Motion immune diffusion imaging using augmented MUSE for high-resolution multi-shot EPI.

Area of Science:

• Medical Imaging
• Neuroimaging
• Diffusion Tensor Imaging

Background:

• Patient motion significantly degrades the quality of high-resolution multishot echo-planar diffusion imaging.
• Existing methods like multiplexed sensitivity encoding (MUSE) have limitations in addressing macroscopic motion and phase errors.

Purpose of the Study:

• To develop advanced techniques for mitigating microscopic and macroscopic patient motion effects in diffusion imaging.
• To enhance the accuracy of quantitative diffusion tensor imaging (DTI) metrics despite subject motion.

Main Methods:

• Extended the MUSE algorithm to create augmented MUSE (AMUSE) to correct for macroscopic misregistrations and motion-induced phase errors.
• Incorporated the impact of altered diffusion encoding due to macroscopic motion into the AMUSE framework.
• Evaluated AMUSE on simulated and in vivo motion-corrupted multishot diffusion data, assessing image quality and DTI metrics.

Main Results:

• AMUSE demonstrated reduced image blurring caused by macroscopic motion compared to standard MUSE.
• Neglecting altered diffusion encoding led to inaccurate tensor estimations with AMUSE.
• Accounting for altered diffusion encoding significantly improved the accuracy of diffusion tensor estimations.

Conclusions:

• Augmented MUSE (AMUSE) effectively improves image quality and diffusion tensor accuracy in multishot diffusion imaging with macroscopic motion.
• The developed techniques are vital for enabling future high-resolution diffusion imaging studies.
• AMUSE offers a promising solution for robust quantitative diffusion MRI in the presence of subject motion.