Data-driven multi-contrast spectral microstructure imaging with InSpect: INtegrated SPECTral component estimation and mapping

Slator, P.J. ORCID: https://orcid.org/0000-0001-6967-989X, Hutter, J., Marinescu, R.V., Palombo, M. ORCID: https://orcid.org/0000-0003-4892-7967, Jackson, L., Ho, A., Chappell, L.C., Rutherford, M., Hajnal, J.V. and Alexander, D.C. 2021. Data-driven multi-contrast spectral microstructure imaging with InSpect: INtegrated SPECTral component estimation and mapping. Medical Image Analysis 71 , 102045. 10.1016/j.media.2021.102045

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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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Psychology
Additional Information:	This is an open access article under the CC BY license
Publisher:	Elsevier
ISSN:	1361-8415
Date of First Compliant Deposit:	2 March 2022
Date of Acceptance:	16 March 2021
Last Modified:	18 Oct 2023 08:37
URI:	https://orca.cardiff.ac.uk/id/eprint/147926

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