Liu Dong, Xin Zhuoyao, Ji Robin, Tsitsos Fotis, Jiménez-Gambín Sergio, Konofagou Elisa E, Ferrera Vincent P, Guo Jia
Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA.
F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA.
Proc IEEE Int Symp Biomed Imaging. 2024 May;2024. doi: 10.1109/isbi56570.2024.10635176. Epub 2024 Aug 22.
Utilizing a multi-task deep learning framework, this study generated synthetic CT (sCT) images from a limited dataset of Ultrashort echo time (UTE) MRI for transcranial focused ultrasound (tFUS) planning. A 3D Transformer U-Net was employed to produce sCT images that closely replicated actual CT scans, demonstrated by an average Dice coefficient of 0.868 for morphological accuracy. The acoustic simulation with sCT images showed mean focus absolute pressure differences of 8.85±7.29 % for the anterior cingulate cortex, 11.81±8.63 % for the precuneus, and 7.27±3.64 % for the supplemental motor cortex, with focus position discrepancies within 0.9±0.5 mm. These results underscore the efficacy of UTE-MRI as a non-radiative, cost-effective alternative for tFUS planning, with significant potential for clinical application.
本研究利用多任务深度学习框架，从用于经颅聚焦超声（tFUS）规划的超短回波时间（UTE）MRI有限数据集中生成合成CT（sCT）图像。采用3D Transformer U-Net生成与实际CT扫描紧密匹配的sCT图像，形态学准确性的平均Dice系数为0.868。使用sCT图像进行的声学模拟显示，前扣带回皮层的平均聚焦绝对压力差异为8.85±7.29%，楔前叶为11.81±8.63%，辅助运动皮层为7.27±3.64%，聚焦位置差异在0.9±0.5毫米以内。这些结果强调了UTE-MRI作为tFUS规划的非辐射、经济高效替代方案的有效性，具有显著的临床应用潜力。
