Project Summary/Abstract:
Functional magnetic resonance imaging (fMRI) is an important tool both clinically and in scientific research,
with broad applications ranging from cognitive neuroscience to presurgical planning. FMRI can generate whole
brain neuronal activation maps, yet it is an inherently low signal to noise ratio (SNR) method due to the
relatively small changes in activation signal relative to the baseline signal. The overarching goal of this project
is to develop and validate robust, high SNR fMRI by using a novel acquisition method, oscillating steady state
(OSS) fMRI, along with machine learning (ML) techniques, to reconstruct high-quality images of the OSS fMRI
data. OSS fMRI can increase SNR by >2x compared to the current state-of-the-art in fMRI acquisition
methods, and this boost is roughly equivalent to the SNR gain going from a 3T MRI scanner to a 7T MRI
scanner. The SNR increase is a direct result of the steady state approach. However, with a more complex,
oscillating acquisition, OSS fMRI can be more susceptible to common MRI artifacts than traditional methods if
left uncorrected. Two of the most common sources of MRI artifacts are changes in the main magnetic field (B0)
due to physiological noise (such as respiration) and patient motion. This project will develop robust, high SNR
fMRI at 3T by incorporating the effects of (1) B0 changes and (2) patient motion into the OSS signal model and
image reconstruction algorithms. A new image reconstruction that incorporates neural networks will correct for
B0 fluctuations and remove B0 induced artifacts. We will train a neural network to generate B0 field maps using
data from a conventional, physics-based two echo field mapping technique to implicitly incorporate prior
information of the physics into the reconstruction, while also providing a fast, ML-based field mapping method.
To correct for subject motion, we will develop a neural network that estimates rigid-body motion parameters
from sequential image frames in the fMRI scan. These motion parameters will be used in an iterative image
reconstruction to produce high-quality resting-state and task-based fMRI, even in the presence of subject
motion. The technology developed in this proposal will result in improved functional MRI that has the potential
to significantly advance both the study of the human brain and the treatment of neurological disorders.
The University of Michigan is one of the top research universities in the US, and provides an ideal environment
and infrastructure to complete the proposed research strategy. The Functional Magnetic Resonance
Laboratory and the Electrical Engineering and Computer Science Department at UM have all the necessary
hardware and computational resources needed for this project, including two state-of-the-art GE 3T MRI
scanners and extensive GPU hardware for the machine learning components of the project. Furthermore, Drs.
Jeffrey Fessler and Douglas Noll have proven expertise in fMRI image acquisition and reconstruction, as well
as extensive mentorship experience, that will help guide this project and my training.
