title: Dosimetric treatment course simulation based on a statistical model of deformable organ motion.
abstract: We present a method of modeling dosimetric consequences of organ deformation and correlated motion of adjacent organ structures in radiotherapy. Based on a few organ geometry samples and the respective deformation fields as determined by deformable registration, principal component analysis (PCA) is used to create a low-dimensional parametric statistical organ deformation model (Söhn et al 2005 Phys. Med. Biol. 50 5893-908). PCA determines the most important geometric variability in terms of eigenmodes, which represent 3D vector fields of correlated organ deformations around the mean geometry. Weighted sums of a few dominating eigenmodes can be used to simulate synthetic geometries, which are statistically meaningful inter- and extrapolations of the input geometries, and predict their probability of occurrence. We present the use of PCA as a versatile treatment simulation tool, which allows comprehensive dosimetric assessment of the detrimental effects that deformable geometric uncertainties can have on a planned dose distribution. For this, a set of random synthetic geometries is generated by a PCA model for each simulated treatment course, and the dose of a given treatment plan is accumulated in the moving tissue elements via dose warping. This enables the calculation of average voxel doses, local dose variability, dose-volume histogram uncertainties, marginal as well as joint probability distributions of organ equivalent uniform doses and thus of TCP and NTCP, and other dosimetric and biologic endpoints. The method is applied to the example of deformable motion of prostate/bladder/rectum in prostate IMRT. Applications include dosimetric assessment of the adequacy of margin recipes, adaptation schemes, etc, as well as prospective 'virtual' evaluation of the possible benefits of new radiotherapy schemes.
authors.affiliation: Department of Radiation Oncology, University Hospital Grosshadern, LMU München, Marchioninistr. 15, 81377 München, Germany. matthias.soehn@med.uni-muenchen.de
authors.forename: M
authors.initials: M
authors.lastname: Söhn
authors.forename: B
authors.initials: B
authors.lastname: Sobotta
authors.forename: M
authors.initials: M
authors.lastname: Alber
mesh_terms: D006801:Humans; D008297:Male; D015233:Models, Statistical; D009068:Movement; D025341:Principal Component Analysis; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging / Q000503:physiopathology / Q000532:radiotherapy; D011874:Radiometry; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D014057:Tomography, X-Ray Computed