التفاصيل البيبلوغرافية
| العنوان: |
Deep learning in MRI‐guided radiation therapy: A systematic review. |
| المؤلفون: |
Eidex, Zach, Ding, Yifu, Wang, Jing, Abouei, Elham, Qiu, Richard L. J., Liu, Tian, Wang, Tonghe, Yang, Xiaofeng |
| المصدر: |
Journal of Applied Clinical Medical Physics; Feb2024, Vol. 25 Issue 2, p1-21, 21p |
| مصطلحات موضوعية: |
DEEP learning, RADIOTHERAPY, IMAGE segmentation, RADIOMICS, TRANSFORMER models, MAGNETIC resonance imaging |
| مستخلص: |
Recent advances in MRI‐guided radiation therapy (MRgRT) and deep learning techniques encourage fully adaptive radiation therapy (ART), real‐time MRI monitoring, and the MRI‐only treatment planning workflow. Given the rapid growth and emergence of new state‐of‐the‐art methods in these fields, we systematically review 197 studies written on or before December 31, 2022, and categorize the studies into the areas of image segmentation, image synthesis, radiomics, and real time MRI. Building from the underlying deep learning methods, we discuss their clinical importance and current challenges in facilitating small tumor segmentation, accurate x‐ray attenuation information from MRI, tumor characterization and prognosis, and tumor motion tracking. In particular, we highlight the recent trends in deep learning such as the emergence of multi‐modal, visual transformer, and diffusion models. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
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