4DCT Geometrical Eigenmode Model for Inter-Fraction Evaluation of Tumor Regression and Breathing Pattern Changes
V Kearney*, X Wang, X Jia, S Jiang, L Cervino, University of California, San Diego, La Jolla, CASU-E-J-185 Sunday 3:00:00 PM - 6:00:00 PM Room: Exhibit Hall
Purpose: Evaluate the feasibility of using intra-fractional respiratory dominant eigenmodes of lung deformation as a method to track inter-fractional tumor regression and other physiological changes. Geometrical evaluation by dominant eigenmodes during free-breathing may be implemented as an IGART tool for assessing inter-fractional non-respiratory anatomical variation.
Methods: Intra-fractional deformable image registration (DIR) is performed on 4DCT scans of lung cancer patients. Principal component analysis is conducted on a set of 10 deformation vector fields for each patient both with and without a lung mask. Leave-one-out cross-validation (LOOCV) is used to assess intra-fractional modeling error among a subset of respiratory phases. The first two dominant eigenmodes of PCA are compared across patients in axial, sagittal, and coronal slices.
Results: Eigenvalues between the first and second dominant respiratory modes decay by a factor of ten for all patients. Intra-fractional LOOCV maximum error is on the order of 1mm inside the lung and 4-6mm for the whole thorax for all patients using the first two dominant eigenmodes. Eigenvalue decay is fastest for patients where diaphragm motion is relatively larger in magnitude. The first two eigenmodes generally demonstrate a strong but smooth superior-inferior motion component with a lesser lateral and anterior-posterior component, while subsequent less dominant eigenmodes produce more random directional eigenvector fields.
Conclusion: The two first principal components are consistent amongst patients. Inter-fractional eigenmode comparison may provide a means to track tumor regression and breathing pattern or physiological changes. We expect that patients with more dominant diaphragmatic respiration will allow for more delineated eigenmode classification. Clear dominant eigenmode delineation may allow subsequent less dominant eigenmodes to be inter-fractionally compared as a means to evaluate and compensate for tumor regression.