Abstract
While 7T diffusion magnetic resonance imaging (dMRI) has high spatial resolution, its diffusion imaging quality is usually affected by signal loss due to B1 inhomogeneity, T2 decay, susceptibility, and chemical shift. In contrast, 3T dMRI has relative higher diffusion angular resolution, but lower spatial resolution. Combination of 3T and 7T dMRI, thus, may provide more detailed and accurate information about the voxel-wise fiber orientations to better understand the structural brain connectivity. However, this topic has not yet been thoroughly explored until now. In this study, we explored the feasibility of fusing 3T and 7T dMRI data to extract voxel-wise quantitative parameters at higher spatial resolution. After 3T and 7T dMRI data was preprocessed, respectively, 3T dMRI volumes were coregistered into 7T dMRI space. Then, 7T dMRI data was harmonized to the coregistered 3T dMRI B0 (b = 0) images. Last, harmonized 7T dMRI data was fused with 3T dMRI data according to four fusion rules proposed in this study. We employed high-quality 3T and 7T dMRI datasets (N = 24) from the Human Connectome Project to test our algorithms. The diffusion tensors (DTs) and orientation distribution functions (ODFs) estimated from the 3T-7T fused dMRI volumes were statistically analyzed. More voxels containing multiple fiber populations were found from the fused dMRI data than from 7T dMRI data set. Moreover, extra fiber directions were extracted in temporal brain regions from the fused dMRI data at Otsu’s thresholds of quantitative anisotropy, but could not be extracted from 7T dMRI dataset. This study provides novel algorithms to fuse intra-subject 3T and 7T dMRI data for extracting more detailed information of voxel-wise quantitative parameters, and a new perspective to build more accurate structural brain networks.
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Data Availability
The data and code that support the findings of this study are openly available at the following URL: https://github.com/freedom1979/7T-dMRI.
References
Cao R, Wang X, Gao Y, Li T, Zhang H, Hussain W, Xie Y, Wang J, Wang B, Xiang J (2020) Abnormal Anatomical Rich-Club Organization and structural-functional coupling in mild cognitive impairment and Alzheimer’s Disease. Front Neurol 11:53. https://doi.org/10.3389/fneur.2020.00053
Celtikci P, Fernandes-Cabral DT, Yeh FC, Panesar SS, Fernandez-Miranda JC (2018) Generalized q-sampling imaging fiber tractography reveals displacement and infiltration of fiber tracts in low-grade gliomas. Neuroradiology 60:267–280. https://doi.org/10.1007/s00234-018-1985-5
Cetin Karayumak S, Bouix S, Ning L, James A, Crow T, Shenton M, Kubicki M, Rathi Y (2019) Retrospective harmonization of multi-site diffusion MRI data acquired with different acquisition parameters. NeuroImage 184:180–200. https://doi.org/10.1016/j.neuroimage.2018.08.073
Chiang CW, Lin SY, Cho KH, Wu KJ, Wang Y, Kuo LW (2019) Effects of signal averaging, gradient encoding scheme, and spatial resolution on diffusion kurtosis imaging: an empirical study using 7T MRI. J Magn Reson Imaging 50:1593–1603. https://doi.org/10.1002/jmri.26755
Daianu M, Jahanshad N, Nir TM, Jack CR Jr, Weiner MW, Bernstein MA, Thompson PM, Alzheimer’s Disease Neuroimaging Initiative (2015a) Rich club analysis in the Alzheimer’s disease connectome reveals a relatively undisturbed structural core network. Hum Brain Mapp 36:3087–3103. https://doi.org/10.1002/hbm.22830
Daianu M, Jahanshad N, Villalon-Reina JE, Prasad G, Jacobs RE, Barners S, Zlokovic BV, Montagne A, Thompson PM (2015b) 7T Multi-shell Hybrid Diffusion Imaging (HYDI) for mapping Brain Connectivity in mice. Proc SPIE Int Soc Opt Eng 9413:941309. https://doi.org/10.1117/12.2081491
Damoiseaux JS (2017) Effects of aging on functional and structural brain connectivity. NeuroImage 160:32–40. https://doi.org/10.1016/j.neuroimage.2017.01.077
Descoteaux M, Angelino E, Fitzgibbons S, Deriche R (2007) Regularized, fast, and robust analytical Q-ball imaging. Magn Reson Med 58:497–510. https://doi.org/10.1002/mrm.21277
Diwakar M, Singh P, Shankar A (2021) Multi-modal medical image fusion framework using co-occurrence filter and local extrema in NSST domain. Biomed Signal Process Control 68:102788. https://doi.org/10.1016/j.bspc.2021.102788
Du J, Li W, Lu K, Xiao B (2016) An overview of multi-modal medical image fusion. Neurocomputing 215:3–20. https://doi.org/10.1016/j.neucom.2015.07.160
Fan Q, Nummenmaa A, Polimeni JR, Witzel T, Huang SY, Wedeen VJ, Rosen BR, Wald LL (2017) HIgh b-value and high Resolution Integrated Diffusion (HIBRID) imaging. NeuroImage 150:162–176. https://doi.org/10.1016/j.neuroimage.2017.02.002
Fang F, Potter T, Nguyen T, Zhang Y (2020) Dynamic reorganization of the cortical functional brain network in affective processing and cognitive reappraisal. Int J Neural Syst 30:2050051. https://doi.org/10.1142/S0129065720500513
Fang F, Gao Y, Schulz PE, Selvaraj S, Zhang Y (2021) Brain controllability distinctiveness between depression and cognitive impairment. J Affect Disord 294:847–856. https://doi.org/10.1016/j.jad.2021.07.106
Gulban OF, De Martino F, Vu AT, Yacoub E, Ugurbil K, Lenglet C (2018) Cortical fibers orientation mapping using in-vivo whole brain 7T diffusion MRI. NeuroImage 178:104–118. https://doi.org/10.1016/j.neuroimage.2018.05.010
Hasan KM, Yamada K (2021) Overview of Diffusion Tensor, Diffusion Kurtosis, and Q-space imaging and Software Tools. Magn Reson Imaging Clin N Am 29:263–268. https://doi.org/10.1016/j.mric.2021.02.003
Hirsiger S, Koppelmans V, Merillat S, Liem F, Erdeniz B, Seidler RD, Jancke L (2016) Structural and functional connectivity in healthy aging: associations for cognition and motor behavior. Humman Brain Mapp 37:855–867. https://doi.org/10.1002/hbm.23067
Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM (2012) FSL Neuroimage 62:782–790. https://doi.org/10.1016/j.neuroimage.2011.09.015
Kleinnijenhuis M, van Mourik T, Norris DG, Ruiter DJ, van Cappellen AM, Barth M (2015) Diffusion tensor characteristics of gyrencephaly using high resolution diffusion MRI in vivo at 7T. NeuroImage 109:378–387. https://doi.org/10.1016/j.neuroimage.2015.01.001
Kraff O, Quick HH (2017) 7T: physics, safety, and potential clinical applications. J Magn Reson Imaging 46:1573–1589. https://doi.org/10.1002/jmri.25723
Le Bihan D, Mangin JF, Poupon C, Clark CA, Pappata S, Molko N, Chabriat H (2001) Diffusion tensor imaging: concepts and applications. J Magn Reson Imaging 13:534–546. https://doi.org/10.1002/jmri.1076
Lutzkendorf R, Heidemann RM, Feiweier T, Luchtmann M, Baecke S, Kaufmann J, Stadler J, Budinger E, Bernarding J (2018) Mapping fine-scale anatomy of gray matter, white matter, and trigeminal-root region applying spherical deconvolution to high-resolution 7-T diffusion MRI. MAGMA 31:701–713. https://doi.org/10.1007/s10334-018-0705-9
Mirzaalian H, Ning L, Savadjiev P, Pasternak O, Bouix S, Michailovich O, Grant G, Marx CE, Morey RA, Flashman LA, George MS, McAllister TW, Andaluz N, Shutter L, Coimbra R, Zafonte RD, Coleman MJ, Kubicki M, Westin CF, Stein MB, Shenton ME, Rathi Y (2016) Inter-site and inter-scanner diffusion MRI data harmonization. NeuroImage 135:311–323. https://doi.org/10.1016/j.neuroimage.2016.04.041
Mirzaalian H, Ning L, Savadjiev P, Pasternak O, Bouix S, Michailovich O, Karmacharya S, Grant G, Marx CE, Morey RA, Flashman LA, George MS, McAllister TW, Andaluz N, Shutter L, Coimbra R, Zafonte RD, Coleman MJ, Kubicki M, Westin CF, Stein MB, Shenton ME, Rathi Y (2018) Multi-site harmonization of diffusion MRI data in a registration framework. Brain Imaging Behav 12:284–295. https://doi.org/10.1007/s11682-016-9670-y
O’Halloran R, Feldman R, Marcuse L, Fields M, Delman B, Frangou S, Balchandani P (2017) A method for u-fiber quantification from 7 T diffusion-weighted MRI data tested in patients with nonlesional focal epilepsy. NeuroReport 28:457–461. https://doi.org/10.1097/WNR.0000000000000788
Obusez EC, Lowe M, Oh SH, Wang I, Jennifer Bullen, Ruggieri P, Hill V, Lockwood D, Emch T, Moon D, Loy G, Lee J, Kiczek M, Manoj Massand, Statsevych V, Stultz T, Jones SE (2018) 7T MR of intracranial pathology: preliminary observations and comparisons to 3T and 1.5T. NeuroImage 168:459–476. https://doi.org/10.1016/j.neuroimage.2016.11.030
Ozarslan E, Shepherd TM, Vemuri BC, Blackband SJ, Mareci TH (2006) Resolution of complex tissue microarchitecture using the diffusion orientation transform (DOT). NeuroImage 31:1086–1103. https://doi.org/10.1016/j.neuroimage.2006.01.024
Qu L, Zhang Y, Wang S, Yap PT, Shen D (2020) Synthesized 7T MRI from 3T MRI via deep learning in spatial and wavelet domains. Med Image Anal 62:101663. https://doi.org/10.1016/j.media.2020.101663
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, Zhang Y, De Stefano N, Brady JM, Matthews PM (2004) Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage 23:S208–S219. https://doi.org/10.1016/j.neuroimage.2004.07.051
Sotiropoulos SN, Jbabdi S, Xu J, Andersson JL, Moeller S, Auerbach EJ, Glasser MF, Hernandez M, Sapiro G, Jenkinson M, Feinberg DA, Yacoub E, Lenglet C, Van Essen DC, Ugurbil K, Behrens TE, WU-Minn HCP Consortium (2013) Advances in diffusion MRI acquisition and processing in the human Connectome Project. NeuroImage 80:125–143. https://doi.org/10.1016/j.neuroimage.2013.05.057
Sotiropoulos SN, Hernandez-Fernandez M, Vu AT, Andersson JL, Moeller S, Yacoub E, Lenglet C, Ugurbil K, Behrens TEJ, Jbabdi S (2016) Fusion in diffusion MRI for improved fibre orientation estimation: an application to the 3T and 7T data of the human Connectome Project. NeuroImage 134:396–409. https://doi.org/10.1016/j.neuroimage.2016.04.014
Speck O, Tempelmann C (2010) Human 7T MRI: first clinical and neuroscientific applications. Neuroradiol J 23:535–546. https://doi.org/10.1177/197140091002300503
Supekar K, Uddin LQ, Prater K, Amin H, Greicius MD, Menon V (2010) Development of functional and structural connectivity within the default mode network in young children. NeuroImage 52:290–301. https://doi.org/10.1016/j.neuroimage.2010.04.009
Tournier JD, Calamante F, Connelly A (2007) Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution. NeuroImage 35:1459–1472. https://doi.org/10.1016/j.neuroimage.2007.02.016
Tournier JD, Smith R, Raffelt D, Tabbara R, Dhollander T, Pietsch M, Christiaens D, Jeurissen B, Yeh CH, Connelly A (2019) MRtrix3: a fast, flexible and open software framework for medical image processing and visualisation. NeuroImage 202:116137. https://doi.org/10.1016/j.neuroimage.2019.116137
Vu AT, Auerbach E, Lenglet C, Moeller S, Sotiropoulos SN, Jbabdi S, Anderson J, Yacoub E, Ugurbil K (2015) High resolution whole brain diffusion imaging at 7T for the human Connectome Project. NeuroImage 122:318–331. https://doi.org/10.1016/j.neuroimage.2015.08.004
Wu Z, Peng Y, Selvaraj S, Schulz PE, Zhang Y (2020) Development of Brain Structural Networks over Age 8: a preliminary study based on Diffusion Weighted Imaging. Front Aging Neurosci 12:61. https://doi.org/10.3389/fnagi.2020.00061
Wu Z, Gao Y, Potter T, Benoit J, Shen J, Schulz PE, Zhang Y, Alzheimer’s Disease Neuroimaging Initiative (2021) Interactions between aging and Alzheimer’s Disease on Structural Brain Networks. Front Aging Neurosci 13:639795. https://doi.org/10.3389/fnagi.2021.639795
Yang J, Jiang X, Wei S, Deng X, Zhu Y, Chang M, Yin Z, Geng H, Tang Y, Dai X (2021) White matter tracts in bipolar disorder patients: a comparative study based on diffusion kurtosis and tensor imaging. J Affect Disord 292:45–55. https://doi.org/10.1016/j.jad.2021.05.030
Yeh FC, Wedeen VJ, Tseng WY (2010) Generalized q-sampling imaging. IEEE Trans Med Imaging 29:1626–1635. https://doi.org/10.1109/TMI.2010.2045126
Zhang X, Zhao H, Li X, Feng Y, Li H (2017) A multi-scale 3D Otsu thresholding algorithm for medical image segmentation. Digit Signal Proc 60:186–199. https://doi.org/10.1016/j.dsp.2016.08.003
Acknowledgements
The research was supported by Natural Science Foundation of Zhejiang Province (LY20E070005 and LY17E070007), China, National Natural Science Foundation of China (51207038), and the University of Houston. We acknowledge support from the Human Connectome Project (1U54MH091657-01), from the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research, Biotechnology Research Center (BTRC) grant (P41 EB015894) from NIBIB, NIH, and NINDS Institutional Center Core Grants to Support Neuroscience Research (P30NS076408). Also funding from the UK EPSRC (EP/L023067/1) and UK MRC (MR/L009013/1). Members of the WU-Minn HCP Consortium are listed at http://www.humanconnectome.org/about/hcp-investigators.html and http://www.humanconnectome.org/about/hcp-colleagues.html. Intra-subject dMRI datasets used in this study correspond to the following HCP subject: 126426, 130114, 130518, 134627, 135124, 146735, 165436, 167440, 177140, 180533, 239136, 360030, 385046, 401422, 463040, 550439, 644246, 757764, 765864, 878877, 905147, 943862, 971160, 995174.
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Z.W proposed the fusion algorithms, and drafted the article. X.W created the tables and Figures. J.S contributed to the writing of the article, including its critical review. M.H revised the manuscript and supervised this study. All authors approved the final form of the article.
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Wu, Z., Weng, X., Shen, J. et al. Voxel-Wise Fusion of 3T and 7T Diffusion MRI Data to Extract more Accurate Fiber Orientations. Brain Topogr (2024). https://doi.org/10.1007/s10548-024-01046-2
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DOI: https://doi.org/10.1007/s10548-024-01046-2