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Journal Article

Low-distortion diffusion tensor MRI with improved phaseless encoding

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Tian, R., Hennel, F., & Pruessmann, K. (2019). Low-distortion diffusion tensor MRI with improved phaseless encoding. Journal of Magnetic Resonance, 309: 106602. doi:10.1016/j.jmr.2019.106602.

Cite as: https://hdl.handle.net/21.11116/0000-000A-F7A0-A
Due to the motion-related instability of the signal phase, diffusion MRI is usually performed with single-shot techniques such as the echo-planar imaging (EPI), which are resolution-limited and suffer from distortions caused by resonance offsets. Multi-shot methods may improve the images but require time-consuming navigators or a trade-off of the sensitivity encoding to measure shot-dependent phase errors. We have recently introduced an alternative approach to multi-shot MRI called phaseless encoding, which, by analogy to optical super-resolution methods, relies on the magnitude value of images taken in different shots thus discarding the phase error without navigators, and demonstrated its capability to perform diffusion MRI at sub-millimeter scale on a standard 3T scanner. In this work, we apply phaseless encoding in a routine diffusion tensor imaging (DTI) protocol with a moderately high resolution that is still within reach of single-shot EPI with the same hardware, and compare both techniques with respect to image distortions. A qualitative comparison of the phaseless encoding with the established navigator-based readout-segmented EPI is also presented. Several technical improvements are proposed to make phaseless encoding compatible with the routine scanning mode. The tagging radiofrequency pulses used in the encoding sequence are made slice-selective to avoid artefacts caused by saturation effects in multi-slice scans and their flip angle is optimized to reduce the intrinsic SNR loss. The super-resolution reconstruction algorithm is also improved to better suppress Gibbs ringing and to correct for possible signal amplitude fluctuations. Our study shows that the phaseless encoding is a promising approach to diffusion weighted imaging. It can easily be implemented in multi-slice sequences and produces less distorted images than the single-shot EPI at the same resolution and hardware parameters. It provides similar results to readout-segmented EPI but without the need of navigators.