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Dynamic contrast-enhanced CT using a lower-viscosity contrast agent in combination with tracer-kinetic analysis by the steepest-slope model improves pulmonary blood flow measurements and assessment of regional distributions of lung perfusion.

Background: The pulmonary vasculature is essential for gas exchange and impacts both pulmonary and cardiac function. However, it is difficult to assess and its characteristics in the general population are unknown. We measured pulmonary blood volume (PBV) noninvasively using contrast enhanced, dual-energy computed tomography to evaluate its relationship to age and symptoms among older adults in the community. Methods: The MESA (Multi-Ethnic Study of Atherosclerosis) is an ongoing community-based, multicenter cohort. All participants attending the most recent MESA exam were selected for contrast enhanced dual-energy computed tomography except those with estimated glomerular filtration rate 2 . PBV was calculated by material decomposition of dual-energy computed tomography images. Multivariable models included age, sex, race/ethnicity, education, height, weight, smoking status, pack-years, and scanner model. Results: The mean age of the 727 participants was 71 (range 59–94) years, and 55% were male. The race/ethnicity distribution was 41% White, 29% Black, 17% Hispanic, and 13% Asian. The mean±SD PBV in the youngest age quintile was 547±180 versus 433±194 mL in the oldest quintile ( P P P =0.04) and greater composite dyspnea symptom scores ( P =0.02). Greater PBV was also associated with greater height, weight, lung volume, Hispanic race/ethnicity, and nonsmoking history. Conclusions: Pulmonary blood volume was substantially lower with advanced age and was associated independently with greater symptoms scores in the elderly.

Emphysema is a progressive disease characterized by irreversible tissue destruction and airspace enlargement, which manifest as low attenuation area (LAA) on CT images. Previous studies have shown that inflammation, protease imbalance, extracellular matrix remodeling and mechanical forces collectively influence the progression of emphysema. Elastic spring network models incorporating force-based mechanical failure have been applied to investigate the pathogenesis and progression of emphysema. However, these models were general without considering the patient-specific information on lung structure available in CT images. The aim of this work was to develop a novel approach that provides an optimal spring network representation of emphysematous lungs based on the apparent density in CT images, allowing the construction of personalized networks. The proposed method takes into account the size and curvature of LAA clusters on the CT images that correspond to a pre-stressed condition of the lung as opposed to a naïve method that excludes the effects of pre-stress. The main findings of this study are that networks constructed by the new method 1) better preserve LAA cluster sizes and their distribution than the naïve method; and 2) predict different course of emphysema progression compared to the naïve method. We conclude that our new method has the potential to predict patient-specific emphysema progression which needs verification using clinical data.

Out-of-phase ventilation occurs when local regions of the lung reach their maximum or minimum volumes at breathing phases other than the global end inhalation or exhalation phases. This paper presents the N-phase local expansion ratio (LER $_{N}$ ) as a surrogate for lung ventilation. A common approach to estimate lung ventilation is to use image registration to align the end exhalation and inhalation 3DCT images and then analyze the resulting correspondence map. This 2-phase local expansion ratio (LER2) is limited because it ignores out-of-phase ventilation and thus may underestimate local lung ventilation. To overcome this limitation, LER $_{N}$ measures the maximum ratio of local expansion and contraction over the entire breathing cycle. Comparing LER2 to LER $_{N}$ provides a means for detecting and characterizing locations of the lung that experience out-of-phase ventilation. We present a novel in-phase/out-of-phase ventilation (IOV) function plot to visualize and measure the amount of high-function IOV that occurs during a breathing cycle. Treatment planning 4DCT scans collected during coached breathing from 32 human subjects with lung cancer were analyzed in this study. Results show that out-of-phase breathing occurred in all subjects and that the spatial distribution of out-of-phase ventilation varied from subject to subject. For the 32 subjects analyzed, 50% of the out-of-phase regions on average were mislabeled as low-function by LER2 (high-function threshold of 1.1, IOV threshold of 1.05). 4DCT and Xenon-enhanced CT of four sheep showed that LER8 is more accurate than LER2 for measuring lung ventilation.

To describe the effects of timing of intubation in COVID-19 patients that fail helmet continuous positive airway pressure (h-CPAP) on progression and severity of disease.COVID-19 patients that failed h-CPAP, required intubation, and underwent chest computed tomography (CT) at two levels of positive end-expiratory pressure (PEEP, 8 and 16 cmHFifty-two patients were included and classified in early (h-CPAP for ≤2 days, N = 26) and late groups (h-CPAP for2 days, N = 26). Patients in the late compared to early intubation group presented: 1) lower respiratory system compliance (median difference, MD -7 mL/cmHIn COVID-19 patients receiving h-CPAP, late intubation was associated with worse clinical presentation at ICU admission and more advanced disease. The possible detrimental effects of delaying intubation should be carefully considered in these patients.

Local tissue expansion of the lungs is typically derived by registering computed tomography (CT) scans acquired at multiple lung volumes. However, acquiring multiple scans incurs increased radiation dose, time, and cost, and may not be possible in many cases, thus restricting the applicability of registration-based biomechanics. We propose a generative adversarial learning approach for estimating local tissue expansion directly from a single CT scan. The proposed framework was trained and evaluated on 2500 subjects from the SPIROMICS cohort. Once trained, the framework can be used as a registration-free method for predicting local tissue expansion. We evaluated model performance across varying degrees of disease severity and compared its performance with two image-to-image translation frameworks - UNet and Pix2Pix. Our model achieved an overall PSNR of 18.95 decibels, SSIM of 0.840, and Spearman's correlation of 0.61 at a high spatial resolution of 1 mm3., 5 pages, 5 figures

OBJECTIVES It is not known how lung injury progression during mechanical ventilation modifies pulmonary responses to prone positioning. We compared the effects of prone positioning on regional lung aeration in late versus early stages of lung injury. DESIGN Prospective, longitudinal imaging study. SETTING Research imaging facility at The University of Pennsylvania (Philadelphia, PA) and Medical and Surgical ICUs at Massachusetts General Hospital (Boston, MA). SUBJECTS Anesthetized swine and patients with acute respiratory distress syndrome (acute respiratory distress syndrome). INTERVENTIONS Lung injury was induced by bronchial hydrochloric acid (3.5 mL/kg) in 10 ventilated Yorkshire pigs and worsened by supine nonprotective ventilation for 24 hours. Whole-lung CT was performed 2 hours after hydrochloric acid (Day 1) in both prone and supine positions and repeated at 24 hours (Day 2). Prone and supine images were registered (superimposed) in pairs to measure the effects of positioning on the aeration of each tissue unit. Two patients with early acute respiratory distress syndrome were compared with two patients with late acute respiratory distress syndrome, using electrical impedance tomography to measure the effects of body position on regional lung mechanics. MEASUREMENTS AND MAIN RESULTS Gas exchange and respiratory mechanics worsened over 24 hours, indicating lung injury progression. On Day 1, prone positioning reinflated 18.9% ± 5.2% of lung mass in the posterior lung regions. On Day 2, position-associated dorsal reinflation was reduced to 7.3% ± 1.5% (p < 0.05 vs Day 1). Prone positioning decreased aeration in the anterior lungs on both days. Although prone positioning improved posterior lung compliance in the early acute respiratory distress syndrome patients, it had no effect in late acute respiratory distress syndrome subjects. CONCLUSIONS The effects of prone positioning on lung aeration may depend on the stage of lung injury and duration of prior ventilation; this may limit the clinical efficacy of this treatment if applied late.

This study reports systematic longitudinal pathophysiology of lung parenchymal and vascular effects of asymptomatic COVID-19 pneumonia in a young, healthy never-smoking male. Inspiratory and expiratory noncontrast along with contrast dual-energy computed tomography (DECT) scans of the chest were performed at baseline on the day of acute COVID-19 diagnosis (day 0), and across a 90-day period. Despite normal vital signs and pulmonary function tests on the day of diagnosis, the CT scans and corresponding quantification metrics detected abnormalities in parenchymal expansion based on image registration, ground-glass (GGO) texture (inflammation) as well as DECT-derived pulmonary blood volume (PBV). Follow-up scans on day 30 showed improvement in the lung parenchymal mechanics as well as reduced GGO and improved PBV distribution. Improvements in lung PBV continued until day 90. However, the heterogeneity of parenchymal mechanics and texture-derived GGO increased on days 60 and 90. We highlight that even asymptomatic COVID-19 infection with unremarkable vital signs and pulmonary function tests can have measurable effects on lung parenchymal mechanics and vascular pathophysiology, which may follow apparently different clinical courses. For this asymptomatic subject, post COVID-19 regional mechanics demonstrated persistent increased heterogeneity concomitant with return of elevated GGOs, despite early improvements in vascular derangement. NEW & NOTEWORTHY We characterized the temporal changes of lung parenchyma and microvascular pathophysiology from COVID-19 infection in an asymptomatic young, healthy nonsmoking male using dual-energy CT. Lung parenchymal mechanics and microvascular disease followed different clinical courses. Heterogeneous perfused blood volume became more uniform on follow-up visits up to 90 days. However, post COVID-19 mechanical heterogeneity of the lung parenchyma increased after apparent improvements in vascular abnormalities, even with normal spirometric indices.

Pulmonary fissure detection in computed tomography (CT) is a critical component for automatic lobar segmentation. The majority of fissure detection methods use feature descriptors that are hand-crafted, low-level, and have local spatial extent. The design of such feature detectors is typically targeted toward normal fissure anatomy, yielding low sensitivity to weak, and abnormal fissures that are common in clinical data sets. Furthermore, local features commonly suffer from low specificity, as the complex textures in the lung can be indistinguishable from the fissure when the global context is not considered. We propose a supervised discriminative learning framework for simultaneous feature extraction and classification. The proposed framework, called FissureNet, is a coarse-to-fine cascade of two convolutional neural networks. The coarse-to-fine strategy alleviates the challenges associated with training a network to segment a thin structure that represents a small fraction of the image voxels. FissureNet was evaluated on a cohort of 3706 subjects with inspiration and expiration 3DCT scans from the COPDGene clinical trial and a cohort of 20 subjects with 4DCT scans from a lung cancer clinical trial. On both data sets, FissureNet showed superior performance compared with a deep learning approach using the U-Net architecture and a Hessian-based fissure detection method in terms of area under the precision-recall curve (PR-AUC). The overall PR-AUC for FissureNet, U-Net, and Hessian on the COPDGene (lung cancer) data set was 0.980 (0.966), 0.963 (0.937), and 0.158 (0.182), respectively. On a subset of 30 COPDGene scans, FissureNet was compared with a recently proposed advanced fissure detection method called derivative of sticks (DoS) and showed superior performance with a PR-AUC of 0.991 compared with 0.668 for DoS.

Enhanced intrapulmonary gas transport enables oscillatory ventilation modalities to support gas exchange using extremely low tidal volumes at high frequencies. However, it is unknown whether gas transport rates can be improved by combining multiple frequencies of oscillation simultaneously. The goal of this study was to investigate distributed gas transport in vivo during multi-frequency oscillatory ventilation (MFOV) as compared with conventional mechanical ventilation (CMV) or high-frequency oscillatory ventilation (HFOV). We hypothesized that MFOV would result in more uniform rates of gas transport compared to HFOV, measured using contrast-enhanced CT imaging during wash-in of xenon gas. In 13 pigs, xenon wash-in equilibration rates were comparable between CMV and MFOV, but 21 to 39% slower for HFOV. By contrast, the root-mean-square delivered volume was lowest for MFOV, increased by 70% during HFOV and 365% during CMV. Overall gas transport heterogeneity was similar across all modalities, but gravitational gradients and regional patchiness of specific ventilation contributed to regional ventilation heterogeneity, depending on ventilator modality. We conclude that MFOV combines benefits of low lung stretch, similar to HFOV, but with fast rates of gas transport, similar to CMV.

The purpose of this study was to develop a fully-automated segmentation algorithm, robust to various density enhancing lung abnormalities, to facilitate rapid quantitative analysis of computed tomography images. A polymorphic training approach is proposed, in which both specifically labeled left and right lungs of humans with COPD, and nonspecifically labeled lungs of animals with acute lung injury, were incorporated into training a single neural network. The resulting network is intended for predicting left and right lung regions in humans with or without diffuse opacification and consolidation. Performance of the proposed lung segmentation algorithm was extensively evaluated on CT scans of subjects with COPD, confirmed COVID-19, lung cancer, and IPF, despite no labeled training data of the latter three diseases. Lobar segmentations were obtained using the left and right lung segmentation as input to the LobeNet algorithm. Regional lobar analysis was performed using hierarchical clustering to identify radiographic subtypes of COVID-19. The proposed lung segmentation algorithm was quantitatively evaluated using semi-automated and manually-corrected segmentations in 87 COVID-19 CT images, achieving an average symmetric surface distance of $$0.495\pm 0.309$$ 0.495 ± 0.309 mm and Dice coefficient of $$0.985\pm 0.011$$ 0.985 ± 0.011 . Hierarchical clustering identified four radiographical phenotypes of COVID-19 based on lobar fractions of consolidated and poorly aerated tissue. Lower left and lower right lobes were consistently more afflicted with poor aeration and consolidation. However, the most severe cases demonstrated involvement of all lobes. The polymorphic training approach was able to accurately segment COVID-19 cases with diffuse consolidation without requiring COVID-19 cases for training.

Lung biomechanical properties can be used to detect disease, assess abnormal lung function, and track disease progression. In this work, we used computed tomography (CT) imaging to measure three biomechanical properties in the lungs of subjects with varying degrees of chronic obstructive pulmonary disease (COPD): the Jacobian determinant (J), a measure of volumetric expansion or contraction; the anisotropic deformation index (ADI), a measure of the magnitude of anisotropic deformation; and the the slab-rod index (SRI), a measure of the nature of anisotropy (i.e., whether the volume is deformed to a rod-like or slab-like shape). We analyzed CT data from 247 subjects collected as part of the Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS). The results show that mean $J$ and mean ADI decrease as disease severity increases, indicating less volumetric expansion and more isotropic expansion with increased disease. No differences in average SRI index were observed across the different levels of disease. The methods and analysis described in this study may provide new insights into our understanding of the biomechanical behavior of the lung for COPD patients with various Global Initiative for Chronic Obstructive Lung Disease (GOLD) stages.

Registration of lungs in thoracic computed tomography (CT) images produces a dense correspondence which can be analyzed to estimate local tissue expansion. However, the validity of this local expansion estimate is dependent on the accuracy of the image registration. In this work, a convolutional neural network (CNN) model is used to directly estimate the local tissue expansion between lungs imaged at two lung volumes, without requiring image registration. The network was trained with 5705 subjects from COPDGene with varying degrees of disease severity. The CNN-based model was evaluated with 3046 subjects from COPDGene. At the global scale, the mean lung expansion estimated from the CNN-based and registration-based models were highly correlated ( $r_{s}=0.945$ ). At the local scale, the proposed method achieved a voxelwise Spearman correlation of $0.871\pm 0.080$ . At the regional scale, Dice coefficient for low- and high-functioning regions was $0.805\pm 0.066$ and $0.806\pm 0.065$ , respectively. The results indicate the CNN-based model was able to reproduce image registration derived tissue expansion images without explicitly estimating the correspondence.

The pulmonary fissures divide the lungs into lobes and can vary widely in shape, appearance, and completeness. Fissure completeness, or integrity, has been studied to assess relationships with airway function measurements, chronic obstructive pulmonary disease (COPD) progression, and collateral ventilation between lobes. Fissure integrity measured from computed tomography (CT) images is already used as a non-invasive method to screen emphysema patients for endobronchial valve treatment, as the procedure is not effective when collateral ventilation is present. We describe a method for automatically computing fissure integrity from lung CT images. Our method is tested using 60 subjects from a COPD study. We examine the repeatability of fissure integrity measurements across inspiration and expiration images, assess changes in fissure integrity over time using a longitudinal dataset, and explore fissure integrity's relationship with COPD severity.

Purpose Regional ventilation and its response to radiation dose can be estimated using four‐dimensional computed tomography (4DCT) and image registration. This study investigated the impact of radiation therapy (RT) on ventilation and the dependence of radiation‐induced ventilation change on pre‐RT ventilation derived from 4DCT. Methods and materials Three 4DCT scans were acquired from each of 12 subjects: two scans before RT and one scan 3 months after RT. The 4DCT datasets were used to generate the pre‐RT and post‐RT ventilation maps by registering the inhale phase image to the exhale phase image and computing the Jacobian determinant of the resulting transformation. The ventilation change between pre‐RT and post‐RT was calculated by taking a ratio of the post‐RT Jacobian map to the pre‐RT Jacobian map. The voxel‐wise ventilation change between pre‐ and post‐RT was investigated as a function of dose and pre‐RT ventilation. Results Lung regions receiving over 20 Gy exhibited a significant decrease in function (3.3%, P < 0.01) compared to those receiving less than 20 Gy. When the voxels were stratified into high and low pre‐RT function by thresholding the Jacobian map at 10% volume expansion (Jacobian = 1.1), high‐function voxels exhibited 4.8% reduction in function for voxels receiving over 20 Gy, a significantly greater decline (P = 0.037) than the 2.4% reduction in function for low‐function voxels. Ventilation decreased linearly with dose in both high‐function and low‐function regions. High‐function regions showed a significantly larger decline in ventilation (P ≪ 0.001) as dose increased (1.4% ventilation reduction/10 Gy) compared to low‐function regions (0.3% ventilation reduction/10 Gy). With further stratification of pre‐RT ventilation, voxels exhibited increasing dose‐dependent ventilation reduction with increasing pre‐RT ventilation, with the largest pre‐RT Jacobian bin (pre‐RT Jacobian between 1.5 and 1.6) exhibiting a ventilation reduction of 4.8% per 10 Gy. Conclusions Significant ventilation reductions were measured after radiation therapy treatments, and were dependent on the dose delivered to the tissue and the pre‐RT ventilation of the tissue. For a fixed radiation dose, lung tissue with high pre‐RT ventilation experienced larger decreases in post‐RT ventilation than lung tissue with low pre‐RT ventilation.

We present an open-source framework for pulmonary fissure completeness assessment. Fissure incompleteness has been shown to associate with emphysema treatment outcomes, motivating the development of tools that facilitate completeness estimation. Generally, the task of fissure completeness assessment requires accurate detection of fissures and definition of the boundary surfaces separating the lung lobes. The framework we describe acknowledges a) the modular nature of fissure detection and lung lobe segmentation (lobe boundary detection), and b) that methods to address these challenges are varied and continually developing. It is designed to be readily deployable on existing lung lobe segmentation and fissure detection data sets. The framework consists of multiple components: a flexible quality control module that enables rapid assessment of lung lobe segmentations, an interactive lobe segmentation tool exposed through 3D Slicer for handling challenging cases, a flexible fissure representation using particles-based sampling that can handle fissure feature-strength or binary fissure detection images, and a module that performs fissure completeness estimation using voxel counting and a novel surface area estimation approach. We demonstrate the usage of the proposed framework by deploying on 100 cases exhibiting various levels of fissure completeness. We compare the two completeness level approaches and also compare to visual reads. The code is available to the community via github as part of the Chest Imaging Platform and a 3D Slicer extension module.

Segmentation of lungs with acute respiratory distress syndrome (ARDS) is a challenging task due to diffuse opacification in dependent regions which results in little to no contrast at the lung boundary. For segmentation of severely injured lungs, local intensity and texture information, as well as global contextual information, are important factors for consistent inclusion of intrapulmonary structures. In this study, we propose a deep learning framework which uses a novel multi-resolution convolutional neural network (ConvNet) for automated segmentation of lungs in multiple mammalian species with injury models similar to ARDS. The multi-resolution model eliminates the need to tradeoff between high-resolution and global context by using a cascade of low-resolution to high-resolution networks. Transfer learning is used to accommodate the limited number of training datasets. The model was initially pre-trained on human CT images, and subsequently fine-tuned on canine, porcine, and ovine CT images with lung injuries similar to ARDS. The multi-resolution model was compared to both high-resolution and low-resolution networks alone. The multi-resolution model outperformed both the low- and high-resolution models, achieving an overall mean Jacaard index of 0.963 ± 0.025 compared to 0.919 ± 0.027 and 0.950 ± 0.036, respectively, for the animal dataset ( N = 287 ). The multi-resolution model achieves an overall average symmetric surface distance of 0.438 ± 0.315 mm, compared to 0.971 ± 0.368 mm and 0.657 ± 0.519 mm for the low-resolution and high-resolution models, respectively. We conclude that the multi-resolution model produces accurate segmentations in severely injured lungs, which is attributed to the inclusion of both local and global features.

Functional avoidance radiation therapy (RT) uses lung function images to identify and minimize irradiation of high-function lung tissue. Lung function can be estimated by local expansion ratio (LER) of the lung, which we define in this paper as the ratio of the maximum to the minimum local lung volume in a breathing cycle. LER is computed using deformable image registration. The end exhale (0EX) and the end inhale (100IN) phases of four-dimensional computed tomography (4DCT) are often used to estimate LER, which we refer to as LER3D. However, the lung may have out-of-phase ventilation, i.e., local lung volume change is out of phase with respect to global lung expansion and contraction. We propose the LER4D measure which estimates the LER measure using all phases of 4DCT. The purpose of this paper is to quantify the amount of out-of-phase ventilation of the lung. Out-of-phase ventilation is defined to occur when the LER4D measure is \(5\%\) or more than the LER3D measure. 4DCT scans of 14 human subjects were used in this study. Low-function (high-function) regions are defined as regions that have less (greater) than \(10\%\) expansion. Our results show that on average \(19.3\%\) of the lung had out-of-phase ventilation; \(3.8\%\) of the lung had out-of-phase ventilation and is labeled as low-function by both LER3D and LER4D; \(9.6\%\) of the lung is labeled as low-function by LER3D while high-function by LER4D; and \(5.9\%\) of the lung had out-of-phase ventilation and is labeled as high-function by both LER3D and LER4D. We conclude that out-of-phase ventilation is common in all 14 human subjects we have investigated.

Carbohydrate response element binding protein (ChREBP) regulates cellular glucose and lipid homeostasis. Although ChREBP is highly expressed in many key metabolic tissues, the role of ChREBP in most of those tissues and the consequent effects on whole-body glucose and lipid metabolism are not well understood. Therefore, we generated a transgenic mouse that overexpresses a constitutively active ChREBP isoform under the control of the fatty acid binding protein 4-Cre-driven promoter (FaChOX). Weight gain was blunted in male, but not female, FaChOX mice when placed on either a normal chow diet or an obesogenic Western diet. Respiratory exchange ratios were increased in Western diet-fed FaChOX mice, indicating a shift in whole-body substrate use favoring carbohydrate metabolism. Western diet-fed FaChOX mice showed improved insulin sensitivity and glucose tolerance in comparison with controls. Hepatic triglyceride content was reduced in Western diet-fed FaChOX mice in comparison with controls, suggesting protection from fatty liver. Epididymal adipose tissue exhibited differential expression of genes involved in differentiation, browning, metabolism, lipid homeostasis, and inflammation between Western diet-fed FaChOX mice and controls. Our findings support a role for ChREBP in modulating adipocyte differentiation and adipose tissue metabolism and inflammation as well as consequent risks for obesity and insulin resistance.

Registering lung CT images is an important problem for many applications including tracking lung motion over the breathing cycle, tracking anatomical and function changes over time, and detecting abnormal mechanical properties of the lung. This paper compares and contrasts current-and varifold-based diffeomorphic image registration approaches for registering tree-like structures of the lung. In these approaches, curve-like structures in the lung—for example, the skeletons of vessels and airways segmentation—are represented by currents or varifolds in the dual space of a Reproducing Kernel Hilbert Space (RKHS). Current and varifold representations are discretized and are parameterized via of a collection of momenta. A momenta corresponds to a line segment via the coordinates of the center of the line segment and the tangent direction of the line segment at the center. A varifold-based registration approach is similar to currents except that two varifold representations are aligned independent of the tangent vector orientation. An advantage of varifolds over currents is that the orientation of the tangent vectors can be difficult to determine especially when the vessel and airway trees are not connected. In this paper, we examine the image registration sensitivity and accuracy of current-and varifold-based registration as a function of the number and location of momentum used to represent tree like-structures in the lung. The registrations presented in this paper were generated using the Deformetrica software package ([Durrleman et al. 2014]).

Purpose: To assess regional changes in human lung ventilation and mechanics using four-dimensional computed tomography (4DCT) and deformable image registration. This work extends our prior analysis of the entire lung to a lobe-based analysis. Methods: 4DCT images acquired from 20 patients prior to radiation therapy (RT) were used for this analysis. Jacobian ventilation and motion maps were computed from the displacement field after deformable image registration between the end of expiration breathing phase and the end of inspiration breathing phase. The lobes were manually segmented on the reference phase by a medical physicist expert. The voxel-by-voxel ventilation and motion magnitude for all subjects were grouped by lobes and plotted into cumulative voxel frequency curves respectively. In addition, to eliminate the effect of different breathing efforts across subjects, we applied the inter-subject equivalent lung volume (ELV) method on a subset of the cohort and reevaluated the lobar ventilation. Results: 95% of voxels in the lung are expanding during inspiration. However, some local regions of lung tissue show far more expansion than others. The greatest expansion with respiration occurs within the lower lobes; between exhale and inhale the median expansion in lower lobes is approximately 15%, while the median expansion in uppermore » lobes is 10%. This appears to be driven by a subset of lung tissues within the lobe that have greater expansion; twice the number of voxels in the lower lobes (20%) expand by > 30% when compared to the upper lobes (10%). Conclusion: Lung ventilation and motion show significant difference on the lobar level. There are different lobar fractions of driving voxels that contribute to the major expansion of the lung. This work was supported by NIH grant CA166703.« less