title: Quantitation of the a priori dosimetric capabilities of spatial points in inverse planning and its significant implication in defining IMRT solution space.
abstract: In inverse planning, the likelihood for the points in a target or sensitive structure to meet their dosimetric goals is generally heterogeneous and represents the a priori knowledge of the system once the patient and beam configuration are chosen. Because of this intrinsic heterogeneity, in some extreme cases, a region in a target may never meet the prescribed dose without seriously deteriorating the doses in other areas. Conversely, the prescription in a region may be easily met without violating the tolerance of any sensitive structure. In this work, we introduce the concept of dosimetric capability to quantify the a priori information and develop a strategy to integrate the data into the inverse planning process. An iterative algorithm is implemented to numerically compute the capability distribution on a case specific basis. A method of incorporating the capability data into inverse planning is developed by heuristically modulating the importance of the individual voxels according to the a priori capability distribution. The formalism is applied to a few specific examples to illustrate the technical details of the new inverse planning technique. Our study indicates that the dosimetric capability is a useful concept to better understand the complex inverse planning problem and an effective use of the information allows us to construct a clinically more meaningful objective function to improve IMRT dose optimization techniques.
authors.affiliation: Department of Radiation Oncology, Stanford University, Stanford, CA 94305-5847, USA.
authors.forename: Z
authors.initials: Z
authors.lastname: Shou
authors.forename: Y
authors.initials: Y
authors.lastname: Yang
authors.forename: C
authors.initials: C
authors.lastname: Cotrutz
authors.forename: D
authors.initials: D
authors.lastname: Levy
authors.forename: Lei
authors.initials: L
authors.lastname: Xing
mesh_terms: D000465:Algorithms; D001822:Body Burden; D003198:Computer Simulation; D006801:Humans; D008297:Male; D008954:Models, Biological; D011471:Prostatic Neoplasms / Q000503:physiopathology* / Q000532:radiotherapy*; D011874:Radiometry / Q000379:methods*; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D020266:Radiotherapy, Conformal / Q000379:methods*; D012062:Relative Biological Effectiveness