Computational Fluid Dynamics Simulations with Applications in Virtual Reality Aided Health Care Diagnostics

  • Vishwanath Panwar
  • Seshu Kumar Vandrangi
  • Sampath EmaniEmail author
  • Gurunadh Velidi
  • Jaseer Hamza
Part of the Studies in Computational Intelligence book series (SCI, volume 875)


Currently, medical scans yield large 3D data volumes. To analyze the data, image processing techniques are worth employing. Also, the data could be visualized to offer non-invasive and accurate 3D anatomical views regarding the inside of patients. Through this visualization approach, several medical processes or healthcare diagnostic procedures (including virtual reality (VR) aided operations) can be supported. The main aim of this study has been to discuss and provide a critical review of some of the recent scholarly insights surrounding the subject of CFD simulations with applications of VR-aided health care diagnostics. The study’s specific objective has been to unearth how CFD simulations have been applied to different areas of health care diagnostics, with VR environments on the focus. Some of the VR-based health care areas that CFD simulations have been observed to gain increasing application include medical device performance and diseases or health conditions such as colorectal cancer, cancer of the liver, and heart failure. From the review, an emerging theme is that CFD simulations form a promising path whereby they sensitize VR operators in health care regarding some of the best paths that are worth taking to minimize patient harm or risk. Hence, CFD simulations have paved the way for VR operators to make more informed and accurate decisions regarding disease diagnosis and treatment tailoring relative to the needs and conditions with which patients present.


CFD Health-care Medicine Virtual reality Image-processing 


  1. 1.
    Pareek, T. G., Mehta, U., & Gupta, A. (2018). A survey: Virtual reality model for medical diagnosis. Biomedical and Pharmacology Journal, 11(4), 2091–2100.CrossRefGoogle Scholar
  2. 2.
    Antoniadis, A. P., Mortier, P., Kassab, G., Dubini, G., Foin, N., et al. (2015). Biomechanical modeling to improve coronary artery bifurcation stenting: Expert review document on techniques and clinical implementation. JACC: Cardiovascular Interventions, 8(10), 1281–1296.Google Scholar
  3. 3.
    Bavo, A., Pouch, A. M., Degroote, J., Vierendeels, J., Gorman, J. H., et al. (2017). Patient-specific CFD models for intraventricular flow analysis from 3D ultrasound imaging: Comparison of three clinical cases. Journal of Biomechanics, 50(11), 144–150.CrossRefGoogle Scholar
  4. 4.
    Chnafa, C., Mendez, S., & Nicoud, F. (2014). Image-based large-eddy simulation in a realistic left heart. Computers & Fluids, 94(6), 173–187.Google Scholar
  5. 5.
    Belinha, J. (2016). Meshless methods: The future of computational biomechanical simulation. Journal of Biometrics and Biostatistics, 7(4), 1–3.Google Scholar
  6. 6.
    Doost, S. N., Ghista, D., Su, B., Zhong, L., & Morsi, Y. S. (2016). Heart blood flow simulation: a perspective review. Biomedical Engineering Online, 15(1), 101.Google Scholar
  7. 7.
    Doost, S. N., Zhong, L., Su, B., & Morsi, Y. S. (2017). Two-dimensional intraventricular flow pattern visualization using the image-based computational fluid dynamics. Computer Methods in Biomechanics and Biomedical Engineering, 20(5), 492–507.CrossRefGoogle Scholar
  8. 8.
    Douglas, P. S., Pontone, G., Hlatky, M. A., Patel, M. R., Norgaard, B. L., et al. (2015). Clinical outcomes of fractional flow reserve by computed tomographic angiography-guided diagnostic strategies vs. usual care in patients with suspected coronary artery disease: The prospective longitudinal trial of FFRCT: Outcome and resource impacts study. European Heart Journal, 36(47), 3359–3367.CrossRefGoogle Scholar
  9. 9.
    Galassi, F., Alkhalil, M., Lee, R., Martindale, P., Kharbanda, R. K., et al. (2018). 3D reconstruction of coronary arteries from 2D angiographic projections using non-uniform rational basis splines (NURBS) for accurate modelling of coronary stenoses. PloS one, 13(1), e0190650.CrossRefGoogle Scholar
  10. 10.
    Imanparast, A., Fatouraee, N., & Sharif, F. (2016). The impact of valve simplifications on left ventricular hemodynamics in a three dimensional simulation based on in vivo MRI data. Journal of Biomechanics, 49(9), 1482–1489.CrossRefGoogle Scholar
  11. 11.
    Lewis, M. A., Pascoal, A., Keevil, S. F., & Lewis, C. A. (2016). Selecting a CT scanner for cardiac imaging: The heart of the matter. The British Journal of Radiology, 89(1065), 20160376.Google Scholar
  12. 12.
    Leng, S., Jiang, M., Zhao, X.-D., Allen, J. C., Kassab, G. S., Ouyang, R.-Z., et al. (2016). Three-dimensional tricuspid annular motion analysis from cardiac magnetic resonance feature-tracking. Annals of Biomedical Engineering, 44(12), 3522–3538.CrossRefGoogle Scholar
  13. 13.
    Mittal, R., Seo, J. H., Vedula, V., Choi, Y. J., Liu, H., et al. (2016). Computational modeling of cardiac hemodynamics: Current status and future outlook. Journal of Computational Physics, 305(2), 1065–1082.MathSciNetCrossRefGoogle Scholar
  14. 14.
    Nguyen, V.-T., Loon, C. J., Nguyen, H. H., Liang, Z., & Leo, H. L. (2015). A semi-automated method for patient-specific computational flow modelling of left ventricles. Computer Methods in Biomechanics and Biomedical Engineering, 18(4), 401–413.CrossRefGoogle Scholar
  15. 15.
    Morris, P. D., Narracott, A., von Tengg-Kobligk, H., Soto, D. A. S., Hsiao, S., Lungu, A., et al. (2016). Computational fluid dynamics modelling in cardiovascular medicine. Heart, 102(1), 18–28.CrossRefGoogle Scholar
  16. 16.
    Moosavi, M.-H., Fatouraee, N., Katoozian, H., Pashaei, A., Camara, O., & Frangi, A. F. (2014). Numerical simulation of blood flow in the left ventricle and aortic sinus using magnetic resonance imaging and computational fluid dynamics. Computer Methods in Biomechanics and Biomedical Engineering, 17(7), 740–749.CrossRefGoogle Scholar
  17. 17.
    Itu, L., Rapaka, S., Passerini, T., Georgescu, B., Schwemmer, C., Schoebinger, M., et al. (2016). A machine-learning approach for computation of fractional flow reserve from coronary computed tomography. Journal of Applied Physiology, 121(1), 42–52.CrossRefGoogle Scholar
  18. 18.
    Su, B., Zhang, J.-M., Tang, H. C., Wan, M., Lim, C. C. W., et al. (2014). Patient-specific blood flows and vortex formations in patients with hypertrophic cardiomyopathy using computational fluid dynamics. In 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES). IEEE.Google Scholar
  19. 19.
    Kawaji, T., Shiomi, H., Morishita, H., Morimoto, T., Taylor, C. A., Kanao, S., et al. (2017). Feasibility and diagnostic performance of fractional flow reserve measurement derived from coronary computed tomography angiography in real clinical practice. The International Journal of Cardiovascular Imaging, 33(2), 271–281.CrossRefGoogle Scholar
  20. 20.
    Khalafvand, S., Zhong, L., & Ng, E. (2014). Three-dimensional CFD/MRI modeling reveals that ventricular surgical restoration improves ventricular function by modifying intraventricular blood flow. International Journal for Numerical Methods in Biomedical Engineering, 30(10), 1044–1056.CrossRefGoogle Scholar
  21. 21.
    Koo, B.-K., Erglis, A., Doh, J.-H., Daniels, D. V., Jegere, S., Kim, H.-S., et al. (2011). Diagnosis of ischemia-causing coronary stenoses by noninvasive fractional flow reserve computed from coronary computed tomographic angiograms: results from the prospective multicenter DISCOVER-FLOW (Diagnosis of ischemia-causing stenoses obtained via noninvasive fractional flow reserve) study. Journal of the American College of Cardiology, 58(19), 1989–1997.CrossRefGoogle Scholar
  22. 22.
    Tu, S., Westra, J., Yang, J., von Birgelen, C., Ferrara, A., et al. (2016). Diagnostic accuracy of fast computational approaches to derive fractional flow reserve from diagnostic coronary angiography: The international multicenter FAVOR pilot study. JACC: Cardiovascular Interventions, 9(19), 2024–2035.Google Scholar
  23. 23.
    Wittek, A., Grosland, N. M., Joldes, G. R., Magnotta, V., & Miller, K. (2016). From finite element meshes to clouds of points: A review of methods for generation of computational biomechanics models for patient-specific applications. Annals of Biomedical Engineering, 44(1), 3–15.CrossRefGoogle Scholar
  24. 24.
    Zhang, J. M., Luo, T., Tan, S. Y., Lomarda, A. M., Wong, A. S. L., et al. (2015). Hemodynamic analysis of patient‐specific coronary artery tree. International Journal for Numerical Methods in Biomedical Engineering, 31(4), e02708.CrossRefGoogle Scholar
  25. 25.
    Wong, K. K., Wang, D., Ko, J. K., Mazumdar, J., Le, T.-T., et al. (2017). Computational medical imaging and hemodynamics framework for functional analysis and assessment of cardiovascular structures. Biomedical Engineering Online, 16(1), 35.Google Scholar
  26. 26.
    Zhang, J.-M., Shuang, D., Baskaran, L., Wu, W., Teo, S.-K., et al. (2018). Advanced analyses of computed tomography coronary angiography can help discriminate ischemic lesions. International Journal of Cardiology, 267(18), 208–214.CrossRefGoogle Scholar
  27. 27.
    Wexelblat, A. (2014). Virtual reality: Applications and explorations. Academic Press.Google Scholar
  28. 28.
    Bush, J. (2008). Viability of virtual reality exposure therapy as a treatment alternative. Computers in Human Behavior, 24(3), 1032–1040.MathSciNetCrossRefGoogle Scholar
  29. 29.
    Fluet, G., Merians, A., Patel, J., Van Wingerden, A., Qiu, Q., et al. (2014). Virtual reality-augmented rehabilitation for patients in sub-acute phase post stroke: A feasibility study. In 10th International Conference on Disability, Virtual Reality & Associated Technologies, Gothenburg, Sweden.Google Scholar
  30. 30.
    Dascal, J., Reid, M., IsHak, W.W., Spiegel, B., Recacho, J., et al. (2017). Virtual reality and medical inpatients: A systematic review of randomized, controlled trials. Innovations in Clinical Neuroscience, 14(1–2), 14.Google Scholar
  31. 31.
    Miloff, A., Lindner, P., Hamilton, W., Reuterskiöld, L., Andersson, G., et al. (2016). Single-session gamified virtual reality exposure therapy for spider phobia vs. traditional exposure therapy: Study protocol for a randomized controlled non-inferiority trial. Trials, 17(1), 60.Google Scholar
  32. 32.
    Hawkins, R. P., Han, J.-Y., Pingree, S., Shaw, B. R., Baker, T. B., & Roberts, L. J. (2010). Interactivity and presence of three eHealth interventions. Computers in Human Behavior, 26(5), 1081–1088.CrossRefGoogle Scholar
  33. 33.
    Garcia, A. P., Ganança, M. M., Cusin, F. S., Tomaz, A., Ganança, F. F., & Caovilla, H. H. (2013). Vestibular rehabilitation with virtual reality in Ménière’s disease. Brazilian Journal of Otorhinolaryngology, 79(3), 366–374.CrossRefGoogle Scholar
  34. 34.
    Cameirao, M. S., Badia, S. B. I., Duarte, E., Frisoli, A., & Verschure, P. F. (2012). The combined impact of virtual reality neurorehabilitation and its interfaces on upper extremity functional recovery in patients with chronic stroke. Stroke, 43(10), 2720–2728.Google Scholar
  35. 35.
    Kim, Y. M., Chun, M. H., Yun, G. J., Song, Y. J., & Young, H. E. (2011). The effect of virtual reality training on unilateral spatial neglect in stroke patients. Annals of Rehabilitation Medicine, 35(3), 309.Google Scholar
  36. 36.
    Subramanian, S. K., Lourenço, C. B., Chilingaryan, G., Sveistrup, H., & Levin, M. F. (2013). Arm motor recovery using a virtual reality intervention in chronic stroke: Randomized control trial. Neurorehabilitation and Neural Repair, 27(1), 13–23.CrossRefGoogle Scholar
  37. 37.
    Nolin, P., Stipanicic, A., Henry, M., Joyal, C. C., & Allain, P. (2012). Virtual reality as a screening tool for sports concussion in adolescents. Brain Injury, 26(13–14), 1564–1573.CrossRefGoogle Scholar
  38. 38.
    Steuperaert, M., Debbaut, C., Segers, P., & Ceelen, W. (2017). Modelling drug transport during intraperitoneal chemotherapy. Pleura and Peritoneum, 2(2), 73–83.CrossRefGoogle Scholar
  39. 39.
    Magdoom, K., Pishko, G. L., Kim, J.H., & Sarntinoranont, M. (2012). Evaluation of a voxelized model based on DCE-MRI for tracer transport in tumor. Journal of Biomechanical Engineering, 134(9), 091004.Google Scholar
  40. 40.
    Kim, M., Gillies, R. J., & Rejniak, K. A. (2013). Current advances in mathematical modeling of anti-cancer drug penetration into tumor tissues. Frontiers in Oncology, 3(11), 278.Google Scholar
  41. 41.
    Pishko, G. L., Astary, G. W., Mareci, T. H., & Sarntinoranont, M. (2011). Sensitivity analysis of an image-based solid tumor computational model with heterogeneous vasculature and porosity. Annals of Biomedical Engineering, 39(9), 2360.Google Scholar
  42. 42.
    Stylianopoulos, T., Martin, J. D., Chauhan, V. P., Jain, S. R., Diop-Frimpong, B., Bardeesy, N., et al. (2012). Causes, consequences, and remedies for growth-induced solid stress in murine and human tumors. Proceedings of the National Academy of Sciences, 109(38), 15101–15108.CrossRefGoogle Scholar
  43. 43.
    Steuperaert, M., Falvo D’Urso Labate, G., Debbaut, C., De Wever, O., Vanhove, C., et al. (2017). Mathematical modeling of intraperitoneal drug delivery: Simulation of drug distribution in a single tumor nodule. Drug Delivery, 24(1), 491–501.CrossRefGoogle Scholar
  44. 44.
    Stylianopoulos, T. (2017). The solid mechanics of cancer and strategies for improved therapy. Journal of Biomechanical Engineering, 139(2), 021004.Google Scholar
  45. 45.
    Winner, K. R. K., Steinkamp, M. P., Lee, R. J., Swat, M., Muller, C. Y., Moses, M. E., et al. (2016). Spatial modeling of drug delivery routes for treatment of disseminated ovarian cancer. Cancer Research, 76(6), 1320–1334.CrossRefGoogle Scholar
  46. 46.
    Zhan, W., Gedroyc, W., & Xu, X. Y. (2014). Effect of heterogeneous microvasculature distribution on drug delivery to solid tumour. Journal of Physics D: Applied Physics, 47(47), 475401.Google Scholar
  47. 47.
    Au, J. L.-S., Guo, P., Gao, Y., Lu, Z., Wientjes, M. G., Tsai, M., et al. (2014). Multiscale tumor spatiokinetic model for intraperitoneal therapy. The AAPS Journal, 16(3), 424–439.CrossRefGoogle Scholar
  48. 48.
    Zhang, Y., Furusawa, T., Sia, S. F., Umezu, M., & Qian, Y. (2013). Proposition of an outflow boundary approach for carotid artery stenosis CFD simulation. Computer Methods in Biomechanics and Biomedical Engineering, 16(5), 488–494.CrossRefGoogle Scholar
  49. 49.
    Tabakova, S., Nikolova, E., & Radev, S. (2014). Carreau model for oscillatory blood flow in a tube. In AIP Conference Proceedings. AIP.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Vishwanath Panwar
    • 1
  • Seshu Kumar Vandrangi
    • 2
  • Sampath Emani
    • 3
    Email author
  • Gurunadh Velidi
    • 4
  • Jaseer Hamza
    • 2
  1. 1.VTU-RRCBelagaviIndia
  2. 2.Department of Mechanical EngineeringUniversiti Teknologi PteronasSeri IskandarMalaysia
  3. 3.Department of Chemical EngineeringUniversiti Teknologi PteronasSeri IskandarMalaysia
  4. 4.University of Petroleum and Energy StudiesDehradunIndia

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