Project Summary
The goal of this project is to enhance the capabilities of diffusion-weighted magnetic resonance imaging
(dMRI)for neonatal and pediatric subjects. Currently, dMRI is the only viable non-invasive method for probing
brain microstructure. The past two decades have witnessed development of more powerful and more complex
modelsof brain microstructure based on dMRI signal. Unfortunately, accurate and reliable estimation of these
models require large numbers of high-quality measurements, which may be difficult or impossible to obtain in
neonatal and pediatric subjects. Therefore, there is an urgent need for methods that can accurately and
robustly estimatethe micro-structural biomarkers from reduced and low-quality measurements. To address this
need, this researchwill develop and validate data-driven and machine learning (ML) techniques methods for
estimating dMRI biomarkers for neonatal and pediatric subjects. The potential of these methods has greatly
increased by the availability of large high-quality dMRI datasets such as the Human Connectome Project
(HCP) data. Recent works, including our own studies, have demonstrated that ML techniques have a great
potential to overcome limitations of the existing analysis tools and to achieve superior estimation accuracy.
This research will substantially extend our preliminary work and generate important new capabilities that
currently do not exist. Specifically, we will develop and validate novel methods for estimating important micro-
structural models and biomarkers, ranging from diffusion tensor to advanced multi-compartment models, with
far fewer measurements.In this regard, the two main novel aspects of our work will include 1) the use of spatio-
temporal atlases to improvethe accuracy of subject-level analysis and 2) development of new deep neural
network architectures based on self-attention. Furthermore, we will develop new techniques for enhancing the
reliability, robustness, and explainability of ML methods for dMRI analysis. This will include techniques for
computing well-calibrated uncertainty estimations, techniques that can detect corrupt, noisy, and out-of-
distribution measurements, and techniques that enable interpretation and explanation of the predictions of
these ML methods. We will evaluate the new methods using test-retest and bootstrapping methods and via
assessment by experts in brain anatomyand micro-structure. The methods developed in this research will
enable quantitative assessment of neonatal and pediatric brain micro-structure and the impact of
developmental factors and neurological disorders at thesecritical stages in brain development with accuracy,
detail, and reproducibility that is currently beyond reach.
