人工智能
图像配准
计算机科学
深度学习
人工神经网络
磁共振成像
相似性(几何)
计算机视觉
基本事实
模式识别(心理学)
放射科
图像(数学)
医学
作者
Shadab Momin,Yang Lei,Tonghe Wang,Yabo Fu,Pretesh Patel,Ashesh B. Jani,Walter J. Curran,Tian Liu,Xiaofeng Yang
摘要
In this study, we propose a novel unsupervised deep learning-based method to register pelvic MRI and CBCT images. No ground truth deformation vector field (DVF) is needed during training. To perform registration between CBCT and MRI, a self-similarity image similarity loss, called as self-correlation descriptor, is used as loss function to learn the trainable parameters in the unsupervised deep neural networks. After training, for a new patient’s CBCT and MRI, the deformed MRI is obtained via first feeding the MRI and CBCT into the unsupervised deep neural networks to derive the DVF, then deformed via spatial transformation on MRI and DVF. Our results show that the proposed method has outperformed manual rigid registration. Target registration error calculated between CBCT and deformed MRI is used for evaluation. The average TRE is 2.95±0.94 mm among the 20 prostate patients we retrospectively investigated. The proposed method has great potential in providing accurate image registration and potentially facilitating adaptive radiation therapy by multi-imaging modality.
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