The goal of this project is to improve the quality and spatial accuracy of echo-planar imaging (EPI), so that accurate quantitative information can be derived from EPI based medical research and clinical diagnosis. EPI is one of the fastest MR imaging techniques, and has been popularly applied to various dynamic studies that require high temporal-resolution, such as functional MRI (fMRI), contrast-enhanced imaging, and MR based interventional procedures. However, EPI data quality is usually degraded by various artifacts, such as geometric distortions and susceptibility signal loss. Furthermore, the sensitivity of EPI to susceptibility field nhomogeneities makes it less reliable in EPI based longitudinal studies. Several techniques have been previously reported for EPI quality improvement and artifact reduction. However, most previously reported EPI artifact reduction methods require time-consuming field mapping scans, and therefore may not always be practical (e.g. for clinical scans and EPI based interventional MRI procedures). Here we propose to use a novel k-space energy spectrum analysis to quantify (1) the k-space energy distribution, (2) susceptibility field gradients, (3) the spatially-dependent echo time values, and (4) artifact levels directly from the acquired EPI data, without the need of additional field mapping procedure or pulse sequence modification. Various EPI artifacts (e.g. distortions and Gibb's ripple artifact) can be effectively removed using the proposed approach. Furthermore, the developed k-space energy spectrum analysis will be applied to design an optimal acquisition strategy for phase-encoded 3D parallel EPI, with an improved signal-to-noise ratio and reduced motion related artifact. We also plan to apply the proposed methods to re-analyze the previously acquired fMRI data, and retrospectively improve the longitudinal reproducibility of grouped activation. The methods developed in the proposed project will be made available to MRI community so that other research groups may use the developed methods to improve their future EPI based quantitative studies or to retrospectively improve the EPI data that were previously obtained. ? ? ?
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