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
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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 |
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