Population-based Correspondence Models for Respiratory Motion Estimation in the Presence of Interfraction Motion Variations

Many respiratory motion compensation approaches in radiation
therapy of thoracic and abdominal tumors are guided by external
breathing signals. Patient-specific correspondence models based on planning
4D data are used to relate signal measurements to internal motion.
The motion estimation accuracy of these models during a treatment fraction
depends on the degree of inter-fraction motion variations. Here, we
investigate whether motion estimation accuracy in the presence of interfraction
motion variations can be improved by (sub)population models,
which incorporate patient-specific motion information and motion data
from selected additional patients. A sparse manifold clustering approach
is integrated into a regression-based correspondence modeling framework
for automated identification of subpopulations of patients with similar
motion characteristics. In an evaluation with repeated 4D CT scans of 13
patients, subpopulation models, on average, outperform patient-specific
correspondence models in the presence of inter-fraction motion variations.