Data-Driven Multi-Contrast Spectral Microstructure Imaging with InSpect

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Data-Driven Multi-Contrast Spectral Microstructure Imaging with InSpect

Slator, P; Hutter, J; Marinescu, R; Palombo, M; Jackson, L; Ho, A; Chappell, L; ... Alexander, D; + view all (2020) Data-Driven Multi-Contrast Spectral Microstructure Imaging with InSpect. In: International Conference on Medical Image Computing and Computer-Assisted Intervention MICCAI 2020: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. (pp. pp. 375-385). Springer, Cham Green open access

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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.

Type:	Proceedings paper
Title:	Data-Driven Multi-Contrast Spectral Microstructure Imaging with InSpect
Event:	23rd International Conference on Medical Image Computing and Computer Assisted Intervention
Open access status:	An open access version is available from UCL Discovery
DOI:	10.1007/978-3-030-59725-2_36
Publisher version:	https://doi.org/10.1007/978-3-030-59725-2_36
Language:	English
Additional information:	This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
Keywords:	Quantitative MRI, Unsupervised learning, 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/10102901