Literature DB >> 25850861

Know your tools--concordance of different methods for measuring brain volume change after ischemic stroke.

Nawaf Yassi1, Bruce C V Campbell, Bradford A Moffat, Christopher Steward, Leonid Churilov, Mark W Parsons, Patricia M Desmond, Stephen M Davis, Andrew Bivard.   

Abstract

INTRODUCTION: Longitudinal brain volume changes have been investigated in a number of cerebral disorders as a surrogate marker of clinical outcome. In stroke, unique methodological challenges are posed by dynamic structural changes occurring after onset, particularly those relating to the infarct lesion. We aimed to evaluate agreement between different analysis methods for the measurement of post-stroke brain volume change, and to explore technical challenges inherent to these methods.
METHODS: Fifteen patients with anterior circulation stroke underwent magnetic resonance imaging within 1 week of onset and at 1 and 3 months. Whole-brain as well as grey- and white-matter volume were estimated separately using both an intensity-based and a surface watershed-based algorithm. In the case of the intensity-based algorithm, the analysis was also performed with and without exclusion of the infarct lesion. Due to the effects of peri-infarct edema at the baseline scan, longitudinal volume change was measured as percentage change between the 1 and 3-month scans. Intra-class and concordance correlation coefficients were used to assess agreement between the different analysis methods. Reduced major axis regression was used to inspect the nature of bias between measurements.
RESULTS: Overall agreement between methods was modest with strong disagreement between some techniques. Measurements were variably impacted by procedures performed to account for infarct lesions.
CONCLUSIONS: Improvements in volumetric methods and consensus between methodologies employed in different studies are necessary in order to increase the validity of conclusions derived from post-stroke cerebral volumetric studies. Readers should be aware of the potential impact of different methods on study conclusions.

Entities:  

Mesh:

Year:  2015        PMID: 25850861     DOI: 10.1007/s00234-015-1522-8

Source DB:  PubMed          Journal:  Neuroradiology        ISSN: 0028-3940            Impact factor:   2.804


  39 in total

1.  Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain.

Authors:  Bruce Fischl; David H Salat; Evelina Busa; Marilyn Albert; Megan Dieterich; Christian Haselgrove; Andre van der Kouwe; Ron Killiany; David Kennedy; Shuna Klaveness; Albert Montillo; Nikos Makris; Bruce Rosen; Anders M Dale
Journal:  Neuron       Date:  2002-01-31       Impact factor: 17.173

2.  Improved optimization for the robust and accurate linear registration and motion correction of brain images.

Authors:  Mark Jenkinson; Peter Bannister; Michael Brady; Stephen Smith
Journal:  Neuroimage       Date:  2002-10       Impact factor: 6.556

3.  Cerebral tissue repair and atrophy after embolic stroke in rat: a magnetic resonance imaging study of erythropoietin therapy.

Authors:  Guangliang Ding; Quan Jiang; Lian Li; Li Zhang; Ying Wang; Zheng Gang Zhang; Mei Lu; Swayamprava Panda; Qingjiang Li; James R Ewing; Michael Chopp
Journal:  J Neurosci Res       Date:  2010-11-01       Impact factor: 4.164

Review 4.  Advances in functional and structural MR image analysis and implementation as FSL.

Authors:  Stephen M Smith; Mark Jenkinson; Mark W Woolrich; Christian F Beckmann; Timothy E J Behrens; Heidi Johansen-Berg; Peter R Bannister; Marilena De Luca; Ivana Drobnjak; David E Flitney; Rami K Niazy; James Saunders; John Vickers; Yongyue Zhang; Nicola De Stefano; J Michael Brady; Paul M Matthews
Journal:  Neuroimage       Date:  2004       Impact factor: 6.556

5.  Geometrically accurate topology-correction of cortical surfaces using nonseparating loops.

Authors:  Florent Ségonne; Jenni Pacheco; Bruce Fischl
Journal:  IEEE Trans Med Imaging       Date:  2007-04       Impact factor: 10.048

6.  A comparison of voxel and surface based cortical thickness estimation methods.

Authors:  Matthew J Clarkson; M Jorge Cardoso; Gerard R Ridgway; Marc Modat; Kelvin K Leung; Jonathan D Rohrer; Nick C Fox; Sébastien Ourselin
Journal:  Neuroimage       Date:  2011-05-26       Impact factor: 6.556

7.  Brain atrophy and lesion load predict long term disability in multiple sclerosis.

Authors:  Veronica Popescu; Federica Agosta; Hanneke E Hulst; Ingrid C Sluimer; Dirk L Knol; Maria Pia Sormani; Christian Enzinger; Stefan Ropele; Julio Alonso; Jaume Sastre-Garriga; Alex Rovira; Xavier Montalban; Benedetta Bodini; Olga Ciccarelli; Zhaleh Khaleeli; Declan T Chard; Lucy Matthews; Jaqueline Palace; Antonio Giorgio; Nicola De Stefano; Philipp Eisele; Achim Gass; Chris H Polman; Bernard M J Uitdehaag; Maria Jose Messina; Giancarlo Comi; Massimo Filippi; Frederik Barkhof; Hugo Vrenken
Journal:  J Neurol Neurosurg Psychiatry       Date:  2013-03-23       Impact factor: 10.154

8.  Evaluating and reducing the impact of white matter lesions on brain volume measurements.

Authors:  Marco Battaglini; Mark Jenkinson; Nicola De Stefano
Journal:  Hum Brain Mapp       Date:  2011-08-31       Impact factor: 5.038

9.  Cortical thickness estimation in longitudinal stroke studies: A comparison of 3 measurement methods.

Authors:  Qi Li; Heath Pardoe; Renee Lichter; Emilio Werden; Audrey Raffelt; Toby Cumming; Amy Brodtmann
Journal:  Neuroimage Clin       Date:  2014-08-23       Impact factor: 4.881

10.  Intercenter agreement of brain atrophy measurement in multiple sclerosis patients using manually-edited SIENA and SIENAX.

Authors:  Bas Jasperse; Paola Valsasina; Veronica Neacsu; Dirk L Knol; Nicola De Stefano; Christian Enzinger; Stephen M Smith; Stefan Ropele; Tijmen Korteweg; Antonio Giorgio; Valerie Anderson; Chris H Polman; Massimo Filippi; David H Miller; Marco Rovaris; Frederik Barkhof; Hugo Vrenken
Journal:  J Magn Reson Imaging       Date:  2007-10       Impact factor: 4.813

View more
  5 in total

1.  Brain atrophy in cerebral small vessel diseases: Extent, consequences, technical limitations and perspectives: The HARNESS initiative.

Authors:  François De Guio; Marco Duering; Franz Fazekas; Frank-Erik De Leeuw; Steven M Greenberg; Leonardo Pantoni; Agnès Aghetti; Eric E Smith; Joanna Wardlaw; Eric Jouvent
Journal:  J Cereb Blood Flow Metab       Date:  2019-11-20       Impact factor: 6.200

2.  Association between baseline peri-infarct magnetic resonance spectroscopy and regional white matter atrophy after stroke.

Authors:  Nawaf Yassi; Bruce C V Campbell; Bradford A Moffat; Christopher Steward; Leonid Churilov; Mark W Parsons; Geoffrey A Donnan; Patricia M Desmond; Stephen M Davis; Andrew Bivard
Journal:  Neuroradiology       Date:  2015-09-16       Impact factor: 2.804

3.  Body composition measurement in young children using quantitative magnetic resonance: a comparison with air displacement plethysmography.

Authors:  L-W Chen; M-T Tint; M V Fortier; I M Aris; L P-C Shek; K H Tan; V S Rajadurai; P D Gluckman; Y-S Chong; K M Godfrey; M S Kramer; C J Henry; F Yap; Y S Lee
Journal:  Pediatr Obes       Date:  2017-10-12       Impact factor: 4.000

4.  Quantifying Infarct Growth and Secondary Injury Volumes: Comparing Multimodal Image Registration Measures.

Authors:  George W J Harston; Davide Carone; Fintan Sheerin; Mark Jenkinson; James Kennedy
Journal:  Stroke       Date:  2018-06-12       Impact factor: 7.914

5.  Assessment of longitudinal hippocampal atrophy in the first year after ischemic stroke using automatic segmentation techniques.

Authors:  Mohamed Salah Khlif; Emilio Werden; Natalia Egorova; Marina Boccardi; Alberto Redolfi; Laura Bird; Amy Brodtmann
Journal:  Neuroimage Clin       Date:  2019-10-22       Impact factor: 4.881

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.