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Abstract #4838

Deep-Learning based Referenceless MR Thermometry Using Residual U-Net with Self-Attention

Yueran Zhao1, Shenyan Zong2, Qiwei Yang1, Hao Wu3, Guofeng Shen1, Nathan Judson McDannold4, and Chang-Sheng Mei4,5
1Shanghai Jiao Tong University, Shanghai, China, 2Fudan University, Shanghai, China, 3Shanghai Shende Medical Technology Co., Ltd, Shanghai, China, 4Brigham and Women's Hospital/Harvard Medical School, Boston, MA, United States, 5Soochow University, Taipei, Taiwan

Synopsis

Keywords: Thermometry/Thermotherapy, Thermometry

Motivation: Clinical use of referenceless MR thermometry is challenged by inconsistent spatial patterns of phase distribution, which limit the accuracy of temperature measurements for motion objects.

Goal(s): This study aims to develop a robust deep-learning model for reconstructing absent phase information on focal regions, regardless of heating spot location, eliminating dependence on specific functions.

Approach: A residual U-Net with a self-attention mechanism was used to restore the background phase based on full-phase data from the subject region.

Results: The model achieved high coherence with ground truth temperatures, particularly when focal areas were adjacent to boundaries where spatial phases are complicated, confirming reliable referenceless thermometry.

Impact: This work enhances MR thermometry in organs with respiratory motion by achieving accurate, real-time temperature measurements and overcoming regional phase variability, demonstrating its potential for broader clinical applications in dynamic thermal assessment.

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Keywords