Slator PJ, Hutter J, Marinescu RV, Palombo M, Jackson LH, Ho A, Chappell LC, Rutherford M, Hajnal JV, Alexander DC (2021)
Publication Type: Journal article
Publication year: 2021
Book Volume: 71
Article Number: 102045
DOI: 10.1016/j.media.2021.102045
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.
APA:
Slator, P.J., Hutter, J., Marinescu, R.V., Palombo, M., Jackson, L.H., Ho, A.,... Alexander, D.C. (2021). Data-Driven multi-Contrast spectral microstructure imaging with InSpect: INtegrated SPECTral component estimation and mapping. Medical Image Analysis, 71. https://doi.org/10.1016/j.media.2021.102045
MLA:
Slator, Paddy J., et al. "Data-Driven multi-Contrast spectral microstructure imaging with InSpect: INtegrated SPECTral component estimation and mapping." Medical Image Analysis 71 (2021).
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