Biomedical Engineering pres.
BME Ph.D. Defense: Amos Cao
Methods for Physiological Artifact Correction in Oscillating Steady State Imaging
First, we develop analytical models and simulations of OSSI, which describe how the signal magnitude varies as a function of frequency. These simulations are then used to study how respiration-induced frequency changes cause artifactual signal fluctuations to a signal timecourse. Our simulations show that the severity of respiration artifacts changes with initial off-resonance. Furthermore, we show that respiration artifacts are primarily caused by transient signal effects rather than changes to steady-state magnitude. These findings inform the two correction strategies proposed in the remainder of the dissertation.
The second portion of this work describes "OSSCOR," a retrospective method to correct timecourse magnitude changes caused by temporally varying frequency. We show how the OSSI signal exhibits a frequency-time duality that can be used to reshape structured physiological noise into a low-rank matrix. We then use principal component analysis in a data-driven correction strategy to create nuisance regressors for subsequent fMRI analysis. We also describe a variation of our method where free induction decay (FID) signals can be used to create nuisance regressors, referred to as ``F-OSSCOR.'' Both OSSCOR and F-OSSCOR were found to significantly improve the functional sensitivity and signal stability compared to polynomial detrending alone, and OSSCOR was also found to significantly outperform a standard data-driven correction method.
Finally, we present a prospective correction method which utilizes FID measurements to estimate and correct for B0 changes in real-time. Prospective correction has the potential to outperform retrospective correction methods by directly reducing perturbations to steady-state magnetization during acquisition. We first present the results of a feasibility analysis where simulation was used to determine how scan parameters would affect correction performance. We then developed a prospective correction application using the RTHawk platform to perform data analysis and parameter adjustment in real-time. Our initial fMRI proof-of-concept shows that real-time correction can increase the number of activated voxels and improve overall image stability as measured by temporal SNR.
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