| List |
| Topic: | Analýza pokročilých MRI dat u pacientů s postižením centrální nervové soustavy - porovnání a testování vhodných algoritmů |
|---|---|
| Department: | Katedra kybernetiky |
| Supervisor: | doc. Ing. Lenka Lhotská, CSc. |
| Announce as: | Diplomová práce, Semestrální projekt |
| Description: | Navrhněte vhodný postup analýzy obrazové informace (segmentace mozkové a okolní tkáně, korekce artefaktů, registrace obrazů) z MRI. Použijte a porovnejte dostupné softwarové nástroje pro vybrané kroky analýzy. |
| Bibliography: | Brown, R.W., Cheng, Y.-C.N., Haacke, E.M., Thompson, M.R., Venkatesan, R., 2014. Classical Response of a Single Nucleus to a Magnetic Field, in: Magnetic Resonance Imaging. John Wiley & Sons, Ltd, pp. 19–36. https://doi.org/10.1002/9781118633953.ch2
Gurney-Champion, O.J., Klaassen, R., Froeling, M., Barbieri, S., Stoker, J., Engelbrecht, M.R.W., Wilmink, J.W., Besselink, M.G., Bel, A., van Laarhoven, H.W.M., Nederveen, A.J., 2018. Comparison of six fit algorithms for the intra-voxel incoherent motion model of diffusion-weighted magnetic resonance imaging data of pancreatic cancer patients. PLoS One 13. https://doi.org/10.1371/journal.pone.0194590 Jesper L.R. Andersson, Stefan Skare, John Ashburner, How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging, NeuroImage, Volume 20, Issue 2, 2003, Pages 870-888, ISSN 1053-8119, https://doi.org/10.1016/S1053-8119(03)00336-7. Kingsley, P.B., 2006a. Introduction to diffusion tensor imaging mathematics: Part I. Tensors, rotations, and eigenvectors. Concepts in Magnetic Resonance Part A 28A, 101–122. https://doi.org/10.1002/cmr.a.20048 Kingsley, P.B., 2006b. Introduction to diffusion tensor imaging mathematics: Part II. Anisotropy, diffusion‐weighting factors, and gradient encoding schemes. Concepts in Magnetic Resonance Part A 28A, 123–154. https://doi.org/10.1002/cmr.a.20049 Kingsley, P.B., 2006c. Introduction to diffusion tensor imaging mathematics: Part III. Tensor calculation, noise, simulations, and optimization. Concepts in Magnetic Resonance Part A 28A, 155–179. https://doi.org/10.1002/cmr.a.20050 Le Bihan, D., 2017. What can we see with IVIM MRI? NeuroImage. https://doi.org/10.1016/j.neuroimage.2017.12.062 Yang, A., Wang Ai-ling and Jincai Chang, "The research on parallel least squares curve fitting algorithm," 2009 International Conference on Test and Measurement, Hong Kong, 2009, pp. 201-204. |