Project Summary
The overarching goal of this project is to improve the reproducibility of quantitative MRI (qMRI).
Secondary goals are to enhance its sensitivity and specificity, and to enable its utilization in clinical
routine imaging by reducing the scan time. Successful completion of this project will help towards the
fulfillment of qMRI's promise to objectify the radiologists' analysis and to improve the comparability of
different scans, which is much needed in the age of large multi-center studies, artificial intelligence, and
precision medicine.
It is well-known that estimates of spin relaxation times for biological tissue vary substantially throughout
the literature, in particular if different pulse sequences are used for their measurement. Our central
hypothesis is that this variability is a result of model oversimplifications, as un-modeled effects bias the
parameter estimates and this bias depends on the sequence. Simple models are commonly chosen for
in vivo qMRI to keep the scan time clinically feasible. This project aims to overcome this variability by
removing the most severe model oversimplifications, and we will enable an efficient encoding of the
more complex model with the "hybrid state," a spin-ensemble state whose discovery we recently
reported in Nature Communications Physics. Our preliminary data suggests that the hybrid state allows
us to disentangle different T1-relaxation paths, on a voxel-by-voxel basis and within a clinically feasible
scan time, and we find that magnetization transfer plays a dominant role in T1-relaxation in brain white
matter.
The three specific aims concern the physics, bio-medical, and the engineering aspects of the project
and will jointly establish the proposed model and hybrid-state pulse sequence for robust and fast
quantitative MRI. We aim to provide an improved understanding of multi-component T1-relaxation in
biological tissue and the clinical implications thereof, as well as to develop tools for quantifying the
developed biomarkers in a clinical imaging setting.
While this work is relevant to the entire field of biomedical MR, the project focuses on neuroimaging,
where the relevant biophysics in best understood; and we will use multiple sclerosis as a test-vehicle
for our model and pulse sequence, as the bio-medical interpretation of the physical parameters is well
understood.
