Investigating motion-induced signal corruption in steady-state diffusion MRI
Purpose: Diffusion-weighted steady-state free precession (DW-SSFP) is a diffusion imaging sequence achieving high SNR efficiency and strong diffusion-weighting. A key challenge for in vivo DW-SSFP is the sequence's severe motion sensitivity, currently limiting investigations to low or no motion regimes. Here we establish a framework to both (1) investigate and (2) correct for the impact of subject motion on the DW-SSFP signal. Theory and Methods: An extended phase graphs (EPG) representation of the DW-SSFP signal was established incorporating a motion operator describing rigid body and pulsatile motion. The representation was validated using Monte Carlo simulations, and subsequently integrated into a data fitting routine for motion estimation and correction. The fitting routine was evaluated using both simulations and experimental 2D low-resolution single-shot timeseries DW-SSFP data acquired in a human brain, with a tensor reconstructed from the motion-corrected experimental DW-SSFP data. Results: The proposed EPG-motion framework gives excellent agreement to complementary Monte Carlo simulations, demonstrating that diffusion coefficient estimation is robust over a range of motion and SNR regimes. Tensor estimates from the motion-corrected experimental DW-SSFP data give excellent visual agreement to complementary DW-SE data acquired in the same subject. Conclusion: Temporal information capturing the evolution of the DW-SSFP signal can be used to retrospectively (1) estimate subject motion and (2) reconstruct motion-corrected DW-SSFP data. Open-source software is provided, facilitating future investigations into the impact of subject-motion on DW-SSFP acquisitions.