Toward Truly Optimal IMRT Dose Distribution: Inverse Planning with Voxel-specific Penalty

Purpose: To establish an inverse planning framework with adjustable voxel penalty for more conformal IMRT dose distribution as well as improved interactive controllability over the regional dose distribution of the resultant plan. Materials and Method: In the proposed coarse-to-fine planning scheme, a conventional inverse planning with organ specific parameters is first performed. The voxel penalty scheme is then “switched on” by allowing the prescription dose to change on an individual voxel scale according to the deviation of the actual voxel dose from the ideally desired dose. The rationale here is intuitive: when the dose at a voxel does not meet its ideal dose, it simply implies that this voxel is not competitive enough when compared with the ones that have met their planning goal. In this case, increasing the penalty of the voxel by varying the prescription can boost its competitiveness and thus improve its dose. After the prescription adjustment, the plan is re-optimized. The dose adjustment/re-optimization procedure is repeated until the resultant dose distribution cannot be improved anymore. The prescription adjustment on a finer scale can be accomplished either automatically or manually. In the latter case, the regions/voxels where a dose improvement is needed are selected visually, unlike in the automatic case where the selection is done purely based on the difference of the actual dose at a given voxel and its ideal prescription. The performance of the proposed method is evaluated using a head and neck and a prostate case. Results: An inverse planning framework with the voxel-specific penalty is established. By adjusting voxel prescriptions iteratively to boost the region where large mismatch between the actual calculated and desired doses occurs, substantial improvements can be achieved in the final dose distribution. The proposed method is applied to a head and neck case and a prostate case. For the former case, a significant reduction in the maximum dose to the brainstem is achieved while the PTV dose coverage is greatly improved. The doses to other organs at risk are also reduced, ranging from 10% to 30%. For the prostate case, the use of the voxel penalty scheme also results in vast improvements to the final dose distribution. The PTV experiences improved dose uniformity and the mean dose to the rectum and bladder is reduced by as much as 15%. Conclusion: Introduction of the spatially non-uniform and adjustable prescription provides room for further improvements of currently achievable dose distributions and equips the planner with an effective tool to modify IMRT dose distributions interactively. The technique is easily implementable in any existing inverse planning platform, which should facilitate clinical IMRT planning process and, in future, off-line/on-line adaptive IMRT.

[1]  Lei Xing,et al.  Plug pattern optimization for gamma knife radiosurgery treatment planning. , 2003, International journal of radiation oncology, biology, physics.

[2]  Y. Censor,et al.  A computational solution of the inverse problem in radiation-therapy treatment planning , 1988 .

[3]  A L Boyer,et al.  Optimization of importance factors in inverse planning. , 1999, Physics in medicine and biology.

[4]  J. Deasy Multiple local minima in radiotherapy optimization problems with dose-volume constraints. , 1997, Medical physics.

[5]  Lei Xing,et al.  Using voxel-dependent importance factors for interactive DVH-based dose optimization. , 2002, Physics in medicine and biology.

[6]  Lei Xing,et al.  Using total-variation regularization for intensity modulated radiation therapy inverse planning with field-specific numbers of segments , 2008, Physics in medicine and biology.

[7]  T. Bortfeld,et al.  Methods of image reconstruction from projections applied to conformation radiotherapy. , 1990, Physics in medicine and biology.

[8]  S. Webb Optimization by simulated annealing of three-dimensional conformal treatment planning for radiation fields defined by a multileaf collimator. , 1991, Physics in medicine and biology.

[9]  David Craft,et al.  Analyzing the main trade-offs in multiobjective radiation therapy treatment planning databases , 2009, Physics in medicine and biology.

[10]  Lei Zhu,et al.  Search for IMRT inverse plans with piecewise constant fluence maps using compressed sensing techniques. , 2009, Medical physics.

[11]  R Mohan,et al.  Algorithms and functionality of an intensity modulated radiotherapy optimization system. , 2000, Medical physics.

[12]  Eva K. Lee,et al.  Simultaneous beam geometry and intensity map optimization in intensity-modulated radiation therapy. , 2006, International journal of radiation oncology, biology, physics.

[13]  Y. Censor,et al.  On the use of Cimmino's simultaneous projections method for computing a solution of the inverse problem in radiation therapy treatment planning , 1988 .

[14]  Z. Moravek,et al.  Application of an inverse kernel concept to Monte Carlo based IMRT. , 2006, Medical physics.

[15]  Lei Xing,et al.  IMRT dose shaping with regionally variable penalty scheme. , 2003, Medical physics.

[16]  Lei Xing,et al.  Clinical knowledge-based inverse treatment planning. , 2004, Physics in medicine and biology.

[17]  Joseph O Deasy,et al.  IMRT treatment planning based on prioritizing prescription goals , 2007, Physics in medicine and biology.

[18]  Thomas Ludwig,et al.  Speed optimized influence matrix processing in inverse treatment planning tools , 2008, Physics in medicine and biology.

[19]  Lei Xing,et al.  Quantitation of the a priori dosimetric capabilities of spatial points in inverse planning and its significant implication in defining IMRT solution space , 2005, Physics in medicine and biology.

[20]  F. Gum,et al.  Verification of IMRT: Techniques and Problems , 2004, Strahlentherapie und Onkologie.

[21]  D Baltas,et al.  A multiobjective gradient-based dose optimization algorithm for external beam conformal radiotherapy. , 2001, Physics in medicine and biology.

[22]  L. Xing,et al.  Multiobjective evolutionary optimization of the number of beams, their orientations and weights for intensity-modulated radiation therapy , 2004, Physics in Medicine and Biology.

[23]  Jürgen Meyer,et al.  Application of influence diagrams to prostate intensity-modulated radiation therapy plan selection , 2004, Physics in medicine and biology.

[24]  A Brahme,et al.  Solution of an integral equation encountered in rotation therapy. , 1982, Physics in medicine and biology.

[25]  Lei Xing,et al.  Inverse treatment planning with adaptively evolving voxel-dependent penalty scheme. , 2004, Medical physics.

[26]  Hui Yan,et al.  AI-guided parameter optimization in inverse treatment planning , 2003, Physics in medicine and biology.

[27]  Radhe Mohan,et al.  A sensitivity-guided algorithm for automated determination of IMRT objective function parameters. , 2006, Medical physics.