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 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 3 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

Chandy, E

Landau, D

McClelland, JR

Stavropoulou, A

Szmul, A

Veiga, C

English

Purpose: Tracking and quantifying local tissue changes in the lungs is imperative in furthering the study of radiation-induced lung damage (RILD). Traditional intensity-based registration approaches fail in this task due to dramatic geometric changes between timepoints. This work aims to successfully register longitudinal pre- and post-radiotherapy (RT) lung CT scans with considerable radiological changes due to RILD. This is achieved by extracting and registering only consistent anatomical features (lung boundaries, main airways, vessels). / Methods: Fifteen pre-RT and 12-month post-RT CT pairs from a chemoradiation trial for lung cancer were available, all with varying degrees of RILD, ranging from mild parenchymal change to extensive consolidation and collapse. For each CT, signed distance transforms from delineations of the lungs and main airways were generated and the Frangi vesselness was calculated. These feature maps were concatenated into multi-channel images and diffeomorphic multichannel registration was performed for each image pair. For the evaluation, the pre- and post- registration landmark distance was calculated for all patients at a mean (range) of 44 (38-65) manually identified landmark pairs. Traditional intensity-based registrations were also performed for all patient datasets and evaluated in the same way as a comparison. / Results: 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. Qualitative improvements in image alignment were observed for all patient datasets. In comparison, the post-registration error of the traditional intensity-based registrations was higher, at 7.90 (8.97) mm and registrations of patients with presence of atelectasis and extensive consolidation failed. / Conclusion: We have demonstrated that our novel registration method can successfully align 12-month follow up scans from RILD patients in the presence of large anatomical changes such as consolidation and atelectasis, outperforming the classical registration approach both quantitatively and through detailed visual inspection

Stavropoulou, K-A

McClelland, J

UCL Discovery

A Multichannel Feature-Based Approach for Longitudinal Lung CT Registration in the Presence of Radiation Induced Lung DamageINTRODUCTIONUnderstanding the relationship between plannedradiotherapy (RT) dose and parenchymal tissueevolution is imperative in furthering the study ofradiation-induced lung damage (RILD). Accuratetracking and quantification of local parenchymal tissuechanges in the lungs is necessary for this purpose, buttraditional intensity-based registration approaches failin this task due to dramatic geometric changesbetween timepoints.CONCLUSIONSWe have demonstrated that our registration method can successfully align 12-month follow up scans from RILD patients in thepresence of large anatomical changes such as consolidation and atelectasis, outperforming the classical intensity-based registrationapproach both quantitatively and through thorough visual inspection. This is the first time that such registration accuracy has beenachieved in baseline and 12-month follow-up pairs that exhibit extensive RILD-induced changes. The results are considered suitablefor longitudinal tracking of parenchymal tissue changes post-radiotherapy.REFERENCESLandau, D. B. et al. (2016). IDEAL-CRT: A Phase 1/2 Trial of Isotoxic Dose-Escalated Radiation Therapy and Concurrent Chemotherapy in Patients With Stage II/III Non-Small Cell Lung Cancer. International Journal of Radiation Oncology Biology Physics, 95(5), 1367–1377. https://doi.org/10.1016/j.ijrobp.2016.03.031Modat, M. et al. (2012). Parametric non-rigid registration using a stationary velocity field. Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis, 145–150. https://doi.org/10.1109/MMBIA.2012.6164745Doel, T. (2012). Developing Clinical Measures of Lung Function in COPD Patients Using Medical Imaging and Computational Modelling. PhD thesis. Oxford University, UKA Stavropoulou1, A Szmul1, E Chandy1,2, C Veiga1, D Landau2, J R McClelland1(1) Radiotherapy Image Computing Group, Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, UK(2) UCL Cancer Institute, University College LondonMETHODSFor the evaluation, the pre- and post- registration landmark distance was calculated for all patients at a mean (range) of 44 (38-65) landmark pairs manually placed on vessel and airway bifurcations. Traditional intensity-based registrations were also performed for all patient datasets and evaluated in the same way as a comparison.RESULTSACKNOWLEDGEMENTSAS- EPSRC-funded UCL i4health CDT (EP/S021930/1). CV- RoyalAcademy of Engineering, Research Fellowship scheme(RF\201718\17140). JRM- Cancer Research UK CentresNetwork Accelerator Award Grant (A21993), ART-NETconsortium. IDEAL CRT trial- Cancer Research UK, grant no.C13530/A10424 and C13530/A17007. AS, CV, EC, AS, JRMreport support from a charitable donation.Figure 1: Baseline (left) and 12-month follow-up (right)scan of RILD patient. The tumour is in the upper rightlobe in the baseline and in the follow up the upper rightlobe has completely collapsed.Figure 4: The distribution of pre- and post-registration landmark distance for each category presented in box plots. Figure 3: 3D rendering of the baseline lungs and main airways of patient dataset with landmark pointsAIMSTo successfully register longitudinal pre- and post-RTlung CT scans with considerable anatomical changesdue to RILD by extracting and registering onlyconsistent anatomical features (lung boundaries, mainairways, vessels).Fifteen pre-RT and 12-month post-RT CT pairs from lung cancer patients treated under a chemoradiation clinical trial were available, all with varying degrees of RILD, ranging from mild parenchymal change to extensive consolidation and collapse. Each patient dataset was placed into one of four categories of increasing expected difficulty of registration between the baseline and 12-month follow up scan. For each CT, signed distance transforms from delineations of the lungs and main airways were generated and the Frangi vesselness was calculated. These feature maps were concatenated into multi-channel images and diffeomorphic multichannel registration was performed for each image pair. Figure 2: Feature images of patient dataset extracted from the CT. The mean (std) distance for all datasets and all categories decreased from 15.95 (8.09) mm pre-registration (Dpre) to 4.56 (5.70) mm post-registration (Dreg). Qualitative improvements in image alignment were observed for patient datasets in all categories. The lung boundaries and main airways were remarkably well aligned even in the most difficult cases, but small-scale vessels and deeper airways could be misaligned in patients demonstrating extreme deformations. In comparison, the Dreg of the traditional intensity-based registrations was higher, at 7.90 (8.97) mm. Registrations of patients with presence of atelectasis and extensive consolidation failed, producing highly implausible deformations such as the example presented in figure 6.Figure 5: Coronal slices from source CT (12 month follow up), target CT (baseline), result CT, colour overlay of target (red) and result CT (cyan), landmark points displayed over the result CT (size of the landmark ∝ point magnitude of Dreg at that point).Figure 6: Results of our multichannel vs results of the intensity-based algorithm. The intensity algorithm has erroneously stretchedthe airway into the shape of the upper right lobe. In comparison,our multichannel algorithm has fully recovered the shape of thecollapsed lobe with smooth and plausible deformations.VesselsLungsCTAirwaysIntensity MultichannelTarget and Result OverlayDisplacement FieldSource TargetResult Overlay Dreg DistributionBaseline 12 Months

A Multichannel Feature-Based Approach for Longitudinal Lung CT Registration in the Presence of Radiation Induced Lung Damage

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

https://discovery.ucl.ac.uk/10135019/1/Stavropoulou_registrations_poster.pdf

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

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