The 2nd UPLIFT School for “Artificial Intelligence in Radiotherapy”

Session

Session D

7 Nov 2026, 09:30

Congressi Stefano Franscini (CSF)

90. Real-Time Range Prediction for a Mixed Helium–Carbon Beam

Maximilian Dick (GSI Helmholtz Center for Heavy Ion Research GmbH)

07/11/2026, 09:30

oral/poster

Objective

Mixed helium-carbon beams enable online range monitoring and imaging. Both ions share the same mass-to-charge ratio, allowing simultaneous acceleration, while helium has approximately three times the range in water. However, anatomical changes beyond the carbon Bragg peak only affect the helium range. We present a deep learning approach for spot-by-spot carbon range prediction...

93. Deep-learning assisted single-scenario robust optimization for carbon ion therapy

Lennart Volz (GSI Helmholtz Center for Heavy Ion Research GmbH)

07/11/2026, 09:50

oral/poster

Objective: Robust optimization in particle therapy conventionally uses a voxel wise-worst case approach, where the plan is optimized to meet the clinical criteria even under the worst of a set of uncertainty scenarios. This entails significant computational overhead, most of which is only needed to compute the worst-case scenario. We hypothesize that optimizing only on the nominal case and a...

91. From Plausible to Controllable: Multimodal Digital Phantom Generation for Radiotherapy

Ronja Stern (PSI - Paul Scherrer Institut)

07/11/2026, 10:10

oral/poster

Objective: Generative models can create synthetic patient cohorts for radiotherapy research, but current multimodal generators primarily sample plausible patients without explicit control over the generated anatomy (Li, 2026). This limits their use for systematic investigation of radiotherapy scenarios. We investigate a conditioned multimodal head-and-neck model that generates anatomically...