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Purpose: Computed tomography (CT)-derived ventilation methods compute respiratory induced volume changes as a surrogate for pulmonary ventilation. Currently, there are no known methods to derive perfusion information from noncontrast CT. We introduce a novel CT-Perfusion (CT-P) method for computing the magnitude mass changes apparent on dynamic noncontrast CT as a surrogate for pulmonary perfusion., Methods: CT-Perfusion is based on a mass conservation model which describes the unknown mass change as a linear combination of spatially corresponding inhale and exhale HU estimated voxel densities. CT-P requires a deformable image registration (DIR) between the inhale/exhale lung CT pair, a preprocessing lung volume segmentation, and an estimate for the Jacobian of the DIR transformation. Given this information, the CT-P image, which provides the magnitude mass change for each voxel within the lung volume, is formulated as the solution to a constrained linear least squares problem defined by a series of subregional mean magnitude mass change measurements. Similar to previous robust CT-ventilation methods, the amount of uncertainty in a subregional sample mean measurement is related to measurement resolution and can be characterized with respect to a tolerance parameter τ . Spatial Spearman correlation between single photon emission CT perfusion (SPECT-P) and the proposed CT-P method was assessed in two patient cohorts via a parameter sweep of τ . The first cohort was comprised of 15 patients diagnosed with pulmonary embolism (PE) who had SPECT-P and 4DCT imaging acquired within 24 h of PE diagnosis. The second cohort was comprised of 15 nonsmall cell lung cancer patients who had SPECT-P and 4DCT images acquired prior to radiotherapy. For each test case, CT-P images were computed for 30 different uncertainty parameter values, uniformly sampled from the range [0.01, 0.125], and the Spearman correlation between the SPECT-P and the resulting CT-P images were computed., Results: The median correlations between CT-P and SPECT-P taken over all 30 test cases ranged between 0.49 and 0.57 across the parameter sweep. For the optimal tolerance τ = 0.0385, the CT-P and SPECT-P correlations across all 30 test cases ranged between 0.02 and 0.82. A one-sample sign test was applied separately to the PE and lung cancer cohorts. A low Spearmen correlation of 15% was set as the null median value and two-sided alternative was tested. The PE patients showed a median correlation of 0.57 (IQR = 0.305). One-sample sign test was statistically significant with 96.5 % confidence interval: 0.20-0.63, P < 0.00001. Lung cancer patients had a median correlation of 0.57(IQR = 0.230). Again, a one-sample sign test for median was statistically significant with 96.5 percent confidence interval: 0.45-0.71, P < 0.00001., Conclusion: CT-Perfusion is the first mechanistic model designed to quantify magnitude blood mass changes on noncontrast dynamic CT as a surrogate for pulmonary perfusion. While the reported correlations with SPECT-P are promising, further investigation is required to determine the optimal CT acquisition protocol and numerical method implementation for CT-P imaging., (© 2021 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Purpose: The computed tomography (CT)-derived ventilation imaging methodology employs deformable image registration (DIR) to recover respiratory motion-induced volume changes from an inhale/exhale CT image pair, as a surrogate for ventilation. The Integrated Jacobian Formulation (IJF) and Mass Conserving Volume Change (MCVC) numerical methods for volume change estimation represent two classes of ventilation methods, namely transformation based and intensity (Hounsfield Unit) based, respectively. Both the IJF and MCVC methods utilize subregional volume change measurements that satisfy a specified uncertainty tolerance. In previous publications, the ventilation images resulting from this numerical strategy demonstrated robustness to DIR variations. However, the reduced measurement uncertainty comes at the expense of measurement resolution. The purpose of this study was to examine the spatial correlation between robust CT-ventilation images and single photon emission CT-ventilation (SPECT-V)., Methods: Previously described implementations of IJF and MCVC require the solution of a large scale, constrained linear least squares problem defined by a series of robust subregional volume change measurements. We introduce a simpler parameterized implementation that reduces the number of unknowns while increasing the number of data points in the resulting least squares problem. A parameter sweep of the measurement uncertainty tolerance, τ , was conducted using the 4DCT and SPECT-V images acquired for 15 non-small cell lung cancer patients prior to radiotherapy. For each test case, MCVC and IJF CT-ventilation images were created for 30 different uncertainty parameter values, uniformly sampled from the range 0.01 , 0.25 . Voxel-wise Spearman correlation between the SPECT-V and the resulting CT-ventilation images was computed., Results: The median correlations between MCVC and SPECT-V ranged from 0.20 to 0.48 across the parameter sweep, while the median correlations for IJF and SPECT-V ranged between 0.79 and 0.82. For the optimal IJF tolerance τ = 0.07 , the IJF and SPECT-V correlations across all 15 test cases ranged between 0.12 and 0.90. For the optimal MCVC tolerance τ = 0.03 , the MCVC and SPECT-V correlations across all 15 test cases ranged between -0.06 and 0.84., Conclusion: The reported correlations indicate that robust methods generate ventilation images that are spatially consistent with SPECT-V, with the transformation-based IJF method yielding higher correlations than those previously reported in the literature. For both methods, overall correlations were found to marginally vary for τ ∈ [ 0.03 , 0.15 ] , indicating that the clinical utility of both methods is robust to both uncertainty tolerance and DIR solution., (© 2020 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Computed tomography (CT) ventilation algorithms estimate volume changes induced by respiratory motion. Existing Hounsfield Unit (HU) methods approximate volume change from the measured HU variations between spatially corresponding voxel locations within a temporally resolved CT image pair, assuming that volume changes are caused solely by changes in air content. Numerical implementations require a deformable image registration to determine the inhale/exhale spatial correspondence, a preprocessing lung volume segmentation, a preprocessing high-intensity vessel segmentation, and a post-processing smoothing applied to the raw volume change estimates obtained for each lung tissue voxel., Purpose: We introduce the novel mass-conserving volume change (MCVC) method for estimating voxel volume changes from the HU values within an inhale/exhale CT image pair. MCVC is based on subregional volume change estimates that possess quantitatively characterized and controllable levels of uncertainty. MCVC is therefore robust to small variations in DIR solutions and the resulting ventilation images are overall more reproducible. In contrast to existing HU methods, MCVC does not require a preprocessing lung vessel segmentation or pre/post-processing Gaussian smoothing., Methods: Subregional volume change estimates are defined in terms of mean density ratios. As such, the corresponding uncertainty is characterized using Gaussian statistics and standard error analysis of the sample density means. A numerical solution is obtained from the MCVC formulation by solving a constrained linear least squares problem defined by a series of subregional volume change estimates. Reproducibility of the MCVC method with respect to DIR solution was assessed using expert-determined landmark point pairs and inhale/exhale phases from 10 four-dimensional CTs (4DCTs) available on www.dir-lab.com. MCVC was also compared to the robust Integrated Jacobian Formulation (IJF), a transformation-based ventilation method., Results: The ten Dir-Lab 4DCT cases were registered twice with the same DIR algorithm, but using different degrees of freedom (DIR 1 and DIR 2). Standard HU ventilation (HUV) and MCVC ventilation images were computed for both solutions. The average spatial errors (300 landmarks per case) for DIR 1 ranged between 0.74 and 1.50 mm, whereas for DIR 2 they ranged between 0.68 and 1.18 mm. Despite the differences in spatial errors between the two DIR solutions, the average Pearson correlation between the corresponding HUV images was 0.94 (0.03), while for the MCVC images it was 1.00 (0.00). The average correlation between MCVC and IJF ventilation over the ten test cases was 0.81 (0.14), whereas for HUV and IJF it was 0.56 (1.11)., Conclusion: While HUV is robust to DIR solution, its implementation depends on heuristic Gaussian smoothing and vessel segmentation. MCVC is based on subregional volume change measurements with quantifiable and controllable levels of uncertainty. The subregional approach eliminates the need for Gaussian smoothing and lung vasculature segmentation. Numerical experiments are consistent with the underlying mathematical theory and indicate that MCVC ventilation is more reproducible with respect to DIR algorithm than standard HU methods. MCVC results are also more consistent with the robust IJF method, which suggests that incorporating robustness leads to more consistent results across both DIRs and ventilation algorithms., (© 2019 The Authors. Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.)

Purpose: Ventilation images can be derived from four-dimensional computed tomography (4DCT) by analyzing the change in HU values and deformable vector fields between different respiration phases of computed tomography (CT). As deformable image registration (DIR) is involved, accuracy of 4DCT-derived ventilation image is sensitive to the choice of DIR algorithms. To overcome the uncertainty associated with DIR, we develop a method based on deep convolutional neural network (CNN) to derive ventilation images directly from the 4DCT without explicit image registration., Methods: A total of 82 sets of 4DCT and ventilation images from patients with lung cancer were used in this study. In the proposed CNN architecture, the CT two-channel input data consist of CT at the end of exhale and the end of inhale phases. The first convolutional layer has 32 different kernels of size 5 × 5 × 5, followed by another eight convolutional layers each of which is equipped with an activation layer (ReLU). The loss function is the mean-squared-error (MSE) to measure the intensity difference between the predicted and reference ventilation images., Results: The predicted images were comparable to the label images of the test data. The similarity index, correlation coefficient, and Gamma index passing rate averaged over the tenfold cross validation were 0.880 ± 0.035, 0.874 ± 0.024, and 0.806 ± 0.014, respectively., Conclusions: The results demonstrate that deep CNN can generate ventilation imaging from 4DCT without explicit deformable image registration, reducing the associated uncertainty., (© 2019 American Association of Physicists in Medicine.)

Intensity-based deformable image registration (DIR) requires minimizing an image dissimilarity metric. Imaged anatomy, such as bones and vasculature, as well as the resolution of the digital grid, can often cause discontinuities in the corresponding objective function. Consequently, the application of a gradient-based optimization algorithm requires a preprocessing image smoothing to ensure the existence of necessary image derivatives. Simple block matching (exhaustive search) methods do not require image derivative approximations, but their general effectiveness is often hindered by erroneous solutions (outliers). Block match methods are therefore often coupled with a statistical outlier detection method to improve results., Purpose: The purpose of this work is to present a spatially accurate, intensity-based DIR optimization formulation that can be solved with a straightforward gradient-free quadratic penalty algorithm and is suitable for 4D thoracic computed tomography (4DCT) registration. Additionally, a novel regularization strategy based on the well-known leave-one-out robust statistical model cross-validation method is introduced., Methods: The proposed Quadratic Penalty DIR (QPDIR) method minimizes both an image dissimilarity term, which is separable with respect to individual voxel displacements, and a regularization term derived from the classical leave-one-out cross-validation statistical method. The resulting DIR problem lends itself to a quadratic penalty function optimization approach, where each subproblem can be solved by straightforward block coordinate descent iteration., Results: The spatial accuracy of the method was assessed using expert-determined landmarks on ten 4DCT datasets available on www.dir-lab.com. The QPDIR algorithm achieved average millimeter spatial errors between 0.69 (0.91) and 1.19 (1.26) on the ten test cases. On all ten 4DCT test cases, the QPDIR method produced spatial accuracies that are superior or equivalent to those produced by current state-of-the-art methods. Moreover, QPDIR achieved accuracies at the resolution of the landmark error assessment (i.e., the interobserver error) on six of the ten cases., Conclusion: The QPDIR algorithm is based on a simple quadratic penalty function formulation and a regularization term inspired by leave-one-out cross validation. The formulation lends itself to a parallelizable, gradient-free, block coordinate descent numerical optimization method. Numerical results indicate that the method achieves a high spatial accuracy on 4DCT inhale/exhale phases., (© 2019 American Association of Physicists in Medicine.)

Computed tomography (CT) derived ventilation algorithms estimate the apparent voxel volume changes within an inhale/exhale CT image pair. Transformation-based methods compute these estimates solely from the spatial transformation acquired by applying a deformable image registration (DIR) algorithm to the image pair. However, approaches based on finite difference approximations of the transformation's Jacobian have been shown to be numerically unstable. As a result, transformation-based CT ventilation is poorly reproducible with respect to both DIR algorithm and CT acquisition method., Purpose: We introduce a novel Integrated Jacobian Formulation (IJF) method for estimating voxel volume changes under a DIR-recovered spatial transformation. The method is based on computing volume estimates of DIR mapped subregions using the hit-or-miss sampling algorithm for integral approximation. The novel approach allows for regional volume change estimates that (a) respect the resolution of the digital grid and (b) are based on approximations with quantitatively characterized and controllable levels of uncertainty. As such, the IJF method is designed to be robust to variations in DIR solutions and thus overall more reproducible., Methods: Numerically, Jacobian estimates are recovered by solving a simple constrained linear least squares problem that guarantees the recovered global volume change is equal to the global volume change obtained from the inhale and exhale lung segmentation masks. Reproducibility of the IJF method with respect to DIR solution was assessed using the expert-determined landmark point pairs and inhale/exhale phases from 10 four-dimensional computed tomographies (4DCTs) available on www.dir-lab.com. Reproducibility with respect to CT acquisition was assessed on the 4DCT and 4D cone beam CT (4DCBCT) images acquired for five lung cancer patients prior to radiotherapy., Results: The ten Dir-Lab 4DCT cases were registered twice with the same DIR algorithm, but with different smoothing parameter. Finite difference Jacobian (FDJ) and IFJ images were computed for both solutions. The average spatial errors (300 landmarks per case) for the two DIR solution methods were 0.98 (1.10) and 1.02 (1.11). The average Pearson correlation between the FDJ images computed from the two DIR solutions was 0.83 (0.03), while for the IJF images it was 1.00 (0.00). For intermodality assessment, the IJF and FDJ images were computed from the 4DCT and 4DCBCT of five patients. The average Pearson correlation of the spatially aligned FDJ images was 0.27 (0.11), while it was 0.77 (0.13) for the IFJ method., Conclusion: The mathematical theory underpinning the IJF method allows for the generation of ventilation images that are (a) computed with respect to DIR spatial accuracy on the digital voxel grid and (b) based on DIR-measured subregional volume change estimates acquired with quantifiable and controllable levels of uncertainty. Analyses of the experiments are consistent with the mathematical theory and indicate that IJF ventilation imaging has a higher reproducibility with respect to both DIR algorithm and CT acquisition method, in comparison to the standard finite difference approach., (© 2019 The Authors. Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.)

Purpose: A primary treatment option for lung cancer patients is surgical resection. Patients who have poor lung function prior to surgery are at increased risk of developing serious and life-threatening complications after surgical resection. Surgeons use nuclear medicine ventilation-perfusion (VQ) scans along with pulmonary function test (PFT) information to assess a patient's pre-surgical lung function. The nuclear medicine images and pre-surgery PFTs are used to calculate percent predicted postoperative (%PPO) PFT values by estimating the amount of functioning lung tissue that would be lost with surgical resection. Nuclear medicine imaging is currently considered the standard of care when evaluating the amount of ventilation that would be lost due to surgery. A novel lung function imaging modality has been developed in radiation oncology that uses 4-Dimensional computed tomography data to calculate ventilation maps (4DCT-ventilation). Compared to nuclear medicine, 4DCT-ventilation is cheaper, does not require a radioactive contrast agent, provides a faster imaging procedure, and has improved spatial resolution. In this work we perform a retrospective study to assess the use of 4DCT-ventilation as a pre-operative surgical lung function evaluation tool. Specifically, the purpose of our study was to compare %PPO PFT values calculated with 4DCT-ventilation and %PPO PFT values calculated with nuclear medicine ventilation-perfusion imaging., Methods: The study included 16 lung cancer patients that had undergone 4DCT imaging, nuclear medicine imaging, and had Forced Expiratory Volume in 1 second (FEV 1 ) acquired as part of a standard PFT. The 4DCT datasets, spatial registration, and a density-change-based model were used to compute 4DCT-ventilation maps. Both 4DCT-ventilation and nuclear medicine images were used to calculate %PPO FEV 1 . The %PPO FEV 1 was calculated by scaling the pre-surgical FEV 1 by (1-fraction of total resected ventilation); where the resected ventilation was determined using either the 4DCT-ventilation or nuclear medicine imaging. Calculations were done assuming both lobectomy and pneumonectomy resections. The %PPO FEV 1 values were compared between the 4DCT-ventilation-based calculations and the nuclear medicine-based calculations using correlation coefficients, average differences, and Receiver Operating Characteristic (ROC) analysis., Results: Overall the 4DCT-ventilation derived %PPO FEV 1 values agreed well with nuclear medicine-derived %PPO FEV 1 data with correlations of 0.99 and 0.81 for lobectomy and pneumonectomy, respectively. The average differences between the 4DCT-ventilation and nuclear medicine-based calculation for %PPO FEV 1 were less than 5%. ROC analysis revealed predictive accuracy that ranged from 87.5% to 100% when assessing the ability of 4DCT-ventilation to predict for nuclear medicine-based %PPO FEV 1 values., Conclusions: 4DCT-ventilation is an innovative technology developed in radiation oncology that has great potential to translate to the surgical domain. The high correlation results when comparing 4DCT-ventilation to the current standard of care provide a strong rationale for a prospective clinical trial assessing 4DCT-ventilation in the clinical setting. 4DCT-ventilation can reduce the cost and imaging time for patients while providing improved spatial accuracy and quantitative results for surgeons., (© 2016 American Association of Physicists in Medicine.)

Purpose: Block matching is a well-known strategy for estimating corresponding voxel locations between a pair of images according to an image similarity metric. Though robust to issues such as image noise and large magnitude voxel displacements, the estimated point matches are not guaranteed to be spatially accurate. However, the underlying optimization problem solved by the block matching procedure is similar in structure to the class of optimization problem associated with B-spline based registration methods. By exploiting this relationship, the authors derive a numerical method for computing a global minimizer to a constrained B-spline registration problem that incorporates the robustness of block matching with the global smoothness properties inherent to B-spline parameterization., Methods: The method reformulates the traditional B-spline registration problem as a basis pursuit problem describing the minimall1-perturbation to block match pairs required to produce a B-spline fitting error within a given tolerance. The sparsity pattern of the optimal perturbation then defines a voxel point cloud subset on which the B-spline fit is a global minimizer to a constrained variant of the B-spline registration problem. As opposed to traditional B-spline algorithms, the optimization step involving the actual image data is addressed by block matching., Results: The performance of the method is measured in terms of spatial accuracy using ten inhale/exhale thoracic CT image pairs (available for download atwww.dir-lab.com) obtained from the COPDgene dataset and corresponding sets of expert-determined landmark point pairs. The results of the validation procedure demonstrate that the method can achieve a high spatial accuracy on a significantly complex image set., Conclusions: The proposed methodology is demonstrated to achieve a high spatial accuracy and is generalizable in that in can employ any displacement field parameterization described as a least squares fit to block match generated estimates. Thus, the framework allows for a wide range of image similarity block match metric and physical modeling combinations., (© 2014 American Association of Physicists in Medicine.)

Purpose: Respiratory motion introduces uncertainties in tumor location and lung deformation, which often results in difficulties calculating dose distributions in thoracic radiation therapy. Deformable image registration (DIR) has ability to describe respiratory-induced lung deformation, with which the radiotherapy techniques can deliver high dose to tumors while reducing radiation in surrounding normal tissue. The authors' goal is to propose a DIR method to overcome two main challenges of the previous biomechanical model for lung deformation, i.e., the requirement of precise boundary conditions and the lack of elasticity distribution., Methods: As opposed to typical methods in biomechanical modeling, the authors' method assumes that lung tissue is inhomogeneous. The authors thus propose a DIR method combining a varying intensity flow (VF) block-matching algorithm with the finite element method (FEM) for lung deformation from end-expiratory phase to end-inspiratory phase. Specifically, the lung deformation is formulated as a stress-strain problem, for which the boundary conditions are obtained from the VF block-matching algorithm and the element specific Young's modulus distribution is estimated by solving an optimization problem with a quasi-Newton method. The authors measure the spatial accuracy of their nonuniform model as well as a standard uniform model by applying both methods to four-dimensional computed tomography images of six patients. The spatial errors produced by the registrations are computed using large numbers (>1000) of expert-determined landmark point pairs., Results: In right-left, anterior-posterior, and superior-inferior directions, the mean errors (standard deviation) produced by the standard uniform FEM model are 1.42(1.42), 1.06(1.05), and 1.98(2.10) mm whereas the authors' proposed nonuniform model reduces these errors to 0.59(0.61), 0.52(0.51), and 0.78(0.89) mm. The overall 3D mean errors are 3.05(2.36) and 1.30(0.97) mm for the uniform and nonuniform models, respectively., Conclusions: The results indicate that the proposed nonuniform model can simulate patient-specific and position-specific lung deformation via spatially varying Young's modulus estimates, which improves registration accuracy compared to the uniform model and is therefore a more suitable description of lung deformation.

Dynamic imaging methods such as four-dimensional computed tomography (4DCT) and static imaging methods such as noble gas magnetic resonance imaging (MRI) deliver direct and regional measurements of lung function even in lung cancer patients in whom global lung function measurements are dominated by tumour burden. The purpose of this study was to directly compare quantitative measurements of gas distribution from static hyperpolarized 3 He MRI and dynamic 4DCT in a small group of lung cancer patients. MRI and 4DCT were performed in 11 subjects prior to radiation therapy. MRI was performed at 3.0T in breath-hold after inhalation 1L of hyperpolarized 3 He gas. Gas distribution in 3 He MRI was quantified using a semi-automated segmentation algorithm to generate percent-ventilated volume (PVV), reflecting the volume of gas in the lung normalized to the thoracic cavity volume. 4DCT pulmonary function maps were generated using deformable image registration of six expiratory phase images. The correspondence between identical tissue elements at inspiratory and expiratory phases was used to estimate regional gas distribution and PVV was quantified from these images. After accounting for differences in lung volumes between 3 He MRI (1.9±0.5L ipsilateral, 2.3±0.7 contralateral) and 4DCT (1.2±0.3L ipsilateral, 1.3±0.4L contralateral) during image acquisition, there was no statistically significant difference in PVV between 3 He MRI (72±11% ipsilateral, 79±12% contralateral) and 4DCT (74±3% ipsilateral, 75±4% contralateral). Our results indicate quantitative agreement in the regional distribution of inhaled gas in both static and dynamic imaging methods. PVV may be considered as a regional surrogate measurement of lung function or ventilation., (© 2012 American Association of Physicists in Medicine.)

Purpose: A method has been proposed to calculate ventilation maps from four-dimensional computed tomography (4DCT) images. Weekly 4DCT data were acquired throughout the course of radiation therapy for patients with lung cancer. The purpose of our work was to use ventilation maps calculated from weekly 4DCT data to study how ventilation changed throughout radiation therapy., Methods: Quantitative maps representing ventilation were generated for six patients. Deformable registration was used to link corresponding lung volume elements between the inhale and exhale phases of the 4DCT dataset. Following spatial registration, corresponding Hounsfield units were input into a density-change-based model for quantifying the local ventilation. The ventilation data for all weeks were registered to the pretreatment ventilation image set. We quantitatively analyzed the data by defining regions of interest (ROIs) according to dose (V(20)) and lung lobe and by tracking the weekly ventilation of each ROI throughout treatment. The slope of the linear fit to the weekly ventilation data was used to evaluate the change in ventilation throughout treatment. A positive slope indicated an increase in ventilation, a negative slope indicated a decrease in ventilation, and a slope of 0 indicated no change. The dose ROI ventilation and slope data were used to study how ventilation changed throughout treatment as a function of dose. The lung lobe ROI ventilation data were used to study the impact of the presence of tumor on pretreatment ventilation. In addition, the lobe ROI data were used to study the impact of tumor reduction on ventilation change throughout treatment., Results: Using the dose ROI data, we found that three patients had an increase in weekly ventilation as a function of dose (slopes of 1.1, 1.4, and 1.5) and three patients had no change or a slight decrease in ventilation as a function of dose (slopes of 0.3, -0.6, -0.5). Visually, pretreatment ventilation appeared to be lower in the lobes that contained tumor. Pretreatment ventilation was 39% for lobes that contained tumor and 54% for lobes that did not contain tumor. The difference in ventilation between the two groups was statistically significant (p = 0.017). When the weekly lobe ventilation data were qualitatively observed, two distinct patterns emerged. When the tumor volume in a lobe was reduced, ventilation increased in the lobe. When the tumor volume was not reduced, the ventilation distribution did not change. The average slope of the group of lobes that contained tumors that shrank was 1.18, while the average slope of the group that did not contain tumors (or contained tumors that did not shrink) was -0.32. The slopes for the two groups were significantly different (p = 0.014)., Conclusions: We did not find a consistent pattern of ventilation change as a function of radiation dose. Pretreatment ventilation was significantly lower for lobes that contained tumor, due to occlusion of the central airway. The weekly lobe ventilation data indicated that when tumor volume shrinks, ventilation increases, and when the thoracic anatomy is not visibly changed, ventilation is likely to remain unchanged.