Skip to main content
SpringerLink
Log in

Principles of Imaging for Epidemiologists

  • Chapter
  • First Online:
Principles of Genetics and Molecular Epidemiology

Abstract

Applications of imaging methods in medicine are not new. However, recent advances in analytical techniques and in the implementation and applicability of imaging findings at the population level have increased exponentially the applications of imaging into large-scale epidemiological studies. Implementing imaging data acquired at large-scale settings comprises several logistic and technical challenges which may be offset by the valuable insights they provide into pathophysiology, diagnosis, disease, and treatment monitoring and prognosis. In this chapter, we discuss the applications of imaging in different medical fields and how these have translated into clinical applications. We also discuss the implementation of imaging in population-based studies, the analytic techniques useful for processing and analyzing imaging data, and the potential limitations of its applicability and reproducibility in epidemiological research. We aim to provide a primer for any researcher interested in implementing imaging methods into population-based epidemiological studies.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 139.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Abbreviations

AD:

Alzheimer’s Disease

BOLD:

Blood Oxygen Level Dependent

CT:

Computed Tomography

DWI:

Diffusion Weighted Imaging

MRI:

Magnetic Resonance Imaging fMRI

Functional MRI

PET:

Positron Emission Tomography

SPECT:

Single-Photon Emission Computed Tomography

References

  1. Wehrl HF, Sauter AW, Judenhofer MS, Pichler BJ. Combined PET/MR imaging — technology and applications. Technol Cancer Res Treat. 2010;9(1):5–20.

    Article  CAS  PubMed  Google Scholar 

  2. Rudin M. Noninvasive structural, functional, and molecular imaging in drug development. Curr Opin Chem Biol. 2009;13(3):360–71.

    Article  CAS  PubMed  Google Scholar 

  3. Gillam LD, Leipsic J, Weissman NJ. Use of imaging endpoints in clinical trials. JACC Cardiovasc Imaging. 2017;10(3):296–303.

    Article  PubMed  Google Scholar 

  4. Murphy P, Koh D-M. Imaging in clinical trials. Cancer Imaging. 2010;10(1A):S74–82.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Wehrl HF, Judenhofer MS, Wiehr S, Pichler BJ. Pre-clinical PET/MR: technological advances and new perspectives in biomedical research. Eur J Nucl Med Mol Imaging. 2009;36(1):56–68.

    Article  Google Scholar 

  6. Yip SSF, Aerts HJWL. Applications and limitations of radiomics. Phys Med Biol. 2016;61(13):R150–66.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Albanese E. Chapter 3 – Advanced epidemiologic and analytical methods. En: Aminoff MJ, Boller F, Swaab DF, editores. Handbook of clinical neurology [internet]. Elsevier; 2016 [citado 2 de abril de 2021]. p. 39–52. (Neuroepidemiology; vol. 138). Disponible en: https://www.sciencedirect.com/science/article/pii/B9780128029732000033.

  8. Cohen AB, Klein JP, Mukundan S. A guide to imaging for common neurological problems. BMJ. 2010;341:c4113.

    Article  PubMed  Google Scholar 

  9. Kerr JND, Denk W. Imaging in vivo : watching the brain in action. Nat Rev Neurosci. 2008;9(3):195–205.

    Article  CAS  PubMed  Google Scholar 

  10. Schwarz CG. Uses of human MR and PET imaging in research of neurodegenerative brain diseases. Neurotherapeutics [Internet]. 15 de marzo de 2e021 [citado 2 de abril de 2021]; Disponible en: https://doi.org/10.1007/s13311-021-01030-9.

  11. Tsushima Y, Taketomi-Takahashi A, Endo K. Prevalence of abnormal findings on brain magnetic resonance (MR) examinations in adult participants of brain docking. BMC Neurol. 2005;5(1):18.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Horga G, Kaur T, Peterson BS. Annual research review: current limitations and future directions in MRI studies of child- and adult-onset developmental psychopathologies. J Child Psychol Psychiatry. 2014;55(6):659–80.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Poldrack RA, Farah MJ. Progress and challenges in probing the human brain. Nature. 2015;526(7573):371–9.

    Article  CAS  PubMed  Google Scholar 

  14. Chandra A, Dervenoulas G, Politis M. Alzheimer’s Disease Neuroimaging Initiative. Magnetic resonance imaging in Alzheimer’s disease and mild cognitive impairment. J Neurol. 2019;266(6):1293–302.

    Article  PubMed  Google Scholar 

  15. van Oostveen WM, de Lange ECM. Imaging techniques in Alzheimer’s disease: a review of applications in early diagnosis and longitudinal monitoring. Int J Mol Sci. 2021;22(4):2110.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Chan D, Janssen JC, Whitwell JL, Watt HC, Jenkins R, Frost C, et al. Change in rates of cerebral atrophy over time in early-onset Alzheimer’s disease: longitudinal MRI study. Lancet. 4 de octubre de 2003;362(9390):1121–2.

    Google Scholar 

  17. Jack CR, Weigand SD, Shiung MM, Przybelski SA, O’Brien PC, Gunter JL, et al. Atrophy rates accelerate in amnestic mild cognitive impairment. Neurology. 6 de mayo de 2008;70(19 Pt 2):1740–52.

    Google Scholar 

  18. Sluimer JD, van der Flier WM, Karas GB, Fox NC, Scheltens P, Barkhof F, et al. Whole-brain atrophy rate and cognitive decline: longitudinal MR study of memory clinic patients. Radiology. 2008;248(2):590–8.

    Article  PubMed  Google Scholar 

  19. Lawrence E, Vegvari C, Ower A, Hadjichrysanthou C, De Wolf F, Anderson RM. A systematic review of longitudinal studies which measure Alzheimer’s disease biomarkers. J Alzheimers Dis. 2017;59(4):1359–79.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Schleim S, Roiser JP. FMRI in translation: the challenges facing real-world applications. Front Hum Neurosci. 2009;3:63.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Johnson KA, Fox NC, Sperling RA, Klunk WE. Brain imaging in Alzheimer disease. Cold Spring Harb Perspect Med [Internet]. abril de 2012 [citado 2 de abril de 2021];2(4). Disponible en: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3312396/.

  22. Zaret BL. Nuclear cardiology: history and milestones [Internet]. Nuclear Cardiac Imaging. Oxford University Press; [citado 2 de abril de 2021]. Disponible en: https://oxfordmedicine.com/view/10.1093/med/9780199392094.001.0001/med-9780199392094-chapter-1.

  23. Di Carli MF. Challenges and opportunities for nuclear cardiology. J Nucl Cardiol. 2019;26(4):1043–6.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Ikram MA, van der Lugt A, Niessen WJ, Krestin GP, Koudstaal PJ, Hofman A, et al. The Rotterdam Scan Study: design and update up to 2012. Eur J Epidemiol. 2011;26(10):811–24.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Bohnen S, Avanesov M, Jagodzinski A, Schnabel RB, Zeller T, Karakas M, et al. Cardiovascular magnetic resonance imaging in the prospective, population-based, Hamburg City Health cohort study: objectives and design. J Cardiovasc Magn Res. 2018;20(1):68.

    Article  Google Scholar 

  26. Bogowicz M, Vuong D, Huellner MW, Pavic M, Andratschke N, Gabrys HS, et al. CT radiomics and PET radiomics: ready for clinical implementation? Q J Nucl Med Mol Imaging. 2019;63(4):355–70.

    Article  PubMed  Google Scholar 

  27. Ashrafinia S, Dalaie P, Yan R, Ghazi P, Marcus C, Taghipour M, et al. Radiomics analysis of clinical myocardial perfusion spect to predict coronary artery calcification. J Nucl Med. 2018;59(Suppl 1):512.

    Google Scholar 

  28. Yaribeygi H, Farrokhi FR, Butler AE, Sahebkar A. Insulin resistance: review of the underlying molecular mechanisms. J Cell Physiol. 2019;234(6):8152–61.

    Article  CAS  PubMed  Google Scholar 

  29. Yazıcı D, Sezer H. Insulin resistance, obesity and lipotoxicity. Adv Exp Med Biol. 2017;960:277–304.

    Article  PubMed  Google Scholar 

  30. Antonio-Villa NE, Bello-Chavolla OY, Vargas-Vázquez A, Mehta R, Fermín-Martínez CA, Martagón-Rosado AJ, et al. Increased visceral fat accumulation modifies the effect of insulin resistance on arterial stiffness and hypertension risk. Nutr Metab Cardiovasc Dis. 2021;31(2):506–17.

    Article  CAS  PubMed  Google Scholar 

  31. Fernández-Chirino L, Antonio-Villa NE, Vargas-Vázquez A, Almeda-Valdés P, Gómez-Velasco D, Viveros-Ruiz TL, et al. Elevated serum uric acid is a facilitating mechanism for insulin resistance mediated accumulation of visceral adipose tissue. medRxiv. 2020;2020:09.20.20198499.

    Google Scholar 

  32. Chumlea WC, Guo SS, Kuczmarski RJ, Flegal KM, Johnson CL, Heymsfield SB, et al. Body composition estimates from NHANES III bioelectrical impedance data. Int J Obes. 2002;26(12):1596–609.

    Article  CAS  Google Scholar 

  33. Hinton BJ, Fan B, Ng BK, Shepherd JA. Dual energy X-ray absorptiometry body composition reference values of limbs and trunk from NHANES 1999–2004 with additional visualization methods. Plos One. 2017;12(3):e0174180.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Després J-P. Body fat distribution and risk of cardiovascular disease: an update. Circulation. 2012;126(10):1301–13.

    Article  PubMed  Google Scholar 

  35. Jean-Pierre D. Body fat distribution and risk of cardiovascular disease. Circulation. 2012;126(10):1301–13.

    Article  Google Scholar 

  36. Katzmarzyk PT, Mire E, Bouchard C. Abdominal obesity and mortality: the Pennington Center Longitudinal Study. Nutr Diabetes. 2012;2:e42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Kay SJ, Fiatarone Singh MA. The influence of physical activity on abdominal fat: a systematic review of the literature. Obes Rev. 2006;7(2):183–200.

    Article  CAS  PubMed  Google Scholar 

  38. Fox CS, Massaro JM, Hoffmann U, Pou KM, Maurovich-Horvat P, Liu C-Y, et al. Abdominal visceral and subcutaneous adipose tissue compartments: association with metabolic risk factors in the Framingham Heart Study. Circulation. 2007;116(1):39–48.

    Article  PubMed  Google Scholar 

  39. Rinella ME. Nonalcoholic fatty liver disease: a systematic review. JAMA. 2015;313(22):2263–73.

    Article  CAS  PubMed  Google Scholar 

  40. Bamberg F, Hetterich H, Rospleszcz S, Lorbeer R, Auweter SD, Schlett CL, et al. Subclinical disease burden as assessed by whole-body MRI in subjects with prediabetes, subjects with diabetes, and normal control subjects from the general population: the KORA-MRI study. Diabetes. 2017;66(1):158–69.

    Article  CAS  PubMed  Google Scholar 

  41. Schram MT, Henry Ronald MA, van Dijk Rob AJM, Kostense Piet J, Dekker Jacqueline M, Giel N, et al. Increased central artery stiffness in impaired glucose metabolism and type 2 diabetes. Hypertension. 2004;43(2):176–81.

    Article  CAS  PubMed  Google Scholar 

  42. Fox ER, Sarpong DF, Cook JC, Samdarshi TE, Nagarajarao HS, Liebson PR, et al. The relation of diabetes, impaired fasting blood glucose, and insulin resistance to left ventricular structure and function in African Americans: the Jackson heart study. Diabetes Care. 2011;34(2):507–9.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Grundy SM. Pre-diabetes, metabolic syndrome, and cardiovascular risk. J Am Coll Cardiol. 2012;59(7):635–43.

    Article  CAS  PubMed  Google Scholar 

  44. O’Connor JPB, Aboagye EO, Adams JE, Aerts HJWL, Barrington SF, Beer AJ, et al. Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol. 2017;14(3):169–86.

    Article  PubMed  Google Scholar 

  45. Wang Y, Chen H, Li N, Ren J, Zhang K, Dai M, et al. Ultrasound for breast cancer screening in high-risk women: results from a population-based cancer screening program in China. Front Oncol. 2019;9:286.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Arasu VA, Miglioretti DL, Sprague BL, Alsheik NH, Buist DSM, Henderson LM, et al. Population-based assessment of the association between magnetic resonance imaging background parenchymal enhancement and future primary breast cancer risk. J Clin Oncol. 2019;37(12):954–63.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Ouedraogo S, Dabakuyo TS, Gentil J, Poillot M-L, Dancourt V, Arveux P. Population-based study of breast cancer screening in Côte d’Or (France): clinical implications and factors affecting screening round adequacy. Eur J Cancer Prev. 2011;20(6):462–74.

    Article  PubMed  Google Scholar 

  48. Daamen LA, Groot VP, Goense L, Wessels FJ, Rinkes IHB, Intven MPW, et al. The diagnostic performance of CT versus FDG PET-CT for the detection of recurrent pancreatic cancer: a systematic review and meta-analysis. Eur J Radiol. 2018;106:128–36.

    Article  PubMed  Google Scholar 

  49. Han S, Choi JY. Impact of 18F-FDG PET, PET/CT, and PET/MRI on staging and management as an initial staging modality in breast cancer: a systematic review and meta-analysis. Clin Nucl Med. 2021;46(4):271–82.

    Article  PubMed  PubMed Central  Google Scholar 

  50. McDonnell LA, Angel PM, Lou S, Drake RR. Chapter eleven – mass spectrometry imaging in cancer research: future perspectives. En: Drake RR, McDonnell LA, editores. Advances in cancer research [internet]. Academic press; 2017 [citado 3 de abril de 2021]. p. 283-90. (applications of mass spectrometry imaging to cancer; vol. 134). Disponible en: https://www.sciencedirect.com/science/article/pii/S0065230X16300835.

  51. Karlsson O, Hanrieder J. Imaging mass spectrometry in drug development and toxicology. Arch Toxicol. 2017;91(6):2283–94.

    Article  CAS  PubMed  Google Scholar 

  52. Solomon M, Liu Y, Berezin MY, Achilefu S. Optical imaging in cancer research: basic principles, tumor detection, and therapeutic monitoring. MPP. 2011;20(5):397–415.

    Google Scholar 

  53. Soliman H, Gunasekara A, Rycroft M, Zubovits J, Dent R, Spayne J, et al. Functional imaging using diffuse optical spectroscopy of neoadjuvant chemotherapy response in women with locally advanced breast cancer. Clin Cancer Res. 2010;16(9):2605–14.

    Article  CAS  PubMed  Google Scholar 

  54. He K, Zeng S, Qian L. Recent progress in the molecular imaging of therapeutic monoclonal antibodies. J Pharm Anal. 2020;10(5):397–413.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Eldred-Evans D, Burak P, Connor MJ, Day E, Evans M, Fiorentino F, et al. Population-based prostate cancer screening with magnetic resonance imaging or ultrasonography: the IP1-PROSTAGRAM study. JAMA Oncol. 2021;7(3):395.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Wu N-Y, Cheng H-C, Ko JS, Cheng Y-C, Lin P-W, Lin W-C, et al. Magnetic resonance imaging for lung cancer detection: Experience in a population of more than 10,000 healthy individuals. BMC Cancer. 2011;11(1):242.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Yi X, Guan X, Zhang Y, Liu L, Long X, Yin H, et al. Radiomics improves efficiency for differentiating subclinical pheochromocytoma from lipid-poor adenoma: a predictive, preventive and personalized medical approach in adrenal incidentalomas. EPMA J. 2018;9(4):421–9.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Zhao W, Xu Y, Yang Z, Sun Y, Li C, Jin L, et al. Development and validation of a radiomics nomogram for identifying invasiveness of pulmonary adenocarcinomas appearing as subcentimeter ground-glass opacity nodules. Eur J Radiol. 2019;112:161–8.

    Article  PubMed  Google Scholar 

  59. Zhou L, Zhang Z, Chen Y-C, Zhao Z-Y, Yin X-D, Jiang H-B. A deep learning-based radiomics model for differentiating benign and malignant renal tumors. Transl Oncol. 2019;12(2):292–300.

    Article  PubMed  Google Scholar 

  60. Biswas M, Kuppili V, Saba L, Edla DR, Suri HS, Cuadrado-Godia E, et al. State-of-the-art review on deep learning in medical imaging. Front Biosci (Landmark Ed). 2019;24:392–426.

    Article  Google Scholar 

  61. Avanzo M, Stancanello J, Pirrone G, Sartor G. Radiomics and deep learning in lung cancer. Strahlenther Onkol. 2020;196(10):879–87.

    Article  PubMed  Google Scholar 

  62. Völzke H, Alte D, Schmidt CO, Radke D, Lorbeer R, Friedrich N, et al. Cohort profile: the study of health in pomerania. Int J Epidemiol. 2011;40(2):294–307.

    Article  PubMed  Google Scholar 

  63. Hetterich H, Bayerl C, Peters A, Heier M, Linkohr B, Meisinger C, et al. Feasibility of a three-step magnetic resonance imaging approach for the assessment of hepatic steatosis in an asymptomatic study population. Eur Radiol. 2016;26(6):1895–904.

    Article  PubMed  Google Scholar 

  64. Jaddoe VWV, van Duijn CM, Franco OH, van der Heijden AJ, van IIzendoorn MH, de Jongste JC, et al. The Generation R Study: design and cohort update 2012. Eur J Epidemiol. 2012, 27;(9):739–56.

    Google Scholar 

  65. Jaddoe VWV, Bakker R, van Duijn CM, van der Heijden AJ, Lindemans J, Mackenbach JP, et al. The generation R study biobank: a resource for epidemiological studies in children and their parents. Eur J Epidemiol. 2007;22(12):917–23.

    Article  PubMed  PubMed Central  Google Scholar 

  66. European Society of Radiology (ESR). ESR position paper on imaging biobanks. Insights Imaging. 2015;6(4):403–10.

    Article  Google Scholar 

  67. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLOS Med. 2015;12(3):e1001779.

    Article  PubMed  PubMed Central  Google Scholar 

  68. The German National Cohort: aims, study design and organization. Eur J Epidemiol. 2014;29(5):371–82.

    Google Scholar 

  69. Big data in meedical image processing [internet]. CRC Press; 2018 [citado 4 de abril de 2021]. Disponible en: https://www.taylorfrancis.com/https://www.taylorfrancis.com/books/mono/10.1201/b22456/big-data-medical-image-processing-suganya-rajaram-sheik-abdullah

  70. Ezhilraman SV, Srinivasan S. State of the art in image processing & big data analytics: issues and challenges. Int J Eng Technol. 2018;7(2.33):195–9.

    Article  Google Scholar 

  71. Kharat AT, Singhal S. A peek into the future of radiology using big data applications. Indian J Radiol Imaging. 2017;27(2):241–8.

    PubMed  PubMed Central  Google Scholar 

  72. Brown LG. A survey of image registration techniques. ACM Comput Surv. 1992;24(4):325–76.

    Article  Google Scholar 

  73. Levman J, Takahashi E. Multivariate analyses applied to healthy neurodevelopment in fetal, neonatal, and pediatric MRI. Front Neuroanat. 2015;9:163.

    PubMed  Google Scholar 

  74. Muir ER, Ndiour IJ, Goasduff NAL, Moffitt RA, Liu Y, Sullards MC, et al. Multivariate analysis of imaging mass spectrometry data. En: 2007 IEEE 7th international symposium on bioinformatics and bioengineering. 2007. p. 472–9.

    Google Scholar 

  75. Willemink MJ, Koszek WA, Hardell C, Wu J, Fleischmann D, Harvey H, et al. Preparing medical imaging data for machine learning. Radiology. 2020;295(1):4–15.

    Article  PubMed  Google Scholar 

  76. Holzinger A, Langs G, Denk H, Zatloukal K, Müller H. Causability and explainability of artificial intelligence in medicine. WIREs Data Min Knowl Discov. 2019;9(4):e1312.

    Google Scholar 

  77. Tahmassebi A, Ehtemami A, Mohebali B, Gandomi AH, Pinker K, Meyer-Baese A. Big data analytics in medical imaging using deep learning. En: Big data: learning, analytics, and applications [internet]. International Society for Optics and Photonics; 2019 [citado 4 de abril de 2021]. p. 109890E. Disponible en: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10989/109890E/Big-data-analytics-in-medical-imaging-using-deep-learning/10.1117/12.2516014.short.

  78. Hanson KM. Introduction to Bayesian image analysis. En: Medical imaging 1993: image processing [Internet]. International Society for Optics and Photonics; 1993 [citado 4 de abril de 2021]. p. 716-31. Disponible en: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/1898/0000/Introduction-to-Bayesian-image-analysis/10.1117/12.154577.short.

  79. Ma J, Feng Q, Feng Y, Huang J, Chen W. Generalized Gibbs priors based positron emission tomography reconstruction. Comput Biol Med. 2010;40(6):565–71.

    Article  PubMed  Google Scholar 

  80. Zhang L, Guindani M, Vannucci M. Bayesian models for functional magnetic resonance imaging data analysis. WIREs Comput Stat. 2015;7(1):21–41.

    Article  Google Scholar 

  81. Illes J, Kirschen MP, Edwards E, Stanford LR, Bandettini P, Cho MK, et al. Incidental findings in brain imaging research. Science. 2006;311(5762):783–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Frisoni GB. Structural imaging in the clinical diagnosis of Alzheimer’s disease: problems and tools. J Neurol Neurosurg Psychiatry. 2001;70(6):711–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Völzke H, Schmidt CO, Hegenscheid K, Kühn J-P, Bamberg F, Lieb W, et al. Population imaging as valuable tool for personalized medicine. Clin Pharmacol Ther. 2012;92(4):422–4.

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Research Division, Instituto Nacional de Geriatría, Mexico City, Mexico

    Omar Yaxmehen Bello-Chavolla

  2. (PECEM) Program, Faculty of Medicine, National Autonomous University of Mexico, Mexico City, Mexico

    Arsenio Vargas-Vázquez, Mónica Itzel Martínez-Gutiérrez, Enrique C. Guerra, Carlos Alberto Fermín-Martínez & Alejandro Márquez-Salinas

Authors
  1. Omar Yaxmehen Bello-Chavolla
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arsenio Vargas-Vázquez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mónica Itzel Martínez-Gutiérrez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Enrique C. Guerra
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Carlos Alberto Fermín-Martínez
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Alejandro Márquez-Salinas
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dirección de Investigación Instituto Nacional de Geriatría (INGER), Ciudad de México, Mexico

    Juan Carlos Gomez-Verjan

  2. Dirección de Investigación Instituto Nacional de Geriatría (INGER) Instituto Nacional de Geriatría, Ciudad de México, Mexico

    Nadia Alejandra Rivero-Segura

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Bello-Chavolla, O.Y., Vargas-Vázquez, A., Martínez-Gutiérrez, M.I., Guerra, E.C., Fermín-Martínez, C.A., Márquez-Salinas, A. (2022). Principles of Imaging for Epidemiologists. In: Gomez-Verjan, J.C., Rivero-Segura, N.A. (eds) Principles of Genetics and Molecular Epidemiology. Springer, Cham. https://doi.org/10.1007/978-3-030-89601-0_11

Download citation

Publish with us

Policies and ethics

Search

Navigation