Propuesta metodológica para la creación de una base de datos de pacientes normales en protocolo cerebral PET/MR FDG / Methodological proposal for the creation of a database of normal patients in the cerebral PET/MR FDG protocol

González , Juan P. (2021) Propuesta metodológica para la creación de una base de datos de pacientes normales en protocolo cerebral PET/MR FDG / Methodological proposal for the creation of a database of normal patients in the cerebral PET/MR FDG protocol. Maestría en Física Médica, Universidad Nacional de Cuyo, Instituto Balseiro.

[img]
Vista previa
PDF (Tesis)
Español
9Mb

Resumen en español

El análisis de las de imágenes de Tomografía por Emisión de Positrones (PET) de cerebro es complejo, sobre todo en etapas tempranas de patologías neurodegenerativas como enfermedad de Alzheimer (EA), demencia frontotemporal (DFT) y demencia con cuerpos de Lewy (DCL), por lo que es necesario recurrir a herramientas de soporte al diagnóstico. Actualmente, el Servicio PET-Medicina Nuclear de la Fundación Escuela de Medicina Nuclear (FUESMEN) posee a disposición el software CortexID del fabricante General Electric que es utilizado para el análisis estadístico de la distribución metabólica de FDG-[18"F] en el cerebro. Sin embargo, este software utiliza una base de datos de pacientes normales generada a partir de imágenes adquiridas con sistemas PET/ CT, siendo estas cerradas sin la posibilidad de modificarlas con pacientes normales de una población de la región. Además, los métodos de corrección de atenuación (AC) en sistemas PET/MR difieren con respecto a los sistemas PET/CT. En la clínica, esta discrepancia se ve reflejada al momento de realizar análisis estadísticos en imágenes cerebrales con protocolo PET/MR. En este trabajo se desarrolló el procedimiento para la creación de una base de datos de pacientes normales de estudios PET/MR cerebrales con FDG-[18"F], sobre la cual se sustentaría el análisis paramétrico estadístico. Con este objetivo, se realizó en primer medida un estudio de optimización del método de reconstrucción PET utilizando el fantoma Hoffman y los software Duetto 2.15 y PMOD. Luego, se seleccionó a los voluntarios que formaron el grupo muestral, trabajándose con 9 sujetos normales, 6 femeninos y 3 masculinos, con una edad media de 32 años. Se trabajó en conjunto con profesionales de psicología y psicopedagogía para la elaboración de una versión reducida del test neurocognitivo Weiss III para la evaluación de la normalidad clínica. Se establecieron los protocolos de preparación, adquisición y reconstrucción de imágenes PET/CT y PET/MR. A partir de los estudios PET/CT y utilizando PMOD, se confeccionó una base de datos y su correspondiente plantilla de valores promedios y desvíos estándares. Se verificó que los valores de z-score de cada uno de los participantes estuviese comprendido en el intervalo de ±2σ. Posteriormente, se comparó el resultado de analizar a una paciente diagnosticada con EA tomando como referencia la plantilla y la BDCN CortexID. Se verificó la correspondencia entre las tendencias del análisis con ambas plantillas cualitativa y cuantitativamente. Una vez validado el proceso, se generó a partir de los mismos sujetos una base de datos PET con los estudios adquiridos en equipo PET/MR. Se evaluó la validez de las muestras a partir del análisis de los mapas de z-score de cada sujeto con respecto a la plantilla. Finalmente, se efectuó la comparación entre la plantilla de la base de datos generada a partir de imágenes PET/MR con la perteneciente a la base de datos de CortexID. Con este fin, se analizaron imágenes de PET/MR de un paciente de 76 años diagnosticado con DCLEA y de una paciente de 70 años con EA. Se verificó la coherencia de los resultados utilizando ambas plantillas, validando de esta manera el proceso de generación de la base de datos de PET/MR.

Resumen en inglés

The analysis of Positron Emission Tomography images of the brain is complex, especially in early stages of neurodegenerative diseases such as Alzheimer’s disease, frontotemporal dementia and dementia with Lewy bodies, so it is necessary to resort to diagnostic support tools. Currently, the PET-Nuclear Medicine Service of the Fundación Escuela de Medicina Nuclear (FUESMEN) has at its disposal the CortexID software from the manufacturer General Electric, which is used for statistical analysis of the metabolic distribution of FDG-[18"F] in the brain. However, this software uses a database of normal patients generated from images acquired with PET/CT systems, which are closed without the possibility of modifying them with normal patients from a population in the region. In addition, attenuation correction methods in PET/MR systems differ from PET/CT systems. In the clinic, this discrepancy is reflected when performing statistical analysis on PET/MR protocol brain images. In this work we developed the procedure for the creation of a database of normal patients from FDG-[18"F] PET/MR brain studies, on which the statistical parametric analysis will be based. To this end, a PET reconstruction method optimisation study was first performed using the Hoffman phantom and the Duetto 2.15 and PMOD software. Then, volunteers were selected to form the sample group, working with 9 normal subjects, 6 females and 3 males, with an average age of 32 years. We worked together with psychology and psychopedagogy professionals to develop a reduced version of the Weiss III neurocognitive test for the assessment of clinical normality. Protocols for the preparation, acquisition and reconstruction of PET/CT and PET/MR images were established. From the PET/CT studies and using PMOD, a database and its corresponding template of mean values and standard deviations were constructed. The z-score values of each participant were checked to ensure that they were within the range of ±2σ. Subsequently, the result of analysing a patient diagnosed with Alzheimer’s disease using the template and the CortexID database as a reference was compared. The correspondence between the trends of the analysis with both templates was checked qualitatively and quantitatively. Once the process was validated, a PET database was generated from the same subjects with the studies acquired on PET/MR equipment. The validity of the samples was assessed by analysing the z-score maps of each subject with respect to the template. Finally, a comparison was made between the template of the database generated from PET/MR images and the one belonging to the CortexID database. For this purpose, PET/MR images of a 76 year old patient diagnosed with dementia with Lewy bodies-Alzheimer’s disease and a 70 year old patient with Alzheimer’s disease were analysed. The consistency of the results was verified using both templates, thus validating the PET/MR database generation process.

Tipo de objeto:Tesis (Maestría en Física Médica)
Palabras Clave:Nuclear medicine, Medicina nuclear; Positron computed tomography; Tomografía computarizada con positron; Brain; Cerebro; [Positron emission tomography; Tomografía por emisión de positrones; Statical parametric mapping; Mapeo paramétrico estadístico; Neurodegenerative diseases; Enfermedades neurodegenerativas; Attenuation corretion; Corrección de atenuación; Normal brains database; Base de datos de cerebros normales]
Referencias:[1] Della Rosa, P. A., Cerami, C., Gallivanone, F., Prestia, A., Caroli, A., Castiglioni,I., et al. A standardized [18 f]-fdg-pet template for spatial normalization instatistical parametric mapping of dementia. Neuroinformatics, 12 (4), 575–593,2014. 1 [2] Saha, G. B. Physics and radiobiology of nuclear medicine. Springer Science &Business Media, 2012. 5, 12, 13 [3] Cherry, S. R., Sorenson, J. A., Phelps, M. E. Physics in nuclear medicine e-Book.Elsevier Health Sciences, 2012. 5, 8, 11 [4] Bailey, D. L., Humm, J. Nuclear medicine physics: a handbook for teachers andstudents. Iaea, 2014. 5 [5] Imagen extraida de University of Washington, Division of Nuclear Medicine. Introductionto PET Physics.https://depts.washington.edu/imreslab/from%20old%20SITE/pet_intro/intro_src/section2.html. Accedido el 15-09-2021.6 [6] Imagen extraida del libro Saha, G. B. Physics and radiobiology of nuclear medicine.Springer Science & Business Media, 2012. 7 [7] Imagen extraida de Tomography, P. E. Basic science and clinical practice, 2003. 8 [8] Loyd, M. S. The development of cesium calcium bromo-iodide scintillator for x-ray and gamma ray detection, 2017. 10 [9] Imagen extraida de Loyd, M. S. The development of cesium calcium bromo-iodide scintillator for x-ray and gamma ray detection, 2017. 10 [10] Khalil, M. M. Pet/mr: basics and new developments. En: Basic Science of PET Imaging, p´ags. 199–228. Springer, 2017. 11 [11] Imagen extraida de Khalil, M. M. Pet/mr: basics and new developments. En: Basic Science of PET Imaging, p´ags. 199–228. Springer, 2017. 11 [12] Alessio, A. M., Kinahan, P. E. Improved quantitation for pet/ct image reconstruction with system modeling and anatomical priors. Medical physics, 33 (11), 4095–4103, 2006. 13 [13] Mergenthaler, P., Lindauer, U., Dienel, G. A., Meisel, A. Sugar for the brain: the role of glucose in physiological and pathological brain function. Trends in neurosciences, 36 (10), 587–597, 2013. 14, 15 [14] Mayfield Foundation, E. . R. Anatomy of the Brain. https://mayfieldclinic.com/pe-anatbrain.htm. Accedido el 20-09-2021. 15, 16 [15] Imágenes extraidas de Olson, T. R., Pawlina, W. ADAM student atlas of anatomy. Cambridge University Press, 2008. 16 [16] Imagen extraida de Mayfield Foundation, E. . R. Anatomy of the Brain. https://mayfieldclinic.com/pe-anatbrain.htm. Accedido el 20-09-2021. 16 [17] Brown, R. K., Bohnen, N. I., Wong, K. K., Minoshima, S., Frey, K. A. Brain petin suspected dementia: patterns of altered fdg metabolism. Radiographics, 34 (3),684–701, 2014. 17 [18] Shivamurthy, V. K., Tahari, A. K., Marcus, C., Subramaniam, R. M. Brain fdg pet and the diagnosis of dementia. American Journal of Roentgenology, 204 (1), W76–W85, 2015. 17, 19, 20, 21 [19] World Earth Organization. Dementia. https://www.who.int/news-room/ fact-sheets/detail/dementia. Accedido el 28-08-2021. 17 [20] Castellani, R. J., Rolston, R. K., Smith, M. A. Alzheimer disease. Disease-amonth: DM, 56 (9), 484, 2010. 18 [21] Im´agenes extraida de Shivamurthy, V. K., Tahari, A. K., Marcus, C., Subramaniam, R. M. Brain fdg pet and the diagnosis of dementia. American Journal of Roentgenology, 204 (1), W76–W85, 2015. 19, 20, 21 [22] Snowden, J. S., Neary, D., Mann, D. M. Frontotemporal dementia. The British journal of psychiatry, 180 (2), 140–143, 2002. 19 [23] McKeith, I. Dementia with lewy bodies. Handbook of clinical Neurology, 84, 531–548, 2007. 20 [24] William, M., Robert, B., et al. Introduccion a la probabilidad y estadistica. Inf. tec., 1987. 21 [25] F¨allmar, D., Lilja, J., Danfors, T., Kilander, L., Iyer, V., Lubberink, M., et al. Zscore maps from low-dose 18f-fdg pet of the brain in neurodegenerative dementia. American journal of nuclear medicine and molecular imaging, 8 (4), 239, 2018. 22 [26] Manual, S. The fil methods group. Functional Imaging Laboratory, Wellcome Trust Centre for Neuroimaging, Institute of Neurology, London, UK, 2009. 22 [27] M´erida, I., Jung, J., Bouvard, S., Le Bars, D., Lancelot, S., Lavenne, F., et al. Cermep-idb-mrxfdg: A database of 37 normal adult human brain [18f] fdg pet, t1 and flair mri, and ct images available for research. EJNMMI research, 11 (1), 1–10, 2021. 23 [28] Imagen extraida de Della Rosa, P. A., Cerami, C., Gallivanone, F., Prestia, A., Caroli, A., Castiglioni, I., et al. A standardized [18 f]-fdg-pet template for spatial normalization in statistical parametric mapping of dementia. Neuroinformatics, 12 (4), 575–593, 2014. 23 [29] Verwer, E., Golla, S., Kaalep, A., Lubberink, M., van Velden, F., Bettinardi, V., et al. Harmonisation of pet/ct contrast recovery performance for brain studies. European Journal of Nuclear Medicine and Molecular Imaging, p´ags. 1–15, 2021. 27, 29, 30, 35 [30] Kogan, R. V., de Jong, B. A., Renken, R. J., Meles, S. K., van Snick, P. J., Golla, S., et al. Factors affecting the harmonization of disease-related metabolic brain pattern expression quantification in [18f] fdg-pet (petmetpat). Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 11, 472–482, 2019. 29 [31] Healthcare, G. E. Duetto Tool Box: User Manual. General Electric, 2021. 32 [32] Hammers, A., Allom, R., Koepp, M. J., Free, S. L., Myers, R., Lemieux, L., et al. Three-dimensional maximum probability atlas of the human brain, with particular reference to the temporal lobe. Human brain mapping, 19 (4), 224–247, 2003. 38 [33] Imagen extraida de PMOD Neuro Tool User Manual Version 4,2. PMOD Technologies, 2020. 38 [34] PMOD. PMOD Neuro Tool User Manual Version 4,2. PMOD Technologies, 2020. 39, 61 [35] Ryan, J. J., Lopez, S. J. Wechsler adult intelligence scale-iii. En: Understanding Psychological Assessment, p´ags. 19–42. Springer, 2001. 51 [36] Nunnally, J. C. Psychometric theory 2nd ed., 1978. 51 [37] System, G. M. Signa PET/MR Operator Manual. General Electric, 2014. 82 [25] F¨allmar, D., Lilja, J., Danfors, T., Kilander, L., Iyer, V., Lubberink, M., et al. Zscore maps from low-dose 18f-fdg pet of the brain in neurodegenerative dementia. American journal of nuclear medicine and molecular imaging, 8 (4), 239, 2018. 22 [26] Manual, S. The fil methods group. Functional Imaging Laboratory, Wellcome Trust Centre for Neuroimaging, Institute of Neurology, London, UK, 2009. 22 [27] M´erida, I., Jung, J., Bouvard, S., Le Bars, D., Lancelot, S., Lavenne, F., et al. Cermep-idb-mrxfdg: A database of 37 normal adult human brain [18f] fdg pet, t1 and flair mri, and ct images available for research. EJNMMI research, 11 (1), 1–10, 2021. 23 [28] Imagen extraida de Della Rosa, P. A., Cerami, C., Gallivanone, F., Prestia, A., Caroli, A., Castiglioni, I., et al. A standardized [18 f]-fdg-pet template for spatial normalization in statistical parametric mapping of dementia. Neuroinformatics, 12 (4), 575–593, 2014. 23 [29] Verwer, E., Golla, S., Kaalep, A., Lubberink, M., van Velden, F., Bettinardi, V., et al. Harmonisation of pet/ct contrast recovery performance for brain studies. European Journal of Nuclear Medicine and Molecular Imaging, pags. 1–15, 2021. 27, 29, 30, 35 [30] Kogan, R. V., de Jong, B. A., Renken, R. J., Meles, S. K., van Snick, P. J., Golla, S., et al. Factors affecting the harmonization of disease-related metabolic brain pattern expression quantification in [18f] fdg-pet (petmetpat). Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 11, 472–482, 2019. 29 [31] Healthcare, G. E. Duetto Tool Box: User Manual. General Electric, 2021. 32 [32] Hammers, A., Allom, R., Koepp, M. J., Free, S. L., Myers, R., Lemieux, L., et al. Three-dimensional maximum probability atlas of the human brain, with particular reference to the temporal lobe. Human brain mapping, 19 (4), 224–247, 2003. 38 [33] Imagen extraida de PMOD Neuro Tool User Manual Version 4,2. PMOD Technologies, 2020. 38 [34] PMOD. PMOD Neuro Tool User Manual Version 4,2. PMOD Technologies, 2020. 39, 61 [35] Ryan, J. J., Lopez, S. J. Wechsler adult intelligence scale-iii. En: Understanding Psychological Assessment, p´ags. 19–42. Springer, 2001. 51 [36] Nunnally, J. C. Psychometric theory 2nd ed., 1978. 51 [37] System, G. M. Signa PET/MR Operator Manual. General Electric, 2014. 82
Materias:Medicina > Medicina nuclear
Divisiones:FUESMEN
Código ID:1035
Depositado Por:Tamara Cárcamo
Depositado En:14 Jun 2022 15:11
Última Modificación:14 Jun 2022 15:31

Personal del repositorio solamente: página de control del documento