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DC Field | Value | Language |
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dc.citation.endPage | 227 | - |
dc.citation.startPage | 220 | - |
dc.citation.title | MAGNETIC RESONANCE IMAGING | - |
dc.citation.volume | 62 | - |
dc.contributor.author | Nath, Vishwesh | - |
dc.contributor.author | Schilling, Kurt G. | - |
dc.contributor.author | Parvathaneni, Prasanna | - |
dc.contributor.author | Hansen, Colin B. | - |
dc.contributor.author | Hainline, Allison E. | - |
dc.contributor.author | Huo, Yuankai | - |
dc.contributor.author | Blaber, Justin A. | - |
dc.contributor.author | Lyu, Ilwoo | - |
dc.contributor.author | Janve, Vaibhav | - |
dc.contributor.author | Gao, Yurui | - |
dc.contributor.author | Stepniewska, Iwona | - |
dc.contributor.author | Anderson, Adam W. | - |
dc.contributor.author | Landman, Bennett A. | - |
dc.date.accessioned | 2023-12-21T18:36:58Z | - |
dc.date.available | 2023-12-21T18:36:58Z | - |
dc.date.created | 2021-03-05 | - |
dc.date.issued | 2019-10 | - |
dc.description.abstract | Purpose: Diffusion-weighted magnetic resonance imaging (DW-MRI) is of critical importance for characterizing in-vivo white matter. Models relating microarchitecture to observed DW-MRI signals as a function of diffusion sensitization are the lens through which DW-MRI data are interpreted. Numerous modem approaches offer opportunities to assess more complex intra-voxel structures. Nevertheless, there remains a substantial gap between intra-voxel estimated structures and ground truth captured by 3-D histology. Methods: Herein, we propose a novel data-driven approach to model the non-linear mapping between observed DW-MRI signals and ground truth structures using a sequential deep neural network regression using residual block deep neural network (ResDNN). Training was performed on two 3-D histology datasets of squirrel monkey brains and validated on a third. A second validation was performed using scan-rescan datasets of 12 subjects from Human Connectome Project. The ResDNN was compared with multiple micro-structure reconstruction methods and super resolved-constrained spherical deconvolution (sCSD) in particular as baseline for both the validations. Results: Angular correlation coefficient (ACC) is a correlation/similarity measure and can be interpreted as accuracy when compared with a ground truth. The median ACC of ResDNN is 0.82 and median ACC's of different variants of CSD are 0.75, 0.77, 0.79. The mean, median and std. of ResDNN & sCSD ACC across 12 subjects from HCP are 0.74, 0.88, 0.31 and 0.61, 0.71, 0.31 respectively. Conclusion: This work highlights the ability of deep learning to capture linkages between ex-vivo ground truth data with feasible MRI sequences. The data-driven approach is applicable to human in-vivo data and results in intriguingly high reproducibility of orientation structure. | - |
dc.identifier.bibliographicCitation | MAGNETIC RESONANCE IMAGING, v.62, pp.220 - 227 | - |
dc.identifier.doi | 10.1016/j.mri.2019.07.012 | - |
dc.identifier.issn | 0730-725X | - |
dc.identifier.scopusid | 2-s2.0-85069697248 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/50102 | - |
dc.identifier.wosid | 000481725200026 | - |
dc.language | 영어 | - |
dc.publisher | ELSEVIER SCIENCE INC | - |
dc.title | Deep learning reveals untapped information for local white-matter fiber reconstruction in diffusion-weighted MRI | - |
dc.type | Article | - |
dc.description.isOpenAccess | FALSE | - |
dc.relation.journalWebOfScienceCategory | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.relation.journalResearchArea | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | DW-MRI | - |
dc.subject.keywordAuthor | HARDI | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Spherical harmonics | - |
dc.subject.keywordAuthor | Histology | - |
dc.subject.keywordAuthor | Ground truth | - |
dc.subject.keywordPlus | PRINCIPAL EIGENVECTOR MEASUREMENTS | - |
dc.subject.keywordPlus | FRACTIONAL ANISOTROPY | - |
dc.subject.keywordPlus | MEAN DIFFUSIVITY | - |
dc.subject.keywordPlus | REPRODUCIBILITY | - |
dc.subject.keywordPlus | MICROSCOPY | - |
dc.subject.keywordPlus | ACCURACY | - |
dc.subject.keywordPlus | PITFALLS | - |
dc.subject.keywordPlus | IMAGES | - |
dc.subject.keywordPlus | BRAIN | - |
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