Multi-model sequential analysis of MRI data for microstructure prediction in heterogeneous tissue

We propose a general method for combining multiple models to predict tissue microstructure, with an exemplar using in vivo diffusion-relaxation MRI data. The proposed method obviates the need to select a single ’optimum’ structure model for data analysis in heterogeneous tissues where the best model...

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Bibliographic Details
Published in:Scientific reports 2023-10, Vol.13 (1), p.16486-14, Article 16486
Main Authors: Enríquez-Mier-y-Terán, Francisco E., Chatterjee, Aritrick, Antic, Tatjana, Oto, Aytekin, Karczmar, Gregory, Bourne, Roger
Format: Article
Language:English
Subjects:
692/4028/67
692/700/1421/1628
Brain
Diffusion Magnetic Resonance Imaging - methods
Humanities and Social Sciences
Humans
Magnetic Resonance Imaging
Male
multidisciplinary
Physical properties
Predictions
Science
Science (multidisciplinary)
Tissues
Water - chemistry
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Summary:We propose a general method for combining multiple models to predict tissue microstructure, with an exemplar using in vivo diffusion-relaxation MRI data. The proposed method obviates the need to select a single ’optimum’ structure model for data analysis in heterogeneous tissues where the best model varies according to local environment. We break signal interpretation into a three-stage sequence: (1) application of multiple semi-phenomenological models to predict the physical properties of tissue water pools contributing to the observed signal; (2) from each Stage-1 semi-phenomenological model, application of a tissue microstructure model to predict the relative volumes of tissue structure components that make up each water pool; and (3) aggregation of the predictions of tissue structure, with weightings based on model likelihood and fractional volumes of the water pools from Stage-1. The multiple model approach is expected to reduce prediction variance in tissue regions where a complex model is overparameterised, and bias where a model is underparameterised. The separation of signal characterisation (Stage-1) from biological assignment (Stage-2) enables alternative biological interpretations of the observed physical properties of the system, by application of different tissue structure models. The proposed method is exemplified with human prostate diffusion-relaxation MRI data, but has potential application to a wide range of analyses where a single model may not be optimal throughout the sampled domain.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-43329-x