Addressing Gradient Imperfection Related Bias in Stack-of-Stars MRI for Free-Breathing, Confounder-Corrected T1 Mapping

Motivation: Non-Cartesian sampling strategies enable free-breathing imaging due to their robustness against motion but are susceptible to artifacts related to gradient imperfections. Such artifacts may manifest as bias or structured noise in quantitative imaging applications. Goal(s): In this study, we explore the effects of gradient imperfections and evaluate the performance of gradient correction methods in non-Cartesian, confounder-corrected T1 mapping using both phantom and in vivo imaging experiments. Approach: For this purpose, we compare confounder-corrected T1 maps reconstructed with data-driven and calibration-based gradient correction approaches. Results: Our initial results indicate that gradient correction methods are essential for mitigating the bias due to gradient imperfections. Impact: This study confirms that gradient imperfections result in bias in non-Cartesian quantitative imaging applications. Our findings indicate that T1 relaxometry is less susceptible to gradient imperfections than PDFF and R2* quantification. The application of gradient correction methods mitigates this bias.