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Background: Chest computed tomography (CT) enables characterization of pulmonary diseases by producing high‐resolution and high‐contrast images of the intricate lung structures. Deformable image registration is used to align chest CT scans at different lung volumes, yielding estimates of local tissue expansion and contraction. Purpose: We investigated the utility of deep generative models for directly predicting local tissue volume change from lung CT images, bypassing computationally expensive iterative image registration and providing a method that can be utilized in scenarios where either one or two CT scans are available. Methods: A residual regression convolutional neural network, called Reg3DNet+, is proposed for directly regressing high‐resolution images of local tissue volume change (i.e., Jacobian) from CT images. Image registration was performed between lung volumes at total lung capacity (TLC) and functional residual capacity (FRC) using a tissue mass‐ and structure‐preserving registration algorithm. The Jacobian image was calculated from the registration‐derived displacement field and used as the ground truth for local tissue volume change. Four separate Reg3DNet+ models were trained to predict Jacobian images using a multifactorial study design to compare the effects of network input (i.e., single image vs. paired images) and output space (i.e., FRC vs. TLC). The models were trained and evaluated on image datasets from the COPDGene study. Models were evaluated against the registration‐derived Jacobian images using local, regional, and global evaluation metrics. Results: Statistical analysis revealed that both factors – network input and output space – were significant determinants for change in evaluation metrics. Paired‐input models performed better than single‐input models, and model performance was better in the output space of FRC rather than TLC. Mean structural similarity index for paired‐input models was 0.959 and 0.956 for FRC and TLC output spaces, respectively, and for single‐input models was 0.951 and 0.937. Global evaluation metrics demonstrated correlation between registration‐derived Jacobian mean and predicted Jacobian mean: coefficient of determination (r2) for paired‐input models was 0.974 and 0.938 for FRC and TLC output spaces, respectively, and for single‐input models was 0.598 and 0.346. After correcting for effort, registration‐derived lobar volume change was strongly correlated with the predicted lobar volume change: for paired‐input models r2 was 0.899 for both FRC and TLC output spaces, and for single‐input models r2 was 0.803 and 0.862, respectively. Conclusions: Convolutional neural networks can be used to directly predict local tissue mechanics, eliminating the need for computationally expensive image registration. Networks that use paired CT images acquired at TLC and FRC allow for more accurate prediction of local tissue expansion compared to networks that use a single image. Networks that only require a single input image still show promising results, particularly after correcting for effort, and allow for local tissue expansion estimation in cases where multiple CT scans are not available. For single‐input networks, the FRC image is more predictive of local tissue volume change compared to the TLC image. [ABSTRACT FROM AUTHOR]

Sliding between lung lobes along lobar fissures is a poorly understood aspect of lung mechanics. The objective of this study was to test the hypothesis that lobar sliding helps reduce distortion in the lung parenchyma during breathing. Finite element models of left lungs with geometries and boundary conditions derived from medical images of human subjects were developed. Effect of lobar sliding was studied by comparing nonlinear finite elastic contact mechanics simulations that allowed and disallowed lobar sliding. Lung parenchymal distortion during simulated breath-holds and tidal breathing was quantified with the model's spatial mean anisotropic deformation index (ADI), a measure of directional preference in volume change that varies spatially in the lung. Models that allowed lobar sliding had significantly lower mean ADI (i.e., lesser parenchymal distortion) than models that disallowed lobar sliding under simulations of both tidal breathing (5.3% median difference, P = 0.008, n = 8) and lung deformation between breath-holds at total lung capacity and functional residual capacity (3.2% median difference, P = 0.03, n = 6). This effect was most pronounced in the lower lobe where lobar sliding reduced parenchymal distortion with statistical significance, but not in the upper lobe. In addition, more lobar sliding was correlated with greater reduction in distortion between sliding and nonsliding models in our study cohorts (Pearson's correlation coefficient of 0.95 for tidal breathing, 0.87 for breathholds, and 0.91 for the combined dataset). These findings are consistent with the hypothesis that lung lobar sliding reduces parenchymal distortion during breathing. [ABSTRACT FROM AUTHOR]

Vessel segmentation in the lung is an ongoing challenge. While many methods have been able to successfully identify vessels in normal, healthy, lungs, these methods struggle in the presence of abnormalities. Following radiotherapy, these methods tend to identify regions of radiographic change due to postradiation therapytoxicities as vasculature falsely. By combining texture analysis and existing vasculature and masking techniques, we have developed a novel vasculature segmentation workflow that improves specificity in irradiated lung while preserving the sensitivity of detection in the rest of the lung. Furthermore, radiation dose has been shown to cause vascular injury as well as reduce pulmonary function post-RT. This work shows the improvements our novel vascular segmentation method provides relative to existing methods. Additionally, we use this workflow to show a dose dependent radiationinduced change in vasculature which is correlated with previously measured perfusion changes (R2 = 0.72) in both directly irradiated and indirectly damaged regions of perfusion. These results present an opportunity to extend noncontrast CT-derived models of functional change following radiation therapy. [ABSTRACT FROM AUTHOR]

Recent functional lung imaging studies have presented evidence of an "indirect effect" on perfusion damage, where regions that are unirradiated or lowly irradiated but that are supplied by highly irradiated regions observe perfusion damage post-radiation therapy (RT). The purpose of this work was to investigate this effect using a contrast-enhanced dynamic CT protocol to measure perfusion change in five novel swine subjects. A cohort of five Wisconsin Miniature Swine (WMS) were given a research course of 60 Gy in five fractions delivered locally to a vessel in the lung using an Accuray Radixact tomotherapy system with Synchrony motion tracking to increase delivery accuracy. Imaging was performed prior to delivering RT and 3 months post-RT to yield a 28–36 frame image series showing contrast flowing in and out of the vasculature. Using MIM software, contours were placed in six vessels on each animal to yield a contrast flow curve for each vessel. The contours were placed as follows: one at the point of max dose, one low-irradiated (5–20 Gy) branching from the max dose vessel, one low-irradiated (5–20 Gy) not branching from the max dose vessel, one unirradiated (<5 Gy) branching from the max dose vessel, one unirradiated (<5 Gy) not branching from the max dose vessel, and one in the contralateral lung. Seven measurements (baseline-to-baseline time and difference, slope up and down, max rise and value, and area under the curve) were acquired for each vessel's contrast flow curve in each subject. Paired Student t-tests showed statistically significant (p < 0.05) reductions in the area under the curve in the max dose, and both fed contours indicating an overall reduction in contrast in these regions. Additionally, there were statistically significant reductions observed when comparing pre- and post-RT in slope up and down in the max dose, low-dose fed, and no-dose fed contours but not the low-dose not-fed, no-dose not-fed, or contralateral contours. These findings suggest an indirect damage effect where irradiation of the vasculature causes a reduction in perfusion in irradiated regions as well as regions fed by the irradiated vasculature. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Purpose: To create a dose‐response model that predicts lung ventilation change following radiation therapy, and examine the effects of out‐of‐phase ventilation. Methods: The dose‐response model was built using 27 human subjects who underwent radiation therapy (RT) from an IRB‐approved trial. For each four‐dimensional computed tomography, two ventilation maps were created by calculating the N‐phase local expansion ratio (LERN) using most or all breathing phases and the 2‐phase LER (LER2) using only the end inspiration and end expiration breathing phases. A polynomial regression model was created using the LERN ventilation maps pre‐RT and post‐RT and dose distributions for each subject, and crossvalidated with a leave‐one‐out method. Further validation of the model was performed using 15 additional human subjects using common statistical operating characteristics and gamma pass rates. Results: For voxels receiving 20 Gy or greater, there was a significant increase from 52% to 59% (P = 0.03) in the gamma pass rates of the LERN model predicted post‐RT Jacobian maps to the actual post‐RT Jacobian maps, relative to the LER2 model. Additionally, accuracy significantly increased (P = 0.03) from 68% to 75% using the LERN model, relative to the LER2 model. Conclusions: The LERN model was significantly more accurate than the LER2 model at predicting post‐RT ventilation maps. More accurate post‐RT ventilation maps will aid in producing a higher quality functional avoidance treatment plan, allowing for potentially better normal tissue sparing. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Mechanical ventilation strategies that reduce the heterogeneity of regional lung stress and strain may reduce the risk of ventilator-induced lung injury (VILI). In this study, we used registration of four-dimensional computed tomographic (4DCT) images to assess regional lung aeration and deformation in 10 pigs under baseline conditions and following acute lung injury induced with oleic acid. CT images were obtained via dynamic axial imaging (Siemens SOMATOM Force) during conventional pressure-controlled mechanical ventilation (CMV), as well as high-frequency and multi-frequency oscillatory ventilation modalities (HFOV and MFOV, respectively). Our results demonstrate that oscillatory modalities reduce intratidal strain throughout the lung in comparison to conventional ventilation, as well as the spatial gradients of dynamic strain along the dorsal-ventral axis. Harmonic distortion of parenchymal deformation was observed during HFOV with a single discrete sinusoid delivered at the airway opening, suggesting inherent mechanical nonlinearity of the lung tissues. MFOV may therefore provide improved lung-protective ventilation by reducing strain magnitudes and spatial gradients of strain compared to either CMV or HFOV. [ABSTRACT FROM AUTHOR]

Rationale: The rate of decline of lung function is greater than age-related change in a substantial proportion of patients with chronic obstructive pulmonary disease, even after smoking cessation. Regions of the lung adjacent to emphysematous areas are subject to abnormal stretch during respiration, and this biomechanical stress likely influences emphysema initiation and progression.Objectives: To assess whether quantifying this penumbra of lung at risk would predict FEV1 decline.Methods: We analyzed paired inspiratory-expiratory computed tomography images at baseline of 680 subjects participating in a large multicenter study (COPDGene) over approximately 5 years. By matching inspiratory and expiratory images voxel by voxel using image registration, we calculated the Jacobian determinant, a measure of local lung expansion and contraction with respiration. We measured the distance between each normal voxel to the nearest emphysematous voxel, and quantified the percentage of normal voxels within each millimeter distance from emphysematous voxels as mechanically affected lung (MAL). Multivariable regression analyses were performed to assess the relationship between the Jacobian determinant, MAL, and FEV1 decline.Measurements and Main Results: The mean (SD) rate of decline in FEV1 was 39.0 (58.6) ml/yr. There was a progressive decrease in the mean Jacobian determinant of both emphysematous and normal voxels with increasing disease stage (P < 0.001). On multivariable analyses, the mean Jacobian determinant of normal voxels within 2 mm of emphysematous voxels (MAL2) was significantly associated with FEV1 decline. In mild-moderate disease, for participants at or above the median MAL2 (threshold, 36.9%), the mean decline in FEV1 was 56.4 (68.0) ml/yr versus 43.2 (59.9) ml/yr for those below the median (P = 0.044).Conclusions: Areas of normal-appearing lung are mechanically influenced by emphysematous areas and this lung at risk is associated with lung function decline. Clinical trial registered with www.clinicaltrials.gov (NCT00608764). [ABSTRACT FROM AUTHOR]

Purpose: Four-dimensional computed tomography (4DCT) can be used to make measurements of pulmonary function longitudinally. The sensitivity of such measurements to identify change depends on measurement uncertainty. Previously, intrasubject reproducibility of Jacobian-based measures of lung tissue expansion was studied in two repeat prior-RT 4DCT human acquisitions. Difference in respiratory effort such as breathing amplitude and frequency may affect longitudinal function assessment. In this study, the authors present normalization schemes that correct ventilation images for variations in respiratory effort and assess the reproducibility improvement after effort correction. Methods: Repeat 4DCT image data acquired within a short time interval from 24 patients prior to radiation therapy (RT) were used for this analysis. Using a tissue volume preserving deformable image registration algorithm, Jacobian ventilation maps in two scanning sessions were computed and compared on the same coordinate for reproducibility analysis. In addition to computing the ventilation maps from end expiration to end inspiration, the authors investigated the effort normalization strategies using other intermediated inspiration phases upon the principles of equivalent tidal volume (ETV) and equivalent lung volume (ELV). Scatter plots and mean square error of the repeat ventilation maps and the Jacobian ratio map were generated for four conditions: no effort correction, global normalization, ETV, and ELV. In addition, gamma pass rate was calculated from a modified gamma index evaluation between two ventilation maps, using acceptance criterions of 2 mm distance-to-agreement and 5% ventilation difference. Results: The pattern of regional pulmonary ventilation changes as lung volume changes. All effort correction strategies improved reproducibility when changes in respiratory effort were greater than 150 cc (p < 0.005 with regard to the gamma pass rate). Improvement of reproducibility was correlated with respiratory effort difference (R = 0.744 for ELV in the cohort with tidal volume difference greater than 100 cc). In general for all subjects, global normalization, ETV and ELV significantly improved reproducibility compared to no effort correction (p = 0.009, 0.002, 0.005 respectively). When tidal volume difference was small (less than 100 cc), none of the three effort correction strategies improved reproducibility significantly (p = 0.52, 0.46, 0.46 respectively). For the cohort (N = 13) with tidal volume difference greater than 100 cc, the average gamma pass rate improves from 57.3% before correction to 66.3% after global normalization, and 76.3% after ELV. ELV was found to be significantly better than global normalization (p = 0.04 for all subjects, and p = 0.003 for the cohort with tidal volume difference greater than 100 cc). Conclusions: All effort correction strategies improve the reproducibility of the authors' pulmonary ventilation measures, and the improvement of reproducibility is highly correlated with the changes in respiratory effort. ELV gives better results as effort difference increase, followed by ETV, then global. However, based on the spatial and temporal heterogeneity in the lung expansion rate, a single scaling factor (e.g., global normalization) appears to be less accurate to correct the ventilation map when changes in respiratory effort are large. [ABSTRACT FROM AUTHOR]

Purpose: Lung function depends on lung expansion and contraction during the respiratory cycle. Respiratory-gated CT imaging and image registration can be used to estimate the regional lung volume change by observing CT voxel density changes during inspiration or expiration. In this study, the authors examine the reproducibility of intensity-based estimates of lung tissue expansion and contraction in three mechanically ventilated sheep and ten spontaneously breathing humans. The intensity-based estimates are compared to the estimates of lung function derived from image registration deformation field. Methods: 4DCT data set was acquired for a cohort of spontaneously breathing humans and anesthetized and mechanically ventilated sheep. For each subject, two 4DCT scans were performed with a short time interval between acquisitions. From each 4DCT data set, an image pair consisting of a volume reconstructed near end inspiration and a volume reconstructed near end exhalation was selected. The end inspiration and end exhalation images were registered using a tissue volume preserving deformable registration algorithm. The CT density change in the registered image pair was used to compute intensity-based specific air volume change (SAC) and the intensity-based Jacobian (IJAC), while the transformation-based Jacobian (TJAC) was computed directly from the image registration deformation field. IJAC is introduced to make the intensity-based and transformation-based methods comparable since SAC and Jacobian may not be associated with the same physiological phenomenon and have different units. Scan-to-scan variations in respiratory effort were corrected using a global scaling factor for normalization. A gamma index metric was introduced to quantify voxel-by-voxel reproducibility considering both differences in ventilation and distance between matching voxels. The authors also tested how different CT prefiltering levels affected intensity-based ventilation reproducibility. Results: Higher reproducibility was found for anesthetized mechanically ventilated animals than for the humans for both the intensity-based (IJAC) and transformation-based (TJAC) ventilation estimates. The human IJAC maps had scan-to-scan correlation coefficients of 0.45 ± 0.14, a gamma pass rate 70 ± 8 without normalization and 75 ± 5 with normalization. The human TJAC maps had correlation coefficients 0.81 ± 0.10, a gamma pass rate 86 ± 11 without normalization and 93 ± 4 with normalization. The gamma pass rate and correlation coefficient of the IJAC maps gradually increased with increased smoothing, but were still much lower than those of the TJAC maps. Conclusions: The transformation-based ventilation maps show better reproducibility than the intensity-based maps, especially in human subjects. Reproducibility was also found to depend on variations in respiratory effort; all techniques were better when applied to images from mechanically ventilated sheep compared to spontaneously breathing human subjects. Nevertheless, intensity-based techniques applied to mechanically ventilated sheep were less reproducible than the transformation-based applied to spontaneously breathing humans, suggesting the method used to determine ventilation maps is important. Prefiltering of the CT images may help to improve the reproducibility of the intensity-based ventilation estimates, but even with filtering the reproducibility of the intensity-based ventilation estimates is not as good as that of transformation-based ventilation estimates. [ABSTRACT FROM AUTHOR]

This paper describes a framework for establishing a reference airway tree segmentation, which was used to quantitatively evaluate 15 different airway tree extraction algorithms in a standardized manner. Because of the sheer difficulty involved in manually constructing a complete reference standard from scratch, we propose to construct the reference using results from all algorithms that are to be evaluated. We start by subdividing each segmented airway tree into its individual branch segments. Each branch segment is then visually scored by trained observers to determine whether or not it is a correctly segmented part of the airway tree. Finally, the reference airway trees are constructed by taking the union of all correctly extracted branch segments. Fifteen airway tree extraction algorithms from different research groups are evaluated on a diverse set of 20 chest computed tomography (CT) scans of subjects ranging from healthy volunteers to patients with severe pathologies, scanned at different sites, with different CT scanner brands, models, and scanning protocols. Three performance measures covering different aspects of segmentation quality were computed for all participating algorithms. Results from the evaluation showed that no single algorithm could extract more than an average of 74% of the total length of all branches in the reference standard, indicating substantial differences between the algorithms. A fusion scheme that obtained superior results is presented, demonstrating that there is complementary information provided by the different algorithms and there is still room for further improvements in airway segmentation algorithms. [ABSTRACT FROM AUTHOR]

EMPIRE10 (Evaluation of Methods for Pulmonary Image REgistration 2010) is a public platform for fair and meaningful comparison of registration algorithms which are applied to a database of intrapatient thoracic CT image pairs. Evaluation of nonrigid registration techniques is a nontrivial task. This is compounded by the fact that researchers typically test only on their own data, which varies widely. For this reason, reliable assessment and comparison of different registration algorithms has been virtually impossible in the past. In this work we present the results of the launch phase of EMPIRE10, which comprised the comprehensive evaluation and comparison of 20 individual algorithms from leading academic and industrial research groups. All algorithms are applied to the same set of 30 thoracic CT pairs. Algorithm settings and parameters are chosen by researchers expert in the configuration of their own method and the evaluation is independent, using the same criteria for all participants. All results are published on the EMPIRE10 website (xlink:type="simple" xlink:href="http://empire10.isi.uu.nl" xmlns:xlink="http://www.w3.org/1999/xlink"http://empire10.isi.uu.nl). The challenge remains ongoing and open to new participants. Full results from 24 algorithms have been published at the time of writing. This paper details the organization of the challenge, the data and evaluation methods and the outcome of the initial launch with 20 algorithms. The gain in knowledge and future work are discussed. [ABSTRACT FROM AUTHOR]

Within pulmonary research, the development of mouse models has provided insight into disease development, progression, and treatment. Structural phenotypes of the lung in healthy inbred mouse strains are necessary for comparison to disease models. To date, progress in the assessment of lung function in these small animals using whole lung function tests has been made. However, assessment of in vivo lung structure of inbred mouse strains has yet to be well defined. Therefore, the link between the structure and function phenotypes is still unclear. With advancements in small animal imaging it is now possible to investigate lung structures such as the central and peripheral airways, whole lung, and lobar volumes of mice in vivo, through the use of micro-CT imaging. In this study, we performed in vivo micro-CT imaging of the C57BL/6, A/J, and BALB/c mouse strains using the intermittent iso-pressure breath hold (IIBH) technique. The resulting high-resolution images were used to extract lung structure phenotypes. The three-dimensional lobar structures and airways were defined and a meaningful mouse airway nomenclature was developed. In addition, using these techniques we have uncovered significant differences in the airway structures between inbred mouse strains in vivo. [ABSTRACT FROM AUTHOR]

The human lungs are divided into five distinct anatomic compartments called the lobes, which are separated by the pulmonary fissures. The accurate identification of the fissures is of increasing importance in the early detection of pathologies, and in the regional functional analysis of the lungs. We have developed an automatic method for the segmentation and analysis of the fissures, based on the information provided by the segmentation and analysis of the airway and vascular trees. This information is used to provide a close initial approximation to the fissures, using a watershed transform on a distance map of the vasculature. In a further refinement step, this estimate is used to construct a region of interest (ROl) encompassing the fissures. The ROl is enhanced using a ridgeness measure, which is followed by a 3-D graph search to find the optimal surface within the ROl. We have also developed an automatic method to detect incomplete fissures, using a fast-marching based segmentation of a projection of the optimal surface. The detected incomplete fissure is used to extrapolate and smoothly complete the fissure. We evaluate the method by testing on data sets from normal subjects and subjects with mild to moderate emphysema. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: Automatic lung segmentation in volumetric computed tomography (CT) images has been extensively investigated, and several methods have been proposed. Most methods distinguish the lung parenchyma from the surrounding anatomy based on the difference in CT attenuation values. This leads to an irregular and inconsistent lung boundary for the regions near the mediastinum, which can cause inconsistent boundaries both across subjects and within subjects scanned at different intervals of time. Processes like lung image registration and lung atlas construction can be affected by such inconsistencies. Therefore there is a need for a more consistent lung surface near the mediastinum.Materials and Methods: This paper presents a fully automatic method for the three-dimensional smoothing of the lung boundary using information from the segmented human airway tree. First, using the segmented airway tree, we define a bounding box around the mediastinum for each lung, within which all operations are performed. We then define all generations of the airway tree distal to the right and left main stem bronchi to be part of the respective lungs and exclude all other segments. Finally, we perform a fast morphological closing with an ellipsoidal kernel to smooth the surface of the lung.Results: This method has been tested by processing the segmented lungs from eight normal datasets. The mean value of the magnitude of curvedness of the lung contours, averaged over all the datasets, is 0.13 postsmoothing, compared with 4.91 before smoothing. The accuracy of the lung contours after smoothing is assessed by comparing the automatic results to manually traced smooth lung borders by a human analyst. Averaged over all volumes, the root mean square difference between human and computer borders is 0.8691 mm after smoothing, compared with 1.3012 mm before smoothing. The mean similarity index, which is an area overlap measure based on the kappa statistic, is 0.9958 (SD 0.0032), after smoothing.Conclusions: We have described a novel scheme for smoothing the lung contour around the mediastinum. The method is based on using anatomic information from the segmented airway tree. The validation results show that there is good agreement between manual and computer results. Because there are no accepted criteria for defining the lung boundary near the mediastinum, we believe our method of defining the boundary based on the structure of the airway tree provides a good basis for three-dimensional smoothing. [ABSTRACT FROM AUTHOR]

: Rationale and ObjectivesTo establish the range of normal values for quantitative CT-based measures of lung structure and function, the authors developed a method for matching pulmonary structures across individuals and creating a normative human lung atlas.: Materials and MethodsA computerized human lung atlas was synthesized from computed tomographic (CT) images from six subjects by means of three-dimensional image registration. The authors identified a set of reproducible feature points for each CT image and used these points to establish correspondences across subjects, used a landmark- and intensity-based consistent image registration algorithm to register a template image volume from the population to the rest of the pulmonary CT volumes in the population, averaged these transformations, and constructed an atlas by deforming the template with the average transformation.: ResultsThe effectiveness of the authors'' method was evaluated and visualized by means of both gray-level and segmented CT images. The method reduced the average landmark registration error from 10.5 mm to 0.4 mm and the average relative volume overlap error from 0.7 to 0.11 for the six data sets studied.: ConclusionThe method, and the computerized human lung atlas constructed and visualized by the authors with this method, provides a basis for establishing regional ranges of normative values for structural and functional measures of the human lung. [Copyright &y& Elsevier]

Presents a fully automatic method for identifying the lungs in three-dimensional pulmonary X-ray computed tomography (CT) images. Comparison with manual analysis; Reproducibility and calculation of lung volumes; Factors that influence CT lung density.

To determine whether 4-dimensional computed tomography (4DCT) ventilation-based functional lung avoidance radiation therapy preserves pulmonary function compared with standard radiation therapy for non-small cell lung cancer (NSCLC). This single center, randomized, phase 2 trial enrolled patients with NSCLC receiving curative intent radiation therapy with either stereotactic body radiation therapy or conventionally fractionated radiation therapy between 2016 and 2022. Patients were randomized 1:1 to standard of care radiation therapy or functional lung avoidance radiation therapy. The primary endpoint was the change in Jacobian-based ventilation as measured on 4DCT from baseline to 3 months postradiation. Secondary endpoints included changes in volume of high- and low-ventilating lung, pulmonary toxicity, and changes in pulmonary function tests (PFTs). A total of 122 patients were randomized and 116 were available for analysis. Median follow up was 29.9 months. Functional avoidance plans significantly (P <.05) reduced dose to high-functioning lung without compromising target coverage or organs at risk constraints. When analyzing all patients, there was no difference in the amount of lung showing a reduction in ventilation from baseline to 3 months between the 2 arms (1.91% vs 1.87%; P =.90). Overall grade ≥2 and grade ≥3 pulmonary toxicities for all patients were 24.1% and 8.6%, respectively. There was no significant difference in pulmonary toxicity or changes in PFTs between the 2 study arms. In the conventionally fractionated cohort, there was a lower rate of grade ≥2 pneumonitis (8.2% vs 32.3%; P =.049) and less of a decline in change in forced expiratory volume in 1 second (–3 vs –5; P =.042) and forced vital capacity (1.5 vs –6; P =.005) at 3 months, favoring the functional avoidance arm. There was no difference in posttreatment ventilation as measured by 4DCT between the arms. In the cohort of patients treated with conventionally fractionated radiation therapy with functional lung avoidance, there was reduced pulmonary toxicity, and less decline in PFTs suggesting a clinical benefit in patients with locally advanced NSCLC. [ABSTRACT FROM AUTHOR]

Background: Chronic obstructive pulmonary disease (COPD) is characterized by airway remodeling. Characterization of airway changes on computed tomography has been challenging due to the complexity of the recurring branching patterns, and this can be better measured using fractal dimensions.Methods: We analyzed segmented airway trees of 8,135 participants enrolled in the COPDGene cohort. The fractal complexity of the segmented airway tree was measured by the Airway Fractal Dimension (AFD) using the Minkowski-Bougliand box-counting dimension. We examined associations between AFD and lung function and respiratory morbidity using multivariable regression analyses. We further estimated the extent of peribronchial emphysema (%) within 5 mm of the airway tree, as this is likely to affect AFD. We classified participants into 4 groups based on median AFD, percentage of peribronchial emphysema, and estimated survival.Results: AFD was significantly associated with forced expiratory volume in one second (FEV1; P < 0.001) and FEV1/forced vital capacity (FEV1/FVC; P < 0.001) after adjusting for age, race, sex, smoking status, pack-years of smoking, BMI, CT emphysema, air trapping, airway thickness, and CT scanner type. On multivariable analysis, AFD was also associated with respiratory quality of life and 6-minute walk distance, as well as exacerbations, lung function decline, and mortality on longitudinal follow-up. We identified a subset of participants with AFD below the median and peribronchial emphysema above the median who had worse survival compared with participants with high AFD and low peribronchial emphysema (adjusted hazards ratio [HR]: 2.72; 95% CI: 2.20-3.35; P < 0.001), a substantial number of whom were not identified by traditional spirometry severity grades.Conclusion: Airway fractal dimension as a measure of airway branching complexity and remodeling in smokers is associated with respiratory morbidity and lung function change, offers prognostic information additional to traditional CT measures of airway wall thickness, and can be used to estimate mortality risk.Trial Registration: ClinicalTrials.gov identifier: NCT00608764.Funding: This study was supported by NIH K23 HL133438 (SPB) and the COPDGene study (NIH Grant Numbers R01 HL089897 and R01 HL089856). The COPDGene project is also supported by the COPD Foundation through contributions made to an Industry Advisory Board comprised of AstraZeneca, Boehringer Ingelheim, Novartis, Pfizer, Siemens, Sunovion and GlaxoSmithKline. [ABSTRACT FROM AUTHOR]

• Current lung cancer radiotherapy planning does not account for regional variation in pulmonary function. • Regional lung function can be quantified through derivation of ventilation obtained from 4DCT imaging. • In this study dose to highly ventilated lung was found to be associated with symptomatic pneumonitis. • These findings provide important metrics to be used in functional lung avoidance radiotherapy planning and in designing clinical trials. To identify metrics of radiation dose delivered to highly ventilated lung that are predictive of radiation-induced pneumonitis. A cohort of 90 patients with locally advanced non-small cell lung cancer treated with standard fractionated radiation therapy (RT) (60–66 Gy in 30–33 fractions) were evaluated. Regional lung ventilation was determined from pre-RT 4-dimensional computed tomography (4DCT) using the Jacobian determinant of a B-spline deformable image registration to estimate lung tissue expansion during respiration. Multiple voxel-wise population- and individual-based thresholds for defining high functioning lung were considered. Mean dose and volumes receiving dose ≥ 5–60 Gy were analyzed for both total lung-ITV (MLD,V5-V60) and highly ventilated functional lung-ITV (fMLD,fV5-fV60). The primary endpoint was symptomatic grade 2+ (G2+) pneumonitis. Receiver operator curve (ROC) analyses were used to identify predictors of pneumonitis. G2+ pneumonitis occurred in 22.2% of patients, with no differences between stage, smoking status, COPD, or chemo/immunotherapy use between G<2 and G2+ patients (P ≥ 0.18). Highly ventilated lung was defined as voxels exceeding the population-wide median of 18% voxel-level expansion. All total and functional metrics were significantly different between patients with and without pneumonitis (P ≤ 0.039). Optimal ROC points predicting pneumonitis from functional lung dose were fMLD ≤ 12.3 Gy, fV5 ≤ 54% and fV20 ≤ 19 %. Patients with fMLD ≤ 12.3 Gy had a 14% risk of developing G2+ pneumonitis whereas risk significantly increased to 35% for those with fMLD > 12.3 Gy (P = 0.035). Dose to highly ventilated lung is associated with symptomatic pneumonitis and treatment planning strategies should focus on limiting dose to functional regions. These findings provide important metrics to be used in functional lung avoidance RT planning and designing clinical trials. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]