In Medical physics ; h5-index 59.0
PURPOSE : Respiration has a major impact on the accuracy of radiation treatment for thorax and abdominal tumors. Instantaneous volumetric imaging could provide precise knowledge of tumor and normal organs' three-dimensional (3D) movement, which is the key to reducing the negative effect of breathing motion. Therefore, this study proposed a real-time 3D MRI reconstruction method from cine-MRI using an unsupervised network.
METHODS AND MATERIALS : Cine-MRI and setup 3D-MRI from eight patients with liver cancer were utilized to establish and validate the deep learning network for 3D-MRI reconstruction. Unlike previous methods that required 4D-MRI for network training, the proposed method utilized a reference 3D-MRI and cine-MRI to generate the training data. Then, a network was trained in an unsupervised manner to estimate the relationship between the cine-MRI acquired on coronal plane and deformation vector field (DVF) that describes the patient's breathing motion. After the training process, the coronal cine-MRI were inputted into the network, and the corresponding DVF was obtained. By wrapping the reference 3D-MRI with the generated DVF, the 3D-MRI could be reconstructed.
RESULTS : The reconstructed 3D-MRI slices were compared with the corresponding phase-sorted cine-MRI using dice similarity coefficients (DSCs) of liver contours and blood vessel localization error. In all patients, the liver DSC had mean value >96.1% and standard deviation <1.3%; the blood vessel localization error had mean value < 2.6 mm, and standard deviation was <2.0 mm. Moreover, the time for 3D-MRI reconstruction was approximately 100 ms. These results indicated that the proposed method could accurately reconstruct the 3D-MRI in real time.
CONCLUSIONS : The proposed method could accurately reconstruct the 3D-MRI from cine-MRI in real time. This method has great potential in improving the accuracy of radiotherapy for moving tumors. This article is protected by copyright. All rights reserved.
Wei Ran, Chen Jiayun, Liang Bin, Chen Xinyuan, Men Kuo, Dai Jianrong
2022-Dec-12
3D-MRI reconstruction, cine-MRI, unsupervised learning
