Improving Estimation of Fiber Orientations in Diffusion MRI Using Inter-Subject Information Sharing
Diffusion magnetic resonance imaging is widely used to investigate diffusion patterns of water molecules in the human brain. It provides information that is useful for tracing axonal bundles and inferring brain connectivity. Diffusion axonal tracing, namely tractography, relies on local directional...
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| Published in | Scientific reports Vol. 6; no. 1; p. 37847 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
28.11.2016
Nature Publishing Group |
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| Online Access | Get full text |
| ISSN | 2045-2322 2045-2322 |
| DOI | 10.1038/srep37847 |
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| Abstract | Diffusion magnetic resonance imaging is widely used to investigate diffusion patterns of water molecules in the human brain. It provides information that is useful for tracing axonal bundles and inferring brain connectivity. Diffusion axonal tracing, namely tractography, relies on local directional information provided by the orientation distribution functions (ODFs) estimated at each voxel. To accurately estimate ODFs, data of good signal-to-noise ratio and sufficient angular samples are desired. This is however not always available in practice. In this paper, we propose to improve ODF estimation by using inter-subject image correlation. Specifically, we demonstrate that diffusion-weighted images acquired from different subjects can be transformed to the space of a target subject to drastically increase the number of angular samples to improve ODF estimation. This is largely due to the incoherence of the angular samples generated when the diffusion signals are reoriented and warped to the target space. To reorient the diffusion signals, we propose a new spatial normalization method that directly acts on diffusion signals using local affine transforms. Experiments on both synthetic data and real data show that our method can reduce noise-induced artifacts, such as spurious ODF peaks, and yield more coherent orientations. |
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| AbstractList | Diffusion magnetic resonance imaging is widely used to investigate diffusion patterns of water molecules in the human brain. It provides information that is useful for tracing axonal bundles and inferring brain connectivity. Diffusion axonal tracing, namely tractography, relies on local directional information provided by the orientation distribution functions (ODFs) estimated at each voxel. To accurately estimate ODFs, data of good signal-to-noise ratio and sufficient angular samples are desired. This is however not always available in practice. In this paper, we propose to improve ODF estimation by using inter-subject image correlation. Specifically, we demonstrate that diffusion-weighted images acquired from different subjects can be transformed to the space of a target subject to drastically increase the number of angular samples to improve ODF estimation. This is largely due to the incoherence of the angular samples generated when the diffusion signals are reoriented and warped to the target space. To reorient the diffusion signals, we propose a new spatial normalization method that directly acts on diffusion signals using local affine transforms. Experiments on both synthetic data and real data show that our method can reduce noise-induced artifacts, such as spurious ODF peaks, and yield more coherent orientations. Diffusion magnetic resonance imaging is widely used to investigate diffusion patterns of water molecules in the human brain. It provides information that is useful for tracing axonal bundles and inferring brain connectivity. Diffusion axonal tracing, namely tractography, relies on local directional information provided by the orientation distribution functions (ODFs) estimated at each voxel. To accurately estimate ODFs, data of good signal-to-noise ratio and sufficient angular samples are desired. This is however not always available in practice. In this paper, we propose to improve ODF estimation by using inter-subject image correlation. Specifically, we demonstrate that diffusion-weighted images acquired from different subjects can be transformed to the space of a target subject to drastically increase the number of angular samples to improve ODF estimation. This is largely due to the incoherence of the angular samples generated when the diffusion signals are reoriented and warped to the target space. To reorient the diffusion signals, we propose a new spatial normalization method that directly acts on diffusion signals using local affine transforms. Experiments on both synthetic data and real data show that our method can reduce noise-induced artifacts, such as spurious ODF peaks, and yield more coherent orientations.Diffusion magnetic resonance imaging is widely used to investigate diffusion patterns of water molecules in the human brain. It provides information that is useful for tracing axonal bundles and inferring brain connectivity. Diffusion axonal tracing, namely tractography, relies on local directional information provided by the orientation distribution functions (ODFs) estimated at each voxel. To accurately estimate ODFs, data of good signal-to-noise ratio and sufficient angular samples are desired. This is however not always available in practice. In this paper, we propose to improve ODF estimation by using inter-subject image correlation. Specifically, we demonstrate that diffusion-weighted images acquired from different subjects can be transformed to the space of a target subject to drastically increase the number of angular samples to improve ODF estimation. This is largely due to the incoherence of the angular samples generated when the diffusion signals are reoriented and warped to the target space. To reorient the diffusion signals, we propose a new spatial normalization method that directly acts on diffusion signals using local affine transforms. Experiments on both synthetic data and real data show that our method can reduce noise-induced artifacts, such as spurious ODF peaks, and yield more coherent orientations. |
| ArticleNumber | 37847 |
| Author | Yap, Pew-Thian Li, Ke Zhang, Pei Shen, Dinggang Chen, Geng Wee, Chong-Yaw Wu, Yafeng |
| Author_xml | – sequence: 1 givenname: Geng surname: Chen fullname: Chen, Geng organization: Data Processing Center, Northwestern Polytechnical University, Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill – sequence: 2 givenname: Pei surname: Zhang fullname: Zhang, Pei organization: Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill – sequence: 3 givenname: Ke surname: Li fullname: Li, Ke organization: Fundamental Science on Ergonomics and Environment Control Laboratory, Beihang University – sequence: 4 givenname: Chong-Yaw surname: Wee fullname: Wee, Chong-Yaw organization: Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill – sequence: 5 givenname: Yafeng surname: Wu fullname: Wu, Yafeng organization: Data Processing Center, Northwestern Polytechnical University – sequence: 6 givenname: Dinggang surname: Shen fullname: Shen, Dinggang organization: Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Department of Brain and Cognitive Engineering, Korea University – sequence: 7 givenname: Pew-Thian surname: Yap fullname: Yap, Pew-Thian organization: Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/27892534$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1038_s41598_017_13247_w crossref_primary_10_1109_TMI_2019_2911203 crossref_primary_10_3389_fninf_2018_00057 crossref_primary_10_1002_mrm_28810 crossref_primary_10_1016_j_media_2019_06_010 crossref_primary_10_1016_j_neuroimage_2018_08_072 crossref_primary_10_1016_j_neuroimage_2021_118451 crossref_primary_10_1016_j_media_2019_01_006 |
| Cites_doi | 10.1002/mrm.21916 10.1109/42.963816 10.1523/JNEUROSCI.4136-10.2010 10.1109/TMI.2012.2204766 10.1109/TMI.2014.2320947 10.1007/978-3-642-33454-2_59 10.1007/s00330-009-1483-6 10.1007/978-3-319-28588-7_13 10.1007/978-3-319-46726-9_68 10.1016/j.neuroimage.2016.04.041 10.1137/040616024 10.1016/j.neuroimage.2008.10.040 10.1016/j.neuroimage.2011.10.015 10.1002/mrm.20279 10.1007/978-3-540-85990-1_15 10.1109/MSP.2010.936775 10.1016/j.media.2003.12.001 10.1371/journal.pone.0024678 10.1007/978-3-540-75759-7_42 10.1006/nimg.2001.0861 10.1109/TMI.2007.907552 10.1109/TMI.2007.900461 10.1016/j.neuroimage.2016.01.061 10.1016/j.neuroimage.2010.12.015 10.1002/1531-8249(199902)45:2<265::AID-ANA21>3.0.CO;2-3 10.1109/ISBI.2009.5193327 10.1016/j.neuroimage.2012.05.026 10.1016/j.neuroimage.2010.10.026 10.1002/mrm.20582 10.1002/mrm.23058 10.1016/j.neuroimage.2004.07.037 10.1109/TMI.2007.906087 10.1016/j.neuroimage.2014.01.009 |
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| References_xml | – reference: VercauterenTPennecXPerchantAAyacheNDiffeomorphic demons: Efficient non-parametric image registrationNeuroImage200945S61S7210.1016/j.neuroimage.2008.10.040 – reference: Chen, G. et al. XQ-NLM: Denoising diffusion MRI data via x-q space non-local patch matching. 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| Title | Improving Estimation of Fiber Orientations in Diffusion MRI Using Inter-Subject Information Sharing |
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