TY - JOUR
T1 - Composite radiation dose representation using Fuzzy Set theory
AU - Park, Samuel B.
AU - Monroe, James I.
AU - Yao, Min
AU - MacHtay, Mitchell
AU - Sohn, Jason W.
N1 - Funding Information:
This work benefited from the use of the Insight Segmentation and Registration Toolkit (ITK) and Visualization Toolkit (VTK), the open source software developed as an initiative of the U.S. National Library of Medicine and available at www.itk.org and www.vtk.org respectively. This project is partially supported by the Agency for Healthcare Research and Quality (AHRQ) grant 1R18HS017424-01A2.
PY - 2012/3/15
Y1 - 2012/3/15
N2 - Composite plans created from different image sets are generated through Deformable Image Registration (DIR) and present a challenge in accurately presenting uncertainties, which vary with anatomy. Our effort focuses on the application of Fuzzy Set theory to provide an accurate dose representation of such a composite treatment plan. The accuracy of the DIR is generally verified through geometrical visual checks, including the confirmation of the corresponding anatomies with edge features, such as bone or organ boundaries. However, the remaining volume of the image (mostly soft tissues) has few significant image features and therefore greater uncertainty. We fuzzified the deformation vector and derived a fuzzy composite dose. The fuzzification was implemented using Gaussian functions based on the varying uncertainties in the DIR. After establishing the theoretical basis for this new approach, we present two-and three-dimensional examples as proof-of-concept. Using Fuzzy Set theory, composite dose plans displaying locality-based uncertainties were successfully created, providing information previously unavailable to clinicians. Previous to Fuzzy Set dose presentations, clinicians had no measure of confidence in the accuracy of a composite dose plan. Using fuzzified composite dose presentations, clinicians can determine a safe additional dose to previously treated anatomy. This will possibly increase the treatment success rate and reduce the rate of complications.
AB - Composite plans created from different image sets are generated through Deformable Image Registration (DIR) and present a challenge in accurately presenting uncertainties, which vary with anatomy. Our effort focuses on the application of Fuzzy Set theory to provide an accurate dose representation of such a composite treatment plan. The accuracy of the DIR is generally verified through geometrical visual checks, including the confirmation of the corresponding anatomies with edge features, such as bone or organ boundaries. However, the remaining volume of the image (mostly soft tissues) has few significant image features and therefore greater uncertainty. We fuzzified the deformation vector and derived a fuzzy composite dose. The fuzzification was implemented using Gaussian functions based on the varying uncertainties in the DIR. After establishing the theoretical basis for this new approach, we present two-and three-dimensional examples as proof-of-concept. Using Fuzzy Set theory, composite dose plans displaying locality-based uncertainties were successfully created, providing information previously unavailable to clinicians. Previous to Fuzzy Set dose presentations, clinicians had no measure of confidence in the accuracy of a composite dose plan. Using fuzzified composite dose presentations, clinicians can determine a safe additional dose to previously treated anatomy. This will possibly increase the treatment success rate and reduce the rate of complications.
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U2 - 10.1016/j.ins.2011.10.025
DO - 10.1016/j.ins.2011.10.025
M3 - Article
AN - SCOPUS:84155172801
SN - 0020-0255
VL - 187
SP - 204
EP - 215
JO - Information Sciences
JF - Information Sciences
IS - 1
ER -