Quantitative T1 MRI of the brain provides valuable insight into the health status of brain tissue.
To estimate T1, an image slice is acquired with multiple inversion time (TI) values, and then the
variation in measured intensities versus TI values are t to a MR spin-relaxation model to extract
the underlying quantitative T1 recovery value. The gold standard for T1 mapping is currently
Inversion-Recovery Spin Echo (IR-SE). However, this MRI sequence requires a prohibitively long
data acquisition time, which prevents clinical adoption.
Inversion-recovery data acquisition time can be reduced by both adopting fast MR imaging
methods such as echo planar imaging (EPI) and creative slice ordering in the acquisition of EPI
data that form the full imaged volume. At ultrahigh MRI eld strength, 7 Tesla, T1-Mapping using
EPI is limited by inherent imaging artifacts. One dominant artifact is EPI ghosting, which manifests
as copies of the measured image displaced from the original source location. In certain cases,
these displaced copies can create ripple artifacts that may change over the course of an imaging
session, introducing temporal instability into the data. These artifacts appear more prominently at
higher magnetic eld strength, particularly when imaging structures deep within the brain.
This proposal seeks to improve and validate a new, novel method for rapid T1-Mapping at
ultra-high eld, named Multiple-Inversion EPI (MI-EPI). In Aim 1, we seek to integrate our recent
high-performance EPI technology that we have shown reduce inherent EPI imaging artifacts,
including Dual-Polarity GRAPPA, Dual-Polarity slice-GRAPPA, and FLEET, into a spin-echo (SE)
readout variant of the Multi-Inversion Simultaneous Multi-Slice (SMS) EPI sequence. We seek to
demonstrate that quantitative T1 maps can be consistently and robustly generated from multiple
inversion-time data collected using SE MI-EPI. We then will further extend our high-performance
EPI technology into an SMS-EPI diffusion weighted sequence, and develop an DICOM-compatible
image reconstruction framework to generate FLAIR and MPRAGE images from the quantitative T1
maps. Finally, we seek to develop a segmented spin-echo MI-EPI sequence to reduce geometric
distortion and improve spatial resolution. In Aim 2, we seek to combine all of the above to implement
a 10 minute, all EPI based, whole-brain neurological examination protocol.
The success of our proposal will be identi ed by the ability to characterize an improvement
in image quality and delity with the underlying anatomy in the T1-Mapping application. The
successful completion of this project will directly improve neuroimaging studies, by enabling rapid,
high quality T1-Mapping and a short, whole-brain neurological exam at 7T eld strength.