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
|
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: | 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 |
Citation Data
Cited 2 times in Scopus. View in Scopus. Powered By Scopus® Data
Actions (repository staff only)
 |
Edit Item |
Altmetric
Altmetric