Virtual reality

Virtual Augmented Reality and Simulation (VARS)

Warren Viant
Faculty of Science and Engineering
Mr Warren Viant
Senior Lecturer

The Challenge

We focus on the application of augmented, virtual and mixed reality, simulation and visualization environments to genuine end-user problems, and the new tools, techniques and theories needed for their construction. Our particular strengths in underpinning technologies stem from work on novel systems and algorithms for modelling, simulation and rendering, and expertise in building, validating and integrating innovative display modalities.

The Approach

The strategy for ensuring a reputable and sustainable future for the group is to carry out evidence-based, fundamental computer science research that is grounded in the real world of users’ needs and aspirations. 



Our impact is both theoretical and application oriented. An exemplar is the team’s theoretical framework on high-precision and efficient geometric reconstruction of human’s vascular system, part of which has been used in a wide range of applications, including MIPAV.

We have developed discipline-leading applications in radiotherapy training, radiological interventions, and training systems in general.  VERT - Virtual Environment for Radiotherapy Training, became the foundation of a successful company, Vertual Ltd.  Watch our video here.

VIC (Virtual Incident Command) and LIMA (Large Incident Multiple Agency) software applications, developed in conjunction with Humberside Fire and Rescue Service (HFR) and North Staffordshire Fire and Rescue Services (NSFRS), deliver training for multi-level emergency incidents.

virtual reality


  • To develop new algorithms for precise and efficient geometrical reconstruction from a range of 2D and 3D imaging modalities.
  • To apply virtual, augmented and mixed reality expertise to new and innovative application areas.
  • To explore the use of virtual, augmented and mixed reality within the clinical setting for diagnosis, treatment and training.



Analysing maxillofacial growth using 3D medical image analysis

Wellcome Trust funded project, in collaboration with Dundee Dental Hospital and Dundee University.

Allam Medical Building Midwifery Training Facilities UNI-0968

Orthopaedic surgical training system

In collaboration with Northern Lincolnshire and Goole NHS Foundation Trust.


Application of GPU based Monte Carlo and Lattice Boltzmann physics simulations

View all projects
  • Wellcome Trust funded project analysing maxillofacial growth using 3D medical image analysis in collaboration with Dundee Dental Hospital and Dundee University. Accurate and robust 3D volume and surface land-marking has been developed to measure the external changes in soft tissues using CT, MRI and photogrammetry imaging modalities

  • The i-BIT project, where Virtual Reality and video games combine to provide a potential treatment for Amblyopia (Lay Eye) in children. The team in collaboration with Nottingham University and NHS Trust have secured both Wellcome Trust and Innovation for Industrial funding, completing two successful sets of clinical trials.

  • An assessment tool to measure and analyse the driving habits and reaction times of older drivers with chronic heart failure, was developed in collaboration with Hull and East Yorkshire NHS Trust.

  • An orthopaedic surgical training system, to build key procedure specific navigation skills, has been developed in collaboration with Northern Lincolnshire and Goole NHS Foundation Trust.

  • The application of GPU based Monte Carlo and Lattice Boltzmann physics simulations linked to computer graphics for special effects such as smoke and fire.


Outputs and publications
  1. Pinheiro, M., Ma, X., Fagan, M. J., McIntyre, G. T., Lin, P., Sivamurthy, G., & Mossey, P. A. A 3D cephalometric protocol for the accurate quantification of the craniofacial symmetry and facial growth. Journal of Biological Engineering 2019; 13(1):42.
  2. Ma X, Martin CB, McIntyre GT, Lin P, Mossey PA. Digital three-dimensional automation of the modified Huddart and Bodenham scoring system for patients with cleft lip and palate. Cleft Palate- Craniofacial Journal 2017; 54 (4), 481-486.
  3. Martin CB, Ma X, McIntyre GT, Wang W, Lin P, Chalmers EV, Mossey PA. The validity and reliability of an automated method of scoring dental arch relationships in unilateral cleft lip and palate using the modified Huddart-Bodenham scoring system. European Journal of Orthodontics 2016; 38(4): 353-358.
  4. Mao Q, Liu S, Wang S, Ma X. Surface Fitting for Quasi Scattered Data from Coordinate Measuring Systems. Sensors. 2018; 13;18(1):214.
  5. Qingde Li, Jie Tian, Multilevel refinable triangular PSP-splines (Tri-PSPS), Computers & Mathematics with Applications, Volume 70, Issue 8, 2015, Pages 1781-1798, ISSN 0898-1221,
  6. QI, Q. LI and Q. HONG, "Skeleton Marching: A High-performance Parallel Vascular Geometry Reconstruction Technique," 2018 24th International Conference on Automation and Computing (ICAC), Newcastle upon Tyne, United Kingdom, 2018, pp. 1-6, doi: 10.23919/IConAC.2018.8749008.
  7. Hong, Q., Li, Q., Wang, B. et al. 3D vasculature segmentation using localized hybrid level-set method. BioMed Eng OnLine 13, 169 (2014).
  8. Hong, Q. Li, B. Wang, K. Liu and Q. Qi, "High Precision Implicit Modeling for Patient-Specific Coronary Arteries," in IEEE Access, vol. 7, pp. 72020-72029, 2019, doi: 10.1109/ACCESS.2019.2920113.
  9. Herbison N, MacKeith D, Vivian A, Purdy J, Fakis A, Ash IM, Cobb SV, Eastgate RM, Haworth SM, Gregson RM, Foss AJ. Randomised controlled trial of video clips and interactive games to improve vision in children with amblyopia using the I-BiT system. Br J Ophthalmol. 2016 Nov;100(11):1511-1516. doi: 10.1136/bjophthalmol-2015-307798. Epub 2016 Mar 7. PMID: 26951772; PMCID: PMC5136691.
  10. Rambani R, Ward J, Viant W. Desktop-based computer-assisted orthopedic training system for spinal surgery. J Surg Educ. 2014 Nov-Dec;71(6):805-9. doi: 10.1016/j.jsurg.2014.04.012. Epub 2014 Jun 23. PMID: 24969310.
  11. Zeng, X. Ma, B. Cheng, E. Zhou and W. Pang, "GANs-Based Data Augmentation for Citrus Disease Severity Detection Using Deep Learning," in IEEE Access, vol. 8, pp. 172882-172891, 2020, doi: 10.1109/ACCESS.2020.3025196.
  12. S. S. Rana, X. Ma, W. Pang and E. Wolverson, "A Multi-Modal Deep Learning Approach to the Early Prediction of Mild Cognitive Impairment Conversion to Alzheimer’s Disease," 2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT), Leicester, United Kingdom, 2020, pp. 9-18, doi: 10.1109/BDCAT50828.2020.00013.
  13. Han Z, Wang Y, Ma X, Liu S, Zhang X, Zhang G. T-Spline Based Unifying Registration Procedure for Free-Form Surface Workpieces in Intelligent CMM. Applied Sciences 2017; 7 (10), 1092.
  14. Ma, X., Keates, , Jiang, Y., Konsinka, J., 2015. Subdivision surface fitting to a dense mesh using ridges and umbilics, Computer Aided Geometric Design, 32(1), 5-21.
  15. Daniel Pan, Pierpaolo Pellicori, Claire Walklett, Andrew Green, Anais R. Masse, Jason Wood, Jon Purdy, Andrew L. Clark, Driving Habits and Reaction Times on a Driving Simulation in Older Drivers With Chronic Heart Failure, Journal of Cardiac Failure, Volume 26, Issue 7, 2020, Pages 555-563, ISSN 1071-9164.
Research Students

Eman Gholoum

“Artificial Intelligence in Diagnosing Mental Health Disorders”

Xinhui Ma

Lee Smith

“Monte-Carlo Simulations of Two-Dimensional Electron Gasses in Gallium Nitride High Electron Mobility Transistors via General-Purpose Computing on Graphics Processing Units”

Warren Viant and Angela Dyson


Our research impacts the world. Come and join us.

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