Advanced Motion Correction Methods for Magnetic Resonance Imaging of the Brain
Abstract
Magnetic resonance imaging (MRI) is an invaluable medical imaging modality providing excellent soft tissue contrast for diagnostic and research purposes. However, the long scan times required for image acquisition increase the possibility for subject motion, with the resulting artifacts potentially impeding diagnosis or other analysis. Even for brain imaging, where involuntary motion (e.g. breathing) is not expected to greatly influence acquisition, it is estimated that one in five MRI exams require at least one scan be repeated due to motion artifacts.
This thesis is centered on the development of motion correction methods for brain MRI. The first objective was to extend spherical navigators (SNAVs), a form of MR-based motion tracking previously validated for retrospective motion correction, to more advantageous prospective applications. The next objective was to demonstrate the first purely navigator-based approach to prospective motion correction (PMC) for R2* and susceptibility mapping. The last objective, for cases where retrospective correction is preferable, was to improve deep-learning-based motion correction by leveraging the additional degree of spatial encoding embedded in multichannel data.
Results demonstrated that optimized processing of SNAVs for real-time application enables PMC of brain MRI with sub millimeter accuracy and low latency (within 60 MS). Furthermore, implementing SNAV-based PMC in a multi-echo gradient echo protocol for R2* and susceptibility mapping in the presence of motion was shown to consistently reduce quantitative mapping error, further improved with retrospective correction of motion-induced field offsets. Finally, in the context of deep-learning-based correction, results showed a 40% improvement in error reduction when applying the networks prior to coil combination.
In summary, this thesis presents three notable developments in motion correction for brain MRI. Improved motion correction methods could reduce the need for repeat scans, which increase costs and can delay diagnosis in a clinical setting.