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Advanced Visualization Basics in Medical Imaging

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Basic Knowledge of Medical Imaging Informatics

Part of the book series: Imaging Informatics for Healthcare Professionals ((IIHP))

Abstract

Unlike other medical imaging domains such as pathology and dermatology, radiology data is inherently in 3D, even 4D and n-dimensional (nD) depending on the modality used for images acquisition. More and more evidences exist on the advantages of studying volume through advanced visualization techniques rather than exploring 2D data alone in different clinical scenarios of diagnosis, treatment planning, and treatment response evaluation (response criteria).

New techniques have appeared in the research and innovation panorama that constitute a disruptive way in the spatial data interpretation. The innovation in the visualization has been brought by the integration of new technologies such as Augmented/Virtual reality (AR/VR), 3D printing, cinematic rendering, and the characterization of tissues and lesions through quantitative parametric maps.

In this chapter, an educational review of the most relevant advanced visualization techniques existing nowadays in the radiology workflow is presented.

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References

  1. Steger S, Franco F, Sverzellati N, Chiari G, Colomer R. 3D assessment of lymph nodes vs. RECIST 1.1. Acad Radiol. 2011;18:391–4.

    Article  Google Scholar 

  2. Hayes SA, Pietanza MC, O’Driscoll D, Zheng J, Moskowitz CS, Kris MG, Ginsberg MS. Comparison of CT volumetric measurement with RECIST response in patients with lung cancer. Eur J Radiol. 2016;85:524–33.

    Article  CAS  Google Scholar 

  3. Aghighi M, Boe J, Rosenberg J, Von Eyben R, Gawande RS, Petit P, Sethi TK, Sharib J, Marina NM, DuBois SG, Daldrup-Link HE. Three-dimensional radiologic assessment of chemotherapy response in Ewing sarcoma can be used to predict clinical outcome. Radiology. 2016;280:905–15.

    Article  Google Scholar 

  4. Comaniciu D, Engel K, Georgescu B, Mansi T. Shaping the future through innovations: from medical imaging to precision medicine. Med Image Anal. 2016;33:19–26.

    Article  Google Scholar 

  5. Dappa E, Higashigaito K, Fornaro J, Leschka S, Wildermuth S, Alkadhi H. Cinematic rendering - an alternative to volume rendering for 3D computed tomography imaging. Insights Imaging. 2016;7(6):849–56.

    Article  Google Scholar 

  6. Eid M, De Cecco CN, Nance JW Jr, et al. Cinematic rendering in CT: a novel, lifelike 3D visualization technique. AJR Am J Roentgenol. 2017;209(2):370–9.

    Article  Google Scholar 

  7. Jhala K, Madan R, Hammer M. A pictorial review of lung torsion using 3D CT cinematic rendering. Emerg Radiol. 2020; https://doi.org/10.1007/s10140-020-01805-1.

  8. Böven J, Boos J, Steuwe A, et al. Diagnostic value and forensic relevance of a novel photorealistic 3D reconstruction technique in post-mortem CT. Br J Radiol. 2020;2020:20200204.

    Article  Google Scholar 

  9. Baldi D, Tramontano L, Punzo B, Orsini M, Cavaliere C. CT cinematic rendering for glomus jugulare tumor with intracranial extension. Quant Imaging Med Surg. 2020;10(2):522–6.

    Article  Google Scholar 

  10. Wollschlaeger LM, Boos J, Jungbluth P, et al. Is CT-based cinematic rendering superior to volume rendering technique in the preoperative evaluation of multifragmentary intraarticular lower extremity fractures? Eur J Radiol. 2020;126:108911.

    Article  Google Scholar 

  11. Caton MT Jr, Wiggins WF, Nunez D. Three-dimensional cinematic rendering to optimize visualization of cerebrovascular anatomy and disease in CT angiography. J Neuroimaging. 2020;30(3):286–96.

    Article  Google Scholar 

  12. Hakim A, Mosimann PJ. Intracranial arteriovenous malformation: cinematic rendering with digital subtraction angiography. Radiology. 2020;294(3):506.

    Article  Google Scholar 

  13. Rowe SP, Chu LC, Meyer AR, Gorin MA, Fishman EK. The application of cinematic rendering to CT evaluation of upper tract urothelial tumors: principles and practice. Abdom Radiol (NY). 2019;44(12):3886–92.

    Article  Google Scholar 

  14. de Spiegeleire X, Vanhaebost J, Coche E. Post-mortem CT angiography of mesenteric vessels using cinematic rendering vision. Diagn Interv Imaging. 2019;100(9):533–4.

    Article  Google Scholar 

  15. Chu LC, Rowe SP, Fishman EK. Cinematic rendering of focal liver masses. Diagn Interv Imaging. 2019;100(9):467–76.

    Article  CAS  Google Scholar 

  16. Colli A, Tua L, Punzo B, Baldi D, Cademartiri F, Gerosa G. Cinematic rendering: an alternative to classical volume rendering for acute aortic dissection. Ann Thorac Surg. 2019;108(2):e121.

    Article  Google Scholar 

  17. Uppot RN, Laguna B, McCarthy CJ, De Novi G, Phelps A, Siegel E, Courtier J. Implementing virtual and augmented reality tools for radiology education and training, communication, and clinical care. Radiology. 2019;291(3):570–80.

    Article  Google Scholar 

  18. Nakarada-Kordic I, Reay S, Bennett G, Kruse J, Lydon AM, Sim J. Can virtual reality simulation prepare patients for an MRI experience? Radiography (Lond). 2019; S1078-8174(19)30166-X.

    Google Scholar 

  19. Pratt P, Ives M, Lawton G, Simmons J, Radev N, Spyropoulou L, Amiras D. Through the HoloLens™ looking glass: augmented reality for extremity reconstruction surgery using 3D vascular models with perforating vessels. Version 2. Eur Radiol Exp. 2018;2(1):2.

    Article  Google Scholar 

  20. Sánchez Y, Anvari A, Samir AE, Arellano RS, Prabhakar AM, Uppot RN. Navigational guidance and ablation planning tools for interventional radiology. Curr Probl Diagn Radiol. 2017;46(3):225–33.

    Article  Google Scholar 

  21. Elmi-Terander A, Skulason H, Söderman M, Racadio J, Homan R, Babic D, van der Vaart N, Nachabe R. Surgical navigation technology based on augmented reality and integrated 3D intraoperative imaging: a spine cadaveric feasibility and accuracy study. Spine (Phila Pa 1976). 2016;41(21):E1303–11.

    Article  Google Scholar 

  22. Teber D, Guven S, Simpfendörfer T, Baumhauer M, Güven EO, Yencilek F, Gözen AS, Rassweiler J. Augmented reality: a new tool to improve surgical accuracy during laparoscopic partial nephrectomy? Preliminary in vitro and in vivo results. Eur Urol. 2009;56(2):332–8.

    Article  Google Scholar 

  23. Mitsouras D, Liacouras P, Imanzadeh A, Giannopoulos AA, Cai T, Kumamaru KK, George E, Wake N, Caterson EJ, Pomahac B, Ho VB, Grant GT, Rybicki FJ. Medical 3D printing for the radiologist. Radiographics. 2015;35(7):1965–88.

    Article  Google Scholar 

  24. Hiller J, Lipson H. STL 2.0: a proposal for a universal multimaterial Additive Manufacturing File format. Proceedings of the Solid Freeform Fabrication Symposium 2009. Austin, Texas; 2009. p. 266–78.

    Google Scholar 

  25. Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells W, Frangi A, editors. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, Lecture notes in computer science, vol. 9351. Cham: Springer; 2015.

    Google Scholar 

  26. Marti-Bonmati L, Alberich-Bayarri A, García-Martí G, Sanz-Requena R. Multiparametric imaging. In: Luna A, Vilanova JC, Celso Hyginio da Cruz Jr L, Rossi SE, editors. Functional imaging in oncology: biophysical basis and technical approaches. New York. ISBN: 978-3-642-40411-5: Springer; 2013. p. 524–35.

    Google Scholar 

  27. Juan-Albarracín J, Fuster-Garcia E, Pérez-Girbés A, Aparici-Robles F, Alberich-Bayarri Á, Revert-Ventura A, Martí-Bonmatí L, García-Gómez JM. Glioblastoma: vascular habitats detected at preoperative dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging predict survival. Radiology. 2018;287(3):944–54.

    Article  Google Scholar 

  28. Fuster-Garcia E, Juan-Albarracín J, García-Ferrando GA, Martí-Bonmatí L, Aparici-Robles F, García-Gómez JM. Improving the estimation of prognosis for glioblastoma patients by MR based hemodynamic tissue signatures. NMR Biomed. 2018;31(12):e4006.

    Article  Google Scholar 

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Authors and Affiliations

  1. Quantitative Imaging Biomarkers in Medicine, QUIBIM SL, Valencia, Spain

    Angel Alberich-Bayarri

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  1. Angel Alberich-Bayarri
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Correspondence to Angel Alberich-Bayarri .

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Editors and Affiliations

  1. University Medical Center Groningen, University of Groningen, GRONINGEN, Groningen, The Netherlands

    Peter M. A. van Ooijen

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© 2021 European Society of Medical Imaging Informatics (EuSoMII)

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Alberich-Bayarri, A. (2021). Advanced Visualization Basics in Medical Imaging. In: van Ooijen, P.M.A. (eds) Basic Knowledge of Medical Imaging Informatics. Imaging Informatics for Healthcare Professionals. Springer, Cham. https://doi.org/10.1007/978-3-030-71885-5_5

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  • DOI: https://doi.org/10.1007/978-3-030-71885-5_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-71884-8

  • Online ISBN: 978-3-030-71885-5

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