Maxwell-compensated design of asymmetric gradient waveforms for tensor-valued diffusion encoding.

Area of Science:

• Magnetic Resonance Imaging (MRI)
• Diffusion Tensor Imaging (DTI)

Background:

• Asymmetric gradient waveforms in diffusion encoding offer superior efficiency.
• Concomitant gradients can introduce signal errors and artifacts in MRI due to residual gradient moments.

Purpose of the Study:

• To develop asymmetric waveform designs for tensor-valued diffusion encoding that are insensitive to concomitant gradients.
• To mitigate signal errors and image artifacts caused by concomitant gradients in diffusion MRI.

Main Methods:

• Proposed the "Maxwell index" as a scalar invariant to quantify concomitant gradient effects.
• Optimized "Maxwell-compensated" waveforms by constraining the Maxwell index.
• Compared optimized waveforms against existing literature using numerical analysis and phantom/human brain experiments.

Main Results:

• Maxwell-compensated waveforms (Maxwell index < 100 (mT/m)² ms) demonstrated negligible signal bias in simulations and experiments.
• Waveforms from literature exhibited significant signal bias, impacting parameter maps.
• Theoretical predictions accurately matched experimental findings.

Conclusions:

• Constraining the Maxwell index during asymmetric gradient waveform optimization effectively negates concomitant field effects.
• This approach enables efficient diffusion encoding with arbitrary b-tensor shapes.
• The waveform design is particularly beneficial for advanced MRI techniques like strong gradients, long encoding times, and simultaneous multi-slice acquisition.