Multiresolution residual deep neural network for improving pelvic CBCT image quality

作者：Wangjiang Wu, Junda Qu, Jing Cai, Ruijie Yang · 发表于：Medical Physics · 年份：2022 · DOI：10.1002/mp.15460 · 被引用次数：18 · 研究领域：Advanced Radiotherapy Techniques、Medical Imaging Techniques and Applications、Prostate Cancer Diagnosis and Treatment

PURPOSE: Cone-beam computed tomography (CBCT) is frequently used for accurate image-guided radiation therapy. However, the poor CBCT image quality prevents its further clinical use. Thus, it is important to improve the HU accuracy and structure preservation of CBCT images. METHODS: In this study, we proposed a novel method to generate synthetic CT (sCT) images from CBCT images. A multiresolution residual deep neural network (RDNN) was adopted for image regression from CBCT images to planning CT (pCT) images. At the coarse level, RDNN was first trained with a large amount of lower resolution images, which can make the network focus on coarse information and prevent overfitting problems. More fine information was obtained gradually by fine-tuning the coarse model using fewer number of higher resolution images. Our model was optimized by using aligned pCT and CBCT image pairs of a particular body region of 153 prostate cancer patients treated in our hospital (120 for training and 33 for testing). Five-fold cross-validation was used to tune the hyperparameters and the testing data were used to evaluate the performance of the final models. RESULTS: The mean absolute error (MAE) between CBCT and pCT on the testing data was 352.56 HU, while the MAE between the sCT and pCT images was 52.18 HU for our proposed multiresolution RDNN model, which reduced the MAE by 85.20% (p < 0.01). In addition, the average structural similarity index measure between the sCT and CBCT was 19.64% (p = 0.0...