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.
Functional magnetic resonance imaging (fMRI) is used to measure brain function, and it has revolutionized our understanding of cognitive processes during the last 25 years and has also been used as a tool for presurgical mapping of language and other sensitive brain regions. More recently, fMRI is being used as a biomarker for the progression of neurologic and psychiatric diseases, for example, in Alzheimer?s disease, multiple sclerosis, and major depression. In this project we will improve fMRI techniques by developing new methods that are more robust to common sources of MRI image degradation, which will improve the sharpness of the fMRI images without increasing instrumentation costs.
