Literature DB >> 28642263

Volumetric Analysis from a Harmonized Multisite Brain MRI Study of a Single Subject with Multiple Sclerosis.

R T Shinohara1, J Oh2,3, G Nair4, P A Calabresi2, C Davatzikos5, J Doshi5, R G Henry6, G Kim7, K A Linn8, N Papinutto6, D Pelletier9, D L Pham10, D S Reich2,4, W Rooney11, S Roy10, W Stern6, S Tummala7, F Yousuf7, A Zhu6, N L Sicotte12, R Bakshi7,13.   

Abstract

BACKGROUND AND
PURPOSE: MR imaging can be used to measure structural changes in the brains of individuals with multiple sclerosis and is essential for diagnosis, longitudinal monitoring, and therapy evaluation. The North American Imaging in Multiple Sclerosis Cooperative steering committee developed a uniform high-resolution 3T MR imaging protocol relevant to the quantification of cerebral lesions and atrophy and implemented it at 7 sites across the United States. To assess intersite variability in scan data, we imaged a volunteer with relapsing-remitting MS with a scan-rescan at each site.
MATERIALS AND METHODS: All imaging was acquired on Siemens scanners (4 Skyra, 2 Tim Trio, and 1 Verio). Expert segmentations were manually obtained for T1-hypointense and T2 (FLAIR) hyperintense lesions. Several automated lesion-detection and whole-brain, cortical, and deep gray matter volumetric pipelines were applied. Statistical analyses were conducted to assess variability across sites, as well as systematic biases in the volumetric measurements that were site-related.
RESULTS: Systematic biases due to site differences in expert-traced lesion measurements were significant (P < .01 for both T1 and T2 lesion volumes), with site explaining >90% of the variation (range, 13.0-16.4 mL in T1 and 15.9-20.1 mL in T2) in lesion volumes. Site also explained >80% of the variation in most automated volumetric measurements. Output measures clustered according to scanner models, with similar results from the Skyra versus the other 2 units.
CONCLUSIONS: Even in multicenter studies with consistent scanner field strength and manufacturer after protocol harmonization, systematic differences can lead to severe biases in volumetric analyses.
© 2017 by American Journal of Neuroradiology.

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Year:  2017        PMID: 28642263      PMCID: PMC5557658          DOI: 10.3174/ajnr.A5254

Source DB:  PubMed          Journal:  AJNR Am J Neuroradiol        ISSN: 0195-6108            Impact factor:   3.825


  55 in total

1.  Reliability and reproducibility of brain tissue volumetry from segmented MR scans.

Authors:  I Agartz; G Okuguwa; M Nordström; D Greitz; V Magnotta; G Sedvall
Journal:  Eur Arch Psychiatry Clin Neurosci       Date:  2001-12       Impact factor: 5.270

2.  Removing inter-subject technical variability in magnetic resonance imaging studies.

Authors:  Jean-Philippe Fortin; Elizabeth M Sweeney; John Muschelli; Ciprian M Crainiceanu; Russell T Shinohara
Journal:  Neuroimage       Date:  2016-02-23       Impact factor: 6.556

3.  Accumulation of hypointense lesions ("black holes") on T1 spin-echo MRI correlates with disease progression in multiple sclerosis.

Authors:  L Truyen; J H van Waesberghe; M A van Walderveen; B W van Oosten; C H Polman; O R Hommes; H J Adèr; F Barkhof
Journal:  Neurology       Date:  1996-12       Impact factor: 9.910

4.  Gray matter atrophy in multiple sclerosis: a longitudinal study.

Authors:  Elizabeth Fisher; Jar-Chi Lee; Kunio Nakamura; Richard A Rudick
Journal:  Ann Neurol       Date:  2008-09       Impact factor: 10.422

5.  Computer-assisted segmentation of white matter lesions in 3D MR images using support vector machine.

Authors:  Zhiqiang Lao; Dinggang Shen; Dengfeng Liu; Abbas F Jawad; Elias R Melhem; Lenore J Launer; R Nick Bryan; Christos Davatzikos
Journal:  Acad Radiol       Date:  2008-03       Impact factor: 3.173

6.  Brain MRI lesion load at 1.5T and 3T versus clinical status in multiple sclerosis.

Authors:  James M Stankiewicz; Bonnie I Glanz; Brian C Healy; Ashish Arora; Mohit Neema; Ralph H B Benedict; Zachary D Guss; Shahamat Tauhid; Guy J Buckle; Maria K Houtchens; Samia J Khoury; Howard L Weiner; Charles R G Guttmann; Rohit Bakshi
Journal:  J Neuroimaging       Date:  2011-04       Impact factor: 2.486

7.  Evidence of early cortical atrophy in MS: relevance to white matter changes and disability.

Authors:  N De Stefano; P M Matthews; M Filippi; F Agosta; M De Luca; M L Bartolozzi; L Guidi; A Ghezzi; E Montanari; A Cifelli; A Federico; S M Smith
Journal:  Neurology       Date:  2003-04-08       Impact factor: 9.910

8.  Feasibility of multi-site clinical structural neuroimaging studies of aging using legacy data.

Authors:  Christine Fennema-Notestine; Anthony C Gamst; Brian T Quinn; Jenni Pacheco; Terry L Jernigan; Leon Thal; Randy Buckner; Ron Killiany; Deborah Blacker; Anders M Dale; Bruce Fischl; Brad Dickerson; Randy L Gollub
Journal:  Neuroinformatics       Date:  2007-11-13

9.  Handling changes in MRI acquisition parameters in modeling whole brain lesion volume and atrophy data in multiple sclerosis subjects: Comparison of linear mixed-effect models.

Authors:  Alicia S Chua; Svetlana Egorova; Mark C Anderson; Mariann Polgar-Turcsanyi; Tanuja Chitnis; Howard L Weiner; Charles R G Guttmann; Rohit Bakshi; Brian C Healy
Journal:  Neuroimage Clin       Date:  2015-07-02       Impact factor: 4.881

10.  OASIS is Automated Statistical Inference for Segmentation, with applications to multiple sclerosis lesion segmentation in MRI.

Authors:  Elizabeth M Sweeney; Russell T Shinohara; Navid Shiee; Farrah J Mateen; Avni A Chudgar; Jennifer L Cuzzocreo; Peter A Calabresi; Dzung L Pham; Daniel S Reich; Ciprian M Crainiceanu
Journal:  Neuroimage Clin       Date:  2013-03-15       Impact factor: 4.881

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  28 in total

1.  Voxel-Based Morphometry-from Hype to Hope. A Study on Hippocampal Atrophy in Mesial Temporal Lobe Epilepsy.

Authors:  F Riederer; R Seiger; R Lanzenberger; E Pataraia; G Kasprian; L Michels; J Beiersdorf; S Kollias; T Czech; J Hainfellner; C Baumgartner
Journal:  AJNR Am J Neuroradiol       Date:  2020-06       Impact factor: 3.825

2.  Quantitative assessment of field strength, total intracranial volume, sex, and age effects on the goodness of harmonization for volumetric analysis on the ADNI database.

Authors:  Da Ma; Karteek Popuri; Mahadev Bhalla; Oshin Sangha; Donghuan Lu; Jiguo Cao; Claudia Jacova; Lei Wang; Mirza Faisal Beg
Journal:  Hum Brain Mapp       Date:  2018-11-15       Impact factor: 5.038

3.  The Canadian Biomarker Integration Network in Depression (CAN-BIND): magnetic resonance imaging protocols

Authors:  Glenda M. MacQueen; Stefanie Hassel; Stephen R. Arnott; Addington Jean; Christopher R. Bowie; Signe L. Bray; Andrew D. Davis; Jonathan Downar; Jane A. Foster; Benicio N. Frey; Benjamin I. Goldstein; Geoffrey B. Hall; Kate L. Harkness; Jacqueline Harris; Raymond W. Lam; Catherine Lebel; Roumen Milev; Daniel J. Müller; Sagar V. Parikh; Sakina Rizvi; Susan Rotzinger; Gulshan B. Sharma; Claudio N. Soares; Gustavo Turecki; Fidel Vila-Rodriguez; Joanna Yu; Mojdeh Zamyadi; Stephen C. Strother; Sidney H. Kennedy
Journal:  J Psychiatry Neurosci       Date:  2019-07-01       Impact factor: 6.186

4.  Multisite reliability and repeatability of an advanced brain MRI protocol.

Authors:  Daniel L Schwartz; Ian Tagge; Katherine Powers; Sinyeob Ahn; Rohit Bakshi; Peter A Calabresi; R Todd Constable; John Grinstead; Roland G Henry; Govind Nair; Nico Papinutto; Daniel Pelletier; Russell Shinohara; Jiwon Oh; Daniel S Reich; Nancy L Sicotte; William D Rooney
Journal:  J Magn Reson Imaging       Date:  2019-01-16       Impact factor: 4.813

5.  DeepHarmony: A deep learning approach to contrast harmonization across scanner changes.

Authors:  Blake E Dewey; Can Zhao; Jacob C Reinhold; Aaron Carass; Kathryn C Fitzgerald; Elias S Sotirchos; Shiv Saidha; Jiwon Oh; Dzung L Pham; Peter A Calabresi; Peter C M van Zijl; Jerry L Prince
Journal:  Magn Reson Imaging       Date:  2019-07-10       Impact factor: 2.546

Review 6.  Imaging outcome measures of neuroprotection and repair in MS: A consensus statement from NAIMS.

Authors:  Jiwon Oh; Daniel Ontaneda; Christina Azevedo; Eric C Klawiter; Martina Absinta; Douglas L Arnold; Rohit Bakshi; Peter A Calabresi; Ciprian Crainiceanu; Blake Dewey; Leorah Freeman; Susan Gauthier; Roland Henry; Mathilde Inglese; Shannon Kolind; David K B Li; Caterina Mainero; Ravi S Menon; Govind Nair; Sridar Narayanan; Flavia Nelson; Daniel Pelletier; Alexander Rauscher; William Rooney; Pascal Sati; Daniel Schwartz; Russell T Shinohara; Ian Tagge; Anthony Traboulsee; Yi Wang; Youngjin Yoo; Tarek Yousry; Yunyan Zhang; Nancy L Sicotte; Daniel S Reich
Journal:  Neurology       Date:  2019-02-20       Impact factor: 9.910

7.  Harmonization of cortical thickness measurements across scanners and sites.

Authors:  Jean-Philippe Fortin; Nicholas Cullen; Yvette I Sheline; Warren D Taylor; Irem Aselcioglu; Philip A Cook; Phil Adams; Crystal Cooper; Maurizio Fava; Patrick J McGrath; Melvin McInnis; Mary L Phillips; Madhukar H Trivedi; Myrna M Weissman; Russell T Shinohara
Journal:  Neuroimage       Date:  2017-11-17       Impact factor: 6.556

8.  The NAIMS cooperative pilot project: Design, implementation and future directions.

Authors:  Jiwon Oh; Rohit Bakshi; Peter A Calabresi; Ciprian Crainiceanu; Roland G Henry; Govind Nair; Nico Papinutto; R Todd Constable; Daniel S Reich; Daniel Pelletier; William Rooney; Daniel Schwartz; Ian Tagge; Russell T Shinohara; Jack H Simon; Nancy L Sicotte
Journal:  Mult Scler       Date:  2017-11-06       Impact factor: 6.312

9.  Standardized Brain MRI Acquisition Protocols Improve Statistical Power in Multicenter Quantitative Morphometry Studies.

Authors:  Allan George; Ruben Kuzniecky; Henry Rusinek; Heath R Pardoe
Journal:  J Neuroimaging       Date:  2019-10-30       Impact factor: 2.486

10.  An Automated Statistical Technique for Counting Distinct Multiple Sclerosis Lesions.

Authors:  J D Dworkin; K A Linn; I Oguz; G M Fleishman; R Bakshi; G Nair; P A Calabresi; R G Henry; J Oh; N Papinutto; D Pelletier; W Rooney; W Stern; N L Sicotte; D S Reich; R T Shinohara
Journal:  AJNR Am J Neuroradiol       Date:  2018-02-22       Impact factor: 3.825

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