| 10:40 - 11:10 |
: A deep learning based model for predicting machine failures in proton therapy system |
| 10:38 - 11:08 |
: Motion Adapted Reconstruction |
| 10:36 - 11:06 |
: Deformable image registration of the treatment planning CT with proton radiographies in perspective of adaptive proton therapy |
| 10:35 - 11:05 |
: The single sided digital tracking calorimeter designed and developed by the Bergen pCT group |
| 10:33 - 11:03 |
: Scattering Proton CT |
| 10:31 - 11:01 |
: An overview on Jet Counter experimental nanodosimetry and track structure simulations |
| 10:29 - 10:59 |
: An update on the use of matRad for MC proton dose calculations (TBC) |
| 10:28 - 10:58 |
: Comparing imaging modalities in homogeneous and heterogeneous tissues: status report on our PTCOG funded project |
| 10:26 - 10:56 |
: Applications of Artificial Intelligence in Imaging and Treatment Planning |
| 10:22 - 10:52 |
: Removing the Front Tracker |
| 10:21 - 10:51 |
: Joint dose minimization and variance optimization for fluence-modulated proton CT |
| 10:14 - 10:44 |
: Prescribing image noise using dynamic fluence field optimization: experimental results using a pre-clinical proton CT scanner |
| 10:12 - 10:42 |
: A deconvolution method to improve spatial resolution in pCT |
| 10:09 - 10:39 |
: Particle versus photon imaging for proton radiotherapy - an experimental comparison |
| 10:08 - 10:38 |
: Replacing 4DCT: It’s about time |