Using machine learning to ensure safety of patients who cannot remain still during magnetic resonance imaging

Ultrahigh field magnetic resonance imaging (UHF-MRI, >=7T) offers previously unattainable levels of image quality and tissue detail. However, several patient populations may not stay still during magnetic resonance imaging, including paediatric patients and people with dementia, Parkinson's, Tourette's and Huntington's. We have recently demonstrated that within-scan patient motion may increase patient heating by up to 3.1-fold above the safety limits (Kopanoglu,MRM,2020) Although sedation can mitigate movement, it may cause adverse side effects and even, with an adverse effect, hospitalization. We demonstrated that, if we know the effect of patient motion on UHF-MRI scan dynamics, we could instead correct them during the scan (Kopanoglu,ISMRM,2018). Unfortunately, these effects cannot be measured, but our feasibility study on using a Machine Learning method (Method-1) to estimate these changes yielded exciting initial results. With this missing link complete, we can ensure safety of patients who cannot remain still while acquiring high quality images.