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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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cMeta-INR:基于队列信息的元学习的隐性神经表示,用于可变形的记录驱动的实时体积核磁共振估计.

Xiaoxue Qian1, Hua-Chieh Shao1, Jing Cai2

  • 1The Medical Artificial Intelligence and Automation (MAIA) Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America.

Physics in medicine and biology
|December 8, 2025
PubMed
概括

这项研究引入了队列信息化超学习 (cMeta-INR) 来从有限的k空间数据中快速准确地重建3D磁共振 (MR) 图像. 这种新型框架增强了隐性神经表示,用于患者特定的可变形图像注册,改善了解剖细节的保存.

关键词:
可变形图像的注册 变形图像的注册隐式神经表示 (INR)超级学习是一种超级学习.人口之前的前期人口.测试时间的适应.

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