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Quantifying parenchymal tissue changes in the lungs is imperative in furthering the study of radiation induced lung damage RILD Registering lung images from different time-points is a key step of this process Traditional intensity-based registration approaches fail this task due to the considerable anatomical changes that occur between timepoints This work proposes a novel method to successfully register longitudinal pre- and post-radiotherapy RT lung computed tomography CT scans that exhibit large changes due to RILD by extracting consistent anatomical features from CT lung boundaries main airways vessels and using these features to optimise the registrations Pre-RT and 12 month post-RT CT pairs from fifteen lung cancer patients were used for this study all with varying degrees of RILD ranging from mild parenchymal change to extensive consolidation and collapse For each CT signed distance transforms from segmentations of the lungs and main airways were generated and the Frangi vesselness map was calculated These were concatenated into multi-channel images and diffeomorphic multichannel registration was performed for each image pair using NiftyReg Traditional intensity-based registrations were also performed for comparison purposes For the evaluation the pre- and post-registration landmark distance was calculated for all patients using an average of 44 manually identified landmark pairs per patient The mean standard deviation distance for all datasets decreased from 15 95 8 09 mm pre-registration to 4 56 5 70 mm post-registration compared to 7 90 8 97 mm for the intensity-based registrations Qualitative improvements in image alignment were observed for all patient datasets For four representative subjects registrations were performed for three additional follow-up timepoints up to 48 months post-RT and similar accuracy was achieved We have demonstrated that our novel multichannel registration method can successfully align longitudinal scans from RILD patients in the presence of large anatomical changes such as consolidation and atelectasis outperforming the traditional registration approach both quantitatively and through thorough visual inspection

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A multichannel feature-based approach for longitudinal lung CT registration in the presence of radiation induced lung da...

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with longitudinal image registration as keyword