Wave-CAIPI ViSTa: highly accelerated whole-brain direct myelin water imaging with zero-padding reconstruction


Zhe Wu, Berkin Bilgic, Hongjian He, Qiqi Tong, Yi Sun, Yiping Du, Kawin Setsompop, and Jianhui Zhong. 2018. “Wave-CAIPI ViSTa: highly accelerated whole-brain direct myelin water imaging with zero-padding reconstruction.” Magn Reson Med, 80, 3, Pp. 1061-1073.


PURPOSE: This study introduces a highly accelerated whole-brain direct visualization of short transverse relaxation time component (ViSTa) imaging using a wave controlled aliasing in parallel imaging (CAIPI) technique, for acquisition within a clinically acceptable scan time, with the preservation of high image quality and sufficient spatial resolution, and reduced residual point spread function artifacts. METHODS: Double inversion RF pulses were applied to preserve the signal from short T components for directly extracting myelin water signal in ViSTa imaging. A 2D simultaneous multislice and a 3D acquisition of ViSTa images incorporating wave-encoding were used for data acquisition. Improvements brought by a zero-padding method in wave-CAIPI reconstruction were also investigated. RESULTS: The zero-padding method in wave-CAIPI reconstruction reduced the root-mean-square errors between the wave-encoded and Cartesian gradient echoes for all wave gradient configurations in simulation, and reduced the side-main lobe intensity ratio from 34.5 to 16% in the thin-slab in vivo ViSTa images. In a 4 × acceleration simultaneous-multislice scenario, wave-CAIPI ViSTa achieved negligible g-factors (g /g  = 1.03/1.10), while retaining minimal interslice artifacts. An 8 × accelerated acquisition of 3D wave-CAIPI ViSTa imaging covering the whole brain with 1.1 × 1.1 × 3 mm voxel size was achieved within 15 minutes, and only incurred a small g-factor penalty (g /g  = 1.05/1.16). CONCLUSION: Whole-brain ViSTa images were obtained within 15 minutes with negligible g-factor penalty by using wave-CAIPI acquisition and zero-padding reconstruction. The proposed zero-padding method was shown to be effective in reducing residual point spread function for wave-encoded images, particularly for ViSTa.