  Modeling of respiratory motion is important for a more accurate understanding
and accounting of its effect on dose to cancers in the thorax and abdomen by
radiotherapy. We have developed a model of respiration-induced organ motion in
the thorax, without the commonly adopted assumption of repeatable breath
cycles. The model describes the motion of a volume of interest within the
patient, based on a reference 3-dimensional image (at end-expiration), and the
diaphragm positions at different time points. The input data are
respiration-correlated CT images of patients treated for nonsmall cell lung
cancer, consisting of 3D images, including the diaphragm positions, at 10
phases of the respiratory cycle. A deformable image registration algorithm
calculates the deformation field that maps each 3D image to the reference 3D
image. A principle component analysis is performed to parameterize the 3D
deformation field in terms of the diaphragm motion. We show that the first two
principal components are adequate to accurately and completely describe the
organ motion in the data of 4 patients. Artifacts in the RCCT images that
commonly occur at the mid-respiration states are reduced in the model-generated
images. Further validation of the model is demonstrated in the successful
application of the parameterized 3D deformation field to RCCT data of the same
patient but acquired several days later. We have developed a method for
predicting respiration-induced organ motion in patients that has potential for
improving the accuracy of dose calculation in radiotherapy.
