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A technique for computing dose volume histograms for structure combinations
Author(s) -
Mohan Radhe,
Brewster Linda J.,
Barest Glenn D.
Publication year - 1987
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.595984
Subject(s) - histogram , dose volume histogram , volume (thermodynamics) , subtraction , disjoint sets , intersection (aeronautics) , algorithm , computer science , data structure , point (geometry) , hierarchy , mathematics , artificial intelligence , radiation treatment planning , radiation therapy , physics , image (mathematics) , medicine , discrete mathematics , geometry , arithmetic , quantum mechanics , engineering , programming language , aerospace engineering , economics , market economy
Graphical displays of three‐dimensional dose distribution data are often too complex to be easily assimilated and interpreted for the evaluation of radiation treatment plans. Histograms showing dose versus volume are convenient and useful tools for summarizing dose distribution information throughout the entire volume of a given anatomic structure. They can quickly highlight characteristics such as dose uniformity and hot and cold spots, and can be used to produce statistics including tumor control and normal tissue complication probabilities. To obtain a dose volume histogram for a given structure, it may be necessary to examine its spatial relationships with neighboring structures. They may overlap, be completely disjoint, or one may be contained within another. To resolve potential ambiguities, a procedure has been developed that assigns hierarchies to anatomical structures for the purpose of histogram calculation. The hierarchy assigned to each structure is used to determine the structure within which a given dose matrix point is considered to lie. In this manner, regions of structure intersection are assigned to one object or another, and dose volume histograms can be calculated for each structure separately. From this framework, addition and subtraction of histograms can also be performed. Details of the algorithm are presented along with an example using patient data.