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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.07882 |
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| _version_ | 1866917133460439040 |
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| author | Zhao, Yueran Mei, Chang-Sheng McDannold, Nathan J. Zong, Shenyan Shen, Guofeng |
| author_facet | Zhao, Yueran Mei, Chang-Sheng McDannold, Nathan J. Zong, Shenyan Shen, Guofeng |
| contents | Background: Accurate proton resonance frequency (PRF) MR thermometry is essential for monitoring temperature rise during thermal ablation with high intensity focused ultrasound (FUS). Conventional referenceless methods such as complex field estimation (CFE) and phase finite difference (PFD) tend to exhibit errors when susceptibility-induced phase discontinuities occur at tissue interfaces. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_07882 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Referenceless Proton Resonance Frequency Thermometry Using Deep Learning with Self-Attention Zhao, Yueran Mei, Chang-Sheng McDannold, Nathan J. Zong, Shenyan Shen, Guofeng Medical Physics Artificial Intelligence Background: Accurate proton resonance frequency (PRF) MR thermometry is essential for monitoring temperature rise during thermal ablation with high intensity focused ultrasound (FUS). Conventional referenceless methods such as complex field estimation (CFE) and phase finite difference (PFD) tend to exhibit errors when susceptibility-induced phase discontinuities occur at tissue interfaces. |
| title | Referenceless Proton Resonance Frequency Thermometry Using Deep Learning with Self-Attention |
| topic | Medical Physics Artificial Intelligence |
| url | https://arxiv.org/abs/2512.07882 |