Impact of Simulation on MRI Physicists
DOI:
https://doi.org/10.59992//IJSR.2023.v2n7p6الكلمات المفتاحية:
C++، C، CUDA، Java، Krubach's Alpha Method، Bloch Equations، Fourier Transformsالملخص
MRI is a very complex imaging modality in radiography. Although some simulators have been developed for training purposes, we are not sure about any of them. In this research, we tried to measure the performance of different simulations for educational purposes and then quantitatively evaluate them.
With this paper, our study deals with an evaluative review of previous studies related to MRI simulators, which are designed to perform specific functions to help the medical engineer managing radiographic operations to better learn the theoretical and practical concepts of MRI from others. Traditional education, which is a method based on reviewing previous studies, searching for strengths and weaknesses in simulators and possible alternatives to them, and then providing recommendations for future studies that can be conducted to switch to packaging simulation programs, which are virtual laboratories that we hope will be the most efficient simulation devices and models used. Currently in MRI engineer qualification.
المراجع
1. Edelman, R.R. The history of MR imaging as seen through the pages of radiology. Radiology 2014, 273, S181–S200. [CrossRef]
2. Jorritsma, W.; Cnossen, F.; Dierckx, R.A.; Oudkerk, M.; Ooijen, P.M.V. Post-deployment usability evaluation of a radiology workstation. Int. J.Med. Inform. 2016, 85, 28–35. [CrossRef]
3. Hanson, L.G. A graphic simulator for teaching basic and advanced MR imaging techniques. Radiographics 2007, 27, e27. [CrossRef] [PubMed]
4. McKagan, S.; Perkins, K.K.; Dubson, M.; Malley, C.; Reid, S.; LeMaster, R.; Wieman, C. Developing and researching PhET simulations for teaching quantum mechanics. Am. J. Phys 2008, 76, 406–417. [CrossRef]
5. Hackländer, T.; Mertens, H. Virtual MRI: A PC-based simulation of a clinical MR scanner. Acad. Radiol. 2005, 12, 85–96. [CrossRef] [PubMed]
6. Torheim, G.; Rinck, P.A.; Jones, R.A.; Kvaerness, J. A simulator for teaching MR image contrast behavior. Magn. Reason. mater. Phys. Biol. Med. 1994, 2, 515–522. [CrossRef]
7. Liu, F.; Kijowski, R.; Block, W. MRiLab: performing fast 3D parallel MRI numerical simulation on a simple PC. In Proceedings of the ISMRM Scientific Meeting & Exhibition, Salt Lake City, UT, USA, 20–26 April 2013; Volume 2072.
8. Liu, F.; Velikina, J.V.; Block, W.F.; Kijowski, R.; Samsonov, A.A. Fast Realistic MRI Simulations Based on Generalized Multi-Pool Exchange tissue model. IEEE Trans. Med. Imaging 2017, 36, 527–537. [CrossRef]
9. Stocker, T.; Vahedipour, K.; Pflugfelder, D.; Shah, N.J. High-performance computing MRI simulations. Magn. Reason. Med. 2010, 64, 186–193. [CrossRef]
10. Layton, K.J.; Kroboth, S.; Jia, F.; Littin, S.; Yu, H.; Leupold, J.; Nielsen, J.F.; Stocker, T.; Zaitsev, M. Pulseq: a rapid and hardware-independent pulse sequence prototyping framework. Magn. Reason. Med. 2017, 77, 1544–1552. [CrossRef]
11. Fortin, A.; Salmon, S.; Baruthio, J.; Delbany, M.; Durand, E. Flow MRI simulation in complex 3D geometries: application to the cerebral venous network. Magn. Reason. Med. 2018, 80, 1655–1665. [CrossRef]
12. Benoit-Cattin, H.; Collewet, G.; Belarussi, B.; Saint-Jalmes, H.; Odette, C.L. The SIMRI project: A versatile and interactive MRI simulator. J.Magn. Reason. 2005, 173, 97–115. [CrossRef]
13. Xanthis, C.G.; Oannis E. Venetis.; Chalkias, A.; Aletras, A. MRISIMUL: A GPU-Based Parallel Approach to MRI Simulations. IEEE Trans. Med. Imaging 2014, 33, 607–617. [CrossRef]
14. Cao, Z.; Oh, S.; Sica, C.T.; McGarrity, J.M.; Horan, T.; Luo, W.; Collins, C.M. Bloch-based MRI system simulator considering Realistic electromagnetic fields for calculation of signal, noise, and specific absorption rate. Magn. Reason. Med. 2014, 72, 237–247. [CrossRef]
15. Kose, R.; Kose, K. BlochSolver: A GPU-optimized fast 3D MRI simulator for experimentally compatible pulse sequences. J.Magn. Reason. 2017, 281, 51–65. [CrossRef]
16. Jochimsen, T.H.; von Mengershausen, M. ODIN: Object-oriented development interface for NMR. J.Magn. Reason. 2004, 170, 67–78. [. [CrossRef]
17. Drobnjak, I.; Gavaghan, D.; Suli, E.; Pitt-Francis, J.; Jenkinson, M. Development of a functional magnetic resonance imaging simulator for modeling realistic rigid-body motion artifacts. Magn. Reason. Med. 2006, 56, 364–380. [CrossRef]
18. Graham, M.S.; Drobnjak, I.; Zhang, H. Realistic simulation of artefacts in diffusion MRI for validating post-processing correction techniques. NeuroImage 2016, 125, 1079–1094. [CrossRef] [PubMed]
19. Graham, M.S.; Drobnjak, I.; Jenkinson, M.; Zhang, H. Quantitative assessment of the susceptibility artefact and its interaction with motion in diffusion MRI. PLoS ONE 2017, 12, e0185647. [CrossRef] [PubMed]
20. Klepaczko, A.; Szczypi ´nski, P.; Dwojakowski, G.; Strzelecki, M.; Materka, A. Computer simulation of magnetic resonance Angiography imaging: model description and validation. PLoS ONE 2014, 9, e93689. [CrossRef] [PubMed]
21. Treceño-Fernandez, D.; Calabia-del Campo, J.; Bote-Lorenzo, M.L.; Sánchez, E. G.; de Luis-García, R.; Alberola-Lopez, C. A Web-Based Educational Magnetic Resonance Simulator: Design, Implementation, and Testing. J.Med. Syst. 2020, 44, 9. [CrossRef]
22. Treceño-Fernandez, D.; Calabia-del Campo, J.; Bote-Lorenzo, M.L.; Gómez-Sánchez, E.; de Luis-García, R.; Alberola-Lopez, C. Integration of an intelligent tutoring system in a magnetic resonance simulator for education: Technical feasibility and user experience. Comput. Methods Programs Biomed. 2020, 195, 105634. [CrossRef]
23. Burch, C. Django: A Web Framework Using Python: Tutorial Presentation. J. Comput. Sci. Colleges 2010, 25, 154–155.
24. Anderson, C. The Model-View-ViewModel (MVVM) Design Pattern. In Pro Business Applications with Silverlight 5; Express: Berkeley, CA, USA, 2012; pp. 461–499.
25. Liang, Z.-P.; Lauterbur, P.C. Principles of Magnetic Resonance Imaging: A Signal Processing Perspective; IEEE Press Series on Biomedical Engineering; IEEE: New York, NY, USA, 2000.
26. Bernstein, M.A.; King, K.F.; Zhou, X.J. Handbook of MRI pulse sequences; Academic Press: Burlington, NJ, USA, 2004.
27. Fraenkel, J.R.; Wallen, N.E.; Hyun, H.H. How to Design and Evaluate Research in Education, 8th ed.; McGraw-Hill Education: New York, NY, USA, 2011; p. 704.
28. Kuder, G.F.; Richardson, M.W. The theory of the estimation of test reliability. Psychometrika 1937, 2, 151–160. [CrossRef]
29. Cronbach, L.J. Coefficient alpha and the internal structure of tests. Psychometrika 1951, 16, 297–334. [CrossRef]
30. Royston, J.P. Algorithm AS 181: The W Test for Normality. J.R. Stat. Soc. Ser. C (Appl. Stat.) 1982, 31, 176–180. [CrossRef]
31. Torchiano, M. Effsize: Efficient Effect Size Computation; R package version 0.7.4; ResearchGate: Berlin, Germany, 2018.
32. Tavakol, M.; Dennick, R. Making sense of Cronbach's alpha. Int. J.Med. Educ. 2011, 2, 53–55. [CrossRef]
33. Frey, B.B. The SAGE Encyclopedia of Educational Research, Measurement, and Evaluation; SAGE Publications, Inc.: Thousand Oaks, CA, USA, 2018.
34. Ramos-Llorden, G.; Arnold, J.; Van Steenkiste, G.; Jeurissen, B.; Vanhevel, F.; Van Audekerke, J.; Verhoye, M.; Sibers, J. A unified maximum likelihood framework for simultaneous motion and T1 estimation in quantitative MR T1 mapping. IEEE Trans. Med. Imaging 2016, 36, 433–446. [CrossRef]
35. Ramos-Llordén, G.; Vegas-Sánchez-Ferrero, G.; Björk, M.; Vanhevel, F.; Parizel, P.M.; Estépar, R.S.J.; Arnold, J.; Sibers, J. NOVIFAST: A fast algorithm for accurate and precise VFA MRI T1 mapping. IEEE Trans. Med. Imaging 2018, 37, 2414–2427. [CrossRef]
