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Optimal Experimental Design for Biophysical Modelling in Multidimensional Diffusion MRI

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

  • Santiago Coelho, University of Leeds
  • ,
  • Jose M. Pozo, University of Leeds
  • ,
  • Sune N. Jespersen
  • Alejandro F. Frangi, University of Leeds
Computational models of biophysical tissue properties have been widely used in diffusion MRI (dMRI) research to elucidate the link between microstructural properties and MR signal formation. For brain tissue, the research community has developed the so-called Standard Model (SM) that has been widely used. However, in clinically applicable acquisition protocols, the inverse problem that recovers the SM parameters from a set of MR diffusion measurements using pairs of short pulsed field gradients was shown to be ill-posed. Multidimensional dMRI was shown to solve this problem by combining linear and planar tensor encoding data. Given sufficient measurements, multiple choices of b-tensor sets provide enough information to estimate all SM parameters. However, in the presence of noise, some sets will provide better results. In this work, we develop a framework for optimal experimental design of multidimensional dMRI sequences applicable to the SM. This framework is based on maximising the determinant of the Fisher information matrix, which is averaged over the full SM parameter space. This averaging provides a fairly objective information metric tailored for the expected signal but that only depends on the acquisition configuration. The optimisation of this metric can be further restricted to any subclass of desirable design constraints like, for instance, hardware-specific constraints. In this work, we compute the optimal acquisitions over the set of all b-tensors with fixed eigenvectors.
Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2019 : Proceedings
EditorsDinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan
Number of pages9
VolumeIII
Place of publicationCham
PublisherSpringer
Publication year2019
Pages617-625
ISBN (print)978-3-030-32247-2
ISBN (Electronic)978-3-030-32248-9
DOIs
Publication statusPublished - 2019
EventMICCAI 2019 : Medical Image Computing and Computer Assisted Intervention - , China
Duration: 13 Oct 201917 Oct 2019

Workshop

WorkshopMICCAI 2019
LandChina
Periode13/10/201917/10/2019
SeriesLecture Notes in Computer Science
Volume11766
ISSN0302-9743

    Research areas

  • Cumulant expansion, Fisher information, Multidimensional diffusion MRI, Optimal experiment design, Standard Model

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