Monte-Carlo Dose Calculation Algorithm
Based on the GPU-accelerated Monte-Carlo dose calculation algorithm, IMRT plan calculation for nasopharyngeal carcinoma can be completed within 3 minutes. The production performance is superior, and MU is significantly reduced while improving the Conformity Index (CI) and reducing Homogeneity Index (HI), ensuring target area coverage and minimising the dose drop outside the target area.
Automated AI Contouring αC
VenusX utilises AI deep learning algorithms to contour OARs accurately, covering more than 40 OARs in all parts of the body. It is a customisable piece of equipment with a high contouring accuracy. Over 90% of cases can be used directly for planning without further modification; GPU-based acceleration reduces regular contouring time by at least ten folds.
Automatic organs-at-risk (OARs) segmentation in CT images for head and neck (HaN)
Purpose: a novel deep learning model, Siamese Ensemble Boundary Network (SEB-Net), was developed to improve the accuracy of automatic organs-at-risk (OARs) segmentation in CT images for head and neck (HaN) as well as small organs, which was veriﬁed for use in radiation oncology practice.
Wei Wang¹, Qingxin Wang¹˙², Mengyu Jia², Zhongqiu Wang¹, Chengwen Yang¹, Daguang Zhang¹, Shujing Wen¹, Delong Hou¹, Ningbo Liu¹, Ping Wang¹* and Jun Wang¹*
¹Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China, 2School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China