4.7 Article

In-scanner head motion and structural covariance networks

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Summary: A deep learning model was trained to estimate head pose changes during structural MRI scans using video data from an in-scanner eye tracker. Changes in head pose were found to impact cortical thickness and subcortical volume estimates. The method is independent of individual image acquisition parameters and does not require markers to be fixed to the patient, making it suitable for clinical imaging and research environments.

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