Multiple source-to-isocenter-distance (SID) optimization for 4πrobotic radiotherapy

Area of Science:

• Medical Physics
• Radiation Oncology
• Robotics in Medicine

Background:

• Conventional 4π radiotherapy often assumes a fixed source-to-isocenter distance (SID), limiting flexibility on standard C-arm linear accelerators.
• Variable SIDs are achievable with robotic radiotherapy platforms, enabling non-coplanar and non-isocentric delivery without patient repositioning.
• Larger SIDs enhance tumor coverage and field-of-view, while shorter SIDs improve modulation resolution.

Purpose of the Study:

• To develop and evaluate a Multi-SID optimization framework for robotic 4π intensity-modulated radiation therapy (IMRT).
• To simultaneously optimize beam orientation, isocenter selection, SID selection, and fluence maps.
• To assess the dosimetric benefits of Multi-SID planning compared to fixed-SID approaches in head-and-neck cancer cases.

Main Methods:

• A Multi-SID optimization framework was created, incorporating dose fidelity, fluence smoothness, and selection sparsity objectives.
• The planning system utilized a large candidate beam pool with over 1000 orientations, 10 SID levels (50-95 cm), and 5-9 isocenters.
• Multi-SID IMRT plans were compared against fixed-SID (50 cm and 100 cm) plans for 10 head-and-neck cancer patients.

Main Results:

• Multi-SID plans consistently demonstrated superior quality across all evaluated patients.
• Key dosimetric improvements included enhanced target volume homogeneity (D95/D5) and dose conformity (Paddick CI).
• Statistically significant improvements in organ-at-risk (OAR) sparing and reduced high-dose regions (R50) were observed with the Multi-SID method.

Conclusions:

• The Multi-SID optimization framework effectively leverages variable SIDs to achieve superior dosimetric outcomes in robotic 4π IMRT.
• This approach offers better target coverage and modulation resolution compared to traditional fixed-SID techniques.
• Multi-SID planning represents a significant advancement for optimizing radiotherapy treatment plans, potentially leading to more effective and efficient cancer treatment.