Validation of respiratory motion estimation is indispensable for a variety of clinical applications. For CT lung registration, current approaches employ manually defined landmark sets or contours and compute a target registration error (TRE) to quantify registration accuracy. Preferably, the landmark set is well-dispersed to reflect for lung anatomy with its varying degrees of stiffness. A recent comparison study, however, revealed that the TRE is not sufficient for functional lung analysis.
On the basis of a compressible CT phantom functional lung analysis is addressed. Non-plausible expansion patterns as they occur for CT lung data are analyzed. Motivated by the relation of Hounsfield value and local volume change, local stiffness is incorporated into registration such that an improved functional lung analysis is achieved.