Data-driven multi-contrast spectral microstructure imaging with InSpect

Slator, Paddy J. ORCID: https://orcid.org/0000-0001-6967-989X, Hutter, Jana, Marinescu, Razvan V., Palombo, Marco ORCID: https://orcid.org/0000-0003-4892-7967, Jackson, Laurence H., Ho, Alison, Chappell, Lucy C., Rutherford, Mary, Hajnal, Joseph V. and Alexander, Daniel C. 2020. Data-driven multi-contrast spectral microstructure imaging with InSpect. Presented at: MICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention, Lima, Peru, 4–8 October, 2020. Published in: Martel, A.L., Abolmaesumi, P., Stoyanov, D., Mateus, D., Zuluaga, M.A., Zhou, S.K., Racoceanu, D. and Joskowicz, L. eds. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. Lecture Notes in Computer Science. , vol.12266 Cham: Springer, pp. 375-385. 10.1007/978-3-030-59725-2_36

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Abstract

We introduce and demonstrate an unsupervised machine learning method for spectroscopic analysis of quantitative MRI (qMRI) experiments. qMRI data can support estimation of multidimensional correlation (or single-dimensional) spectra, which allow model-free investigation of tissue properties, but this requires an ill-posed calculation. Moreover, in the vast majority of applications ground truth knowledge is unobtainable, preventing the application of supervised machine learning. Here we present a new method that addresses these limitations 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 that our algorithm substantially outperforms current voxelwise spectral approaches. We demonstrate the method on combined diffusion-relaxometry placental MRI scans, revealing anatomically-relevant substructures, and identifying dysfunctional placentas. Our algorithm vastly reduces the data required to reliably estimate multidimensional correlation (or single-dimensional) spectra, opening up the possibility of spectroscopic imaging in a wide range of new applications.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Psychology
Publisher:	Springer
ISBN:	978-3-030-59724-5
ISSN:	0302-9743
Last Modified:	14 Nov 2023 17:12
URI:	https://orca.cardiff.ac.uk/id/eprint/147888

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