Wellcome Centre for Integrative Neuroimaging, FMRIB Division, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.
Magn Reson Med. 2023 Jun;89(6):2376-2390. doi: 10.1002/mrm.29589. Epub 2023 Jan 19.
To assess the accuracy of morphing an established reference electromagnetic head model to a subject-specific morphometry for the estimation of specific absorption rate (SAR) in 7T parallel-transmit (pTx) MRI.
Synthetic T -weighted MR images were created from three high-resolution open-source electromagnetic head voxel models. The accuracy of morphing a "reference" (multimodal image-based detailed anatomical [MIDA]) electromagnetic model into a different subject's native space (Duke and Ella) was compared. Both linear and nonlinear registration methods were evaluated. Maximum 10-g averaged SAR was estimated for circularly polarized mode and for 5000 random RF shim sets in an eight-channel transmit head coil, and comparison made between the morphed MIDA electromagnetic models and the native Duke and Ella electromagnetic models, respectively.
The averaged error in maximum 10-g averaged SAR estimation across pTx MRI shim sets between the MIDA and the Duke target model was reduced from 17.5% with only rigid-body registration, to 11.8% when affine linear registration was used, and further reduced to 10.7% when nonlinear registration was used. The corresponding figures for the Ella model were 16.7%, 11.2%, and 10.1%.
We found that morphometry accounts for up to half of the subject-specific differences in pTx SAR. Both linear and nonlinear morphing of an electromagnetic model into a target subject improved SAR agreement by better matching head size, morphometry, and position. However, differences remained, likely arising from details in tissue composition estimation. Thus, the uncertainty of the head morphometry and tissue composition may need to be considered separately to achieve personalized SAR estimation.
评估将已建立的参考电磁头模型变形为特定个体形态学以估计 7T 并行传输 (pTx) MRI 中的比吸收率 (SAR) 的准确性。
从三个高分辨率开源电磁头体素模型中创建合成 T1 加权 MR 图像。比较了将“参考”（基于多模态图像的详细解剖学 [MIDA]）电磁模型变形为不同个体固有空间（Duke 和 Ella）的准确性。评估了线性和非线性配准方法。在八通道发射头线圈中，对圆极化模式和 5000 个随机 RF 调谐集估计最大 10-g 平均 SAR，并分别比较变形的 MIDA 电磁模型和固有 Duke 和 Ella 电磁模型之间的差异。
在 pTx MRI 调谐集之间，MIDA 和 Duke 目标模型之间的最大 10-g 平均 SAR 估计的平均误差从仅刚体配准的 17.5%降低到使用仿射线性配准时的 11.8%，进一步降低到使用非线性配准时的 10.7%。Ella 模型的相应数字为 16.7%、11.2%和 10.1%。
我们发现形态计量学占 pTx SAR 个体差异的一半以上。电磁模型到目标个体的线性和非线性变形通过更好地匹配头部大小、形态学和位置，提高了 SAR 一致性。然而，仍然存在差异，可能源于组织成分估计的细节。因此，头部形态计量学和组织成分的不确定性可能需要分别考虑，以实现个性化 SAR 估计。
