Motion-compensated gradient waveforms for tensor-valued diffusion encoding by constrained numerical optimization.

Area of Science:

• Magnetic Resonance Imaging (MRI)
• Biomedical Engineering
• Physics

Background:

• Diffusion-weighted MRI is susceptible to motion artifacts, which can distort signal measurements and analysis.
• Tissue motion, both bulk and incoherent, poses a significant challenge in diffusion MRI, particularly in dynamic environments like the heart.

Purpose of the Study:

• To develop and validate a motion-compensated gradient waveform design for tensor-valued diffusion encoding.
• To negate the confounding effects of bulk and incoherent motion in the ballistic regime during diffusion MRI.

Main Methods:

• A numerical optimization framework was employed, incorporating constraints on gradient waveform moment vectors for motion compensation.
• Existing constraints for b-tensor shape, hardware limitations, and concomitant fields were integrated into the framework.
• The proposed method was evaluated using simulations and demonstrated in vivo using linear and planar b-tensor encoding in a healthy heart.

Main Results:

• The optimization produced asymmetric motion-compensated waveforms, enabling arbitrary b-tensor shapes with improved efficiency for tensor-valued encoding.
• Efficiency was comparable to previous designs for conventional linear encoding.
• In vivo demonstration in the heart showed substantially enhanced data quality with motion compensation.

Conclusions:

• The novel gradient waveform design offers greater flexibility and efficiency than prior methods for diffusion MRI.
• It facilitates tensor-valued diffusion encoding in tissues prone to motion-induced signal corruption.
• The design integrates compensation for concomitant gradient effects and utilizes asymmetric encoding times with multiple refocusing pulses.