A linear programming approach to inverse planning in Gamma Knife radiosurgery

Purpose Leksell Gamma Knife® is a stereotactic radiosurgery system that allows fine‐grained control of the delivered dose distribution. We describe a new inverse planning approach that both resolves shortcomings of earlier approaches and unlocks new capabilities. Methods We fix the isocenter positio...

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Veröffentlicht in:Medical physics (Lancaster) Jg. 46; H. 4; S. 1533 - 1544
Hauptverfasser: Sjölund, J., Riad, S., Hennix, M., Nordström, H.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States John Wiley and Sons Inc 01.04.2019
Schlagworte:
Algorithms
Cavernous Sinus - pathology
Cavernous Sinus - surgery
Humans
inverse planning
Leksell Gamma Knife
Meningeal Neoplasms - pathology
Meningeal Neoplasms - surgery
Meningioma - pathology
Meningioma - surgery
optimization
Programming, Linear
Radiosurgery - methods
Radiosurgery - standards
Radiotherapy Dosage
Radiotherapy Planning, Computer-Assisted - standards
stereotactic radiosurgery
THERAPEUTIC INTERVENTIONS
treatment planning
treatment planning
inverse planning
optimization
Leksell Gamma Knife
stereotactic radiosurgery
ISSN:0094-2405, 2473-4209, 2473-4209
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Zusammenfassung:Purpose Leksell Gamma Knife® is a stereotactic radiosurgery system that allows fine‐grained control of the delivered dose distribution. We describe a new inverse planning approach that both resolves shortcomings of earlier approaches and unlocks new capabilities. Methods We fix the isocenter positions and perform sector‐duration optimization using linear programming, and study the effect of beam‐on time penalization on the trade‐off between beam‐on time and plan quality. We also describe two techniques that reduce the problem size and thus further reduce the solution time: dualization and representative subsampling. Results The beam‐on time penalization reduces the beam‐on time by a factor 2–3 compared with the naïve alternative. Dualization and representative subsampling each leads to optimization time‐savings by a factor 5–20. Overall, we find in a comparison with 75 clinical plans that we can always find plans with similar coverage and better selectivity and beam‐on time. In 44 of these, we can even find a plan that also has better gradient index. On a standard GammaPlan workstation, the optimization times ranged from 2.3 to 26 s with a median time of 5.7 s. Conclusion We present a combination of techniques that enables sector‐duration optimization in a clinically feasible time frame.
Bibliographie:ObjectType-Article-1
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ISSN:0094-2405
2473-4209
2473-4209
DOI:10.1002/mp.13440