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Using the wild bootstrap to quantify uncertainty in mean apparent propagator MRI
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0002-9091-4724
2019 (English)In: Frontiers in Neuroinformatics, ISSN 1662-5196, E-ISSN 1662-5196, Vol. 13, article id 43Article in journal (Refereed) Published
Abstract [en]

Purpose: Estimation of uncertainty of MAP-MRI metricsis an important topic, for several reasons. Bootstrap deriveduncertainty, such as the standard deviation, providesvaluable information, and can be incorporated in MAP-MRIstudies to provide more extensive insight.

Methods: In this paper, the uncertainty of different MAPMRImetrics was quantified by estimating the empirical distributionsusing the wild bootstrap. We applied the wildbootstrap to both phantom data and human brain data, andobtain empirical distributions for theMAP-MRImetrics returnto-origin probability (RTOP), non-Gaussianity (NG) and propagatoranisotropy (PA).

Results: We demonstrated the impact of diffusion acquisitionscheme (number of shells and number of measurementsper shell) on the uncertainty of MAP-MRI metrics.We demonstrated how the uncertainty of these metrics canbe used to improve group analyses, and to compare differentpreprocessing pipelines. We demonstrated that withuncertainty considered, the results for a group analysis canbe different.

Conclusion: Bootstrap derived uncertain measures provideadditional information to the MAP-MRI derived metrics, andshould be incorporated in ongoing and future MAP-MRIstudies to provide more extensive insight.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2019. Vol. 13, article id 43
Keywords [en]
Bootstrap, diffusion MRI, MAP-MRI, uncertainty, RtoP, NG, PA
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:liu:diva-157089DOI: 10.3389/fninf.2019.00043ISI: 000471589200001PubMedID: 31244637OAI: oai:DiVA.org:liu-157089DiVA, id: diva2:1318438
Note

Funding agencies:  Swedish Research Council [2015-05356]; Linkoping University Center for Industrial Information Technology (CENIIT); Knut and Alice Wallenberg Foundation project Seeing Organ Function; National Institute of Dental and Craniofacial Research (NIDCR); National

Available from: 2019-05-27 Created: 2019-05-27 Last updated: 2019-11-19
In thesis
1. Advanced analysis of diffusion MRI data
Open this publication in new window or tab >>Advanced analysis of diffusion MRI data
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Diffusion magnetic resonance imaging (diffusion MRI) is a non-invasive imaging modality which can measure diffusion of water molecules, by making the MRI acquisition sensitive to diffusion. Diffusion MRI provides unique possibilities to study structural connectivity of the human brain, e.g. how the white matter connects different parts of the brain. Diffusion MRI enables a range of tools that permit qualitative and quantitative assessments of many neurological disorders, such as stroke and Parkinson.

This thesis introduces novel methods for diffusion MRI data analysis. Prior to estimating a diffusion model in each location (voxel) of the brain, the diffusion data needs to be preprocessed to correct for geometric distortions and head motion. A deep learning approach to synthesize diffusion scalar maps from a T1-weighted MR image is proposed, and it is shown that the distortion-free synthesized images can be used for distortion correction. An evaluation, involving both simulated data and real data, of six methods for susceptibility distortion correction is also presented in this thesis.

A common problem in diffusion MRI is to estimate the uncertainty of a diffusion model. An empirical evaluation of tractography, a technique that permits reconstruction of white matter pathways in the human brain, is presented in this thesis. The evaluation is based on analyzing 32 diffusion datasets from a single healthy subject, to study how reliable tractography is. In most cases only a single dataset is available for each subject. This thesis presents methods based on frequentistic (bootstrap) as well as Bayesian inference, which can provide uncertainty estimates when only a single dataset is available. These uncertainty measures can then, for example, be used in a group analysis to downweight subjects with a higher uncertainty.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2019. p. 93
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 2017
Keywords
Diffusion MRI, Distortion Correction, Deep Learning, Uncertainty Estimation
National Category
Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-161288 (URN)10.3384/diss.diva-161288 (DOI)9789175190037 (ISBN)
Public defence
2019-12-06, Hugo Theorell, Building 448, Campus US, Linköping, 13:15 (English)
Opponent
Supervisors
Available from: 2019-11-19 Created: 2019-11-19 Last updated: 2019-11-19Bibliographically approved

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