Markov-Gibbs model based registration of CT lung images using subsampling for the follow-up assessment of pleural thickenings

Examining the growth rate of pleural thickenings in consecutive 3D-CT images requires the matching of identical thickenings in lung images acquired at two different points in time. The thickenings can be subject to strong deformations caused by their growth. This implies that position information should play a major role in finding correspondences. Here, a MGRF approach is presented to determine a rigid transformation. It aligns the lung volumes by maximizing the probability of the regarded lung tissue to fit an offline trained model. To ensure a symmetrical matching of lung surfaces this probability is calculated reciprocally. Using precalculation, strong sub-sampling and a multiscale approach, the required time can be reduced by a factor of about 80, depending on the image resolution. Due to this speed-up, online follow-up assessment is feasible. We show that this approach results in precise registrations which can be used for a reliable matching of lung thickenings.