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).
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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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