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Deep Brain Stimulation (DBS) has proven its efficiency in the treatment of Parkinson's disease or essential tremor. It requires precise localizations of targets for instance in the thalamus. Since deep brain structures have been shown to be hardly visible on T1 or T2 weighted imaging, most methods rely on atlas based comparison and registration. It is however possible to use direct targeting using a specific MRI sequence called WAIR (White Matter Attenuated Inversion Recovery) even on 1.5 Tesla MRI machine. The direct targeting facilitates the precise segmentation of deep brain structures needed to plan the trajectories of the electrodes for the DBS. But this remains a tedious delineation necessarily done by a neurosurgeon to avoid misinterpretation of the images. In this paper, we propose to build an isotropic super-resolution image for WAIR imaging to facilitate precise direct targeting of anatomical structures in the deep brain. We present a method to perform the reconstruction of a high resolution isotropic WAIR volume from three acquisitions performed on a volunteer subject. The method is based on transfinite interpolation in convex cells of an hyperplane arrangement. Our results show promising quality reconstruction for the computation of a super-resolution WAIR. It allows unambiguous segmentation of the deep brain to be used in DBS surgery.
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Czasopismo
Rocznik
Tom
Strony
41--62
Opis fizyczny
Bibliogr. 26 poz., rys.
Twórcy
autor
- Université Clermont Auvergne CNRS, SIGMA Clermont, Institut Pascal, CHU G. Montpied, F-63000 Clermont-Ferrand, France
autor
- Université Clermont Auvergne CNRS, SIGMA Clermont, Institut Pascal, CHU G. Montpied, F-63000 Clermont-Ferrand, France
Bibliografia
- [1] Guiot G, Derome P. The principles of stereotaxic thalamotomy. Correlative neurosurgery, 1969. pp.376-401.
- [2] Deoni SC, Rutt BK, Parrent AG, Peters TM. Segmentation of thalamic nuclei using a modified k-means clustering algorithm and high-resolution quantitative magnetic resonance imaging at 1.5 T. NeuroImage, 2007;34(1):117-126. doi:10.1016/j.neuroimage.2006.09.016.
- [3] Benabid AL, Koudsie A, Benazzouz A, Le Bas JF, Pollak P. Imaging of subthalamic nucleus and ventralis intermedius of the thalamus. Mov. Disord., 2002;17 Suppl 3:S123-129. URL https://doi.org/10.1002/mds.10153.
- [4] Vassal F, Coste J, Derost P, Mendes V, Gabrillargues J, Nuti C, Durif F, Lemaire JJ. Direct stereotactic targeting of the ventrointermediate nucleus of the thalamus based on anatomic 1.5-T MRI mapping with a white matter attenuated inversion recovery (WAIR) sequence. Brain Stimulation, 2012;5(4):625-633. doi:10.1016/j.brs.2011.10.007.
- [5] Balafar MA, Ramli AR, Saripan MI, Mashohor S. Review of Brain MRI Image Segmentation Methods. Artif. Intell. Rev., 2010;33(3):261-274. doi:10.1007/s10462-010-9155-0.
- [6] Christ MCJ, Parvathi RMS. A Survey on MRI Brain Segmentation. In: Wyld DC, Zizka J, Nagamalai D (eds.), Advances in Computer Science, Engineering & Applications, number 166 in Advances in Intelligent and Soft Computing, pp. 167-177. Springer Berlin Heidelberg, 2012. doi:10.1007/978-3-642-30157-5_18.
- [7] Cabezas M, Oliver A, Lladó X, Freixenet J, Bach Cuadra M. A review of atlas-based segmentation for magnetic resonance brain images. Computer Methods and Programs in Biomedicine, 2011;104(3):158-177. doi:10.1016/j.cmpb.2011.07.015.
- [8] Randrianarivony M. On transfinite interpolations with respect to convex domains. Computer Aided Geometric Design, 2011;28(2):135-149. URL https://doi.org/10.1016/j.cagd.2010.10.003.
- [9] Brant-Zawadzki M, Gillan G, Nitz W. MP RAGE: a three-dimensional, T1-weighted, gradient-echo sequence-initial experience in the brain. Radiology, 1992;182(3):769-775. doi:10.1148/radiology.182.3.1535892.
- [10] Van Steenkiste G, Poot D, Jeurissen B, den Dekker A, Vanhevel F, Parizel P, Sijbers J. Super-resolution T1 estimation: Quantitative high resolution T1 mapping from a set of low resolution T1-weighted images with different slice orientations. Magn Reson Med., 2017;77(5):1818-1830. doi: 10.1117/12.911445.
- [11] Coupé P, Manjón JV, Gedamu E, Arnold D, Robles M, Collins DL. Robust Rician noise estimation for MR images. Medical Image Analysis, 2010;14(4):483-493. URL https://doi.org/10.1016/j.media.2010.03.001.
- [12] Dabov K, Foi A, Katkovnik V, Egiazarian K. Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Transactions on image processing, 2007;16(8):2080-2095. doi:10.1109/TIP.2007.901238.
- [13] Maggioni M, Katkovnik V, Egiazarian K, Foi A. A nonlocal transform-domain filter for volumetric data denoising and reconstruction. IEEE Trans. Image Process., 2013;22(1):119-133.
- [14] Manjón J, Coupé P, Marti-Bonmati L, Robles M, Collins D. Adaptive non-local means denoising of MR images with spatially varying noise levels. J. Magn. Reson. Imaging, 2010;31:192-203. doi:10.1002/jmri.22003.
- [15] Manjón JV, Coupé P, Buades A. MRI noise estimation and denoising using non-local PCA. Medical Image Analysis, 2015;22(1):35-47. doi:10.1016/j.media.2015.01.004.
- [16] Xu L, Lu C, Xu Y, Jia J. Image smoothing via L0 gradient minimization. ACM Transactions on Graphics (TOG), 2011;30(6):174. doi:10.1145/2024156.2024208.
- [17] Romano Y, Elad M. Boosting of image denoising algorithms. SIAM Journal on Imaging Sciences, 2015;8(2):1187-1219. URL https://doi.org/10.1137/140990978.
- [18] Sibson R. A brief description of natural neighbor interpolation. Interpreting multivariate data, 1981. pp.21-36.
- [19] Vuçini E, Möller T, Gröller ME. Efficient reconstruction from non-uniform point sets. The Visual Computer, 2008;24(7-9):555-563. doi:10.1007/s00371-008-0236-x.
- [20] Stanley R. Enumerative Combinatorics 1 (2nd ed.), chapter 3.11 Hyperplane Arrangements. Cambridge University Press, 2011.
- [21] Floater MS, Kós G, Reimers M. Mean value coordinates in 3d. CAGD, 2005;22(7):623-631. URL https://doi.org/10.1016/j.cagd.2005.06.004.
- [22] Getreuer P. Linear Methods for Image Interpolation. Image Processing On Line, 1, 2011. URL https: //doi.org/10.5201/ipol.2011.g_lmii.
- [23] Blu T, Thévenaz P, Unser M. MOMS: Maximal-Order Interpolation of Minimal Support. IEEE Trans.Image Process, 2001;10:1069-1080. doi:10.1109/83.931101.
- [24] Holoborodko P. MPFR C++. Last visited: may, 2018. URL http://www.holoborodko.com/pavel/mpfr.
- [25] Schumaker L. Fitting surfaces to scattered data, pp. 203-262. Approximation Theory II. Academic Press, New York, Lorentz, G.G. and Chui, C. and Schumaker, L. edition, 1976.
- [26] A Algarni D, El hassan IM. Comparison of thin plate spline, polynomial, CIfunction and Shepard’s interpolation techniques with GPS-derived DEM. Intnl J. of Applied Earth Observation and Geoinformation, 2001;3(2):155-161. URL https://doi.org/10.1016/S0303-2434(01)85007-8.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-5cb654dc-4bfb-4bd5-88d5-343848f68a0f
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