Corrección de atenuación en equipos PET-RM. Comparación de métodos mediante simulación Monte Carlo.
DOI:
https://doi.org/10.37004/sefm/2020.21.2.004Palabras clave:
PET, RM, corrección por atenuación, simulación, Monte CarloResumen
La visualización y cuantificación adecuadas de una imagen de tomografía por emisión de positrones (PET) requiere la corrección por la atenuación que sufren los fotones al atravesar el medio. En un equipo híbrido que combina PET con resonancia magnética (RM), la señal de RM no puede convertirse en valores de atenuación de forma directa. En este trabajo se analizaron dos métodos de estimación del mapa de atenuación, el primero basado en segmentación de la imagen RM y el segundo en un promedio de imágenes de tomografía computarizada (TC) a partir de múltiples sujetos. El estudio se realizó utilizando imágenes PET obtenidas mediante simulación Monte Carlo y el parámetro cuantitativo evaluado fue el valor de captación estandarizado relativo (SUVr) tomando como región de referencia el cerebelo.
La comparación de los resultados obtenidos con cada método con los correspondientes al utilizar la imagen TC propia de cada paciente (considerado como gold standard) indica que: 1) ambos métodos pierden exactitud en la zona próxima al tejido óseo, 2) en un análisis de SUVr por regiones, el método que utiliza segmentación a partir de la imagen de RM da mejores resultados con diferencias relativas máximas en torno al 5% frente al gold standard.
Referencias
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.