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@ARTICLE{Wahl:130810,
author = {N. Wahl$^*$ and M. Bangert$^*$ and C. P. Kamerling$^*$ and
P. Ziegenhein$^*$ and G. H. Bol and B. W. Raaymakers and U.
Oelfke$^*$},
title = {{P}hysically constrained voxel-based penalty adaptation for
ultra-fast {IMRT} planning.},
journal = {Journal of applied clinical medical physics},
volume = {17},
number = {4},
issn = {1526-9914},
address = {Reston, Va.},
publisher = {ACMP},
reportid = {DKFZ-2017-05888},
pages = {172 - 189},
year = {2016},
abstract = {Conventional treatment planning in intensity-modulated
radiation therapy (IMRT) is a trial-and-error process that
usually involves tedious tweaking of optimization
parameters. Here, we present an algorithm that automates
part of this process, in particular the adaptation of
voxel-based penalties within normal tissue. Thereby, the
proposed algorithm explicitly considers a priori known
physical limitations of photon irradiation. The efficacy of
the developed algorithm is assessed during treatment
planning studies comprising 16 prostate and 5 head and neck
cases. We study the eradication of hot spots in the normal
tissue, effects on target coverage and target conformity, as
well as selected dose volume points for organs at risk. The
potential of the proposed method to generate class solutions
for the two indications is investigated. Run-times of the
algorithms are reported. Physically constrained voxel-based
penalty adaptation is an adequate means to automatically
detect and eradicate hot-spots during IMRT planning while
maintaining target coverage and conformity. Negative effects
on organs at risk are comparably small and restricted to
lower doses. Using physically constrained voxel-based
penalty adaptation, it was possible to improve the
generation of class solutions for both indications.
Considering the reported run-times of less than 20 s,
physically constrained voxel-based penalty adaptation has
the potential to reduce the clinical workload during
planning and automated treatment plan generation in the long
run, facilitating adaptive radiation treatments.},
cin = {E040},
ddc = {530},
cid = {I:(DE-He78)E040-20160331},
pnm = {315 - Imaging and radiooncology (POF3-315)},
pid = {G:(DE-HGF)POF3-315},
typ = {PUB:(DE-HGF)16},
pubmed = {pmid:27455484},
doi = {10.1120/jacmp.v17i4.6117},
url = {https://inrepo02.dkfz.de/record/130810},
}