A feature-based affine registration method for capturing background lung tissue deformation for ground glass nodule tracking

Apparent changes in lung nodule size assessed via simple image-based measurements from computed tomography (CT) images may be compromised by the effect of the background lung tissue deformation on the nodule, leading to erroneous nodule tracking. We propose a feature-based affine registration method and study its performance vis-a-vis several other registration methods. We implement and test each registration method using a lung- and a lesion-centred region of interest on 10 patient CT datasets featuring 12 nodules. We evaluate each registration method according to the target registration error (TRE) computed across 30-50 homologous fiducial landmarks selected by expert radiologists. Our results show that the proposed feature-based affine lesion-centred registration yielded a 1.11.2 mm TRE, while a Symmetric Normalisation deformable registration yielded a 1.21.2 mm TRE, with a baseline least-square fit of the validation fiducial landmarks of 1.51.2 mm TRE. The proposed feature-based affine registration is computationally efficient, eliminates the need for nodule segmentation, and reduces the susceptibility of artificial deformations. We also conducted a pilot pre-clinical study that showed the proposed feature-based lesion-centred affine registration effectively compensates for the background lung tissue deformation and serves as a reliable baseline registration method prior to assessing lung nodule changes due to disease.