Automated treatment planning for a dedicated multi-source intra-cranial radiosurgery treatment unit accounting for overlapping structures and dose homogeneity.

PURPOSE The purpose of this work is to advance the two-step approach for Gamma Knife(®) Perfexion™ (PFX) optimization to account for dose homogeneity and overlap between the planning target volume (PTV) and organs-at-risk (OARs). METHODS In the first step, a geometry-based algorithm is used to quickly select isocentre locations while explicitly accounting for PTV-OARs overlaps. In this approach, the PTV is divided into subvolumes based on the PTV-OARs overlaps and the distance of voxels to the overlaps. Only a few isocentres are selected in the overlap volume, and a higher number of isocentres are carefully selected among voxels that are immediately close to the overlap volume. In the second step, a convex optimization is solved to find the optimal combination of collimator sizes and their radiation duration for each isocentre location. RESULTS This two-step approach is tested on seven clinical cases (comprising 11 targets) for which the authors assess coverage, OARs dose, and homogeneity index and relate these parameters to the overlap fraction for each case. In terms of coverage, the mean V99 for the gross target volume (GTV) was 99.8% while the V95 for the PTV averaged at 94.6%, thus satisfying the clinical objectives of 99% for GTV and 95% for PTV, respectively. The mean relative dose to the brainstem was 87.7% of the prescription dose (with maximum 108%), while on average, 11.3% of the PTV overlapped with the brainstem. The mean beam-on time per fraction per dose was 8.6 min with calibration dose rate of 3.5 Gy/min, and the computational time averaged at 205 min. Compared with previous work involving single-fraction radiosurgery, the resulting plans were more homogeneous with average homogeneity index of 1.18 compared to 1.47. CONCLUSIONS PFX treatment plans with homogeneous dose distribution can be achieved by inverse planning using geometric isocentre selection and mathematical modeling and optimization techniques. The quality of the obtained treatment plans are clinically satisfactory while the homogeneity index is improved compared to conventional PFX plans.

[1]  D. Song,et al.  Inter- and intrafraction patient positioning uncertainties for intracranial radiotherapy: a study of four frameless, thermoplastic mask-based immobilization strategies using daily cone-beam CT. , 2011, International journal of radiation oncology, biology, physics.

[2]  C. Ménard,et al.  Performance of a novel repositioning head frame for gamma knife perfexion and image-guided linac-based intracranial stereotactic radiotherapy. , 2010, International journal of radiation oncology, biology, physics.

[3]  Q. J. Wu,et al.  Clinical Evaluation of a Gamma Knife Inverse Planning System , 2004 .

[4]  Tamás Terlaky,et al.  An Interior Point Constraint Generation Algorithm for Semi-Infinite Optimization with Health-Care Application , 2011, Oper. Res..

[5]  H. Romeijn,et al.  Intensity modulated radiation therapy treatment plan optimization , 2008 .

[6]  Vira Chankong,et al.  Real-time inverse planning for Gamma Knife radiosurgery. , 2003, Medical physics.

[7]  Michael C. Ferris,et al.  Radiosurgery Treatment Planning via Nonlinear Programming , 2003, Ann. Oper. Res..

[8]  J. Sheehan,et al.  Interfraction and intrafraction performance of the Gamma Knife Extend system for patient positioning and immobilization. , 2012, Journal of neurosurgery.

[9]  Philip T. Komljenovic,et al.  Cone beam computed tomography image guidance system for a dedicated intracranial radiosurgery treatment unit. , 2013, International journal of radiation oncology, biology, physics.

[10]  D. Jaffray,et al.  Automated treatment planning for a dedicated multi-source intracranial radiosurgery treatment unit using projected gradient and grassfire algorithms. , 2012, Medical physics.

[11]  L Souhami,et al.  Radiation Therapy Oncology Group: radiosurgery quality assurance guidelines. , 1993, International journal of radiation oncology, biology, physics.

[12]  Michael C. Ferris,et al.  Optimization of gamma knife radiosurgery , 1999, Discrete Mathematical Problems with Medical Applications.

[13]  Michael C. Ferris,et al.  An Optimization Approach for Radiosurgery Treatment Planning , 2002, SIAM J. Optim..

[14]  Y Chen,et al.  A geometrically based method for automated radiosurgery planning. , 2000, International journal of radiation oncology, biology, physics.

[15]  Russell H. Taylor,et al.  Patient geometry-driven information retrieval for IMRT treatment plan quality control. , 2009, Medical physics.

[16]  R. Maciunas,et al.  12 Gy gamma knife radiosurgical volume is a predictor for radiation necrosis in non-AVM intracranial tumors. , 2006, International journal of radiation oncology, biology, physics.