Scopul nostru este sprijinirea şi promovarea cercetării ştiinţifice şi facilitarea comunicării între cercetătorii români din întreaga lume.
Autori: Indrin J. Chetty, Mihaela Rosu, Daniel L. McShan, Benedick A. Fraass, James M. Balter, Randall K. Ten Haken
Editorial: Medical Physics, 31(4), p.925-932, 2004.
We have applied convolution methods to account for some of the effects of respiratory induced motion in clinical treatment planning of the lung. The 3-D displacement of the GTV center-of-mass (COM) as determined from breath-hold exhale and inhale CT scans was used to approximate the breathing induced motion. The time-course of the GTV-COM was estimated using a probability distribution function (PDF) previously derived from diaphragmatic motion [Med. Phys. 26, 715–720 (1990)] but also used by others for treatment planning in the lung [Int. J. Radiat. Oncol., Biol., Phys. 53, 822–834 (2002); Med. Phys. 30, 1086–1095 (2003)]. We have implemented fluence and dose convolution methods within a Monte Carlo based dose calculation system with the intent of comparing these approaches for planning in the lung. All treatment plans in this study have been calculated with Monte Carlo using the breath-hold exhale CT data sets. An analysis of treatment plans for 3 patients showed substantial differences (hot and cold spots consistently greater than ±15%) between the motion convolved and static treatment plans. As fluence convolution accounts for the spatial variance of the dose distribution in the presence of tissue inhomogeneities, the doses were approximately 5% greater than those calculated with dose convolution in the vicinity of the lung. DVH differences between the static, fluence and dose convolved distributions for the CTV were relatively small, however, larger differences were observed for the PTV. An investigation of the effect of the breathing PDF asymmetry on the motion convolved dose distributions showed that reducing the asymmetry resulted in increased hot and cold spots in the motion convolved distributions relative to the static cases. In particular, changing from an asymmetric breathing function to one that is symmetric results in an increase in the hot/cold spots of ±15% relative to the static plan. This increase is not unexpected considering that the target spends relatively more time at inhale as the asymmetry decreases (note that the treatment plans were generated using the exhale CT scans).
Cuvinte cheie: Monte Carlo, Breathing, Fluence convolution, Dose convolution