RESUMEN
BACKGROUND: Current intensity-modulated radiation therapy (IMRT) treatment planning is still a manual and time/resource consuming task, knowledge-based planning methods with appropriate predictions have been shown to enhance the plan quality consistency and improve planning efficiency. This study aims to develop a novel prediction framework to simultaneously predict dose distribution and fluence for nasopharyngeal carcinoma treated with IMRT, the predicted dose information and fluence can be used as the dose objectives and initial solution for an automatic IMRT plan optimization scheme, respectively. METHODS: We proposed a shared encoder network to simultaneously generate dose distribution and fluence maps. The same inputs (three-dimensional contours and CT images) were used for both dose distribution and fluence prediction. The model was trained with datasets of 340 nasopharyngeal carcinoma patients (260 cases for training, 40 cases for validation, 40 cases for testing) treated with nine-beam IMRT. The predicted fluence was then imported back to treatment planning system to generate the final deliverable plan. Predicted fluence accuracy was quantitatively evaluated within projected planning target volumes in beams-eye-view with 5 mm margin. The comparison between predicted doses, predicted fluence generated doses and ground truth doses were also conducted inside patient body. RESULTS: The proposed network successfully predicted similar dose distribution and fluence maps compared with ground truth. The quantitative evaluation showed that the pixel-based mean absolute error between predicted fluence and ground truth fluence was 0.53% ± 0.13%. The structural similarity index also showed high fluence similarity with values of 0.96 ± 0.02. Meanwhile, the difference in the clinical dose indices for most structures between predicted dose, predicted fluence generated dose and ground truth dose were less than 1 Gy. As a comparison, the predicted dose achieved better target dose coverage and dose hot spot than predicted fluence generated dose compared with ground truth dose. CONCLUSION: We proposed an approach to predict 3D dose distribution and fluence maps simultaneously for nasopharyngeal carcinoma patients. Hence, the proposed method can be potentially integrated in a fast automatic plan generation scheme by using predicted dose as dose objectives and predicted fluence as a warm start.
Asunto(s)
Neoplasias Nasofaríngeas , Radioterapia de Intensidad Modulada , Humanos , Radioterapia de Intensidad Modulada/métodos , Carcinoma Nasofaríngeo/radioterapia , Dosificación Radioterapéutica , Planificación de la Radioterapia Asistida por Computador/métodos , Neoplasias Nasofaríngeas/radioterapiaRESUMEN
Synthesizing computed tomography (CT) images from magnetic resonance imaging (MRI) data can provide the necessary electron density information for accurate dose calculation in the treatment planning of MRI-guided radiation therapy (MRIgRT). Inputting multimodality MRI data can provide sufficient information for accurate CT synthesis: however, obtaining the necessary number of MRI modalities is clinically expensive and time-consuming. In this study, we propose a multimodality MRI synchronous construction based deep learning framework from a single T1-weight (T1) image for MRIgRT synthetic CT (sCT) image generation. The network is mainly based on a generative adversarial network with sequential subtasks of intermediately generating synthetic MRIs and jointly generating the sCT image from the single T1 MRI. It contains a multitask generator and a multibranch discriminator, where the generator consists of a shared encoder and a splitted multibranch decoder. Specific attention modules are designed within the generator for feasible high-dimensional feature representation and fusion. Fifty patients with nasopharyngeal carcinoma who had undergone radiotherapy and had CT and sufficient MRI modalities scanned (5550 image slices for each modality) were used in the experiment. Results showed that our proposed network outperforms state-of-the-art sCT generation methods well with the least MAE, NRMSE, and comparable PSNR and SSIM index measure. Our proposed network exhibits comparable or even superior performance than the multimodality MRI-based generation method although it only takes a single T1 MRI image as input, thereby providing a more effective and economic solution for the laborious and high-cost generation of sCT images in clinical applications.
Asunto(s)
Aprendizaje Profundo , Humanos , Tomografía Computarizada por Rayos X/métodos , Imagen por Resonancia Magnética/métodos , Imagen Multimodal , Planificación de la Radioterapia Asistida por Computador/métodos , Procesamiento de Imagen Asistido por Computador/métodosRESUMEN
Accurate segmentation of brain tumor plays an important role in MRI diagnosis and treatment monitoring of brain tumor. However, the degree of lesions in each patient's brain tumor region is usually inconsistent, with large structural differences, and brain tumor MR images are characterized by low contrast and blur, current deep learning algorithms often cannot achieve accurate segmentation. To address this problem, we propose a novel end-to-end brain tumor segmentation algorithm by integrating the improved 3D U-Net network and super-resolution image reconstruction into one framework. In addition, the coordinate attention module is embedded before the upsampling operation of the backbone network, which enhances the capture ability of local texture feature information and global location feature information. To demonstrate the segmentation results of the proposed algorithm in different brain tumor MR images, we have trained and evaluated the proposed algorithm on BraTS datasets, and compared with other deep learning algorithms by dice similarity scores. On the BraTS2021 dataset, the proposed algorithm achieves the dice similarity score of 89.61%, 88.30%, 91.05%, and the Hausdorff distance (95%) of 1.414 mm, 7.810 mm, 4.583 mm for the enhancing tumors, tumor cores and whole tumors, respectively. The experimental results illuminate that our method outperforms the baseline 3D U-Net method and yields good performance on different datasets. It indicated that it is robust to segmentation of brain tumor MR images with structures vary considerably.