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Abstract #4421

Data-Driven Multi-Contrast Spectral Microstructure Imaging with InSpect: INtegrated SPECTral Component Estimation and Mapping

Paddy J. Slator1, Jana Hutter2,3, Razvan V. Marinescu1, Marco Palombo1, Laurence Jackson2,3, Alison Ho4, Lucy C. Chappell4, Mary A. Rutherford2, Joseph V. Hajnal2,3, and Daniel C. Alexander1
1Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom, 2Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, 3Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, 4Women's Health Department, King's College London, London, United Kingdom

We introduce a novel spectroscopic imaging technique - termed InSpect - for analysing multi-contrast microstructural MRI experiments. Such data potentially supports estimation of multidimensional correlation spectra via a regularised inverse Laplace transform, but this is an ill-posed calculation. InSpect addresses these limitations in a data-driven way. The algorithm simultaneously estimates a canonical basis of spectral components for the whole data set, and maps their spatial distribution across images. Unlike standard approaches, InSpect shares information across voxels, implementing data-driven regularisation of the inverse Laplace transform. We demonstrate the method on combined diffusion-relaxometry placental MRI scans, revealing anatomically-relevant substructures, and identifying dysfunctional placentas.

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