Data-Driven multi-Contrast spectral microstructure imaging with InSpect: INtegrated SPECTral component estimation and mapping

Paddy J. Slator*, Jana Hutter, Razvan V. Marinescu, Marco Palombo, Laurence H. Jackson, Alison Ho, Lucy C. Chappell, Mary Rutherford, Joseph V. Hajnal, Daniel C. Alexander

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number102045
JournalMedical Image Analysis
Volume71
DOIs
Publication statusPublished - Jul 2021

Keywords

  • Diffusion-relaxation MRI
  • Inverse Laplace transform
  • Microstructure imaging
  • MRI
  • Placenta MRI
  • Quantitative MRI
  • Unsupervised learning

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