Towards adaptive IMRT sequencing for the MR-linac.

Area of Science:

• Medical Physics
• Radiation Oncology
• Radiotherapy Technology

Background:

• The integration of Magnetic Resonance Imaging (MRI) with linear accelerators (MR-linacs) offers real-time tracking of targets and organs at risk during radiation delivery.
• Current treatment planning systems require optimization for adaptive radiotherapy (ART) to account for anatomical variations during treatment sessions.

Purpose of the Study:

• To present a novel treatment planning system for intensity-modulated radiotherapy (IMRT) designed for MR-linac systems.
• To enable fully adaptive intra-fraction radiotherapy by compensating for patient anatomy changes during treatment delivery.

Main Methods:

• Development of a treatment planning system incorporating a GPU Monte Carlo dose engine, an inverse dose optimization algorithm, and a novel adaptive sequencing algorithm.
• Testing the system's feasibility in a 0 Tesla environment using various clinical cases (breast, prostate, spinal metastasis, head and neck).
• Implementation of an inter-fraction adaptation scheme to achieve convergence to ideal dose distributions without final segment weight optimization.

Main Results:

• Generated IMRT plans met all clinical constraints for diverse patient cases.
• The new sequencing strategy showed minimal differences between fluence and sequenced distributions, validating its feasibility.
• The inter-fraction adaptation scheme demonstrated convergence within the initial treatment fractions.

Conclusions:

• The developed treatment planning system supports adaptive radiotherapy, enabling immediate radiation delivery from the first calculated segment.
• The system's adaptive capabilities, including intra-fraction planning, can account for patient anatomy updates during treatment.
• This technology promises to enhance the precision and effectiveness of radiotherapy by adapting to real-time anatomical changes.