Literature DB >> 21316471

Quantification of accuracy and precision of multi-center DTI measurements: a diffusion phantom and human brain study.

Tong Zhu1, Rui Hu, Xing Qiu, Michael Taylor, Yuen Tso, Constantin Yiannoutsos, Bradford Navia, Susumu Mori, Sven Ekholm, Giovanni Schifitto, Jianhui Zhong.   

Abstract

The inter-site and intra-site variability of system performance of MRI scanners (due to site-dependent and time-variant variations) can have significant adverse effects on the integration of multi-center DTI data. Measurement errors in accuracy and precision of each acquisition determine both the inter-site and intra-site variability. In this study, multiple scans of an identical isotropic diffusion phantom and of the brain of a traveling human volunteer were acquired at MRI scanners from the same vendor and with similar configurations at three sites. We assessed the feasibility of multi-center DTI studies by direct quantification of accuracy and precision of each dataset. Accuracy was quantified via comparison to carefully constructed gold standard datasets while precision (the within-scan variability) was estimated by wild bootstrap analysis. The results from both the phantom and human data suggest that the inter-site variation in system performance, although relatively small among scanners of the same vendor, significantly affects DTI measurement accuracy and precision and therefore the effectiveness for the integration of multi-center DTI measurements. Our results also highlight the value of a DTI-specific phantom in identifying and quantifying measurement errors due to site-dependent variations in the system performance, and its usefulness for quality assurance/quality control in multi-center DTI studies. In addition, we observed that the within-scan variability of each data acquisition, as assessed by wild bootstrap analysis, is of the same magnitude as the inter-site and intra-site variability. We propose that by weighing datasets based on their variability, as evaluated by wild bootstrap analysis, one can improve the quality of the dataset. This approach will provide a more effective integration of datasets from multi-center DTI studies.
Copyright © 2011 Elsevier Inc. All rights reserved.

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Year:  2011        PMID: 21316471      PMCID: PMC3085553          DOI: 10.1016/j.neuroimage.2011.02.010

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  36 in total

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Authors:  K M Hasan; D L Parker; A L Alexander
Journal:  J Magn Reson Imaging       Date:  2001-05       Impact factor: 4.813

2.  The effect of gradient sampling schemes on measures derived from diffusion tensor MRI: a Monte Carlo study.

Authors:  Derek K Jones
Journal:  Magn Reson Med       Date:  2004-04       Impact factor: 4.668

3.  Simulation and experimental verification of the diffusion in an anisotropic fiber phantom.

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4.  Test-retest and between-site reliability in a multicenter fMRI study.

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Journal:  Hum Brain Mapp       Date:  2008-08       Impact factor: 5.038

5.  An optimized wild bootstrap method for evaluation of measurement uncertainties of DTI-derived parameters in human brain.

Authors:  Tong Zhu; Xiaoxu Liu; Patrick R Connelly; Jianhui Zhong
Journal:  Neuroimage       Date:  2008-01-26       Impact factor: 6.556

6.  Evaluation of measurement uncertainties in human diffusion tensor imaging (DTI)-derived parameters and optimization of clinical DTI protocols with a wild bootstrap analysis.

Authors:  Tong Zhu; Xiaoxu Liu; Michelle D Gaugh; Patrick R Connelly; Hongyan Ni; Sven Ekholm; Giovanni Schifitto; Jianhui Zhong
Journal:  J Magn Reson Imaging       Date:  2009-02       Impact factor: 4.813

7.  Bootstrap quantification of cardiac pulsation artifact in DTI.

Authors:  SungWon Chung; Blandine Courcot; Michael Sdika; Kristin Moffat; Caroline Rae; Roland G Henry
Journal:  Neuroimage       Date:  2009-07-03       Impact factor: 6.556

8.  Tractography gone wild: probabilistic fibre tracking using the wild bootstrap with diffusion tensor MRI.

Authors:  Derek K Jones
Journal:  IEEE Trans Med Imaging       Date:  2008-09       Impact factor: 10.048

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10.  Test liquids for quantitative MRI measurements of self-diffusion coefficient in vivo.

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  57 in total

1.  Harmonizing DTI measurements across scanners to examine the development of white matter microstructure in 803 adolescents of the NCANDA study.

Authors:  Kilian M Pohl; Edith V Sullivan; Torsten Rohlfing; Weiwei Chu; Dongjin Kwon; B Nolan Nichols; Yong Zhang; Sandra A Brown; Susan F Tapert; Kevin Cummins; Wesley K Thompson; Ty Brumback; Ian M Colrain; Fiona C Baker; Devin Prouty; Michael D De Bellis; James T Voyvodic; Duncan B Clark; Claudiu Schirda; Bonnie J Nagel; Adolf Pfefferbaum
Journal:  Neuroimage       Date:  2016-02-10       Impact factor: 6.556

2.  Sex differences in network controllability as a predictor of executive function in youth.

Authors:  Eli J Cornblath; Evelyn Tang; Graham L Baum; Tyler M Moore; Azeez Adebimpe; David R Roalf; Ruben C Gur; Raquel E Gur; Fabio Pasqualetti; Theodore D Satterthwaite; Danielle S Bassett
Journal:  Neuroimage       Date:  2018-12-01       Impact factor: 6.556

3.  Gray matter alterations in early aging: a diffusion magnetic resonance imaging study.

Authors:  Y Rathi; O Pasternak; P Savadjiev; O Michailovich; S Bouix; M Kubicki; C-F Westin; N Makris; M E Shenton
Journal:  Hum Brain Mapp       Date:  2013-12-31       Impact factor: 5.038

4.  A comparison of structural connectivity in anxious depression versus non-anxious depression.

Authors:  Lauren Delaparte; Fang-Cheng Yeh; Phil Adams; Ashley Malchow; Madhukar H Trivedi; Maria A Oquendo; Thilo Deckersbach; Todd Ogden; Diego A Pizzagalli; Maurizio Fava; Crystal Cooper; Melvin McInnis; Benji T Kurian; Myrna M Weissman; Patrick J McGrath; Daniel N Klein; Ramin V Parsey; Christine DeLorenzo
Journal:  J Psychiatr Res       Date:  2017-01-24       Impact factor: 4.791

5.  Multi-center reproducibility of structural, diffusion tensor, and resting state functional magnetic resonance imaging measures.

Authors:  S Deprez; Michiel B de Ruiter; S Bogaert; R Peeters; J Belderbos; D De Ruysscher; S Schagen; S Sunaert; P Pullens; E Achten
Journal:  Neuroradiology       Date:  2018-04-14       Impact factor: 2.804

6.  A quality assurance protocol for diffusion tensor imaging using the head phantom from American College of Radiology.

Authors:  Zhiyue J Wang; Youngseob Seo; Jonathan M Chia; Nancy K Rollins
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Review 7.  Diffusion MRI of the neonate brain: acquisition, processing and analysis techniques.

Authors:  Kerstin Pannek; Andrea Guzzetta; Paul B Colditz; Stephen E Rose
Journal:  Pediatr Radiol       Date:  2012-08-18

8.  Harmonizing Diffusion MRI Data Across Multiple Sites and Scanners.

Authors:  Hengameh Mirzaalian; Amicie de Pierrefeu; Peter Savadjiev; Ofer Pasternak; Sylvain Bouix; Marek Kubicki; Carl-Fredrik Westin; Martha E Shenton; Yogesh Rathi
Journal:  Med Image Comput Comput Assist Interv       Date:  2015-11-18

9.  Uncertainty assessment of gamma-aminobutyric acid concentration of different brain regions in individual and group using residual bootstrap analysis.

Authors:  Meng Chen; Congyu Liao; Song Chen; Qiuping Ding; Darong Zhu; Hui Liu; Xu Yan; Jianhui Zhong
Journal:  Med Biol Eng Comput       Date:  2016-10-01       Impact factor: 2.602

10.  Reprint of "Quantitative evaluation of brain development using anatomical MRI and diffusion tensor imaging".

Authors:  Kenichi Oishi; Andreia V Faria; Shoko Yoshida; Linda Chang; Susumu Mori
Journal:  Int J Dev Neurosci       Date:  2013-12-02       Impact factor: 2.457

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