Titlebook: Artificial Intelligence in Radiation Therapy; First International Dan Nguyen,Lei Xing,Steve Jiang Conference proceedings 2019 Springer Nat

In-Situ

Deriving Lung Perfusion Directly from CT Image Using Deep Convolutional Neural Network: A Preliminal imaging suffers from many shortcomings, including the need of exogenous contrasts, longer processing time, etc. In this study, we present a new approach to derive the lung functional images, using a deep convolutional neural network to learn and exploit the underlying functional information in the

Obstacle

Immunotherapy

招致

Dose Distribution Prediction for Optimal Treamtment of Modern External Beam Radiation Therapy for Nper proposes a new automatic method for predicting of dose distribution of Nasopharyngeal carcinoma (NPC) from contoured computer tomography (CT) images. The proposed method consists of two phases: (1) predicting the 2D optimal dose images of each beam from contoured CT images of a patient by convol

Hla461

完全

UC-GAN for MR to CT Image Synthesis,rsarial network (CycleGAN) is becoming an influential method, however, its image quality of synthesis is not optimal yet. In this study, we proposed a new learning method named U-Net-CycleGAN (UC-GAN) to generate synthetic CT (sCT) image for MRI-only radiation treatment planning, which integrated an

perpetual

粉筆

abreast

dysphagia

https://doi.org/10.1057/978-1-137-46178-0e investigate the feasibility of CT-only dose prediction and the profitability of additional isocenter and contour information. To evaluate the network’s performance, a 5-fold cross-validation is performed on 79 prostate patients, all treated with volumetric modulated arc therapy.