Dynamic range optimization for treatment time reduction in respiratory-gated proton therapy using RayStation v2025.

PURPOSE: This study introduces dynamic range optimization, which constrains the lower limit of the minimum MU per spot, as a planning-based strategy to enhance efficiency in respiratory-gated proton therapy and evaluates the resultant dosimetric trade-offs.

METHODS

We analyzed 101 hepatocellular carcinoma patients who received line-scanning proton therapy. We developed a computational framework to predict the total treatment time, integrating dynamic range-mediated changes in beam-on time (BoT) with respiratory gating dynamics. The model was validated against fully dynamic range-optimized plans generated by RayStation v2025 across five dynamic range values (no constraint, 200, 100, 50, and 10). These values represent the ratio of maximum to minimum MU allowed within an energy layer; specifically, a lower dynamic range value imposes a stricter constraint by elevating the minimum MU floor, thereby reducing intensity modulation flexibility while increasing delivery speed. Dosimetric trade-offs were quantified using fully re-optimized plans for a representative case under dynamic range = 100 and 10 constraints. The study used a layer switching time (TLS) of 2 s and amplitude-based respiratory gating. A RayStation-integrated Python script was developed for clinical decision support.

RESULTS

Decreasing the dynamic range systematically reduced beam-on time in a sigmoid-shape pattern. The computational model's predictions for beam-on time showed a maximum error of 10.4% compared to RayStation v2025 calculations, confirming its accuracy. Total treatment time reduction reached a plateau at certain dynamic range thresholds, particularly when layer delivery plus switching was completed within a single gating-on period, indicating no further efficiency gain from additional dynamic range reduction. While target coverage remained equivalent across dynamic range values, lower dynamic range values systematically increased normal tissue doses due to reduced intensity modulation capability, with organs proximal to the target experiencing the largest relative increases (e.g., gallbladder mean dose increased 16.4% at DR = 10).

CONCLUSION

Dynamic range optimization is an effective, planning-based method to reduce treatment time in respiratory-gated proton therapy, operating independently of patient cooperation or system modifications. The developed RayStation-integrated tool enables clinicians to identify the optimal patient-specific dynamic range, balancing efficiency gains against acceptable dosimetric quality, particularly for organs-at-risk adjacent to targets. This approach provides a technical foundation for individualized dynamic range selection during treatment planning.