3D-MB-MUSE: A robust 3D multi-slab, multi-band and multi-shot reconstruction approach for ultrahigh resolution diffusion MRI.
Neuroimage
; 159: 46-56, 2017 10 01.
Article
em En
| MEDLINE
| ID: mdl-28732674
Recent advances in achieving ultrahigh spatial resolution (e.g. sub-millimeter) diffusion MRI (dMRI) data have proven highly beneficial in characterizing tissue microstructures in organs such as the brain. However, the routine acquisition of in-vivo dMRI data at such high spatial resolutions has been largely prohibited by factors that include prolonged acquisition times, motion induced artifacts, and low SNR. To overcome these limitations, we present here a framework for acquiring and reconstructing 3D multi-slab, multi-band and interleaved multi-shot EPI data, termed 3D-MB-MUSE. Through multi-band excitations, the simultaneous acquisition of multiple 3D slabs enables whole brain dMRI volumes to be acquired in-vivo on a 3 T clinical MRI scanner at high spatial resolution within a reasonably short amount of time. Representing a true 3D model, 3D-MB-MUSE reconstructs an entire 3D multi-band, multi-shot dMRI slab at once while simultaneously accounting for coil sensitivity variations across the slab as well as motion induced artifacts commonly associated with both 3D and multi-shot diffusion imaging. Such a reconstruction fully preserves the SNR advantages of both 3D and multi-shot acquisitions in high resolution dMRI images by removing both motion and aliasing artifacts across multiple dimensions. By enabling ultrahigh resolution dMRI for routine use, the 3D-MB-MUSE framework presented here may prove highly valuable in both clinical and research applications.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Encéfalo
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Interpretação de Imagem Assistida por Computador
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Imageamento Tridimensional
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Imagem de Difusão por Ressonância Magnética
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Neuroimagem
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Neuroimage
Assunto da revista:
DIAGNOSTICO POR IMAGEM
Ano de publicação:
2017
Tipo de documento:
Article