To monitor and strategically control energy deposition in magnetic resonance imaging (MRI), measured as a specific absorption rate (SAR), numerical methods using generic human models have been employed to estimate worst-case values. Radiofrequency (RF) sequences are therefore often designed conservatively with large safety margins, potentially hindering the full potential of high-field systems. To more accurately predict the patient SAR values, we propose the use of image registration techniques, in conjunction with high-resolution image and tissue libraries, to create patient-specific voxel models. To test this, a matching model from the archives was first selected. Its tissue information was then warped to the patient's coordinates by registering the high-resolution library image to the pilot scan of the patient. Results from studying the models' 1 g SAR distribution suggest that the developed patient model can predict regions of elevated SAR within the patient with remarkable accuracy. Additionally, this work also proposes a voxel analytical metric that can assist in the construction of a patient library and the selection of the matching model from the library for a patient. It is hoped that, by developing voxel models with high accuracy in patient-specific anatomy and positioning, the proposed method can accurately predict the safety margins for high-field human applications and, therefore maximize the safe use of RF sequence power in high-field MRI systems. © 2012 Institute of Physics and Engineering in Medicine.
CITATION STYLE
Jin, J., Liu, F., Weber, E., & Crozier, S. (2012). Improving SAR estimations in MRI using subject-specific models. Physics in Medicine and Biology, 57(24), 8153–8171. https://doi.org/10.1088/0031-9155/57/24/8153
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