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Updated: Jun 24, 2025

Movement Retraining using Real-time Feedback of Performance
Published on: January 17, 2013
Sequence-agnostic motion-correction leveraging efficiently calibrated Pilot Tone signals
Yannick Brackenier1,2, Lucilio Cordero-Grande1,2,3, Sarah McElroy1,2,4
1Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
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 ( ) improved motion correction with increased signal to residual ratio for both MPRAGE and SPACE sequences with standard k-space acquisition, especially when motion is large. Additionally, results highlight the benefits of using a distributed calibration approach.
Conclusions:
This study presents a framework for performing motion-corrected brain MRI in sequences with arbitrary k-space encoding and contrast, using externally generated PT signals. The DMC protocol is introduced, promoting observation of patient motion occurring throughout the examination and providing a calibration pipeline suitable for clinical deployment. The method's application is demonstrated in standard volumetric MPRAGE and SPACE sequences.
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