Fast quantitative magnetic resonance imaging of the brain using sparsity and low-rank image reconstruction constraints

Quantitative magnetic resonance imaging (qMRI) is increasingly used in research to map the microstructure and composition of the brain non-invasively. In contrast to conventional “weighted” MR imaging, qMRI requires the acquisition of several images with different contrast weightings. The data at each voxel is subsequently fit to a signal equation to extract features of the tissue. These quantitative maps can be compared directly across subjects and time points to detect subtle differences in tissue microstructure due to experience or disease.

While qMRI has many advantages, its translation to the clinic has been limited due to the long scan times. The objective of this project is to implement novel imaging acquisition and reconstruction techniques that will significantly shorten the scan time without compromising image quality. We will evaluate the result on image quality and qMRI reproducibility in a group of healthy controls scanned at the Montreal Neurological Institute.

Faculty Supervisor:

Christine Tardif

Student:

Partner:

Rheinisch-Westfälische Technische Hochschule Aachen

Discipline:

Engineering

Sector:

Education

University:

McGill University

Program: