Caldart, Carlos S. (2012) Análisis de imágenes cerebrales con FDG-PET en relación a una plantilla de estudios normales. / FDG-PET brain image analysis thought templates of normal patients. Maestría en Física Médica, Universidad Nacional de Cuyo, Instituto Balseiro.
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Resumen en español
En esta tesis se desarrollaron dos algoritmos de registración de imágenes cerebrales obtenidas mediante Tomografía Computada (CT), PET (Tomografía por Emisión de Positrones) y RMN (Resonancia Magnética Nuclear). El primero de ellos usa registración rígida basada en cuaterniones y el segundo utiliza una registración deformable basada en B-Splines, ambos aplicando maximización de la función de información mutua. Los algoritmos fueron validados utilizando datos adquiridos en marco de la tesis: la registración rígida fue validada con fantomas con un error de 1.24 mm para registraciones de CT-RM (T1) y 1.57 mm para registraciones RM (T1)-PET, mientras que el algoritmo de registración deformable fue validado mediante imágenes de pacientes de PET con un error de 2.7 mm. Como parte de los objetivos del trabajo se generó y amplió una base de datos regional de estudios cerebrales con PET-FDG-F18 dentro del esquema de plantillas propuesto por el software SPM (Statistical Parametric Mapping). Esta plantilla regional fue exportada a la plataforma de visualización DICOM (Digital Imaging and Communication in Medicine) OsiriX ® para el análisis de casos patológicos. Se compararon 18 casos normales y 5 casos patológicos respecto de la plantilla generada. Los casos analizados coincidieron con el diagnóstico clínico, pudiendo analizarse patologías como Alzheimer, demencias frontotemporal y demencia de los cuerpos de Lewy. En el análisis estadístico de las imágenes con la plantillas se obtuvieron resultados concordantes con los hallazgos clínicos. Las diferentes técnicas de normalización de las imágenes permitieron realizar un análisis diferencial de los casos patológicos, aportando mayor información diagnóstica.
Resumen en inglés
Two algorithms for brain image registration were developed, involving data obtained with Computed Tomography (CT), Positron Emission Tomography (PET) and Magnetic Resonance (MR). The first is a rigid registration method based on quaternions and the second applies deformable registration based on B-splines. Mutual Information was chosen as a similarity measure for both methods. Algorithms were validated with data acquired throughout this work; rigid registration performance was assessed with physical phantoms, with an error of 1.24 mm for T1-CT registrations and 1.57 mm for T1-PET registrations. Deformable registration was evaluated with clinical PET images instead, yielding an error of 2.7 mm. As part of this work an existing database was expanded and a regional template of PET-FDG-F18 was built according to the templates of the SPM (Statistical Parametric Mapping) software. Once the template was completed, it was exported to a DICOM viewer (OsiriX ®) so as to facilitate the analysis of pathological cases. A group of 18 subject classified as normal, and a group of 5 patients deemed as pathological were confronted with the regional template. All examined cases were in agreement with their clinical diagnosis, suggesting conditions like Alzheimer Disease, frontotemporal dementia and Lewy body Dementia. Image statistical analysis using templates and normalization strategies allow differential assessment of pathological cases, thereby enhancing diagnostic capabilities in this medical imaging field.
| Tipo de objeto: | Tesis (Maestría en Física Médica) |
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| Palabras Clave: | Positron computed tomography; Tomografia computerizada con positron; Nuclear magnetic resonance; Resonancia magnética nuclear; Algorithms; Algoritmos; Brain image; Imágenes cerebrales; Positron emission tomography; Tomografía por emisión de positrón; Template; Plantilla; Registration; Registración; Mutual information; Información mutua |
| Referencias: | [1] - Phelps, Michael E. PET, Physics, Instrumentation and Barridosners. Los Angeles, CA: Springer, 2006. [2] – Maes F., Collignon A., Vandermeulen., Marchal G., Suetens P. Multimodality image registration by maximization of mutual information. IEEE Trans. Med. Imaging 16(2):187-198, 1997. [3] – Andronache, A., Siebenthal, M. von, Szekely, G., Non-rigid registration of multi-modal images using both mutual information and cross-correlation. Medical Image Analysis 12 (2008) 3–15 [4] – Isoardi R.A. – Optimización de análisis y registración de imágenes tomografícas. Tesis en doctorado en física, 2010. [5] – Hua X. et al. 3D characterization of brain atrophy in Alzheimer's disease and mild cognitive impairment using tensor-based morphometry. Neuroimage. 2008 May 15; 41(1): 19–34. [6] - Mosconi Lisa, Wai H. Tsui1, Karl Herholz, Alberto Pupi, Alexander Drzezga, Giovanni Lucignani, Eric M. Reiman, Vjera Holthoff , Elke Kalbe, Sandro Sorbi, Janine Diehl-Schmid, Robert Perneczky, Francesca Clerici, Richard Caselli, Bettina Beuthien-Baumann. "Multicenter Standardized 18F-FDG PET Diagnosis of Mild Cognitive Impairment, Alzheimer’s Disease, and Other Dementias." THE JOURNAL OF NUCLEAR MEDICINE 49, no. 3 (March 2008): 49:390–398. [7] – Chen, Wei-Ping, et al. "Effect of an age-mismatched and sex-mismatched normal database on the diagnostic performance of [18F]-FDG-PET for Alzheimer’s disease: The Ishikawa Brain Imaging Study." Nuclear Medicine Communications 32, no. 12 (December 2011): 1128–1133. [8] – Dukart et al. Differential effects of global and cerebellar normalization on detection anddifferentiation of dementia in FDG-PET studies. NeuroImage 49 (2010) 1490– 1495 [9] - Gispert, J.D., S. Reig, J. Pascau, V. Molina, A. Santos., and M. Desco. "Técnicas de cuantificación de imágenes PET (Tomografía por Emisión de Positrones): Aplicación al estudio de la esquizofrenia." Barcelona PET Conference, 2001. [10] – Medina, R., Bellera, J. Bases del procesamiento de imágenes medicas. Universidad de Los Andes, Facultad de Ingeniería, Grupo de Ingeniería Biomédica de la ULA (GIBULA). [11] - Zitova, B., Flusser, J., “Image registration methods, a survey”. Image and Vision Computing 21 (2003) 977–1000 [12] – Maintz J.B.A., Viergever M. “A survey of medical image registration”. Med Image Anal 2(1):1-36, 1998. [13] - Cellucci C.J., Albano A.M., Rapp P.E. “Statistical validation of mutual information calculations: comparisons of alternative numerical algorithms”. Naval Medical Research Institute. NMRC 2004-002 September 2004. [14] – Hartley, R. V.L., Transmission of information, Bell Syst. Tech. J., vol. 7, pp 535-563, 1928. [15] – Woods, R.P., Cherry, S.R., Maziotta, J.C. , MRI-PET registration with automated algorithm. J. Comput. Assist. Tomogr. Vol 16, no 4, pp. 620-633, 1992. [16] – Hill, D.L.G., Hawkes, D.J., Harrison, N.A., Ruff, C.F., A strategy for automated multimodality image registration incoprporating anatomical knowledge and image characteristics. Information Processing un Medical Imaging, H.H. Barrett y A.F. Gmitro, Eds. Berlin, Alemania: Springer-Verlag, 1993, vol 687, pp 182-196. Lecture Notes in Computer Science. [18] - Joseph V. Hajnal, Derek L.G. Hill, David J. Hawkes, “Medical Image Registration”, Series Editor Michael Neuman, “The BIOMEDICAL ENGINEERING Series”, Boca Raton London New York Washington, D.C, http://www.crcpress.com, 2001. [19]- Fitzpatrick J.M., West B.W, The Distribution of Target Registration Error in Rigid-Body Point-Based Registration, IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 20, NO. 9, SEPTEMBER 200 pp 917-927. [20] – Bailey, D.L., et al., Positron Emission Tomography: Basic Scienses. 2005, Singapore: Springer. [21]- Powsner, R.A. y Powsner E.R, Essential Nuclear Medicine Physics. Segunda ed. 2006, Haryana: Blackwell Publishing. [22] – Ferreira, Jéssica. "Evaluación de los factores metodológicos que afectan la cuantificación de imágenes PET/CT." Tesis Maestría en Física Médica, 2011 [23] – Alessio, A., et al., PET/CT barridosner instrumentation challenges, and solutions. Radiologic Clinics of North America, 2004. 42:p. 1071-1032. [24] – Saraví, Fernando. "Apuntes de cátedra de Radiobiología. Maestría en Física Médica. FU.ES.ME.N - Instituto Balseiro." 2009. [25] – Blanco, Antonio. Química Biológica. 8va. El Ateneo, 2007 [26]- Nicolás Pebet: Resonancia Nuclear Magnética. XIII Seminario de Ing.Biomédica 2004 Facultades de Medicina e Ingeniería = Univ. de la República Oriental del Uruguay. 27 de abril de 2004 [27] – Hornak J.P., The basics of MRI. 1996. http://www.cis.rit.edu/htbooks/mri/index.html [28] – Villamil D.F. – Aplicaciones medicas de resonancia magnética nuclear. FUESMEN 2012. [29] – Siebert J.A., Magnetic Resonance Imaging. ISEP 2012. [30] – Silverman, Daniel H.S. "Brain 18F-FDG PET in the Diagnosis of Neurodegenerative Dementias: Comparison with Perfusion SPECT and with Clinical Evaluations Lacking Nuclear Imaging." J Nucl Med , 2004: 45:594–607. [32] – Farreras, and Rozman. Medicina interna. 15 edición. Vol. (Volumen I). Madrid: Elsevier, 2004. [33] – Luis Ibáñez, Will Schroeder, Lydia Ng, Josh Cates and the Insight Software Consortium, “The ITK Software Guide”, Second Edition, November 21 de 2005, http://www.itk.org, email: insight=users@itk.org [34] – Yokoi T., Soma T., Hiroyuki S., Matsuda H. Accuracy and reproducibility of co-registration techniques based on mutual information and normalized mutual information for MRI and SPECT brain image. Annals of Nuclear Medicine Vol. 18, No. 8, 659–667, 2004 [35] - OsiriX: An Open-Source Software for Navigating in Multidimensional DICOM Images. J Digit Imaging. 2004 Sep;17(3):205-216. [36] - General Consumer Communication Tools for Improved Image Management and Communication in Medicine. J Digit Imaging. 2005 Dec;18(4):270-9. [37] – Wikipedia: The Free Encyclopedia. Wikimedia Foundation. <http://en.wikipedia.org> [38] – Lorusso A. et al, A comparison of four algorithms for estimating 3D rigid transformation. British Machine Vision Conference, 1995. [39] – Friston, K.J., , C. Frith, P.F. Liddle, and R.S.J. Frackowiak. "Comparing functional (PET) images: the assessment of significant change." Journal of Cerebral Blood Flow and Metabolism, 1991: 11:690-699. [40] – Lancaster JL, Woldorff MG, Parsons LM, Liotti M, Freitas CS, Rainey L, Kochunov PV, Nickerson D, Mikiten SA, Fox PT, "Automated Talairach Atlas labels for functional brain mapping". Human Brain Mapping 10:120-131, 2000. [41] - Friston, K.J., J. Ashburner, S.J. Kiebel, and T.E. Nich. Statistical Parametric Mapping: The Analysis of Functional Brain Images. Academic Press, 2007. [42]- Evans, AC, DL Collins, SR Mills, ED Brown, RL Kelly, and M. Peters. "3D statistical neuroanatomical models from 305 MRI volumes." IEEE-Nuclear Science Symposium and Medical Imaging Conference, 1993. [43] – Romero, Gonzalo “Creación de una base de datos de referencia normal de actividad metabólica cerebral y cerebelar con PET-FDG”. Tesis Maestría en Física Médica, 2011 [44] – Crum W.R., Griffin L.D., Hill D.L.G., Hawkes D.J. Zen and the art of medical image registration: correspondence, homology and quality. Neuroimage 20:1425-1437, 2003. [45] – D’Agostino E., Maes F., Vandermeulen D., Suetens P. A viscous fluid model for multimodal non-rigid registration using mutual information. Med Image Anal. 7: 565-575, 2003. [46] – Jeraj R. Image quantification in (radiation) therapy. ISEP 2012. [47] – Tzourio et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage 15, 273–289 (2002) doi:10.1006/nimg.2001.0978 [48] – Yakushev et al. SPM-based count normalization provides excellent discrimination of mild Alzheimer's disease and amnestic mild cognitive impairment from healthy aging. NeuroImage 44 (20 09) 43 – 50 [49] – Bauer, S., Fejes, T.,Reyes, M., A Skull-Stripping Filter for ITK. May 1, 2012. Insight Journal. http://hdl.handle.net/10380/335 [50] – Momojian, S., Vulliemoz, S., Spinelli, L., Pollo, C. Utilization of preoperative imagin for epilepsy surgery. Epileptologie 2007; 24: 73 – 77 [51] – Souplet, J.C., Lebrun, C., Ayache, N., Malandain, G. An automatic segmentation of T2-FLAIR multiple sclerosis lesions. July 15, 2008. [52] – Perez, M. O., Método de registro no rígido basado en funciones de base radial. Aplicaciones a neurocirugía utilizando atlas cerebrales. Tesis Doctoral. Universidad politécnica de Valencia departamento de sistemas informáticos y computación. 2009. [53] – Popa, T., Ibañez, L., Cleary, K., Wong, K. H., ITK Implementation of Deformable Registration Methods for Time-varying (4D) Imaging Data. Insight Journal, 2007. [54] – Namias, Mauro “Aplicación de información mutua para la correlacion de imágenes tomograficas multimodales”. Tesis Maestria en Fisica Medica, 2004. [55] – Lancaster, L.L et al. “Bias Between MNI and Talairach Coordinates Analyzed Using the ICBM-152 Brain Template” - Human Brain Mapping 28:1194–1205 (2007) |
| Materias: | Medicina > Diagnóstico por imagen y medicina nuclear |
| Divisiones: | FUESMEN |
| Código ID: | 396 |
| Depositado Por: | Marisa G. Velazco Aldao |
| Depositado En: | 08 Mar 2013 14:44 |
| Última Modificación: | 08 Mar 2013 14:44 |
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