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  Active acquisition for multimodal neuroimaging

Cole, J. H., Lorenz, R., Geranmayeh, F., Wood, T., Hellyer, P., Williams, S., et al. (2019). Active acquisition for multimodal neuroimaging. Wellcome Open Research, 3: 145. doi:10.12688/wellcomeopenres.14918.2.

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 Creators:
Cole, James H.1, Author
Lorenz, Romy2, 3, Author              
Geranmayeh, Fatemeh4, Author
Wood, Tobias1, Author
Hellyer, Peter1, Author
Williams, Steven1, Author
Turkheimer, Federico1, Author
Leech, Robert1, Author
Affiliations:
1Centre for Neuroimaging Science, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, United Kingdom, ou_persistent22              
2MRC Cognition and Brain Sciences Unit, Cambridge, United Kingdom, ou_persistent22              
3Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_2205649              
4Division of Brain Sciences, Faculty of Medicine, Imperial College London, United Kingdom, ou_persistent22              

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Free keywords: Neuroimaging; Active acquisition; MRI; Brain
 Abstract: In many clinical and scientific situations the optimal neuroimaging sequence may not be known prior to scanning and may differ for each individual being scanned, depending on the exact nature and location of abnormalities. Despite this, the standard approach to data acquisition, in such situations, is to specify the sequence of neuroimaging scans prior to data acquisition and to apply the same scans to all individuals. In this paper, we propose and illustrate an alternative approach, in which data would be analysed as it is acquired and used to choose the future scanning sequence: Active Acquisition. We propose three Active Acquisition scenarios based around multiple MRI modalities. In Scenario 1, we propose a simple use of near-real time analysis to decide whether to acquire more or higher resolution data, or acquire data with a different field-of-view. In Scenario 2, we simulate how multimodal MR data could be actively acquired and combined with a decision tree to classify a known outcome variable (in the simple example here, age). In Scenario 3, we simulate using Bayesian optimisation to actively search across multiple MRI modalities to find those which are most abnormal. These simulations suggest that by actively acquiring data, the scanning sequence can be adapted to each individual. We also consider the many outstanding practical and technical challenges involving normative data acquisition, MR physics, statistical modelling and clinical relevance. Despite these, we argue that Active Acquisition allows for potentially far more powerful, sensitive or rapid data acquisition, and may open up different perspectives on individual differences, clinical conditions, and biomarker discovery.

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Language(s): eng - English
 Dates: 2019-09-23
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.12688/wellcomeopenres.14918.2
Other: eCollection 2018
PMID: 31667357
PMC: PMC6807153.2
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Project name : -
Grant ID : 213996
Funding program : Wellcome Trust Seed Award in Science
Funding organization : Wellcome Trust
Project name : -
Grant ID : 209139 ; 106092
Funding program : Sir Henry Wellcome Postdoctoral Fellowship
Funding organization : Wellcome Trust
Project name : -
Grant ID : -
Funding program : UKRI Innovation Fellowship
Funding organization : Engineering and Physical Sciences Research Council (EPSRC)
Project name : -
Grant ID : MR/R005370/1
Funding program : -
Funding organization : Medical Research Council (MRC)
Project name : -
Grant ID : WT 203148/Z/16/Z
Funding program : -
Funding organization : Wellcome/EPSRC Centre for Medical Engineering
Project name : -
Grant ID : -
Funding program : Data to Early Diagnosis and Precision Medicine Industrial Strategy Challenge Fund
Funding organization : UK Research and Innovation (UKRI)

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Title: Wellcome Open Research
Source Genre: Journal
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Publ. Info: UK : F1000Research
Pages: - Volume / Issue: 3 Sequence Number: 145 Start / End Page: - Identifier: Other: 2398-502X
CoNE: https://pure.mpg.de/cone/journals/resource/2398-502X