Registration of fa and t1-weighted MRI data of healthy human brain based on template matching and normalized cross-correlation
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In this work, we propose a new approach for three-dimensional registration of MR fractional anisotropy images with T1-weighted anatomy images of human brain. From the clinical point of view, this accurate coregistration allows precise detection of nerve fibers that is essential in neuroscience. A template matching algorithm combined with normalized cross-correlation was used for this registration task. To show the suitability of the proposed method, it was compared with the normalized mutual information-based B-spline registration provided by the Elastix software library, considered a reference method. We also propose a general framework for the evaluation of robustness and reliability of both registration methods. Both registration methods were tested by four evaluation criteria on a dataset consisting of 74 healthy subjects. The template matching algorithm has shown more reliable results than the reference method in registration of the MR fractional anisotropy and T1 anatomy image data. Significant differences were observed in the regions splenium of corpus callosum and genu of corpus callosum, considered very important areas of brain connectivity. We demonstrate that, in this registration task, the currently used mutual information-based parametric registration can be replaced by more accurate local template matching utilizing the normalized cross-correlation similarity measure.
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Intermodality registration between dMRI and T1-weighted images can be affected by differences in image resolutions and echo-planar imaging (EPI) distortion in dMRI data.
“For example, the intermodality registration between dMRI and T1-weighted images can be affected by differences in image resolutions (Malinsky et al., 2013) and echo-planar imaging (EPI) distortion in dMRI data (Albi et al., 2018).”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Malinsky et al., 2013
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- Grieve SM, Williams LM, Paul RH, Clark CR, Gordon E: Cognitive aging, executive function, and fractional anisotropy: a diffusion tensor MR imaging study. Am J Neuroradiol 28(2):226- 235, 2007
- Sherbondy A, Akers D, Mackenzie R, Dougherty R, Wandell B: Exploring connectivity of the brain's white matter with dynamic queries. IEEE Trans Vis Comput Graph 11(4):419-430, 2005
- Naik M, Lundervold A, Nygaard H, Geitung JT: Diffusion tensor imaging in dementia patients with frontal lobe symptoms. Acta Radiol 51(6):662-668, 2010
- Archip N, Clatz O, Whalen S, Kacher D, Fedorov A, Kot A, Chrisochoides N, Jolesz F, Golby A, Black PM, Warfield SK: Non-rigid alignment of pre-operative MRI, fMRI, and DT-MRI with intra-operative MRI for enhanced visualization and naviga- tion in image-guided neurosurgery. NeuroImage 35(2):609-624, 2007
- Lee AD, Lepore N, Barysheva M, Chou Y, Brun C, Madsen, SK, McMahon K, de Zubicaray GI, Wright MJ, Toga AW, Thompson PM: Gene effects mapped using fractional and geodesic anisotropy in diffusion tensor images of 92 monozygotic and dizygotic twins. In: Alexander D, Gee J, Whitaker R Eds. Proceedings of the CDMRI'08: Workshop on Computational Diffusion MRI, MICCAI Workshop on Computational Diffusion MRI, New York, United States. University of Pennsylvania 31-40, 2008
- Suárez E, Westin CF, Rovaris E, Ruiz-Alzola J: Nonrigid registra- tion using regularized matching weighted by local structure. In: Proceeding of the MICCAI 2002 2489:581-589, 2002
- Basser P, Mattiello J, Le Bihan D: MR diffusion tensor spectros- copy and imaging. Biophys J 66(1):259-267, 1994
- Hodneland E, Ystad MA, Haasz J, Munthe-Kaas AZ, Lundervold A: Automated workflow for spatial alignment of multimodal MR image acquisitions in a longitudinal study of cognitive aging. In: ASM'10 Proceedings of the 4th International Conference on Applied Mathematics, Simulation, Modelling 2010. Wisconsin, USA, WSEAS Press, 264-269, 2010
- Bozzali M, Parker GJ, Serra L, Embleton K, Gili T, Perri R, Caltagirone C, Cercignani M: Anatomical connectivity mapping: a new tool to assess brain disconnection in Alzheimer's disease. NeuroImage 54(3):2045-2051, 2011
- Li Y, Verma R: Multichannel image registration by feature-based information fusion. IEEE Trans Med Imaging 30(3):707-720, 2011
- Walimuni IS, Abid H, Hasan KM: A computational framework to quantify tissue microstructural integrity using conventional MRI macrostructural volumetry. Comput Biol Med 41(12):1073-1081, 2010
- Jahanshad N, Lee AD, Barysheva M, McMahon KL, de Zubicaray GI, Martin NG, Wright MJ, Toga AW, Thompson PM: Genetic influences on brain asymmetry: a DTI study of 374 twins and siblings. NeuroImage 52(2):455-469, 2010
- Chiang MC, Leow AD, Klunder AD, Dutton RA, Barysheva M, Rose SE, McMahon KL, de Zubicaray GI, Toga AW, Thompson PM: Fluid registration of diffusion tensor images using informa- tion theory. IEEE Trans Med Imaging 27(4):442-456, 2008
- Jin Y, Shi Y, Jahanshad N, Aganj I, Sapiro G, Toga AW, Thompson PM: 3D elastic registration improves HARDI-derived fiber align- ment and automated tract clustering. In: Proceeding of the Biomedical Imaging: Form Nano to Macro, 2011 IEEE International Symposium on, 822-826, 2011
- Klein S, Staring M, Murphy K, Viergever MA, Pluim JP: Elastix: a toolbox for intensity based medical image registration. IEEE Trans Med Imaging 29(1):196-205, 2010
- Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TE, Johansen-Berg H, Bannister PR, De Luca M, Drobnjak I, Flitney DE, Niazy RK, Saunders J, Vickers J, et al: Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage 23:208-219, 2004
- Christensen GE, Kuhl JG, Grabowski TJ, Pirwani IA, Vannier MW, Allen JS, Damasio H: Introduction to the non-rigid image registration evaluation project (NIREP). In: Proceedings of SPIE, WBIR 2006, 128-135, 2006
- Ystad M, Hodneland E, Adolfsdottir S, Haasz J, Lundervold AJ, Eichele T, Lundervold A: Cortico-stricetal connectivity and cog- nition in normal aging: a combined DTI and resting state fMRI study. NeuroImage 55(1):24-31, 2011
- Ystad M, Eichele T, Lundervold AJ, Lundervold A: Subcortical functional connectivity and verbal episodic memory in healthy el- derly-a resting state fMRI study. NeuroImage 52(1):379-388, 2010
- Desikan RS, Segonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ: An automated labeling system for sub- dividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage 31(3):968-980, 2006
- Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, van der Kouwe A, Killiany R, Kennedy D, Klaveness S, Montillo A, Makris N, Rosen B, Dale AM: Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33(3):341-355, 2002
- Ségonne F, Dale AM, Busa E, Glessner M, Salat D, Hahn HK, Fischl B: Hybrid approach to the skull-stripping problem in MRI. NeuroImage 22(3):1160-1175, 2004
- Windyga PS: Fast impulsive noise removal. IEEE Trans Image Proc 10(1):173-179, 2001
- Modersitzki J: Numerical Methods for Image Registration. Oxford: Oxford University Press, 2004
- Sun H, Yushkevich PA, Zhang H, Cook PA, Duda JT, Simon TJ, Gee JC: Shape-based normalization of the corpus callosum for DTI connectivity analysis. IEEE Trans Med Imaging 26(9):1166-1178, 2007
- Anderson RF, Kirtzic JS, Daescu O: Applying parallel design techniques to template matching with GPUs. in: Jose M, Laginha M, Palma MD, Osni M, Joao CL Eds. Proceedings of the 9th international conference on high performance computing for com- putational science (VECPAR'10). Berlin/Heidelberg: Springer, 2010, pp 456-468
- Bican J, Flusser J: 3D rigid registration by cylindrical phase correlation method. Pattern Recognit Lett 30(10):914-921, 2009
- Authors
- Malinsky, M., Peter, R., Hodneland, E., Lundervold, A. J., Lundervold, A., & Jan, J.
- Affiliations
- University of Bergen (NO)
- Haukeland University Hospital (NO)
- Venue
- Journal of Digital Imaging
- Published
- 2013-01-04
- Volume
- 26
- Issue
- 4
- Pages
- 774-785
- ISSN
- 0897-1889 (ISSN-L)
- 1618-727X
- PMC
- PMC3705011
- OpenAlex
- W2073111135
- Cited by
- 33
- References
- 33
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 1 · 2023: 4 · 2022: 4 · 2021: 3 · 2020: 1 · 2019: 2 · 2018: 5 · 2017: 1 · 2016: 1 · 2015: 4 · 2014: 1
- FWCI
- 2.25
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Journal of Imaging Informatics in Medicine (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Norges Forskningsråd (rcn-foriss-202807)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- fractional anisotropy, T1-weighted imaging, template matching, normalized cross-correlation, three-dimensional image registration, corpus callosum, white matter fiber tracking
- MeSH terms
- Aged, Algorithms, Brain, Brain Mapping, Humans, Image Interpretation, Computer-Assisted, Image Processing, Computer-Assisted, Longitudinal Studies, Magnetic Resonance Imaging, Reference Values, Software, Reproducibility of Results, Anisotropy, Imaging, Three-Dimensional
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