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Data-Driven Multi-Contrast Spectral Microstructure Imaging with InSpect: INtegrated SPECTral Component Estimation and Mapping

Slator, PJ; Hutter, J; Marinescu, RV; Palombo, M; Jackson, L; Ho, A; Chappell, LC; ... Alexander, DC; + view all (2021) Data-Driven Multi-Contrast Spectral Microstructure Imaging with InSpect: INtegrated SPECTral Component Estimation and Mapping. Medical Image Analysis , 71 , Article 102045. 10.1016/j.media.2021.102045. (In press). Green open access

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Abstract

We introduce and demonstrate an unsupervised machine learning technique for spectroscopic analysis of quantitative MRI experiments. Our algorithm supports estimation of one-dimensional spectra from single-contrast data, and multidimensional correlation spectra from simultaneous multi-contrast data. These spectrum-based approaches allow model-free investigation of tissue properties, but require regularised inversion of a Laplace transform or Fredholm integral, which is an ill-posed calculation. Here we present a method that addresses this limitation in a data-driven way. The algorithm simultaneously estimates a canonical basis of spectral components and voxelwise maps of their weightings, thereby pooling information across whole images to regularise the ill-posed problem. We show in simulations that our algorithm substantially outperforms current voxelwise spectral approaches. We demonstrate the method on multi-contrast diffusion-relaxometry placental MRI scans, revealing anatomically-relevant sub-structures, and identifying dysfunctional placentas. Our algorithm vastly reduces the data required to reliably estimate spectra, opening up the possibility of quantitative MRI spectroscopy in a wide range of new applications. Our InSpect code is available at github.com/paddyslator/inspect.

Type: Article
Title: Data-Driven Multi-Contrast Spectral Microstructure Imaging with InSpect: INtegrated SPECTral Component Estimation and Mapping
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.media.2021.102045
Publisher version: https://doi.org/10.1016/j.media.2021.102045
Language: English
Additional information: © 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
Keywords: MRI, Microstructure imaging, Diffusion-relaxation MRI, Inverse Laplace transform, Unsupervised learning, Quantitative MRI, Placenta MRI
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10124268
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