Sequence‐agnostic motion‐correction leveraging efficiently calibrated Pilot Tone signals

• Published in: Magnetic resonance in medicine 2024-11, Vol.92 (5), p.1881-1897
• Main Authors: Brackenier, Yannick, Cordero‐Grande, Lucilio, McElroy, Sarah, Tomi‐Tricot, Raphael, Barbaroux, Hugo, Bridgen, Philippa, Malik, Shaihan J., Hajnal, Joseph V.
• Format: Article
• Language: English
• Subjects: Adult; Algorithms; Brain; Brain - diagnostic imaging; Calibration; Data acquisition; Female; Healthy Volunteers; Humans; Image acquisition; Image contrast; Image Processing, Computer-Assisted - methods; In vivo methods and tests; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Medical imaging; Motion; motion correction; Motion sensors; Motion simulation; Neuroimaging; parallel imaging; Parameter robustness; Pilot Tone (PT); reconstruction; Sampling; Trajectory analysis; ultrahigh field; Workflow
• ISSN: 0740-3194; 1522-2594; 1522-2594
• DOI: 10.1002/mrm.30161

Purpose This study leverages externally generated Pilot Tone (PT) signals to perform motion‐corrected brain MRI for sequences with arbitrary k‐space sampling and image contrast. Theory and Methods PT signals are promising external motion sensors due to their cost‐effectiveness, easy workflow, and consistent performance across contrasts and sampling patterns. However, they lack robust calibration pipelines. This work calibrates PT signal to rigid motion parameters acquired during short blocks (˜4 s) of motion calibration (MC) acquisitions, which are short enough to unobstructively fit between acquisitions. MC acquisitions leverage self‐navigated trajectories that enable state‐of‐the‐art motion estimation methods for efficient calibration. To capture the range of patient motion occurring throughout the examination, distributed motion calibration (DMC) uses data acquired from MC scans distributed across the entire examination. After calibration, PT is used to retrospectively motion‐correct sequences with arbitrary k‐space sampling and image contrast. Additionally, a data‐driven calibration refinement is proposed to tailor calibration models to individual acquisitions. In vivo experiments involving 12 healthy volunteers tested the DMC protocol's ability to robustly correct subject motion. Results The proposed calibration pipeline produces pose parameters consistent with reference values, even when distributing only six of these approximately 4‐s MC blocks, resulting in a total acquisition time of 22 s. In vivo motion experiments reveal significant (p