Real-time inverse high-dose-rate brachytherapy planning with catheter optimization by compressed sensing-inspired...

Area of Science:

• Medical Physics
• Computational Imaging
• Radiation Oncology

Background:

• Inverse treatment planning (ITP) is crucial for optimizing radiation delivery in brachytherapy.
• Current ITP methods can be computationally intensive, limiting real-time applications.
• High-dose-rate (HDR) brachytherapy requires efficient and accurate treatment planning.

Purpose of the Study:

• To adapt compressed sensing (CS) optimization strategies for HDR brachytherapy ITP.
• To improve the computational performance and speed of ITP solvers.
• To evaluate the clinical efficacy and dosimetric quality of CS-inspired ITP plans.

Main Methods:

• Reformulated the ITP problem to align with standard CS mathematical structures.
• Developed and implemented two greedy CS methods: hard thresholding and subspace pursuit.
• Compared the performance of novel methods against state-of-the-art ITP solvers using clinical prostate brachytherapy data.

Main Results:

• Achieved speed-ups of 56-350 times compared to existing ITP solvers.
• Demonstrated statistically significant reductions in the final objective function value (p < 0.01).
• Optimization times were under one second, enabling real-time planning capabilities.

Conclusions:

• CS-inspired optimization offers a computationally efficient solution for HDR brachytherapy ITP.
• The novel strategy enables real-time planning, including catheter optimization.
• Generated plans are clinically equivalent or superior in dosimetric performance.