Dynamic respiratory motion estimation using patch-based kernel-pca priors for lung cancer Radiotherapy

Abstract

In traditional radiation therapy of lung cancer, the planned target volume (PTV) is delineated from the average or a single phase of the planning-4D-CT, which is then registered to the intra-procedural 3D-CT for delivery of radiation dose. Because of respiratory motion, the radiation needs to be gated so that the PTV covers the tumor. 4D planning deals with multiple breathing phases, however, since the breathing patterns during treatment can change, there are matching discrepancies between the planned 4D volumes and the actual tumor shape and position. Recent works showed that it is promising to dynamically estimate the lung motion from chest motion. In this paper, we propose a patch-based Kernel-PCA model for estimating lung motion from the chest and upper abdomen motion. First, a statistical model is established from the 4D motion fields of a population. Then, the lung motion of a patient is estimated dynamically based on the patient’s 4D-CT image and chest and upper abdomen motion, using population’s statistical model as prior knowledge. This lung motion estimation algorithm aims to adapt the patient’s planning 4D-CT to his/her current breathing status dynamically during treatment so that the location and shape of the lung tumor can be precisely tracked. Thus, it reduces possible damage to surrounding normal tissue, reduces side-effects, and improves the efficiency of radiation therapy. In experiments, we used the leave-one-out method to evaluate the estimation accuracy from images of 51 male subjects and compared the linear and nonlinear estimation scenarios. The results showed smaller lung field matching errors for the proposed patch-based nonlinear estimation.