NVAM-Net: deep learning networks for reconstructing high-quality fiber orientation distributions.
Purpose: Diffusion magnetic resonance imaging (dMRI) is a widely used non-invasive method for investigating brain anatomical structures. Conventional techniques for estimating fiber orientation distribution (FOD) from dMRI data often neglect voxel-level spatial relationships, leading to ambiguous as...
| Publicado en: | Neuroradiology Vol. 66; no. 7; pp. 1177 - 1188 |
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| Autores principales: | , , |
| Formato: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jul2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=177648387&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 177648387 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: Jul2024 vid: 66 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 177648387 176381847 177648387 177648387 10.1007/s00234-024-03341-y 177648387 ppf: 1177 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: NVAM-Net: deep learning networks for reconstructing high-quality fiber orientation distributions. aug: au: Li, Jiahao Ai, Lingmei Yao, Ruoxia affil: https://ror.org/0170z8493 School of Computer Science, Shaanxi Normal University, 710119, Xi'an, China sug: subj: Deep Learning Attention Magnetic Resonance Imaging Neural Networks (Computer) Image Enhancement Nerve Fibers Human Descriptive Statistics Quantitative Studies Funding Source Imaging, Three-Dimensional Quality Improvement Models, Statistical Comparative Studies ab: Purpose: Diffusion magnetic resonance imaging (dMRI) is a widely used non-invasive method for investigating brain anatomical structures. Conventional techniques for estimating fiber orientation distribution (FOD) from dMRI data often neglect voxel-level spatial relationships, leading to ambiguous associations between target voxels and their neighbors, which, in turn, adversely impacts FOD accuracy. This study aims to address this issue by introducing a novel neural network, the neighboring voxel attention mechanism network (NVAM-Net), designed to reconstruct high-quality FOD images. Methods: The NVAM-Net leverages a Transformer architecture and incorporates two innovative attention mechanisms: voxel attention and surface attention. These mechanisms are specifically designed to capture overlooked features among neighboring voxels. The processed features are subsequently passed through two fully connected layers, further enhancing FOD estimation accuracy by separately estimating spherical harmonics (SH) coefficients of varying orders. Results: The experimental findings, based on the Human Connectome Project (HCP) dataset, reveal that the reconstructed super-resolution FOD images achieve results comparable to those obtained through more advanced dMRI acquisition protocols. These results underscore the NVAM-Net's robust performance in reconstructing multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD). Conclusion: In summary, this research underscores the NVAM-Net's advantages and practical feasibility in reconstructing high-quality FOD images. It provides a reliable reference point for clinical applications in the field of diffusion magnetic resonance imaging. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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