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Statistical methods for vascular magnetic resonance fingerprinting: application to the epileptic brain

on the December 11, 2020
2PM

Thesis defense of Fabien Boux

On friday, the 11th of december 2020, Fabien Boux will defend his thesis "Statistical methods for vascular magnetic resonance fingerprinting: application to the epileptic brain".


This thesis was prepared at GIN in Emmanuel BARBIER team.

Supervisors: Emmanuel Barbier and Florence Forbes, and Julyan Arbel.

Jury members
:

  • Rapporteurs: Ludovic De Rochefort et Simon Warfield.
  • Examinateurs: Olivier François, Benoit Martin et Benjamin Marty.
  • Invités: Julyan Arbel et Lorella Minotti.

Abstract
:

The objective of this thesis is the investigation of magnetic resonance imaging (MRI) for the identification and localization of brain regions involved in mesio-temporal lobe epilepsy (MTLE). Precisely, the work aims 1) at optimizing a vascular MRI protocol on an animal model of epilepsy and 2) at designing a method to quantify vascular MRI maps based on the modeling of the relationship between MRI signals and biophysical parameters.
MRI acquisitions on an experimental mouse model of MTLE with hippocampal sclerosis were performed on a 9.4 T scanner. The data collected allowed the quantification of seven cellular and vascular MRI maps a few days after the epileptic condition and later when the spontaneous seizures emerged. These parameters were used for the automatic identification of epileptogenic regions and regions of seizure propagation. To enhance the detection of small variations in MRI parameters in epileptic subjects, a quantification method based on magnetic resonance fingerprinting has been developed. This method consists in identifying, among a set of simulated signals, the closest one to the acquired signal. It can be seen as an inverse problem that presents the following difficulties: the direct model is non-linear, as a complex series of equations or simulation tools; the inputs are high-dimensional signals; and the output is multidimensional. For these reasons, we used an appropriate inverse regression approach to learn a mapping between signal and biophysical parameter spaces. In a field widely dominated by deep learning approaches, the proposed method is very competitive and provides more accurate results. Moreover, the method allows for the first time to produce a confidence index associated with each estimate. In particular, this index allows to reduce the quantification error by discarding estimates associated with low confidence.
So far no clinical protocol emerges as a consensus to accurately localize epileptic foci. The possibility of a non-invasive identification of these regions is therefore a first step towards a potential clinical transfer.

 

Updated on November 27, 2020

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