title: Incorporating patient breathing variability into a stochastic model of dose deposition for stereotactic body radiation therapy.
abstract: Hypo-fractionated stereotactic body radiation therapy (SBRT) employs precisely-conforming high-level radiation dose delivery to improve tumor control probabilities and sparing of healthy tissue. However, the delivery precision and conformity of SBRT renders dose accumulation particularly susceptible to organ motion, and respiratory-induced motion in the abdomen may result in significant displacement of lesion targets during the breathing cycle. Given the maturity of the technology, sensitivity of dose deposition to respiratory-induced organ motion represents a significant factor in observed discrepancies between predictive treatment plan indicators and clinical patient outcome statistics and one of the major outstanding unsolved problems in SBRT. Techniques intended to compensate for respiratory-induced organ motion have been investigated, but very few have yet reached clinical practice. To improve SBRT, it is necessary to overcome the challenge that uncertainties in dose deposition due to organ motion present. This requires incorporating an accurate prediction of the effects of the random nature of the respiratory process on SBRT dose deposition for improved treatment planning and delivery of SBRT. We introduce a means of characterizing the underlying day-to-day variability of patient breathing and calculate the resulting stochasticity in dose accumulation.
authors.affiliation: Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA. geneser@sci.utah.edu
authors.forename: Sarah E
authors.initials: SE
authors.lastname: Geneser
authors.forename: Robert M
authors.initials: RM
authors.lastname: Kirby
authors.forename: Brian
authors.initials: B
authors.lastname: Wang
authors.forename: Bill
authors.initials: B
authors.lastname: Salter
authors.forename: Sarang
authors.initials: S
authors.lastname: Joshi
mesh_terms: D000465:Algorithms; D016477:Artifacts; D003198:Computer Simulation; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D016634:Radiosurgery / Q000379:methods*; D015203:Reproducibility of Results; D015656:Respiratory Mechanics; D055425:Respiratory-Gated Imaging Techniques / Q000379:methods*; D012680:Sensitivity and Specificity; D025321:Surgery, Computer-Assisted / Q000379:methods*; D014057:Tomography, X-Ray Computed / Q000379:methods*