The goal of this project is to develop a framework for high-performance parallel transmission (pTx) that is trans- ferable to a wide range of MRI scanners, and apply it to push the spatial encoding limits of echo planar imaging (EPI) at 7 Tesla. EPI is by far the most widely used pulse sequence for rapid functional, diffusion, and perfusion imaging, and has been the focus of considerable development in recent years to increase its speed and spatial resolution. Now there is a strong desire to push EPI's spatial resolution down to the micro scale. For functional MRI (fMRI), this would enable imaging of ?ne structures (layers, columns, and nuclei) of cortical and subcortical architecture while better resolving the hemodynamic response. For diffusion MRI (dMRI), micro scale EPI would improve surface and laminar analysis of ?bers in the cortex, as well as brain parcelation using fractional anisotropy differences between gray matter regions, while broadly reducing partial volume effects. It would further enable EPI to be broadly applied to accelerate anatomic scans that are geometrically matched to fMRI and dMRI scans. However, increasing the resolution of single-shot EPI requires longer readouts which extend echo times and re- duce functional contrast in fMRI and signal-to-noise in dMRI at 7 Tesla, while increasing geometric distortions and blurring. Segmented or multishot EPI is a classic method to increase spatial resolution without increasing readout durations, but is underutilized, primarily due to its high sensitivity to motion and dynamic phase changes between shots which cause large image artifacts. We propose to develop a new multishot EPI technique called shuttered EPI, which addresses the lim- itations of conventional multishot EPI by imaging a set of spatially disjoint shutters in each shot. The shutters are produced by a multidimensional excitation pulse and are spatially shifted between shots to cover an entire slice. However, with thin slices the length of the excitation pulses are impractical (20-100 ms). Many-coil pTx (> 8 coils) can shorten the length of these pulses to feasible durations, but current 7 Tesla scanners have only 8 transmit channels due to cost, footprint, cabling, and other constraints. In the ?rst project period we pioneered a technique called array-compressed pTx (acpTx) which overcomes this limitation. Using acpTx, 8 transmit chan- nels can control an arbitrarily large number of coils, where the channels and coils are connected via an array compression network that is optimized with RF pulses for speci?c excitations. In this project, we will develop and apply acpTx methods and hardware (a many-coil head transmit array and an 8 channel-to-many coil array com- pression network) to achieve feasible RF pulse durations when exciting the shutter patterns required for shuttered EPI. These developments will be implemented on two major 7T scanner platforms and evaluated in submillimeter (600 micron) fMRI and dMRI acquisitions. Overall, the project encompasses the synergistic design of RF pulses, hardware, acquisitions and reconstructions to achieve a major advance in spatial encoding.

Public Health Relevance

Diffusion and functional magnetic resonance imaging (MRI) at 7 Tesla ?eld strength using echo planar imaging (EPI) has the potential to deliver clear images of brain structure and function at the level of layers, columns, and nuclei. However, when existing EPI scans are pushed to the spatial resolutions required to resolve these structures, they become highly sensitive to off resonance-induced geometric distortions, relaxation-induced blur- ring, physiological noise and motion. To address this problem, in this project we will develop many-coil array- compressed parallel transmission and apply it to enable shuttered multishot EPI scans that are robust to these effects.

National Institute of Health (NIH)
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Research Project (R01)
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Study Section
Biomedical Imaging Technology Study Section (BMIT)
Program Officer
Wang, Shumin
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Vanderbilt University Medical Center
Schools of Medicine
United States
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Ianni, Julianna D; Cao, Zhipeng; Grissom, William A (2018) Machine learning RF shimming: Prediction by iteratively projected ridge regression. Magn Reson Med 80:1871-1881
Yan, Xinqiang; Cao, Zhipeng; Grissom, William A (2018) Ratio-adjustable power splitters for array-compressed parallel transmission. Magn Reson Med 79:2422-2431
Ianni, Julianna D; Welch, E Brian; Grissom, William A (2018) Ghost reduction in echo-planar imaging by joint reconstruction of images and line-to-line delays and phase errors. Magn Reson Med 79:3114-3121
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Grissom, William A; Setsompop, Kawin; Hurley, Samuel A et al. (2017) Advancing RF pulse design using an open-competition format: Report from the 2015 ISMRM challenge. Magn Reson Med 78:1352-1361
Cao, Zhipeng; Donahue, Manus J; Ma, Jun et al. (2016) Joint design of large-tip-angle parallel RF pulses and blipped gradient trajectories. Magn Reson Med 75:1198-208
Yan, Xinqiang; Cao, Zhipeng; Grissom, William A (2016) Experimental implementation of array-compressed parallel transmission at 7 tesla. Magn Reson Med 75:2545-52

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