Interactive CT/MRI 3D Fusion for cerebral system analysis and as a preoperative surgical strategy and educational tool
Michał Chlebiej 1  
,   Andrzej Rutkowski 1  
,   Anna Żurada 2  
,   Jerzy Gielecki 3  
,   Katarzyna Polak-Boroń 3  
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Faculty of Mathematics and Computer Science, Nicolaus Copernicus University in Toruń, Poland
Department of Radiology, Collegium Medicum, School of Medicine, University of Warmia and Mazury, Olsztyn, Poland
Department of Anatomy, Collegium Medicum, School of Medicine, University of Warmia and Mazury, Olsztyn, Poland
Katarzyna Polak-Boroń   

Jagiellońska 52/11, 10-283 Olsztyn, Poland. Tel.:+48 889261947.
Submission date: 2021-11-02
Final revision date: 2021-11-29
Acceptance date: 2021-11-29
Online publication date: 2021-12-20
The development of systems that merge existing technologies with gathered data may bring some spectacular effects that are usable both in preoperative and educational processes. Augmented reality (AR) is one of the key aspects of the new medical approach. Newly fused data sets draw from it and give users a better overall experience.

The main goal of this study was to enable the interactive presentation of patients’ CT and MRI combined data with the incorporation of AR tools considering the accuracy of the data with an emphasis on vascular structures.

Material and methods:
The registration method, reconstruction of the vascular system using tubular structures, and error analysis using surface distance measurements results were used in the system to provide accurate combined information about bony structures from CT volume and vascular objects and cerebral vessels from MRI.

Results and discussion:
The strategies concern a series of CTI volumes that could be used to analyze bony surgical procedures. The methods are preferred, especially to the most complicated and individually modified bony structures of the skull. Removing, replacing, or modifying these bony structures or elements of the skull could be used as an analysis of operating procedures at the particular stages of the operation during neurosurgical or otolaryngological techniques.

Presented study regarded to an innovative system consisting of a CT and MRI datasets fusion. The distance analysis of the segmented vascular model and proposed method for stabilization of the human head combined with virtual sculpting technique. In conclusion, it was meaningful in many aspects of the scientific-technological merge.

This research was partially supported by the Polish National Center (grant No. 2012/07/D/ST6/02479).
None declared.
Inoue HK, Nakajima A, Sato H, Noda S, Saitoh J, Suzuki Y. Image Fusion for Radiosurgery, Neurosurgery and Hypofractionated Radiotherapy. Cureus. 2015;7(3):e252.
Maes F, Vandermeulen D, Suetens P. Medical image registration using mutual information. Proc IEEE. 2003;91(10):1699–1721.
Pol EJD, Viergever MH. Medical Image Matching – A Review with Classification. IEEE Eng Med Biol Mag. 1993;12(1):26–39.
Sahu S, Pati UC. Intensity-based registration of medical images. Int J Comput Vis Robot. 2016;6(4):319–330.
Bavirisetti DP, Kollu V, Gang X, Dhuli R. Fusion of MRI and CT images using guided image filter and image statistics. Int J Imaging Syst Technol. 2017;27(3):227–237.
Lesage D, Angelini ED, Bloch I, Funka-Lea G. A review of 3D vessel lumen segmentation techniques: Models, features and extraction schemes. Med Image Anal. 2009;13(6):819–845.
Piccinelli M, Veneziani A, Steinman DA, Remuzzi A, Antiga L. A framework for geometric analysis of vascular structures: Application to cerebral aneurysms. IEEE Trans Med Imaging. 2009;28(8):1141–1155.
Aylward S, Pace D, Enquobahrie A, McCormick M, Mullins C, Goodlett C, Reynolds P. Tube TK, segmentation, registration, and analysis of tubular structure in images. Clifton Park 2012.
Hamza A Ben, He Y, Krim H, Willsky A. A multiscale approach to pixel-level image fusion. Integr Comput Aided Eng. 2005;12(2):135–146.
Li H, Manjunath Bs. Multisensor-Image-Fusion-Using-the-Wavelet-Transform_1995_Graphical-Models-and-Image-Processing.pdf. Graph Model Image Process. 1995;57(3):235–245.
Petrovic VS, Xydeas PC. Optimising Multiresolution Pixel-level Image Fusion. Proc SPIE. 2001;4385:96–107.
James AP, Dasarathy B V. Medical image fusion: A survey of the state of the art. Inf Fusion. 2014;19(1):4–19.
Du J, Li W, Lu K, Xiao B. An overview of multi-modal medical image fusion. Neurocomputing. 2016;215:3–20.
Perez J, Mazo C, Trujillo M, Herrera A. Mri and ct fusion in stereotactic electroencephalography: A literature review. Appl Sci. 2021;11(12):5524.
Sasikala M, Kumaravel N. A comparative analysis of feature based image fusion methods. Inf Technol J. 2007;6(8):1224–1230.
Tao Q, Veldhuis R. Threshold-optimized decision-level fusion and its application to biometrics. Pattern Recognit. 2009;42(5):823–836. doi:10.1016/j.patcog.2008.09.036.
Nowinski WL, Volkau I, Marchenko Y, Thirunavuukarasuu A, Ng TT, Runge VM. A 3D model of human cerebrovasculature derived from 3T magnetic resonance angiography. Neuroinformatics. 2009;7(1):23–36.
Press WH, Teukolsky SA, Vettering WT, Flannery BP. NUMERICAL RECIPES The Art of Scientific Computing Third Edition. Cambridge Univ Press. 2007.
Garrido-Jurado S, Muñoz-Salinas R, Madrid-Cuevas FJ, Marín-Jiménez MJ. Automatic generation and detection of highly reliable fiducial markers under occlusion. Pattern Recognit. 2014;47(6):2280–2292.
Serra J. Image analysis and mathematical morphology. Computer Graphics and Image Processing.1982;20:96–97.
Huang X, Qian Z, Huang R, Metaxas D. Deformable-Model Based Textured Object Segmentation. In: Rangarajan A, Vemuri B, Yuille AL, eds. Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2005. Lecture Notes in Computer Science, vol 3757. Springer. 2005
Mystkowska D, Tutas A, Jezierska-Woźniak K, Mikołajczyk A, Bobek-Billewicz B, Jurkowski M. Usefulness of clinical magnetic resonance scanners for imaging experimental changes in laboratory rodents’ central nervous system. Pol Ann Med. 2012;19(1):43–49.
Mystkowska D, Tutasa A, Jezierska-Woniar K, Mikołajczyka A, Bobek-Billewicz B, Jurkowski MK. High resolution small animals dedicated magnetic resonance scanners as a tool for laboratory rodents central nervous system imaging. Pol Ann Med. 2013;20(1):62–68.
Kainz W. A Review of: “The Design and Analysis of Spatial Data Structures”. By H. SAMET. (Addison-Wesley Publishing Company, Inc., 1989.) Int J Geogr Inf Syst. 1991;5(2):253–253.