In the field of radiation therapy treatment planning, treatment planners usually generate an intensity-modulated radiation therapy (IMRT) treatment plan after having knowledge of the to-be-treated target volume in a patient's body, for example by using a computer tomography (CT) scanner. The target volume is usually an organ in a patient's body that is affected by a tumor, for example a cancer. However, studies have shown that the quality of the treatment plans is closely linked to the experience of the treatment planner. This suggests that many treatment plans have room for improvement, especially if prepared by a less experienced planner. Moreover, the use of inadequate planning methods, early termination of the optimization process, or measuring plan quality with improper or blunt fitness functions often lead to inadequate radiation therapy treatment plans. Examples of the latter fitness functions are the semideviation penalties, which penalize volumetric units with doses above or below some threshold, often utilized in treatment plan optimization: When just volumetric units that receive doses higher than a prescribed dose level are penalized, the only incentive for the optimization algorithm is to reduce the dose to that very level, even though lower doses may well be attainable without any sacrifice of other treatment goals.
As an example, the publication “Patient geometry-driven information retrieval for IMRT treatment plan quality control,” from B. Wu, F. Ricchetti, G. Sanguineti, M. Kazhdan, P. Simari, M. Chuang, R. Taylor, R. Jacques, and T. McNutt, of Medical Physics, Vol. 36(12), pages 5497-5505, 2009, presents a method for quality control of treatment plans. In this method, when a new plan has been prepared, its patient geometry is matched against those in a database of previous plans. Structures for which the doses in the new plan are worse than the doses in previous plans with similar geometries are flagged as being potential subjects for improvements. The plans are then reoptimized with stricter dose requirements for the flagged structures.
The methods proposed by the publications “Using voxel-dependent importance factors for interactive DVH-based dose optimization,” from C. Cotrutz and L. Xing, Physics in Medicine and Biology, Vol. 47(10). Pages 1659-1669, 2002, and “IMRT dose shaping with regionally variable penalty scheme,” also from C. Cotrutz and L. Xing, Medical Physics, Vol. 30(4), pages 544-551, 2003 suggest to use voxel-specific weighting factors to convey the importance of different volumes in the patient geometry to the optimization algorithm for dose-volume histograms (DVH). They perform optimizations iteratively to carefully balance the trade-offs between planning criteria. The publication “Treatment plan modification using voxel-based weighting factors/dose prescription,” Physics in Medicine and Biology, Vol. 48(15), pp. 2479, 2003 from C. Wu, G. H. Olivera, R. Jeraj, H. Keller, and T. R. Mackie also suggests to perform optimizations iteratively, in which they update the weighting factors or the prescribed doses to each voxel. They find that the two types of updates are equivalent under certain conditions.
Similarly, the publication “Toward truly optimal IMRT dose distribution: inverse planning with voxel-specific penalty” from P. Lougovski, J. LeNoach, L. Zhu, Y. Ma, Y. Censor, and L. Xing, Technology in Cancer Research and Treatment, Vol. 9(6), pages 629-636, 2010, proposes to construct a scheme in which the prescribed dose levels are iteratively updated for voxels not satisfying the ideal dose prescriptions of uniform target dose and zero dose to healthy tissues. In addition, the method described by the Ph.D. Thesis of Johan L of, entitled “Development of a general framework for optimization of radiation therapy,” of the Department of Medical Radiation Physics of Stockholm University, 2000, the optimization of the complication free tumor control probability is followed by a minimization of the integral dose under the constraint that the complication free tumor control probability can only be marginally worse than in the first optimization.
Despite all the above discussed strategies, quality control, and optimization methods, there is still a strong need for better radiation therapy treatment plans that are generated by using novel methods and strategies that allow to improve or optimize upon existing radiation treatment plans.
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