Chest computed tomography (CT) at inspiration is often complemented by an expiratory CT to identify peripheral airways disease. Additionally, co-registered inspiratory-expiratory volumes can be used to derive various markers of lung function. Expiratory CT scans, however, may not be acquired due to dose or scan time considerations or may be inadequate due to motion or insufficient exhale; leading to a missed opportunity to evaluate underlying small airways disease. Here, we propose LungViT- a generative adversarial learning approach using hierarchical vision transformers for translating inspiratory CT intensities to corresponding expiratory CT intensities. LungViT addresses several limitations of the traditional generative models including slicewise discontinuities, limited size of generated volumes, and their inability to model texture transfer at volumetric level. We propose a shifted-window hierarchical vision transformer architecture with squeeze-and-excitation decoder blocks for modeling dependencies between features. We also propose a multiview texture similarity distance metric for texture and style transfer in 3D. To incorporate global information into the training process and refine the output of our model, we use ensemble cascading. LungViT is able to generate large 3D volumes of size 320×320×320 . We train and validate our model using a diverse cohort of 1500 subjects with varying disease severity. To assess model generalizability beyond the development set biases, we evaluate our model on an out-of-distribution external validation set of 200 subjects. Clinical validation on internal and external testing sets shows that synthetic volumes could be reliably adopted for deriving clinical endpoints of chronic obstructive pulmonary disease.

This paper presents the Population Learning followed by One Shot Learning (PLOSL) pulmonary image registration method. PLOSL is a fast unsupervised learning-based framework for 3D-CT pulmonary image registration algorithm based on combining population learning (PL) and one-shot learning (OSL). The PLOSL image registration has the advantages of the PL and OSL approaches while reducing their respective drawbacks. The advantages of PLOSL include improved performance over PL, substantially reducing OSL training time and reducing the likelihood of OSL getting stuck in local minima. PLOSL pulmonary image registration uses tissue volume preserving and vesselness constraints for registration of inspiration-to-expiration and expiration-to-inspiration pulmonary CT images. A coarse-to-fine convolution encoder-decoder CNN architecture is used to register large and small shape features. During training, the sum of squared tissue volume difference (SSTVD) compensates for intensity differences between inspiration and expiration computed tomography (CT) images and the sum of squared vesselness measure difference (SSVMD) helps match the lung vessel tree. Results show that the PLOSL (SSTVD+SSVMD) algorithm achieved subvoxel landmark error while preserving pulmonary topology on the SPIROMICS data set, the public DIR-LAB COPDGene and 4DCT data sets.

Electrical impedance tomography is clinically used to trace ventilation related changes in electrical conductivity of lung tissue. Estimating regional pulmonary perfusion using electrical impedance tomography is still a matter of research. To support clinical decision making, reliable bedside information of pulmonary perfusion is needed. We introduce a method to robustly detect pulmonary perfusion based on indicator-enhanced electrical impedance tomography and validate it by dynamic multidetector computed tomography in two experimental models of acute respiratory distress syndrome. The acute injury was induced in a sublobar segment of the right lung by saline lavage or endotoxin instillation in eight anesthetized mechanically ventilated pigs. For electrical impedance tomography measurements, a conductive bolus (10% saline solution) was injected into the right ventricle during breath hold. Electrical impedance tomography perfusion images were reconstructed by linear and normalized Gauss-Newton reconstruction on a finite element mesh with subsequent element-wise signal and feature analysis. An iodinated contrast agent was used to compute pulmonary blood flow via dynamic multidetector computed tomography. Spatial perfusion was estimated based on first-pass indicator dilution for both electrical impedance and multidetector computed tomography and compared by Pearson correlation and Bland-Altman analysis. Strong correlation was found in dorsoventral (r = 0.92) and in right-to-left directions (r = 0.85) with good limits of agreement of 8.74% in eight lung segments. With a robust electrical impedance tomography perfusion estimation method, we found strong agreement between multidetector computed and electrical impedance tomography perfusion in healthy and regionally injured lungs and demonstrated feasibility of electrical impedance tomography perfusion imaging.

Chronic obstructive pulmonary disease (COPD) is a common lung disease, and quantitative CT-based bronchial phenotypes are of increasing interest as a means of exploring COPD sub-phenotypes, establishing disease progression, and evaluating intervention outcomes. Reliable, fully automated, and accurate segmentation of pulmonary airway trees is critical to such exploration. We present a novel approach of multi-parametric freeze-and-grow (FG) propagation which starts with a conservative segmentation parameter and captures finer details through iterative parameter relaxation. First, a CT intensity-based FG algorithm is developed and applied for airway tree segmentation. A more efficient version is produced using deep learning methods generating airway lumen likelihood maps from CT images, which are input to the FG algorithm. Both CT intensity- and deep learning-based algorithms are fully automated, and their performance, in terms of repeat scan reproducibility, accuracy, and leakages, is evaluated and compared with results from several state-of-the-art methods including an industry-standard one, where segmentation results were manually reviewed and corrected. Both new algorithms show a reproducibility of 95% or higher for total lung capacity (TLC) repeat CT scans. Experiments on TLC CT scans from different imaging sites at standard and low radiation dosages show that both new algorithms outperform the other methods in terms of leakages and branch-level accuracy. Considering the performance and execution times, the deep learning-based FG algorithm is a fully automated option for large multi-site studies.

Pulmonary emphysema overlaps considerably with chronic obstructive pulmonary disease (COPD), and is traditionally subcategorized into three subtypes previously identified on autopsy. Unsupervised learning of emphysema subtypes on computed tomography (CT) opens the way to new definitions of emphysema subtypes and eliminates the need of thorough manual labeling. However, CT-based emphysema subtypes have been limited to texture-based patterns without considering spatial location. In this work, we introduce a standardized spatial mapping of the lung for quantitative study of lung texture location and propose a novel framework for combining spatial and texture information to discover spatially-informed lung texture patterns (sLTPs) that represent novel emphysema subtype candidates. Exploiting two cohorts of full-lung CT scans from the MESA COPD (n = 317) and EMCAP (n = 22) studies, we first show that our spatial mapping enables population-wide study of emphysema spatial location. We then evaluate the characteristics of the sLTPs discovered on MESA COPD, and show that they are reproducible, able to encode standard emphysema subtypes, and associated with physiological symptoms.

In this paper, we present a semi-supervised deep learning approach to accurately recover high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the generative adversarial network (GAN) as the building block, we enforce the cycle-consistency in terms of the Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs. We also include the joint constraints in the loss function to facilitate structural preservation. In this process, we incorporate deep convolutional neural network (CNN), residual learning, and network in network techniques for feature extraction and restoration. In contrast to the current trend of increasing network depth and complexity to boost the imaging performance, we apply a parallel 1×1 CNN to compress the output of the hidden layer and optimize the number of layers and the number of filters for each convolutional layer. The quantitative and qualitative evaluative results demonstrate that our proposed model is accurate, efficient and robust for super-resolution (SR) image restoration from noisy LR input images. In particular, we validate our composite SR networks on three large-scale CT datasets, and obtain promising results as compared to the other state-of-the-art methods.

Numerous multi-center studies related to chronic obstructive pulmonary disease use computed tomography (CT) based characterization of the lung parenchyma and bronchial tree to understand the disease's status and progression. To our knowledge, there are no fully automated methods for airway tree segmentation that don't require post-segmentation manual revision and intervention. In this paper, we present a novel CT-based airway tree segmentation algorithm using topological leakage detection and volume freezing. The method is fully automated requiring no manual inputs or post-segmentation editing. It uses intensity-based connectivity and novel approaches of leakage detection and volume freezing to iteratively grow an airway tree starting from an initial seed inside the trachea. It begins with a conservative threshold and then, iteratively shifts toward generous threshold values. The method was applied on chest CT scans of ten non-smoking healthy subjects at total lung capacity, and the results were highly promising with no visual segmentation leakages.

We seek to use computed tomography (CT) to characterize regional lung parenchymal deformation during high-frequency and multi-frequency oscillatory ventilation. Periodic motion of thoracic structures results in artifacts of CT images obtained by standard reconstruction algorithms, especially for frequencies exceeding that of the X-ray source rotation. In this paper, we propose an acquisition and reconstruction technique for high-resolution imaging of the thorax during periodic motion. Our technique relies on phase-binning projections according to the frequency of subject motion relative to the scanner rotation, prior to volumetric reconstruction. The mathematical theory and limitations of the proposed technique are presented, and then validated in a simulated phantom as well as a living porcine subject during oscillatory ventilation. The 4-D image sequences obtained using this frequency-selective reconstruction technique yielded high-spatio-temporal resolution of the thorax during periodic motion. We conclude that the frequency-based selection of CT projections is ideal for characterizing dynamic deformations of thoracic structures that are ordinarily obscured by motion artifact using conventional reconstruction techniques.

Theoretical properties of a multi-scale opening (MSO) algorithm for two conjoined fuzzy objects are established, and its extension to separating two conjoined fuzzy objects with different intensity properties is introduced. Also, its applications to artery/vein (A/V) separation in pulmonary CT imaging and carotid vessel segmentation in CT angiograms (CTAs) of patients with intracranial aneurysms are presented. The new algorithm accounts for distinct intensity properties of individual conjoined objects by combining fuzzy distance transform (FDT), a morphologic feature, with fuzzy connectivity, a topologic feature. The algorithm iteratively opens the two conjoined objects starting at large scales and progressing toward finer scales. Results of application of the method in separating arteries and veins in a physical cast phantom of a pig lung are presented. Accuracy of the algorithm is quantitatively evaluated in terms of sensitivity and specificity on patients' CTA data sets and its performance is compared with existing methods. Reproducibility of the algorithm is examined in terms of volumetric agreement between two users' carotid vessel segmentation results. Experimental results using this algorithm on patients' CTA data demonstrate a high average accuracy of 96.3% with 95.1% sensitivity and 97.5% specificity and a high reproducibility of 94.2% average agreement between segmentation results from two mutually independent users. Approximately, twenty-five to thirty-five user-specified seeds/separators are needed for each CTA data through a custom designed graphical interface requiring an average of thirty minutes to complete carotid vascular segmentation in a patient's CTA data set.

Traditional arc skeletonization algorithms using the principle of Blum's transform, often, produce unwanted spurious branches due to boundary irregularities and digital effects on objects and other artifacts. This paper presents a new robust approach of extracting arc skeletons for three-dimensional (3-D) elongated fuzzy objects, which avoids spurious branches without requiring post-pruning. Starting from a root voxel, the method iteratively expands the skeleton by adding a new branch in each iteration that connects the farthest voxel to the current skeleton using a minimum-cost geodesic path. The path-cost function is formulated using a novel measure of local significance factor defined by fuzzy distance transform field, which forces the path to stick to the centerline of the object. The algorithm terminates when dilated skeletal branches fill the entire object volume or the current farthest voxel fails to generate a meaningful branch. Accuracy of the algorithm has been evaluated using computer-generated blurred and noisy phantoms with known skeletons. Performance of the method in terms of false and missing skeletal branches, as defined by human expert, has been examined using in vivo CT imaging of human intrathoracic airways. Experimental results from both experiments have established the superiority of the new method as compared to a widely used conventional method in terms of accuracy of medialness as well as robustness of true and false skeletal branches.

The extent of pulmonary emphysema is commonly estimated from CT scans by computing the proportional area of voxels below a predefined attenuation threshold. However, the reliability of this approach is limited by several factors that affect the CT intensity distributions in the lung. This work presents a novel method for emphysema quantification, based on parametric modeling of intensity distributions and a hidden Markov measure field model to segment emphysematous regions. The framework adapts to the characteristics of an image to ensure a robust quantification of emphysema under varying CT imaging protocols, and differences in parenchymal intensity distributions due to factors such as inspiration level. Compared to standard approaches, the presented model involves a larger number of parameters, most of which can be estimated from data, to handle the variability encountered in lung CT scans. The method was applied on a longitudinal data set with 87 subjects and a total of 365 scans acquired with varying imaging protocols. The resulting emphysema estimates had very high intra-subject correlation values. By reducing sensitivity to changes in imaging protocol, the method provides a more robust estimate than standard approaches. The generated emphysema delineations promise advantages for regional analysis of emphysema extent and progression.

Identification of both the luminal and the wall areas of the bronchial tree structure from volumetric X-ray computed tomography (CT) data sets is of critical importance in distinguishing important phenotypes within numerous major lung diseases including chronic obstructive pulmonary diseases (COPD) and asthma. However, accurate assessment of the inner and outer airway wall surfaces of a complete 3-D tree structure is difficult due to their complex nature, particularly around the branch areas. In this paper, we extend a graph search based technique (LOGISMOS) to simultaneously identify multiple inter-related surfaces of branching airway trees. We first perform a presegmentation of the input 3-D image to obtain basic information about the tree topology. The presegmented image is resampled along judiciously determined paths to produce a set of vectors of voxels (called voxel columns). The resampling process utilizes medial axes to ensure that voxel columns of appropriate lengths and directions are used to capture the object surfaces without interference. A geometric graph is constructed whose edges connect voxels in the resampled voxel columns and enforce validity of the smoothness and separation constraints on the sought surfaces. Cost functions with directional information are employed to distinguish inner and outer walls. The assessment of wall thickness measurement on a CT-scanned double-wall physical phantom (patterned after an in vivo imaged human airway tree) achieved highly accurate results on the entire 3-D tree. The observed mean signed error of wall thickness ranged from -0.09 ±0.24 mm to 0.07 ±0.23 mm in bifurcating/nonbifurcating areas. The mean unsigned errors were 0.16±0.12 mm to 0.20±0.11 mm. When the airway wall surface was partitioned into meaningful subregions, the airway wall thickness accuracy was the same in most tested bifurcation/nonbifurcation and carina/noncarina regions (p=NS). Once validated on phantoms, our method was applied to human in vivo volumetric CT data to demonstrate relationships of airway wall thickness as a function of luminal dimension and airway tree generation. Wall thickness differences between the bifurcation/nonbifurcation regions were statistically significant (p < 0.05) for tree generations 6, 7, 8, and 9. In carina/noncarina regions, the wall thickness was statistically different in generations 1, 4, 5, 6, 7, and 8.

This paper describes a framework for establishing a reference airway tree segmentation, which was used to quantitatively evaluate fifteen 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 twenty 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.

This paper presents a statistical interior tomography (SIT) approach making use of compressed sensing (CS) theory. With the projection data modeled by the Poisson distribution, an objective function with a total variation (TV) regularization term is formulated in the maximization of a posteriori (MAP) framework to solve the interior problem. An alternating minimization method is used to optimize the objective function with an initial image from the direct inversion of the truncated Hilbert transform. The proposed SIT approach is extensively evaluated with both numerical and real datasets. The results demonstrate that SIT is robust with respect to data noise and down-sampling, and has better resolution and less bias than its deterministic counterpart in the case of low count data.

A novel multiscale topomorphologic approach for opening of two isointensity objects fused at different locations and scales is presented and applied to separating arterial and venous trees in 3-D pulmonary multidetector X-ray computed tomography (CT) images. Initialized with seeds, the two isointensity objects (arteries and veins) grow iteratively while maintaining their spatial exclusiveness and eventually form two mutually disjoint objects at convergence. The method is intended to solve the following two fundamental challenges: how to find local size of morphological operators and how to trace continuity of locally separated regions. These challenges are met by combining fuzzy distance transform (FDT), a morphologic feature with a topologic fuzzy connectivity, and a new morphological reconstruction step to iteratively open finer and finer details starting at large scales and progressing toward smaller scales. The method employs efficient user intervention at locations where local morphological separability assumption does not hold due to imaging ambiguities or any other reason. The approach has been validated on mathematically generated tubular objects and applied to clinical pulmonary noncontrast CT data for separating arteries and veins. The tradeoff between accuracy and the required user intervention for the method has been quantitatively examined by comparing with manual outlining. The experimental study, based on a blind seed selection strategy, has demonstrated that above 95% accuracy may be achieved using 25-40 seeds for each of arteries and veins. Our method is very promising for semiautomated separation of arteries and veins in pulmonary CT images even when there is no object-specific intensity variation at conjoining locations.

Our goal is to enhance the ability to differentiate normal lung from subtle pathologies via multidetector row CT (MDCT) by extending a two-dimensional (2-D) texturebased tissue classification [adaptive multiple feature method (AMFM)] to use three-dimensional (3-D) texture features. We performed MDCT on 34 humans and classified volumes of interest (VOIs) in the MDCT images into five categories: EC, emphysema in severe chronic obstructive pulmonary disease (COPD); MC, mild emphysema in mild COPD; NC, normal appearing lung in mild COPD; NN, normal appearing lung in normal nonsmokers; and NS, normal appearing lung in normal smokers. COPD severity was based upon pulmonary function tests (PFTs). Airways and vessels were excluded from VOIs; 24 3-D texture features were calculated; and a Bayesian classifier was used for discrimination. A leave-one-out method was employed for validation. Sensitivity of the four-class classification in the form of 3-D/2-D was: EC: 85%/71%, MC: 90%/82%; NC: 88%/50%; NN: 100%/60%. Sensitivity and specificity for NN using a two-class classification of NN and NS in the form of 3-D/2-D were: 99%/72% and 100%/75%, respectively. We conclude that 3-D AMFM analysis of lung parenchyma improves discrimination compared to 2-D AMFM of the same VOIs. Furthermore, our results suggest that the 3-D AMFM may provide a means of discriminating subtle differences between smokers and nonsmokers both with normal PFTs.

High-resolution X-ray computed tomography (CT) imaging is routinely used for clinical pulmonary applications. Since lung function varies regionally and because pulmonary disease is usually not uniformly distributed in the lungs, it is useful to study the lungs on a lobe-by-lobe basis. Thus, it is important to segment not only the lungs, but the lobar fissures as well. In this paper, we demonstrate the use of an anatomic pulmonary atlas, encoded with a priori information on the pulmonary anatomy, to automatically segment the oblique lobar fissures. Sixteen volumetric CT scans from 16 subjects are used to construct the pulmonary atlas. A ridgeness measure is applied to the original CT images to enhance the fissure contrast. Fissure detection is accomplished in two stages: an initial fissure search and a final fissure search. A fuzzy reasoning system is used in the fissure search to analyze information from three sources: the image intensity, an anatomic smoothness constraint, and the atlas-based search initialization. Our method has been tested on 22 volumetric thin-slice CT scans from 12 subjects, and the results are compared to manual tracings. Averaged across all 22 data sets, the RMS error between the automatically segmented and manually segmented fissures is 1.96 +/- 0.71 mm and the mean of the similarity indices between the manually defined and computer-defined lobe regions is 0.988. The results indicate a strong agreement between the automatic and manual lobe segmentations.

The segmentation of the human airway tree from volumetric computed tomography (CT) images builds an important step for many clinical applications and for physiological studies. Previously proposed algorithms suffer from one or several problems: leaking into the surrounding lung parenchyma, the need for the user to manually adjust parameters, excessive runtime. Low-dose CT scans are increasingly utilized in lung screening studies, but segmenting them with traditional airway segmentation algorithms often yields less than satisfying results. In this paper, a new airway segmentation method based on fuzzy connectivity is presented. Small adaptive regions of interest are used that follow the airway branches as they are segmented. This has several advantages. It makes it possible to detect leaks early and avoid them, the segmentation algorithm can automatically adapt to changing image parameters, and the computing time is kept within moderate values. The new method is robust in the sense that it works on various types of scans (low-dose and regular dose, normal subjects and diseased subjects) without the need for the user to manually adjust any parameters. Comparison with a commonly used region-grow segmentation algorithm shows that the newly proposed method retrieves a significantly higher count of airway branches. A method that conducts accurate cross-sectional airway measurements on airways is presented as an additional processing step. Measurements are conducted in the original gray-level volume. Validation on a phantom shows that subvoxel accuracy is achieved for all airway sizes and airway orientations.

Matching of corresponding branchpoints between two human airway trees, as well as assigning anatomical names to the segments and branchpoints of the human airway tree, are of significant interest for clinical applications and physiological studies. In the past, these tasks were often performed manually due to the lack of automated algorithms that can tolerate false branches and anatomical variability typical for in vivo trees. In this paper, we present algorithms that perform both matching of branchpoints and anatomical labeling of in vivo trees without any human intervention and within a short computing time. No hand-pruning of false branches is required. The results from the automated methods show a high degree of accuracy when validated against reference data provided by human experts. 92.9% of the verifiable branchpoint matches found by the computer agree with experts' results. For anatomical labeling, 97.1% of the automatically assigned segment labels were found to be correct.

Multidetector computed-tomography (MDCT) scanners provide large high-resolution three-dimensional (3-D) images of the chest. MDCT scanning, when used in tandem with bronchoscopy, provides a state-of-the-art approach for lung-cancer assessment. We have been building and validating a lung-cancer assessment system, which enables virtual-bronchoscopic 3-D MDCT image analysis and follow-on image-guided bronchoscopy. A suitable path planning method is needed, however, for using this system. We describe a rapid, robust method for computing a set of 3-D airway-tree paths from MDCT images. The method first defines the skeleton of a given segmented 3-D chest image and then performs a multistage refinement of the skeleton to arrive at a final tree structure. The tree consists of a series of paths and branch structural data, suitable for quantitative airway analysis and smooth virtual navigation. A comparison of the method to a previously devised path-planning approach, using a set of human MDCT images, illustrates the efficacy of the method. Results are also presented for human lung-cancer assessment and the guidance of bronchoscopy.

The lungs exchange air with the external environment via the pulmonary airways. Computed tomography (CT) scanning can be used to obtain detailed images of the pulmonary anatomy, including the airways. These images have been used to measure airway geometry, study airway reactivity, and guide surgical interventions. Prior to these applications, airway segmentation can be used to identify the airway lumen in the CT images. Airway tree segmentation can be performed manually by an image analyst, but the complexity of the tree makes manual segmentation tedious and extremely time-consuming. We describe a fully automatic technique for segmenting the airway tree in three-dimensional (3-D) CT images of the thorax. We use grayscale morphological reconstruction to identify candidate airways on CT slices and then reconstruct a connected 3-D airway tree. After segmentation, we estimate airway branchpoints based on connectivity changes in the reconstructed tree. Compared to manual analysis on 3-mm-thick electron-beam CT images, the automatic approach has an overall airway branch detection sensitivity of approximately 73%.

A system for virtual-endoscopic 3D medical image assessment and follow-on live endoscopy is described. The development and results focus on 3D CT images of the chest and lung-cancer assessment. Two stages are involved in the assessment. In Stage-1, the physician uses a series of tools for visualization and 3D central-axes analysis to build a guidance plan (a "case study"). Next, during Stage-2 endoscopy, the physician links the computer system and case study to the endoscope. The endoscopic video is registered to 3D virtual image data to give the physician "augmented reality" information to better perform the endoscopy. Results illustrate the system's utility for animal and human cases.

Segmentation of pulmonary X-ray computed tomography (CT) images is a precursor to most pulmonary image analysis applications. This paper presents a fully automatic method for identifying the lungs in three-dimensional (3-D) pulmonary X-ray CT images. The method has three main steps. First, the lung region is extracted from the CT images by gray-level thresholding. Then, the left and right lungs are separated by identifying the anterior and posterior junctions by dynamic programming. Finally, a sequence of morphological operations is used to smooth the irregular boundary along the mediastinum in order to obtain results consistent with those obtained by manual analysis, in which only the most central pulmonary arteries are excluded from the lung region. The method has been tested by processing 3-D CT data sets from eight normal subjects, each imaged three times at biweekly intervals with lungs at 90% vital capacity. We present results by comparing our automatic method to manually traced borders from two image analysts. Averaged over all volumes, the root mean square difference between the computer and human analysis is 0.8 pixels (0.54 mm). The mean intrasubject change in tissue content over the three scans was 2.75% +/- 2.29% (mean +/- standard deviation).

Three-dimensional (3-D) analysis of airway trees extracted from computed tomography (CT) image data can provide objective information about lung structure and function. However, manual analysis of 3-D lung CT images is tedious, time consuming and, thus, impractical for routine clinical care. We have previously reported an automated rule-based method for extraction of airway trees from 3-D CT images using a priori knowledge about airway-tree anatomy. Although the method's sensitivity was quite good, its specificity suffered from a large number of falsely detected airways. We present a new approach to airway-tree detection based on fuzzy logic that increases the method's specificity without compromising its sensitivity. The method was validated in 32 CT image slices randomly selected from five volumetric canine electron-beam CT data sets. The fuzzy-logic method significantly outperformed the previously reported rule-based method (p < 0.002).

Airway geometry measurements can provide information regarding pulmonary physiology and pathophysiology. There has been considerable interest in measuring intrathoracic airways in two-dimensional (2-D) slices from volumetric X-ray computed tomography (CT). Such measurements can be used to evaluate and track the progression of diseases affecting the airways. A popular airway measurement method uses the "half-max" criteria, in which the gray level at the airway wall is estimated to be halfway between the minimum and maximum gray levels along a ray crossing the edge. However, because the scanning process introduces blurring, the half-max approach may not be applicable across all airway sizes. We propose a new measurement method based on a model of the scanning process. In our approach, we examine the gray-level profile of a ray crossing the airway wall and use a maximum-likelihood method to estimate the airway inner and outer radius. Using CT scans of a physical phantom, we present results showing that the new approach is more accurate than the half-max method at estimating wall location for thin-walled airways.

This paper describes a new approach that registers two images based on a physically deformable model. No prior knowledge about the types of geometric transformations is required for registration. Instead, our approach simulates the manual registration process and models the registration as the deformation of an elastic material governed by physical laws. Before registration, the contours of the images are extracted. The registration consists of two steps. The physically deformable model is applied first to deform one contour, called the start contour, to match the other, the goal contour. The result is point correspondences between the contours. Then an intensity transformation is estimated from the matching points to warp one image towards the other.

New sensitive and reliable methods for assessing alterations in regional lung structure and function are critically important for the investigation and treatment of pulmonary diseases. Accurate identification of the airway tree will provide an assessment of airway structure and will provide a means by which multiple volumetric images of the lung at the same lung volume over time can be used to assess regional parenchymal changes. The authors describe a novel rule-based method for the segmentation of airway trees from three-dimensional (3-D) sets of computed tomography (CT) images, and its validation. The presented method takes advantage of a priori anatomical knowledge about pulmonary airway and vascular trees and their interrelationships. The method is based on a combination of 3-D seeded region growing that is used to identify large airways, rule-based two-dimensional (2-D) segmentation of individual CT slices to identify probable locations of smaller diameter airways, and merging of airway regions across the 3-D set of slices resulting in a tree-like airway structure. The method was validated in 40 3-mm-thick CT sections from five data sets of canine lungs scanned via electron beam CT in vivo with lung volume held at a constant pressure. The method's performance was compared with that of the conventional 3-D region growing method. The method substantially outperformed an existing conventional approach to airway tree detection.

Describes a graphical user interface system for constructing automatic image-segmentation processes for 3D medical-imaging applications. The system provides a user-friendly environment for prototyping, testing, and executing complex image-analysis processes and can interact with a graphics-based 3D medical image-analysis package. The interface links to a library of 1D, 2D, and 3D image-processing functions and can easily link to other functions created by the user. Demonstrations of the system are given for 3D left-ventricular chamber extraction and 3D upper-airway analysis.>