|
WO2020256750A1
|
|
Predicting radiotherapy control points using projection images
|
|
WO2020246996A1
|
|
Sct image generation using cyclegan with deformable layers
|
|
EP3773216A1
|
|
A method of providing proton radiation therapy utilizing periodic motion
|
|
US2020294678A1
|
|
Automated cancer registry record generation
|
|
US2020276456A1
|
|
Method of providing rotational radiation therapy using particles
|
|
US2020160972A1
|
|
Real-time motion monitoring using deep neural network
|
|
US2020129780A1
|
|
Machine learning approach to real-time patient motion monitoring
|
|
US2020129784A1
|
|
Real-time patient motion monitoring using a magnetic resonance linear accelerator (MR-LINAC)
|
|
US2019333219A1
|
|
Cone-beam ct image enhancement using generative adversarial networks
|
|
AU2018307739A1
|
|
Radiation therapy planning using deep convolutional network
|
|
US2019333623A1
|
|
Radiotherapy treatment plan modeling using generative adversarial networks
|
|
US2019329072A1
|
|
Phantom for adaptive radiotherapy
|
|
US2019318474A1
|
|
Image synthesis using adversarial networks such as for radiation therapy
|
|
US2019030371A1
|
|
Automated image segmentation using DCNN such as for radiation therapy
|
|
US2019251694A1
|
|
Atlas-based segmentation using deep-learning
|
|
US2019220986A1
|
|
Methods and devices for surface motion tracking
|
|
US2018326223A1
|
|
Systems and methods of accounting for shape change during radiotherapy
|
|
US2019175940A1
|
|
Radiation treatment planning or administration electron modeling
|
|
US2019175952A1
|
|
Determining parameters for a beam model of a radiation machine using deep convolutional neural networks
|
|
US2019076671A1
|
|
Radiotherapy treatment plan optimization workflow
|
