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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.

Breathing motion is challenging for radiotherapy planning and delivery. This requires advanced four-dimensional (4D) imaging and motion mitigation strategies and associated validation tools with known deformations. Numerical phantoms such as the XCAT provide reproducible and realistic data for simulation-based validation. However, the XCAT generates partially inconsistent and non-invertible deformations where tumours remain rigid and structures can move through each other. We address these limitations by post-processing the XCAT deformation vector fields (DVF) to generate a breathing phantom with realistic motion and quantifiable deformation. An open-source post-processing framework was developed that corrects and inverts the XCAT-DVFs while preserving sliding motion between organs. Those post-processed DVFs are used to warp the first XCAT-generated image to consecutive time points providing a 4D phantom with a tumour that moves consistently with the anatomy, the ability to scale lung density as well as consistent and invertible DVFs. For a regularly breathing case, the inverse consistency of the DVFs was verified and the tumour motion was compared to the original XCAT. The generated phantom and DVFs were used to validate a motion-including dose reconstruction (MIDR) method using isocenter shifts to emulate rigid motion. Differences between the reconstructed doses with and without lung density scaling were evaluated. The post-processing framework produced DVFs with a maximum 95 t h -percentile inverse-consistency error of 0.02 mm. The generated phantom preserved the dominant sliding motion between the chest wall and inner organs. The tumour of the original XCAT phantom preserved its trajectory while deforming consistently with the underlying tissue. The MIDR was compared to the ground truth dose reconstruction illustrating its limitations. MIDR with and without lung density scaling resulted in small dose differences up to 1 Gy (prescription 54 Gy). The proposed open-source post-processing framework overcomes important limitations of the original XCAT phantom and makes it applicable to a wider range of validation applications within radiotherapy.

Respiratory motion can vary dramatically between the planning stage and the different fractions of radiotherapy treatment. Motion predictions used when constructing the radiotherapy plan may be unsuitable for later fractions of treatment. This paper presents a methodology for constructing patient-specific respiratory motion models and uses these models to evaluate and analyse the inter-fraction variations in the respiratory motion. The internal respiratory motion is determined from the deformable registration of Cine CT data and related to a respiratory surrogate signal derived from 3D skin surface data. Three different models for relating the internal motion to the surrogate signal have been investigated in this work. Data were acquired from six lung cancer patients. Two full datasets were acquired for each patient, one before the course of radiotherapy treatment and one at the end (approximately 6 weeks later). Separate models were built for each dataset. All models could accurately predict the respiratory motion in the same dataset, but had large errors when predicting the motion in the other dataset. Analysis of the inter-fraction variations revealed that most variations were spatially varying base-line shifts, but changes to the anatomy and the motion trajectories were also observed.

This work is a feasibility study to use a four-dimensional computed tomography (4D CT) dataset generated by a continuous motion model for treatment planning in lung radiotherapy. The model-based 4D CT data were derived from multiple breathing cycles. Four patients were included in this retrospective study. Treatment plans were optimized at end-exhale for each patient and the effect of respiratory motion on the dose delivery investigated. The accuracy of the delivered dose as determined by the number of intermediate respiratory phases used for the calculation was considered. The time-averaged geometry of the anatomy representing the mid-ventilation phase of the breathing cycle was generated using the motion model and a treatment plan was optimized for this phase for one patient. With respiratory motion included, the mid-ventilation plan achieved better target coverage than the plan optimized at end-exhale when standard margins were used to expand the clinical target volume (CTV) to planning target volume (PTV). Using a margin to account for set-up uncertainty only, resulted in poorer target coverage and healthy tissue sparing. For this patient cohort, the results suggest that conventional three-dimensional treatment planning was sufficient to maintain target coverage despite respiratory motion. The motion model has proved a useful tool in 4D treatment planning.

PET/CT quantification of lung tissue is limited by several difficulties: the lung density and local volume changes during respiration, the anatomical mismatch between PET and CT and the relative contributions of tissue, air and blood to the PET signal (the tissue fraction effect). Air fraction correction (AFC) has been shown to improve PET image quantification in the lungs. Methods to correct for the movement and anatomical mismatch involve respiratory gating and image registration techniques. While conventional registration methods only account for spatial mismatch, the Jacobian determinant of the deformable registration transformation field can be used to estimate local volume changes and could therefore potentially be used to correct (i.e. Jacobian Correction, JC) the PET signal for changes in concentration due to local volume changes. This work aims to investigate the relationship between variations in the lung due to respiration, specifically density, tracer concentration and local volume changes. In particular, we study the effect of AFC and JC on PET quantitation after registration of respiratory gated PET/CT patient data. Six patients suffering from lung cancer with solitary pulmonary nodules underwent [Formula: see text]F-FDG PET/cine-CT. The PET data were gated into six respiratory gates using displacement gating based on a real-time position management (RPM) signal and reconstructed with matched gated CT. The PET tracer concentration and tissue density were extracted from registered gated PET and CT images before and after corrections (AFC or JC) and compared to the values from the reference images. Before correction, we observed a linear correlation between the PET tracer concentration values and density. Across all gates and patients, the maximum relative change in PET tracer concentration before (after) AFC was found to be 16.2% (4.1%) and the maximum relative change in tissue density and PET tracer concentration before (after) JC was found to be 17.1% (5.5%) and 16.2% (6.8%) respectively. Overall our results show that both AFC or JC largely explain the observed changes in PET tracer activity over the respiratory cycle. We also speculate that a second order effect is related to change in fluid content but this needs further investigation. Consequently, either AFC or JC is recommended when combining lung PET images from different gates to reduce noise.

The uncertainties in water equivalent thickness (WET) and accuracy of dose estimation using a virtual CT (vCT), generated from deforming the planning CT (pCT) onto the daily cone-beam CT (CBCT), were comprehensively evaluated in the context of lung malignancies and passive scattering proton therapy. The validation methodology utilized multiple CBCT datasets to generate the vCTs of twenty lung cancer patients. A correction step was applied to the vCTs to account for anatomical modifications that could not be modeled by deformation alone. The CBCT datasets included a regular CBCT (rCBCT) and synthetic CBCTs created from the rCBCT and rescan CT (rCT), which minimized the variation in setup between the vCT and the gold-standard image (i.e., rCT). The uncertainty in WET was defined as the voxelwise difference in WET between vCT and rCT, and calculated in 3D (planning target volume, PTV) and 2D (distal and proximal surfaces). The uncertainty in WET based dose warping was defined as the difference between the warped dose and a forward dose recalculation on the rCT. The overall root mean square (RMS) uncertainty in WET was 3.6 ± 1.8, 2.2 ± 1.4 and 3.3 ± 1.8 mm for the distal surface, proximal surface and PTV, respectively. For the warped dose, the RMS uncertainty of the voxelwise dose difference was 6% ± 2% of the maximum dose (%mD), using a 20% cut-off. The rCBCT resulted in higher uncertainties due to setup variability with the rCT; the uncertainties reported with the two synthetic CBCTs were similar. The vCT followed by a correction step was found to be an accurate alternative to rCT.

We propose a method to build a fully deformable motion model directly from cone-beam CT (CBCT) projections. This allows inter-fraction variations in the respiratory motion to be accounted for. It is envisaged that the model be used to track the tumour, and monitor organs at risk (OAR), during gated or tracked radiotherapy (RT) treatment of lung cancer. The method is tested on CBCT projections from a simulated phantom in two cases. The simulations are generated from a patient respiratory trace and associated CBCT scanner geometry. Without and with motion correction, l2 norm maximum errors were reduced from 24.5 to 0.698 mm in case 1, and 20.0 to 0.101 mm in case 2, respectively.

The purpose of this work is to validate an in-house deformable image registration (DIR) algorithm for adaptive radiotherapy for head and neck patients. We aim to use the registrations to estimate the "dose of the day" and assess the need to replan. NiftyReg is an open-source implementation of the B-splines deformable registration algorithm, developed in our institution. We registered a planning CT to a CBCT acquired midway through treatment for 5 HN patients that required replanning. We investigated 16 different parameter settings that previously showed promising results. To assess the registrations, structures delineated in the CT were warped and compared with contours manually drawn by the same clinical expert on the CBCT. This structure set contained vertebral bodies and soft tissue. Dice similarity coefficient (DSC), overlap index (OI), centroid position and distance between structures' surfaces were calculated for every registration, and a set of parameters that produces good results for all datasets was found. We achieve a median value of 0.845 in DSC, 0.889 in OI, error smaller than 2 mm in centroid position and over 90% of the warped surface pixels are distanced less than 2 mm of the manually drawn ones. By using appropriate DIR parameters, we are able to register the planning geometry (pCT) to the daily geometry (CBCT).