Corrección de atenuación en equipos PET-RM. Comparación de métodos mediante simulación Monte Carlo.

José Pablo Cabello García; Roser  Sala-Llonch; Raúl Tudela Fernández; Domènec  Ros Puig; Javier Pavía Segura; Aida  Niñerola Baizán

doi:10.37004/sefm/2020.21.2.004

Authors

José Pablo Cabello García Servicio de Medicina Nuclear, Hospital Clínic de Barcelona. C. de Villarroel, 170, 08036 Barcelona.
Roser Sala-Llonch Servicio de Medicina Nuclear, Hospital Clínic de Barcelona. C. de Villarroel, 170, 08036 Barcelona. Unidad de Biofísica y Bioingeniería, Facultad de Medicina, Universidad de Barcelona. Casanova, 143. 08036 Barcelona.
Raúl Tudela Fernández Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Barcelona. C Baldiri Reixac, 10-12. 08028 Barcelona.
Domènec Ros Puig Servicio de Medicina Nuclear, Hospital Clínic de Barcelona. C. de Villarroel, 170, 08036 Barcelona. Unidad de Biofísica y Bioingeniería, Facultad de Medicina, Universidad de Barcelona. Casanova, 143. 08036 Barcelona.
Javier Pavía Segura Servicio de Medicina Nuclear, Hospital Clínic de Barcelona. C. de Villarroel, 170, 08036 Barcelona.
Aida Niñerola Baizán Servicio de Medicina Nuclear, Hospital Clínic de Barcelona. C. de Villarroel, 170, 08036 Barcelona.

DOI:

https://doi.org/10.37004/sefm/2020.21.2.004

Keywords:

PET, MRI, attenuation correction, simulation, Monte Carlo

Abstract

Appropriate visualization and quantification in positron emission tomography (PET) imaging requires the correction by the attenuation of photons when crossing the medium. In a hybrid device that combines the PET technique with magnetic resonance imaging (MRI), the signal from MRI cannot be directly converted to attenuation values. In this work, two methods to estimate the attenuation map have been analysed, the first one, based on segmentation from the MRI and the second one, from an average of computed tomography (CT) images from multiple subjects. The study was carried out using PET images obtained by Monte Carlo simulation and the quantitative parameter evaluated was the standardized uptake value ratio (SUVr), taking the cerebellum as reference region.

The results obtained with both methods compared to those obtained using the CT image of each patient (considered as gold standard) show that: 1) the accuracy in the calculation of the total uptake diminishes in the region near the bone tissue, 2) in a SUVr analysis by regions, the method that uses segmentation from the MRI gives better results with maximum relative differences around 5% compared to the gold standard.

References

Chen Y, An H. Attenuation Correction of PET/MR Imaging. Magn Reson Imaging Clin N Am. 2017;25(2):245–55.

Izquierdo-Garcia D, Catana C. Magnetic resonance imaging-guided attenuation correction of positron emission tomography data in PET/MRI. PET Clin. 2016;11(2):1922–2013.

Liu F, Jang H, Kijowski R, Bradshaw T, McMillan AB. Deep learning MR imaging-based attenuation correction for PET/MR imaging. Radiology. 2018;286(2):676–84.

Ladefoged CN, Law I, Anazodo U, St. Lawrence K, Izquierdo-Garcia D, Catana C, et al. A multi-centre evaluation of eleven clinically feasible brain PET/MRI attenuation correction techniques using a large cohort of patients. Neuroimage [Internet]. 2017;147(June 2016):346–59. Available from: https://dx.doi.org/10.1016/j.neuroimage.2016.12.010

Cabello J, Lukas M, Rota Kops E, Ribeiro A, Shah NJ, Yakushev I, et al. Comparison between MRI-based attenuation correction methods for brain PET in dementia patients. Eur J Nucl Med Mol Imaging [Internet]. 2016 Nov 20 [cited 2019 Sep 9];43(12):2190–200. Available from: http://www.ncbi.nlm.nih.gov/pubmed/27094314

Bai B, Li Q, Leahy RM. MR-Guided PET Image Reconstruction. Semin Nucl Med [Internet]. 2013;43(1):30–44. Available from: http://www.sciencedirect.com/science/article/pii/S0001299812000827

Grimm R, Fürst S, Souvatzoglou M, Forman C, Hutter J, Dregely I, et al. Self-gated MRI motion modeling for respiratory motion compensation in integrated PET/MRI. Med Image Anal [Internet]. 2015;19(1):110–20. Available from: https://dx.doi.org/10.1016/j.media.2014.08.003

Datteri RD, Liu Y, D’Haese PF, Dawant BM. Validation of a nonrigid registration error detection algorithm using clinical MRI brain data. IEEE Trans Med Imaging. 2015 Jan 1;34(1):86–96.

An HJ, Seo S, Kang H, Choi H, Cheon GJ, Kim HJ, et al. MRI-based attenuation correction for PET/MRI using multiphase level-set method. Journal of Nuclear Medicine. 2016;57:587–93.

Okazawa H, Tsujikawa T, Higashino Y, Kikuta K-I, Mori T, Makino A, et al. No significant difference found in PET/MRI CBF values reconstructed with CT-atlas-based and ZTE MR attenuation correction. EJNMMI Res [Internet]. 2019 Mar 19 [cited 2019 Sep 9];9(1):26. Available from: http://www.ncbi.nlm.nih.gov/pubmed/30888559

Montandon ML, Zaidi H. Atlas-guided non-uniform attenuation correction in cerebral 3D PET imaging. Neuroimage. 2005;25(1):278–86.

Sekine T, Burgos N, Warnock G, Huellner M, Buck A, Ter Voert EEGW, et al. Multi-atlas-based attenuation correction for brain 18F-FDG PET imaging using a time-of-flight PET/MR scanner: Comparison with clinical single-atlas-and CT-based attenuation correction. J Nucl Med. 2016;57(8):1258–64.

Haynor DR, Harrison RL, Lewellen TK. The use of importance sampling techniques to improve the efficiency of photon tracking in emission tomography simulations. Med Phys [Internet]. 1991 Sep [cited 2019 Sep 9];18(5):990–1001. Available from: http://www.ncbi.nlm.nih.gov/pubmed/1961165

Smith SM. Fast robust automated brain extraction. Hum Brain Mapp [Internet]. 2002 Nov [cited 2019 Sep 16];17(3):143–55. Available from: http://www.ncbi.nlm.nih.gov/pubmed/12391568

Jenkinson M, Pechaud M, Smith S. BET2: MR-based estimation of brain, skull and scalp surfaces. Elev Annu Meet Organ Hum brain Mapp [Internet]. 2005;17(3): 167. Available from: http://mickaelpechaud.free.fr/these/HBM05.pdf

Acton PD, Friston KJ. Statistical parametric mapping in functional neuroimaging: beyond PET and fMRI activation studies. Eur J Nucl Med [Internet]. 1998 Jul [cited 2019 Sep 9];25(7):663–7. Available from: http://www.ncbi.nlm.nih.gov/pubmed/9741993

Marti-Fuster B, Esteban O, Thielemans K, Setoain X, Santos A, Ros D, et al. Including anatomical and functional information in MC simulation of PET and SPECT brain studies. Brain-VISET: A voxel-based iterative method. IEEE Trans Med Imaging. 2014;33(10):1931–8.

López-González FJ, Moscoso A, Efthimiou N, Fernández-Ferreiro A, Piñeiro-Fiel M, Archibald SJ, et al. Spill-in counts in the quantification of 18F-florbetapir on Ab-negative subjects: the effect of including white matter in the reference region. EJNMMI Phys. 2019;6(1).

Thielemans K, Tsoumpas C, Mustafovic S, Beisel T, Aguiar P, Dikaios N, et al. STIR: software for tomographic image reconstruction release 2. Phys Med Biol [Internet]. 2012 Feb 21 [cited 2019 Sep 9];57(4):867–83. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22290410

Jena A, Taneja S, Goel R, Renjen P, Negi P. Reliability of semiquantitative 18 F-FDG PET parameters derived from simultaneous brain PET/MRI: A feasibility study. Eur J Radiol [Internet]. 2014 Jul [cited 2019 Sep 9];83(7):1269–74. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24813529

Makris N, Goldstein JM, Kennedy D, Hodge SM, Caviness VS, Faraone S V., et al. Decreased volume of left and total anterior insular lobule in schizophrenia. Schizophr Res [Internet]. 2006 Apr [cited 2019 Sep 16];83(2–3):155–71. Available from: http://www.ncbi.nlm.nih.gov/pubmed/16448806

Frazier JA, Chiu S, Breeze JL, Makris N, Lange N, Kennedy DN, et al. Structural Brain Magnetic Resonance Imaging of Limbic and Thalamic Volumes in Pediatric Bipolar Disorder. Am J Psychiatry [Internet]. 2005 Jul [cited 2019 Sep 16];162(7):1256–65. Available from: http://www.ncbi.nlm.nih.gov/pubmed/15994707

Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage [Internet]. 2006 Jul 1 [cited 2019 Sep 16];31(3):968–80. Available from: http://www.ncbi.nlm.nih.gov/pubmed/16530430

Goldstein JM, Seidman LJ, Makris N, Ahern T, O’Brien LM, Caviness VS, et al. Hypothalamic Abnormalities in Schizophrenia: Sex Effects and Genetic Vulnerability. Biol Psychiatry [Internet]. 2007 Apr 15 [cited 2019 Sep 16];61(8):935–45. Available from: http://www.ncbi.nlm.nih.gov/pubmed/17046727

Laforce R, Soucy JP, Sellami L, Dallaire-Théroux C, Brunet F, Bergeron D, et al. Molecular imaging in dementia: Past, present, and future. Vol. 14, Alzheimer’s and Dementia. Elsevier Inc.; 2018. p. 1522–52.

Boston R, Sumner A. STATA: A Statistical Analysis System for Examining Biomedical Data. Adv Exp Med Biol. 2003 Feb 1;537:353–69.

Pagani M, De Carli F, Morbelli S, Öberg J, Chincarini A, Frisoni GB, et al. Volume of interest-based [18F]fluorodeoxyglucose PET discriminates MCI converting to Alzheimer’s disease from healthy controls. A European Alzheimer’s Disease Consortium (EADC) study. NeuroImage Clin. 2015;7:34–42.

Burgos N, Cardoso MJ, Thielemans K, Modat M, Pedemonte S, Dickson J, et al. Attenuation correction synthesis for hybrid PET-MR scanners: Application to brain studies. IEEE Trans Med Imaging. 2014.

Catana C, Van Der Kouwe A, Benner T, Michel CJ, Hamm M, Fenchel M, et al. Toward implementing an MRI-based PET attenuation-correction method for neurologic studies on the MR-PET brain prototype. J Nucl Med. 2010.

Izquierdo-Garcia D, Hansen AE, Förster S, Benoit D, Schachoff S, Fürst S, et al. An SPM8-based approach for attenuation correction combining segmentation and nonrigid template formation: Application to simultaneous PET/MR Brain Imaging. J Nucl Med. 2014;55(11):1825–30.

Martinez-Möller A, Souvatzoglou M, Navab N, Schwaiger M, Nekolla SG. Artifacts from misaligned CT in cardiac perfusion PET/CT studies: Frequency, effects, and potential solutions. J Nucl Med. 2007;48(2):188–93.