Ultra fast MRI acquisition at 7 Tesla

Abstract

Magnetic Resonance Imaging is one of the most widely used imaging modalities nowadays and it performs especially well imaging human organs such as the brain and liver. One of its main limitations is the relatively long imaging times, to overcome this issue and speed up the data acquisition, several techniques such as Parallel Imaging or PI have been developed. These techniques require advanced hardware and software to be able to decrease the acquisition time. On the hardware side, a highly efficient insert gradient coil has been designed and built at the University Medical Center Utrecht. Specialized software has to be implemented to optimally make use of this hardware. One of the recently proposed PI methods called Wave-CAIPI has been proved to achieve a ninth fold acceleration factor without compromising image quality. This project aims to investigate the time gain that can be achieved when combing the insert gradient coil with a Wave-CAIPI strategy. Two main aspects are reviewed. The first one is the maximum achievable under-sampling factor that does not compromise image quality. The second one is the decrease in acquisition time that can be obtained when using the insert gradient coil compared to conventional gradient systems while maintaining image quality. To do so, the strategy has been implemented and extensive simulations have been performed to optimize the MR acquisition parameters. To prove the results from the simulations, the Wave-CAIPI sequence was implemented in a 7T scanner at the UMCU, where the acquired data was retrospectively under-sampled, obtaining the wave image to be further reconstructed. Limitations of previous works on Wave-CAIPI have been the gradient specifications, which can be overcome by the high-efficiency coil. It has been concluded that shorter acquisition times without compromising image quality are possible when using the insert coil compared with conventional systems. The time gain can be up to a factor of five, and sixteen fold under-sampling factors could be possible. The time gain can be especially useful for Echo Planar Imaging sequences, where switching faster gradients allows to acquire more signals in less time. The next steps for this research are to prospectively under-sample the data in a Wave-CAIPI fashion with a sixteen under-sampling factor and corroborate if sequences such as Echo Planar Imaging can be benefited with the time gain.