Motion-insensitive diffusion imaging of the brain using optical tracking and dynamic sequence updates.

PURPOSE: Diffusion-weighted imaging (DWI) is sensitive to head movements, which may cause signal losses because of motion-induced gradient imbalances. Prospective motion correction using fast optical tracking can attenuate these artifacts. Approaches include quasicontinuous updates of gradients and radiofrequency (RF) pulses or dynamically applying a rebalancing gradient to restore the gradient balance, but these prior methods used bipolar diffusion gradients. The goal of this project was to develop and evaluate a motion-insensitive implementation for the more common monopolar diffusion sequence.
METHODS: A monopolar diffusion sequence was developed with motion updates before each RF pulse and each diffusion-weighting gradient. The sequence was tested in a phantom and human brain at b = 1000 s/mm2 and rotational velocities up to 20°/s. Motion sensitivity, signal losses, and in vivo image profiles were compared between scans with and without intrasequence motion updates.
RESULTS: With typical motion parameters, intrasequence motion updates with optimal parameters reduced the motion sensitivity of DWI (motion-induced gradient moment imbalance) sevenfold. Optimal results were achieved by matching the echo time of the pulse sequence to an even multiple of the tracking system frame-to-frame period. Average signal losses and the frequency of signal dropouts in phantom and in vivo measurements were reduced when intrasequence updates were enabled, and quality measures of DTI analyses were improved.
CONCLUSION: A correction scheme for the monopolar DWI sequence can reduce the motion sensitivity of brain DWI up to sevenfold compared with an implementation without intrasequence updates.