Damien C. Weber

Muheng Li, Xia Li, Sairos Safai et al.

In recent advancements in proton therapy, MR-based treatment planning is gaining momentum to minimize additional radiation exposure compared to traditional CT-based methods. This transition highlights the critical need for accurate MR-to-CT image synthesis, which is essential for precise proton dose calculations. Our research introduces the Diffusion Schrödinger Bridge Models (DSBM), an innovative approach for high-quality MR-to-CT synthesis. DSBM learns the nonlinear diffusion processes between MR and CT data distributions. This method improves upon traditional diffusion models by initiating synthesis from the prior distribution rather than the Gaussian distribution, enhancing both generation quality and efficiency. We validated the effectiveness of DSBM on a head and neck cancer dataset, demonstrating its superiority over traditional image synthesis methods through both image-level and dosimetric-level evaluations. The effectiveness of DSBM in MR-based proton treatment planning highlights its potential as a valuable tool in various clinical scenarios.

Xia Li, Fabian Zhang, Muheng Li et al.

Intra-fraction motion in radiotherapy is commonly modeled using deformable image registration (DIR). However, existing methods often struggle to balance speed and accuracy, limiting their applicability in clinical scenarios. This study introduces a novel approach that harnesses Neural Graphics Primitives (NGP) to optimize the displacement vector field (DVF). Our method leverages learned primitives, processed as splats, and interpolates within space using a shallow neural network. Uniquely, it enables self-supervised optimization at an ultra-fast speed, negating the need for pre-training on extensive datasets and allowing seamless adaptation to new cases. We validated this approach on the 4D-CT lung dataset DIR-lab, achieving a target registration error (TRE) of 1.15\pm1.15 mm within a remarkable time of 1.77 seconds. Notably, our method also addresses the sliding boundary problem, a common challenge in conventional DIR methods.

Pablo Fernandez Carmona, Michael Eichin, Alexandre Mayor et al.

At the Centre for Proton Therapy at the Paul Scherrer Institute cancer patients are treated with a fixed beamline and in two gantries for ocular and non-ocular malignancies, respectively. For the installation of a third gantry a new patient safety system (PaSS) was developed and is sequentially being rolled out to update the existing areas. The aim of PaSS is to interrupt the treatment whenever any sub-system detects a hazardous condition. To ensure correct treatment delivery, this system needs to be thoroughly tested as part of the regular quality assurance (QA) protocols as well as after any upgrade. In the legacy safety systems, unit testing required an extensive use of resources: two weeks of work per area in the laboratory in addition to QA beam time. In order to significantly reduce the time, an automated PaSS test stand for unit testing was developed based on a PXI chassis with virtually unlimited IOs that are synchronously stimulated or sampled at 1 MHz. It can emulate the rest of the facility using adapters to connect each type of interface. With it PaSS can be tested under arbitrary conditions. A VHDL-based formal language was developed to describe stimuli, expected behaviour and specific measurements, interpreted by a LabView runtime environment. This article describes the tools and methodology being applied for unit testing and QA release tests for the new PaSS. It shows how automation and formalization made possible an increase in test coverage while significantly cutting down the laboratory testing time and facility's beam usage.