Multi-parametric neuroimaging reproducibility: a 3-T resource study.
Multi-parametric neuroimaging reproducibility: a 3-T resource study.
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DOI:
10.1016/j.neuroimage.2010.11.047
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发表时间:
2011-02-14
期刊:
影响因子:
5.7
通讯作者:
van Zijl PC
中科院分区:
文献类型:
--
作者:
Landman BA;Huang AJ;Gifford A;Vikram DS;Lim IA;Farrell JA;Bogovic JA;Hua J;Chen M;Jarso S;Smith SA;Joel S;Mori S;Pekar JJ;Barker PB;Prince JL;van Zijl PC
Modern MRI image processing methods have yielded quantitative, morphometric, functional, and structural assessments of the human brain. These analyses typically exploit carefully optimized protocols for specific imaging targets. Algorithm investigators have several excellent public data resources to use to test, develop, and optimize their methods. Recently, there has been an increasing focus on combining MRI protocols in multi-parametric studies. Notably, these have included innovative approaches for fusing connectivity inferences with functional and/or anatomical characterizations. Yet, validation of the reproducibility of these interesting and novel methods has been severely hampered by the limited availability of appropriate multi-parametric data. We present an imaging protocol optimized to include state-of-the-art assessment of brain function, structure, micro-architecture, and quantitative parameters within a clinically feasible 60 minute protocol on a 3T MRI scanner. We present scan-rescan reproducibility of these imaging contrasts based on 21 healthy volunteers (11 M/10 F, 22–61 y/o). The cortical gray matter, cortical white matter, ventricular cerebrospinal fluid, thalamus, putamen, caudate, cerebellar gray matter, cerebellar white matter, and brainstem were identified with mean volume-wise reproducibility of 3.5%. We tabulate the mean intensity, variability and reproducibility of each contrast in a region of interest approach, which is essential for prospective study planning and retrospective power analysis considerations. Anatomy was highly consistent on structural acquisition (~1–5% variability), while variation on diffusion and several other quantitative scans was higher (~<10%). Some sequences are particularly variable in specific structures (ASL exhibited variation of 28% in the cerebral white matter) or in thin structures (quantitative T2 varied by up to 73% in the caudate) due, in large part, to variability in automated ROI placement. The richness of the joint distribution of intensities across imaging methods can be best assessed within the context of a particular analysis approach as opposed to a summary table. As such, all imaging data and analysis routines have been made publicly and freely available. This effort provides the neuroimaging community with a resource for optimization of algorithms that exploit the diversity of modern MRI modalities. Additionally, it establishes a baseline for continuing development and optimization of multi-parametric imaging protocols.
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影响因子:
3.3
作者:
Alsop, DC
通讯作者:
Alsop, DC
影响因子:
2.9
作者:
Barkhof, F;Scheltens, P
通讯作者:
Scheltens, P
DOI:
10.1006/jmrb.1994.1037
发表时间:
1994-03-01
期刊:
JOURNAL OF MAGNETIC RESONANCE SERIES B
影响因子:
--
作者:
BASSER, PJ;MATTIELLO, J;LEBIHAN, D
通讯作者:
LEBIHAN, D
影响因子:
3.3
作者:
BUXTON, RB
通讯作者:
BUXTON, RB
影响因子:
10.6
作者:
Bazin, Pierre-Louis;Pham, Dzung L.
通讯作者:
Pham, Dzung L.