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Development of a novel 3D immersive visualisation tool for manual image matching

Published online by Cambridge University Press:  02 May 2019

B. Byrd ,
M. Warren ,
J. Fenwick  and
P. Bridge Open the ORCID record for P. Bridge [Opens in a new window]
B. Byrd
Affiliation:
Institute of Translational Medicine, University of Liverpool, Liverpool L69 3GB, UK
M. Warren
Affiliation:
Institute of Translational Medicine, University of Liverpool, Liverpool L69 3GB, UK
J. Fenwick
Affiliation:
Institute of Translational Medicine, University of Liverpool, Liverpool L69 3GB, UK
P. Bridge*
Affiliation:
Institute of Translational Medicine, University of Liverpool, Liverpool L69 3GB, UK
*
Author for correspondence: P. Bridge, University of Liverpool, Brownlow Hill, Liverpool L69 3GB, UK. Tel: +44(0)1517958366. E-mail: pete.bridge@liverpool.ac.uk
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Abstract

Aim:

The novel Volumetric Image Matching Environment for Radiotherapy (VIMER) was developed to allow users to view both computed tomography (CT) and cone-beam CT (CBCT) datasets within the same 3D model in virtual reality (VR) space. Stereoscopic visualisation of both datasets combined with custom slicing tools and complete freedom in motion enables alternative inspection and matching of the datasets for image-guided radiotherapy (IGRT).

Material and methods:

A qualitative study was conducted to explore the challenges and benefits of VIMER with respect to image registration. Following training and use of the software, an interview session was conducted with a sample group of six university staff members with clinical experience in image matching.

Results:

User discomfort and frustration stemmed from unfamiliarity with the drastically different input tools and matching interface. As the primary advantage, the users reported match inspection efficiency when presented with the 3D volumetric renderings of the planning and secondary CBCT datasets.

Findings:

This study provided initial evidence for the achievable benefits and limitations to consider when implementing a 3D voxel-based dataset comparison VR tool including a need for extensive training and the minimal interruption to IGRT workflow. Key advantages include efficient 3D anatomical interpretation and the capability for volumetric matching.

Keywords

IGRTimage registration3Dvirtual reality
Type
Original Article
Information
Journal of Radiotherapy in Practice , Volume 18 , Issue 4 , December 2019 , pp. 318 - 322
Copyright
© Cambridge University Press 2019 

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References

Hector, C L, Webb, S, Evans, P M. The dosimetric consequences of inter-fractional patient movement on conventional and intensity-modulated breast radiotherapy treatments. Radiother Oncol. 2000; 54: 57–64.10.1016/S0167-8140(99)00167-XCrossRefGoogle ScholarPubMed
Barker, J L, Garden, A S, Ang, K K. Quantification of volumetric and geometric changes occurring during fractionated radiotherapy for head-and-neck cancer using an integrated CT/linear accelerator system. Int J Radiat Oncol Biol Phys. 2004; 59: 60–70.10.1016/j.ijrobp.2003.12.024CrossRefGoogle ScholarPubMed
Becker-Schiebe, M, Abac, A, Ahmad, T, Hoffmann, W. Reducing radiation-associated toxicity using online image guidance (IGRT) in prostate cancer patients undergoing dose-escalated radiation therapy. Rep Pract Oncol Radiother. 2016; 21(3): 188–194.10.1016/j.rpor.2016.01.005CrossRefGoogle ScholarPubMed
Barney, B M, Lee, R J, Handrahan, D, Welsh, K T, Cook, J T, Sause, W T. Image-guided radiotherapy (IGRT) for prostate cancer comparing kV imaging of fiducial markers with cone beam computed tomography (CBCT). Int J Radiat Oncol Biol Phys 2011; 80: 301–305.10.1016/j.ijrobp.2010.06.007CrossRefGoogle Scholar
Schulze, D, Liang, J, Yan, D, Zhang, T. Comparison of various online IGRT strategies: the benefits of online treatment plan re-optimization. Radiother Oncol. 2009; 90: 367–376.10.1016/j.radonc.2008.08.012CrossRefGoogle ScholarPubMed
Cui, Y, Galvin, J M, Straube, W L, et al. Multi-system verification of registrations for image-guided radiotherapy in clinical trials. Int J Radiat Oncol Biol Phys. 2011; 81(1): 305–312.10.1016/j.ijrobp.2010.11.019CrossRefGoogle ScholarPubMed
Barber, J, Sykes, JR, Holloway, L, Thwaites, D I. Automatic image registration performance for two different CBCT systems; variation with imaging dose. J Phys Conf Ser. 2014; 489: 012070–3.10.1088/1742-6596/489/1/012070CrossRefGoogle Scholar
Society and College of Radiographers. (2012). Image guided radiotherapy (IGRT): guidance for implementation and use. National Radiotherapy Implementation Group Report 2012. London: SCOR.Google Scholar
Kaplan, B. Evaluating informatics applications—some alternative approaches: theory, social interactionism, and call for methodological pluralism. Int J Med Informat. 2001; 64(1): 39–56.10.1016/S1386-5056(01)00184-8CrossRefGoogle ScholarPubMed
Heathfield, H, Pitty, D, Hanka, R. Evaluating information technology in health care: barriers and challenges. Brit Med J. 1998; 316(7149): 1959–1961.10.1136/bmj.316.7149.1959CrossRefGoogle ScholarPubMed
Stewart, D W, Shamdasani, P N. Focus Groups: Theory and Practice, Volume 20. Thousand Oaks: Sage, 2014.Google Scholar
Naismith, L M, Cheung, J J, Ringsted, C, Cavalcanti, R B. Limitations of subjective cognitive load measures in simulation-based procedural training. Med Educ. 2015; 49(8): 805–814.10.1111/medu.12732CrossRefGoogle ScholarPubMed
Tolley, E E, Ulin, P R, Mack, N, Robinson, E T, Succop, S M. Qualitative Methods in Public Health: A Field Guide for Applied Research. Hoboken: Wiley, 2016.Google Scholar
Eyal, R, Tendick, F. Spatial ability and learning the use of an angled laparoscope in a virtual environment. Stud Health Technol Inform. 2001; 81: 146–152.Google Scholar
Wheeler, G, Deng, S, Toussaint, N et al. Virtual interaction and visualisation of 3D medical imaging data with VTK and unity. Health Techn Lett. 2018; 5(5): 148–153.10.1049/htl.2018.5064CrossRefGoogle ScholarPubMed
Seo, J H, Smith, B M, Cook, M et al. Anatomy builder VR: applying a constructive learning method in the virtual reality canine skeletal system. In Proceedings, International Conference on Applied Human Factors and Ergonomics 2017. Berlin: Springer, 2017.Google Scholar
Shinomiya, A, Shindo, A, Kawanishi, M et al. Technical notes & surgical techniques: usefulness of the 3D virtual visualization surgical planning simulation and 3D model for endoscopic endonasal transsphenoidal surgery of pituitary adenoma: technical report and review of literature. Interdiscp Neurosurg. 2018; 13: 13–19.10.1016/j.inat.2018.02.002CrossRefGoogle Scholar
Mohamoud, G, Ryan, M, Moseley, D. Inter-observer variability in cone beam CT image matching amongst radiation therapists: a departmental Initiative. J Med Imag Radiat Sci. 2015; 46(1): S8.10.1016/j.jmir.2015.01.028CrossRefGoogle Scholar