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

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.

[1]  Mitsuo Umezu,et al.  Proposition of an outflow boundary approach for carotid artery stenosis CFD simulation , 2013, Computer methods in biomechanics and biomedical engineering.

[2]  Patrick Segers,et al.  Mathematical modeling of intraperitoneal drug delivery: simulation of drug distribution in a single tumor nodule , 2017, Drug delivery.

[3]  S. Keevil,et al.  Selecting a CT scanner for cardiac imaging: the heart of the matter. , 2016, The British journal of radiology.

[4]  Robert P. Hawkins,et al.  Interactivity and presence of three eHealth interventions , 2010, Comput. Hum. Behav..

[5]  Thomas H. Mareci,et al.  Sensitivity Analysis of an Image-Based Solid Tumor Computational Model with Heterogeneous Vasculature and Porosity , 2011, Annals of Biomedical Engineering.

[6]  Triantafyllos Stylianopoulos,et al.  Causes, consequences, and remedies for growth-induced solid stress in murine and human tumors , 2012, Proceedings of the National Academy of Sciences.

[7]  Jimmy Bush,et al.  Viability of virtual reality exposure therapy as a treatment alternative , 2008, Comput. Hum. Behav..

[8]  Jung Hwan Kim,et al.  Evaluation of a voxelized model based on DCE-MRI for tracer transport in tumor. , 2012, Journal of biomechanical engineering.

[9]  L. Zhong,et al.  Three‐dimensional CFD/MRI modeling reveals that ventricular surgical restoration improves ventricular function by modifying intraventricular blood flow , 2014, International journal for numerical methods in biomedical engineering.

[10]  J. Reiber,et al.  Diagnostic Accuracy of Fast Computational Approaches to Derive Fractional Flow Reserve From Diagnostic Coronary Angiography: The International Multicenter FAVOR Pilot Study. , 2016, JACC. Cardiovascular interventions.

[11]  J. Gunn,et al.  Computational fluid dynamics modelling in cardiovascular medicine , 2015, Heart.

[12]  Liang Zhong,et al.  Three-Dimensional Tricuspid Annular Motion Analysis from Cardiac Magnetic Resonance Feature-Tracking , 2016, Annals of Biomedical Engineering.

[13]  Wenbo Zhan,et al.  Effect of heterogeneous microvasculature distribution on drug delivery to solid tumour , 2014 .

[14]  Robert J. Gillies,et al.  Current Advances in Mathematical Modeling of Anti-Cancer Drug Penetration into Tumor Tissues , 2013, Front. Oncol..

[15]  Matthias Gutberlet,et al.  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 , 2015, European heart journal.

[16]  Claudio Chiastra,et al.  Biomechanical Modeling to Improve Coronary Artery Bifurcation Stenting: Expert Review Document on Techniques and Clinical Implementation. , 2015, JACC. Cardiovascular interventions.

[17]  I. Danovitch,et al.  Virtual Reality and Medical Inpatients: A Systematic Review of Randomized, Controlled Trials. , 2017, Innovations in clinical neuroscience.

[18]  J Belinha,et al.  Meshless Methods: The Future of Computational BiomechanicalSimulation , 2016 .

[19]  Vinh-Tan Nguyen,et al.  A semi-automated method for patient-specific computational flow modelling of left ventricles , 2015, Computer methods in biomechanics and biomedical engineering.

[20]  Liang Zhong,et al.  Two-dimensional intraventricular flow pattern visualization using the image-based computational fluid dynamics , 2017, Computer methods in biomechanics and biomedical engineering.

[21]  A. Dunning,et al.  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. , 2011, Journal of the American College of Cardiology.

[22]  G. Andersson,et al.  Single-session gamified virtual reality exposure therapy for spider phobia vs. traditional exposure therapy: study protocol for a randomized controlled non-inferiority trial , 2016, Trials.

[23]  Geraldine Bessie Amali D,et al.  A Survey: Virtual Reality Model for Medical Diagnosis , 2018, Biomedical and Pharmacology Journal.

[24]  Vicente Grau,et al.  3D reconstruction of coronary arteries from 2D angiographic projections using non-uniform rational basis splines (NURBS) for accurate modelling of coronary stenoses , 2018, PloS one.

[25]  D. Comaniciu,et al.  A machine-learning approach for computation of fractional flow reserve from coronary computed tomography. , 2016, Journal of applied physiology.

[26]  Karol Miller,et al.  From Finite Element Meshes to Clouds of Points: A Review of Methods for Generation of Computational Biomechanics Models for Patient-Specific Applications , 2015, Annals of Biomedical Engineering.

[27]  Alan Wexelblat,et al.  Virtual reality applications and explorations , 1993 .

[28]  Liang Zhong,et al.  Advanced analyses of computed tomography coronary angiography can help discriminate ischemic lesions. , 2018, International journal of cardiology.

[29]  Nasser Fatouraee,et al.  The impact of valve simplifications on left ventricular hemodynamics in a three dimensional simulation based on in vivo MRI data. , 2016, Journal of biomechanics.

[30]  Siamak N. Doost,et al.  Heart blood flow simulation: a perspective review , 2016, Biomedical engineering online.

[31]  H. Howie Huang,et al.  Computational modeling of cardiac hemodynamics: Current status and future outlook , 2016, J. Comput. Phys..

[32]  Fernando Freitas Ganança,et al.  Vestibular rehabilitation with virtual reality in Ménière's disease , 2013, Brazilian journal of otorhinolaryngology.

[33]  Charles A. Taylor,et al.  Feasibility and diagnostic performance of fractional flow reserve measurement derived from coronary computed tomography angiography in real clinical practice , 2017, The International Journal of Cardiovascular Imaging.

[34]  Liang Zhong,et al.  Patient-specific blood flows and vortex formations in patients with hypertrophic cardiomyopathy using computational fluid dynamics , 2014, 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES).

[35]  T. Stylianopoulos The Solid Mechanics of Cancer and Strategies for Improved Therapy. , 2017, Journal of biomechanical engineering.

[36]  Defeng Wang,et al.  Computational medical imaging and hemodynamics framework for functional analysis and assessment of cardiovascular structures , 2017, BioMedical Engineering OnLine.

[37]  Alejandro F. Frangi,et al.  Numerical simulation of blood flow in the left ventricle and aortic sinus using magnetic resonance imaging and computational fluid dynamics , 2014, Computer methods in biomechanics and biomedical engineering.

[38]  S. Tabakova,et al.  Carreau model for oscillatory blood flow in a tube , 2014 .

[39]  Mindy F Levin,et al.  Arm Motor Recovery Using a Virtual Reality Intervention in Chronic Stroke , 2013, Neurorehabilitation and neural repair.

[40]  Jan Vierendeels,et al.  Patient-specific CFD models for intraventricular flow analysis from 3D ultrasound imaging: Comparison of three clinical cases. , 2017, Journal of biomechanics.

[41]  Min Ho Chun,et al.  The Effect of Virtual Reality Training on Unilateral Spatial Neglect in Stroke Patients , 2011, Annals of rehabilitation medicine.

[42]  Patrick Segers,et al.  Modelling drug transport during intraperitoneal chemotherapy , 2017, Pleura and peritoneum.

[43]  Kimberly R. Kanigel Winner,et al.  Spatial Modeling of Drug Delivery Routes for Treatment of Disseminated Ovarian Cancer. , 2016, Cancer research.

[44]  Antonio Frisoli,et al.  The Combined Impact of Virtual Reality Neurorehabilitation and Its Interfaces on Upper Extremity Functional Recovery in Patients With Chronic Stroke , 2012, Stroke.

[45]  Philippe Allain,et al.  Virtual reality as a screening tool for sports concussion in adolescents , 2012, Brain injury.

[46]  Liang Zhong,et al.  Hemodynamic analysis of patient‐specific coronary artery tree , 2015, International journal for numerical methods in biomedical engineering.

[47]  Franck Nicoud,et al.  Image-based large-eddy simulation in a realistic left heart , 2014 .

[48]  Ze Lu,et al.  Multiscale Tumor Spatiokinetic Model for Intraperitoneal Therapy , 2014, The AAPS Journal.