolution algorithm for the correction of partial volume effects in positron emission tomography. Med Phys. 2011 Sep;38(9):4920-3.

Boellaard R, van Lingen A, Lammertsma AA. Experimental and clinical evaluation of iterative reconstruction (OSEM) in dynamic PET: quantitative characteristics and effects on kinetic modeling. J Nucl Med. 2001 May;42(5):808-17.

Wallius E, Nyman M, Oikonen V, Hietala J, Ruotsalainen U. Voxel-based NK1 receptor occupancy measurements with [(18)F]SPA-RQ and positron emission tomography: a procedure for assessing errors from image reconstruction and physiological modeling. Mol Imaging Biol. 2007 Sep-Oct;9(5):284-94.

Zaidi H. Comparative evaluation of scatter correction techniques in 3D positron emission tomography. Eur J Nucl Med. 2000 Dec;27(12):1813-26.

Shidahara M, Tsoumpas C, Hammers A, Boussion N, Visvikis D, Suhara T, Kanno I, Turkheimer FE. Functional and structural synergy for resolution recovery and partial volume correction in brain PET. Neuroimage. 2009 Jan 15;44(2):340-8.

Cho ZH, Chan JK, Ericksson L, Singh M, Graham S, MacDonald NS, Yano Y. Positron ranges obtained from biomedically important positron-emitting radionuclides. J Nucl Med. 1975 Dec;16(12):1174-6.

Levin CS, Hoffman EJ. Calculation of positron range and its effect on the fundamental limit of positron emission tomography system spatial resolution. Phys Med Biol. 1999 Mar;44(3):781-99.

Geramifar P, Ay MR, Zafarghandi MS, Loudos G, Rahmim A. Performance comparison of four commercial GE Discovery PET/CT scanners: A Monte Carlo study using GATE. Iran J Nucl Med 2009;17(2):26-33.

Lewellen TK, Harrison RL, Vannoy S. The SimSET program. In: Ljungberg M, Strand SE, King MA, editors. Monte Carlo simulations in Nuclear Medicine. Bristol and Philadelphia: IOP Publishing; 1998. p. 77-92.

 Gunn RN, Gunn SR, Cunningham VJ. Positron emission tomography compartmental models. J Cereb Blood Flow Metab. 2001 Jun;21(6):635-52.

Schmidt KC, Turkheimer FE. Kinetic modeling in positron emission tomography. Q J Nucl Med. 2002 Mar;46(1):70-85.

Fleming JS, Bolt L, Stratford JS, Kemp PM. The specific uptake size index for quantifying radiopharmaceutical uptake. Phys Med Biol. 2004 Jul 21;49(14):N227-34.

Tossici-Bolt L, Hoffmann SM, Kemp PM, Mehta RL, Fleming JS. Quantification of [123I]FP-CIT SPECT brain images: an accurate technique for measurement of the specific binding ratio. Eur J Nucl Med Mol Imaging. 2006 Dec;33(12):1491-9.

Howes OD, Montgomery AJ, Asselin MC, Murray RM, Valli I, Tabraham P, Bramon-Bosch E, Valmaggia L, Johns L, Broome M, McGuire PK, Grasby PM. Elevated striatal dopamine function linked to prodromal signs of schizophrenia. Arch Gen Psychiatry. 2009 Jan;66(1):13-20.

Jacobson M, Levkovitz R, Ben-Tal A, Thielemans K, Spinks T, Belluzzo D, Pagani E, Bettinardi V, Gilardi MC, Zverovich A, Mitra G. Enhanced 3D PET OSEM reconstruction using inter-update Metz filtering. Phys Med Biol. 2000 Aug;45(8):2417-39.

Thielemans K, Tsoumpas C, Mustafovic S, Beisel T, Aguiar P, Dikaios N, Jacobson MW. STIR: software for tomographic image reconstruction release 2. Phys Med Biol. 2012 Feb 21;57(4):867-83.

Turkheimer FE, Aston JA, Asselin MC, Hinz R. Multi-resolution Bayesian regression in PET dynamic studies using wavelets. Neuroimage. 2006 Aug 1;32(1):111-21.

Yu DC, Huang SC, Barrio JR, Phelps ME. The assessment of the non-equilibrium effect in the 'Patlak analysis' of Fdopa PET studies. Phys Med Biol. 1995 Jul;40(7):1243-54.

Patlak CS, Blasberg RG. Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. Generalizations. J Cereb Blood Flow Metab. 1985 Dec;5(4):584-90.

Walker MD, Asselin MC, Julyan PJ, Feldmann M, Talbot PS, Jones T, Matthews JC. Bias in iterative reconstruction of low-statistics PET data: benefits of a resolution model. Phys Med Biol. 2011 Feb 21;56(4):931-49.

Rahmim A, Rousset O, Zaidi H. Strategies for motion tracking and correction in PET. PET Clin. 2007;2(2):251-66.

Shidahara M, Tsoumpas C, McGinnity CJ, Kato T, Tamura H, Hammers A, Watabe H, Turkheimer FE. Wavelet-based resolution recovery using an anatomical prior provides quantitative recovery for human population phantom PET [¹¹C]raclopride data. Phys Med Biol. 2012 May 21;57(10):3107-22.