Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images

Lung Segmentation in 4D CT Volumes Based on Robust Active Shape Model Matching

Dynamic and longitudinal lung CT imaging produce 4D lung image data sets, enabling applications like radiation treatment planning or assessment of response to treatment of lung diseases. In this paper, we present a 4D lung segmentation method that mutually utilizes all individual CT volumes to derive segmentations for each CT data set. Our approach is based on a 3D robust active shape model and extends it to fully utilize 4D lung image data sets. This yields an initial segmentation for the 4D volume, which is then refined by using a 4D optimal surface finding algorithm. The approach was evaluated on a diverse set of 152 CT scans of normal and diseased lungs, consisting of total lung capacity and functional residual capacity scan pairs. In addition, a comparison to a 3D segmentation method and a registration based 4D lung segmentation approach was performed. The proposed 4D method obtained an average Dice coefficient of 0.9773 ± 0.0254, which was statistically significantly better (p value ≪0.001) than the 3D method (0.9659 ± 0.0517). Compared to the registration based 4D method, our method obtained better or similar performance, but was 58.6% faster. Also, the method can be easily expanded to process 4D CT data sets consisting of several volumes.

Registration of lung CT images acquired in different respiratory ranges with 4DCT and HRCT

Pulmonary image registration is challenging because of the unique structure of the lung, its high deformability and its non-uniform intensity change with breathing. In the present work we propose a new method for pulmonary image registration, based on the reconstruction and the combination of the main pulmonary structures to modify parenchyma intensity prior to the application of the registration algorithm. The algorithm has been applied to both four dimensional CT and multi-volume high resolution CT demonstrating an increased accuracy of the results with the application of the pulmonary structure enhancement, evaluated both on landmarks distance in 4DCT and structures' surface distance in HRCT.

Automated compromised right lung segmentation method using a robust atlas-based active volume model with sparse shape composition prior in CT

To resolve challenges in image segmentation in oncologic patients with severely compromised lung, we propose an automated right lung segmentation framework that uses a robust, atlas-based active volume model with a sparse shape composition prior. The robust atlas is achieved by combining the atlas with the output of sparse shape composition. Thoracic computed tomography images (n=38) from patients with lung tumors were collected. The right lung in each scan was manually segmented to build a reference training dataset against which the performance of the automated segmentation method was assessed. The quantitative results of this proposed segmentation method with sparse shape composition achieved mean Dice similarity coefficient (DSC) of (0.72, 0.81) with 95% CI, mean accuracy (ACC) of (0.97, 0.98) with 95% CI, and mean relative error (RE) of (0.46, 0.74) with 95% CI. Both qualitative and quantitative comparisons suggest that this proposed method can achieve better segmentation accuracy with less variance than other atlas-based segmentation methods in the compromised lung segmentation.

Thoracic cavity definition for 3D PET/CT analysis and visualization

X-ray computed tomography (CT) and positron emission tomography (PET) serve as the standard imaging modalities for lung-cancer management. CT gives anatomical details on diagnostic regions of interest (ROIs), while PET gives highly specific functional information. During the lung-cancer management process, a patient receives a co-registered whole-body PET/CT scan pair and a dedicated high-resolution chest CT scan. With these data, multimodal PET/CT ROI information can be gleaned to facilitate disease management. Effective image segmentation of the thoracic cavity, however, is needed to focus attention on the central chest. We present an automatic method for thoracic cavity segmentation from 3D CT scans. We then demonstrate how the method facilitates 3D ROI localization and visualization in patient multimodal imaging studies. Our segmentation method draws upon digital topological and morphological operations, active-contour analysis, and key organ landmarks. Using a large patient database, the method showed high agreement to ground-truth regions, with a mean coverage=99.2% and leakage=0.52%. Furthermore, it enabled extremely fast computation. For PET/CT lesion analysis, the segmentation method reduced ROI search space by 97.7% for a whole-body scan, or nearly 3 times greater than that achieved by a lung mask. Despite this reduction, we achieved 100% true-positive ROI detection, while also reducing the false-positive (FP) detection rate by >5 times over that achieved with a lung mask. Finally, the method greatly improved PET/CT visualization by eliminating false PET-avid obscurations arising from the heart, bones, and liver. In particular, PET MIP views and fused PET/CT renderings depicted unprecedented clarity of the lesions and neighboring anatomical structures truly relevant to lung-cancer assessment.

NUFFT-Based Iterative Image Reconstruction via Alternating Direction Total Variation Minimization for Sparse-View CT

Sparse-view imaging is a promising scanning method which can reduce the radiation dose in X-ray computed tomography (CT). Reconstruction algorithm for sparse-view imaging system is of significant importance. The adoption of the spatial iterative algorithm for CT image reconstruction has a low operation efficiency and high computation requirement. A novel Fourier-based iterative reconstruction technique that utilizes nonuniform fast Fourier transform is presented in this study along with the advanced total variation (TV) regularization for sparse-view CT. Combined with the alternating direction method, the proposed approach shows excellent efficiency and rapid convergence property. Numerical simulations and real data experiments are performed on a parallel beam CT. Experimental results validate that the proposed method has higher computational efficiency and better reconstruction quality than the conventional algorithms, such as simultaneous algebraic reconstruction technique using TV method and the alternating direction total variation minimization approach, with the same time duration. The proposed method appears to have extensive applications in X-ray CT imaging.

A Method for Lung Boundary Correction Using Split Bregman Method and Geometric Active Contour Model

In order to get the extracted lung region from CT images more accurately, a model that contains lung region extraction and edge boundary correction is proposed. Firstly, a new edge detection function is presented with the help of the classic structure tensor theory. Secondly, the initial lung mask is automatically extracted by an improved active contour model which combines the global intensity information, local intensity information, the new edge information, and an adaptive weight. It is worth noting that the objective function of the improved model is converted to a convex model, which makes the proposed model get the global minimum. Then, the central airway was excluded according to the spatial context messages and the position relationship between every segmented region and the rib. Thirdly, a mesh and the fractal theory are used to detect the boundary that surrounds the juxtapleural nodule. Finally, the geometric active contour model is employed to correct the detected boundary and reinclude juxtapleural nodules. We also evaluated the performance of the proposed segmentation and correction model by comparing with their popular counterparts. Efficient computing capability and robustness property prove that our model can correct the lung boundary reliably and reproducibly.

A method for avoiding overlap of left and right lungs in shape model guided segmentation of lungs in CT volumes

PURPOSE

The automated correct segmentation of left and right lungs is a nontrivial problem, because the tissue layer between both lungs can be quite thin. In the case of lung segmentation with left and right lung models, overlapping segmentations can occur. In this paper, the authors address this issue and propose a solution for a model-based lung segmentation method.

METHODS

The thin tissue layer between left and right lungs is detected by means of a classification approach and utilized to selectively modify the cost function of the lung segmentation method. The approach was evaluated on a diverse set of 212 CT scans of normal and diseased lungs. Performance was assessed by utilizing an independent reference standard and by means of comparison to the standard segmentation method without overlap avoidance.

RESULTS

For cases where the standard approach produced overlapping segmentations, the proposed method significantly (p = 1.65 × 10(-9)) reduced the overlap by 97.13% on average (median: 99.96%). In addition, segmentation accuracy assessed with the Dice coefficient showed a statistically significant improvement (p = 7.5 × 10(-5)) and was 0.9845 ± 0.0111. For cases where the standard approach did not produce an overlap, performance of the proposed method was not found to be significantly different.

CONCLUSIONS

The proposed method improves the quality of the lung segmentations, which is important for subsequent quantitative analysis steps.

Intrapulmonary administration of bone-marrow derived M1/M2 macrophages to enhance the resolution of LPS-induced lung inflammation: noninvasive monitoring using free-breathing MR and CT imaging protocols

Background

Alveolar macrophages, with their high functional plasticity, were reported to orchestrate the induction and resolution of inflammatory processes in chronic pulmonary diseases. Noninvasive imaging modalities that offer simultaneous monitoring of inflammation progression and tracking of macrophages subpopulations involved in the inflammatory cascade, can provide an ideal and specific diagnostic tool to visualize the action mechanism in its initial stages. Therefore, the purpose of the current study was to evaluate the role of M1 and M2 macrophages in the resolution of lipopolysaccharide (LPS)-induced lung inflammation and monitor this process using noninvasive free-breathing MRI and CT protocols.

Methods

Bone-marrow derived macrophages were first polarized to M1 and M2 macrophages and then labeled with superparamagnetic iron oxide nanoparticles. BALB/c mice with lung inflammation received an intrapulmonary instillation of these ex vivo polarized M1 or M2 macrophages. The biodistribution of macrophages subpopulations and the subsequent resolution of lung inflammation were noninvasively monitored using MRI and micro-CT. Confirmatory immunohistochemistry analyses were performed on lung tissue sections using specific macrophage markers.

Results

As expected, large inflammatory areas noninvasively imaged using pulmonary MR and micro-CT were observed within the lungs following LPS challenge. Subsequent intrapulmonary administration of M1 and M2 macrophages resulted in a significant decrease in inflammation starting from 72 h. Confirmatory immunohistochemistry analyses established a progression of lung inflammation with LPS and its subsequent reduction with both macrophages subsets. An enhanced resolution of inflammation was observed with M2 macrophages compared to M1.

Conclusions

The current study demonstrated that ex vivo polarized macrophages decreased LPS-induced lung inflammation. Noninvasive free-breathing MR and CT imaging protocols enabled efficient monitoring of progression and resolution of lung inflammation.

Automatic left and right lung separation using free-formed surface fitting on volumetric CT

This study presents a completely automated method for separating the left and right lungs using free-formed surface fitting on volumetric computed tomography (CT). The left and right lungs are roughly divided using iterative 3-dimensional morphological operator and a Hessian matrix analysis. A point set traversing between the initial left and right lungs is then detected with a Euclidean distance transform to determine the optimal separating surface, which is then modeled from the point set using a free-formed surface-fitting algorithm. Subsequently, the left and right lung volumes are smoothly and directly separated using the separating surface. The performance of the proposed method was estimated by comparison with that of a human expert on 44 CT examinations. For all data sets, averages of the root mean square surface distance, maximum surface distance, and volumetric overlap error between the results of the automatic and the manual methods were 0.032 mm, 2.418 mm, and 0.017 %, respectively. Our study showed the feasibility of automatically separating the left and right lungs by identifying the 3D continuous separating surface on volumetric chest CT images.

Relative contributions of emphysema and airway remodelling to airflow limitation in COPD: Consistent results from two cohorts

BACKGROUND AND OBJECTIVE

The relative contributions of emphysema and airway remodelling to airflow limitation remain unclear in chronic obstructive pulmonary disease (COPD). We aimed to evaluate the relative contributions of emphysema and airway wall thickness measured by quantitative computed tomography (CT) to the prediction of airflow limitation in two separate COPD cohorts.

METHODS

Pulmonary function tests and whole-lung CT were performed in 250 male smokers with COPD, including 167 from University Medical Center at Ho Chi Minh City, Vietnam, and 83 from Shiga University of Medical Science Hospital, Japan. The same CT analysis software was used to measure the percentage of low attenuation volume (%LAV) at the threshold of -950 Hounsfield units and the square root of wall area of a hypothetical airway with an internal perimeter of 10 mm (Pi10). The standardized coefficients in multiple linear regressions were used to evaluate the relative contributions of %LAV and Pi10 to predictions of FEV1 /FVC and FEV1 % predicted.

RESULTS

Both %LAV and Pi10 independently predicted either forced expiratory volume in 1 s/forced vital capacity (FEV1 /FVC) or FEV1 % predicted (P ≤ 0.001 for all standardized coefficients). However, the absolute values of the standardized coefficients were 2-3 times higher for %LAV than for Pi10 in all prediction models. The results were consistent in the two COPD cohorts.

CONCLUSIONS

%LAV predicts both FEV1 /FVC and FEV1 better than Pi10 in patients with COPD. Thus, emphysema may make a greater contribution to airflow limitation than airway remodelling in COPD.

Heterogeneity of specific gas volume changes: a new tool to plan lung volume reduction in COPD

OBJECTIVE

The aim of this work was to investigate if regional differences of specific gas volume (SVg) in the different regions (lobes and bronchopulmonary segments) in healthy volunteers and patients with severe emphysema can be used as a tool for planning lung volume reduction (LVR) in emphysema.

METHODS

CT scans of 10 healthy subjects and 10 subjects with severe COPD were obtained at end-inspiration (total lung capacity [TLC]) and end-expiration (residual volume [RV]). For each subject, ΔSVg (ΔSVg = SVg,TLC - SVg,RV, where SVg,TLC and SVg,RV are specific gas volume at TLC and RV, respectively) vs ΔV (ΔV = V,TLC-V,RV, where V,TLC and V,RV are lung volume at TLC and RV, respectively) was plotted for the entire lung, each lobe, and all bronchopulmonary segments. For each subject, a heterogeneity index (HI) was defined to quantify the range of variability of ΔSVg/ΔV in all bronchopulmonary regions.

RESULTS

In patients with COPD, SVg,TLC and SVg,RV were significantly higher and ΔSVg variations lower than in healthy subjects (P < .001). In COPD, ΔSVg/ΔV slopes were lower in upper lobes than in lower lobes. In healthy subjects, the entire lung, lobes, and bronchopulmonary segments all showed similar ΔSVg/ΔV slopes, whereas in COPD a high variance was found. As a consequence, HI was significantly higher in subjects with COPD than in healthy subjects (0.80 ± 0.34 vs 0.15 ± 0.10, respectively; P < .001).

CONCLUSIONS

SVg variations within the lung are highly homogeneous in healthy subjects. Regions with low ΔSVg/ΔV (ie, more pronounced gas trapping) should be considered as target areas for LVR. Regions with negative values of ΔSVg/ΔV identify where collateral ventilation is present. HI is helpful to assess the patient in the different stages of disease and the effect of different LVR treatments.

Shape and texture based novel features for automated juxtapleural nodule detection in lung CTs

Lung cancer is one of the types of cancer with highest mortality rate in the world. In case of early detection and diagnosis, the survival rate of patients significantly increases. In this study, a novel method and system that provides automatic detection of juxtapleural nodule pattern have been developed from cross-sectional images of lung CT (Computerized Tomography). Shape-based and both shape and texture based 7 features are contributed to the literature for lung nodules. System that we developed consists of six main stages called preprocessing, lung segmentation, detection of nodule candidate regions, feature extraction, feature selection (with five feature ranking criteria) and classification. LIDC dataset containing cross-sectional images of lung CT has been utilized, 1410 nodule candidate regions and 40 features have been extracted from 138 cross-sectional images for 24 patients. Experimental results for 10 classifiers are obtained and presented. Adding our derived features to known 33 features has increased nodule recognition performance from 0.9639 to 0.9679 AUC value on generalized linear model regression (GLMR) for 22 selected features and being reached one of the most successful results in the literature.

Review of automatic pulmonary lobe segmentation methods from CT

The computational detection of pulmonary lobes from CT images is a challenging segmentation problem with important respiratory health care applications, including surgical planning and regional image analysis. Several authors have proposed automated algorithms and we present a methodological review. These algorithms share a number of common stages and we consider each stage in turn, comparing the methods applied by each author and discussing their relative strengths. No standard method has yet emerged and none of the published methods have been demonstrated across a full range of clinical pathologies and imaging protocols. We discuss how improved methods could be developed by combining different approaches, and we use this to propose a workflow for the development of new algorithms.

Automatic lung segmentation using control feedback system: morphology and texture paradigm

Interstitial Lung Disease (ILD) encompasses a wide array of diseases that share some common radiologic characteristics. When diagnosing such diseases, radiologists can be affected by heavy workload and fatigue thus decreasing diagnostic accuracy. Automatic segmentation is the first step in implementing a Computer Aided Diagnosis (CAD) that will help radiologists to improve diagnostic accuracy thereby reducing manual interpretation. Automatic segmentation proposed uses an initial thresholding and morphology based segmentation coupled with feedback that detects large deviations with a corrective segmentation. This feedback is analogous to a control system which allows detection of abnormal or severe lung disease and provides a feedback to an online segmentation improving the overall performance of the system. This feedback system encompasses a texture paradigm. In this study we studied 48 males and 48 female patients consisting of 15 normal and 81 abnormal patients. A senior radiologist chose the five levels needed for ILD diagnosis. The results of segmentation were displayed by showing the comparison of the automated and ground truth boundaries (courtesy of ImgTracer™ 1.0, AtheroPoint™ LLC, Roseville, CA, USA). The left lung's performance of segmentation was 96.52% for Jaccard Index and 98.21% for Dice Similarity, 0.61 mm for Polyline Distance Metric (PDM), -1.15% for Relative Area Error and 4.09% Area Overlap Error. The right lung's performance of segmentation was 97.24% for Jaccard Index, 98.58% for Dice Similarity, 0.61 mm for PDM, -0.03% for Relative Area Error and 3.53% for Area Overlap Error. The segmentation overall has an overall similarity of 98.4%. The segmentation proposed is an accurate and fully automated system.

Classification of thresholded regions based on selective use of PET, CT and PET-CT image features

Fluorodeoxyglucose positron emission tomography - computed tomography (FDG PET-CT) is the preferred image modality for lymphoma diagnosis. Sites of disease generally appear as foci of increased FDG uptake. Thresholding methods are often applied to robustly separate these regions. However, its main limitation is that it also includes sites of FDG excretion and physiological FDG uptake regions, which we define as FEPU - sites of FEPU include the bladder, renal, papillae, ureters, brain, heart and brown fat. FEPU can make image interpretation problematic. The ability to identify and label FEPU sites and separate them from abnormal regions is an important process that could improve image interpretation. We propose a new method to automatically separate and label FEPU sites from the thresholded PET images. Our method is based on the selective use of features extracted from data types comprising of PET, CT and PET-CT. Our FEPU classification of 43 clinical lymphoma patient studies revealed higher accuracy when compared to non-selective image features.

An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy

Computer-aided detection and diagnosis (CAD) has been widely investigated to improve radiologists׳ diagnostic accuracy in detecting and characterizing lung disease, as well as to assist with the processing of increasingly sizable volumes of imaging. Lung segmentation is a requisite preprocessing step for most CAD schemes. This paper proposes a parameter-free lung segmentation algorithm with the aim of improving lung nodule detection accuracy, focusing on juxtapleural nodules. A bidirectional chain coding method combined with a support vector machine (SVM) classifier is used to selectively smooth the lung border while minimizing the over-segmentation of adjacent regions. This automated method was tested on 233 computed tomography (CT) studies from the lung imaging database consortium (LIDC), representing 403 juxtapleural nodules. The approach obtained a 92.6% re-inclusion rate. Segmentation accuracy was further validated on 10 randomly selected CT series, finding a 0.3% average over-segmentation ratio and 2.4% under-segmentation rate when compared to manually segmented reference standards done by an expert.

Automatic segmentation of anatomical structures from CT scans of thorax for RTP

Modern radiotherapy techniques are vulnerable to delineation inaccuracies owing to the steep dose gradient around the target. In this aspect, accurate contouring comprises an indispensable part of optimal radiation treatment planning (RTP). We suggest a fully automated method to segment the lungs, trachea/main bronchi, and spinal canal accurately from computed tomography (CT) scans of patients with lung cancer to use for RTP. For this purpose, we developed a new algorithm for inclusion of excluded pathological areas into the segmented lungs and a modified version of the fuzzy segmentation by morphological reconstruction for spinal canal segmentation and implemented some image processing algorithms along with them. To assess the accuracy, we performed two comparisons between the automatically obtained results and the results obtained manually by an expert. The average volume overlap ratio values range between 94.30 ± 3.93% and 99.11 ± 0.26% on the two different datasets. We obtained the average symmetric surface distance values between the ranges of 0.28 ± 0.21 mm and 0.89 ± 0.32 mm by using the same datasets. Our method provides favorable results in the segmentation of CT scans of patients with lung cancer and can avoid heavy computational load and might offer expedited segmentation that can be used in RTP.

Semiautomatic segmentation of longitudinal computed tomography images in a rat model of lung injury by surfactant depletion

Quantitative analysis of computed tomography (CT) is essential to the study of acute lung injury. However, quantitative CT is made difficult by poor lung aeration, which complicates the critical step of image segmentation. To overcome this obstacle, this study sought to develop and validate a semiautomated, multilandmark, registration-based scheme for lung segmentation that is effective in conditions of poor aeration. Expiratory and inspiratory CT images were obtained in rats (n = 8) with surfactant depletion of incremental severity to mimic worsening aeration. Trained operators manually delineated the images to provide a comparative landmark. Semiautomatic segmentation originated from a single, previously segmented reference image obtained at healthy baseline. Deformable registration of the target images (after surfactant depletion) was performed using the symmetric diffeomorphic transformation model with B-spline regularization. Registration used multiple landmarks (i.e., rib cage, spine, and lung parenchyma) to minimize the effect of poor aeration. Then target images were automatically segmented by applying the calculated transformation function to the reference image contour. Semiautomatically and manually segmented contours proved to be highly similar in all aeration conditions, including those characterized by more severe surfactant depletion and expiration. The Dice similarity coefficient was over 0.9 in most conditions, confirming high agreement, irrespective of poor aeration. Furthermore, CT density-based measurements of gas volume, tissue mass, and lung aeration distribution were minimally affected by the method of segmentation. Moving forward, multilandmark registration has the potential to streamline quantitative CT analysis by enabling semiautomatic image segmentation of lungs with a broad range of injury severity.

Pneumothorax risk factors in smokers with and without chronic obstructive pulmonary disease

RATIONALE

The demographic, physiological, and computed tomography (CT) features associated with pneumothorax in smokers with and without chronic obstructive pulmonary disease (COPD) are not clearly defined.

OBJECTIVES

We evaluated the hypothesis that pneumothorax in smokers is associated with male sex, tall and thin stature, airflow obstruction, and increased total and subpleural emphysema.

METHODS

The study included smokers with and without COPD from the COPDGene Study, with quantitative chest CT analysis. Pleural-based emphysema was assessed on the basis of local histogram measures of emphysema. Pneumothorax history was defined by subject self-report.

MEASUREMENTS AND MAIN RESULTS

Pneumothorax was reported in 286 (3.2%) of 9,062 participants. In all participants, risk of prior pneumothorax was significantly higher in men (odds ratio [OR], 1.55; 95% confidence interval [CI], 1.08-2.22) and non-Hispanic white subjects (OR, 1.90; 95% CI, 1.34-2.69). Risk of prior pneumothorax was associated with increased percent CT emphysema in all participants and participants with COPD (OR, 1.04 for each 1% increase in emphysema; 95% CI, 1.03-1.06). Increased pleural-based emphysema was independently associated with risk of past pneumothorax in all participants (OR, 1.05 for each 1% increase; 95% CI, 1.01-1.10). In smokers with normal spirometry, risk of past pneumothorax was associated with non-Hispanic white race and lifetime smoking intensity (OR, 1.20 for every 10 pack-years; 95% CI, 1.09-1.33).

CONCLUSIONS

Among smokers, pneumothorax is associated with male sex, non-Hispanic white race, and increased percentage of total and subpleural CT emphysema. Pneumothorax was not independently associated with height or lung function, even in participants with COPD. Clinical trial registered with www.clinicaltrials.gov (NCT00608764).

Comparison between multivolume CT-based surrogates of regional ventilation in healthy subjects

RATIONALE AND OBJECTIVES

The assessment of regional ventilation is of critical importance when investigating lung function during disease progression and planning of pulmonary interventions. Recently, different computed tomography (CT)-based parameters have been proposed as surrogates of lung ventilation. The aim of the present study was to compare these parameters, namely variations of density (ΔHU), specific volume (sVol), and specific gas volume (ΔSVg) between different lung volumes, in relation to their topographic distribution within the lung.

MATERIALS AND METHODS

Ten healthy volunteers were scanned via high-resolution CT at residual volume (RV) and total lung capacity (TLC); ΔHU, sVol, and ΔSVg were mapped voxel by voxel after registering TLC onto RV. Variations of the three parameters along the vertical and horizontal directions were analyzed.

RESULTS

Along the vertical direction (from ventral to dorsal regions), a strong dependence on gravity was found in ΔHU and sVol, with greater values in the dorsal regions of the lung (P < .001), whereas ΔSVg was more homogeneously distributed within the lung. Conversely, along the caudocranial direction (from lung bases to apexes) where no gravitational gradient is present, the three parameters behaved similarly, with lower values at the apices.

CONCLUSIONS

ΔHU, sVol, and ΔSVg behave differently along the gravity direction. As the greater amount of air delivered to the dependent portion of the lung supplies a larger number of alveoli, the amount of gas delivered to alveoli compared to the mass of tissue is not gravity dependent. The minimization of gravity dependence in the distribution of ventilation when using ΔSVg suggests that this parameter is more reliable to discriminate healthy from pathologic regions.

Towards local progression estimation of pulmonary emphysema using CT

PURPOSE

Whole lung densitometry on chest CT images is an accepted method for measuring tissue destruction in patients with pulmonary emphysema in clinical trials. Progression measurement is required for evaluation of change in health condition and the effect of drug treatment. Information about the location of emphysema progression within the lung may be important for the correct interpretation of drug efficacy, or for determining a treatment plan. The purpose of this study is therefore to develop and validate methods that enable the local measurement of lung density changes, which requires proper modeling of the effect of respiration on density.

METHODS

Four methods, all based on registration of baseline and follow-up chest CT scans, are compared. The first naïve method subtracts registered images. The second employs the so-called dry sponge model, where volume correction is performed using the determinant of the Jacobian of the transformation. The third and the fourth introduce a novel adaptation of the dry sponge model that circumvents its constant-mass assumption, which is shown to be invalid. The latter two methods require a third CT scan at a different inspiration level to estimate the patient-specific density-volume slope, where one method employs a global and the other a local slope. The methods were validated on CT scans of a phantom mimicking the lung, where mass and volume could be controlled. In addition, validation was performed on data of 21 patients with pulmonary emphysema.

RESULTS

The image registration method was optimized leaving a registration error below half the slice increment (median 1.0 mm). The phantom study showed that the locally adapted slope model most accurately measured local progression. The systematic error in estimating progression, as measured on the phantom data, was below 2 gr/l for a 70 ml (6%) volume difference, and 5 gr/l for a 210 ml (19%) difference, if volume correction was applied. On the patient data an underlying linearity assumption relating lung volume change with density change was shown to hold (fitR(2) = 0.94), and globalized versions of the local models are consistent with global results (R(2) of 0.865 and 0.882 for the two adapted slope models, respectively).

CONCLUSIONS

In conclusion, image matching and subsequent analysis of differences according to the proposed lung models (i) has good local registration accuracy on patient data, (ii) effectively eliminates a dependency on inspiration level at acquisition time, (iii) accurately predicts progression in phantom data, and (iv) is reasonably consistent with global results in patient data. It is therefore a potential future tool for assessing local emphysema progression in drug evaluation trials and in clinical practice.

Comparison of manual and semiautomated techniques for analyzing gastric volumes with MRI in humans

Gastric emptying, accommodation, and motility can be quantified with magnetic resonance imaging (MRI). The first step in image analysis entails segmenting the stomach from surrounding structures, usually by a time-consuming manual process. We have developed a semiautomated process to segment and measure gastric volumes with MRI. Gastric images were acquired with a three-dimensional gradient echo MRI sequence at 5, 10, 20, and 30 min after ingestion of a liquid nutrient (Ensure, 296 ml) labeled with gadolinium in 20 healthy volunteers and 29 patients with dyspeptic symptoms. The agreement between gastric volumes measured by manual segmentation and our new semiautomated algorithm was assessed with Lin's concordance correlation coefficient (CCC) and the Bland Altman test. At 5 min after a meal, food volumes measured by manual (352 ± 4 ml) and semiautomated (346 ± 4 ml) techniques were correlated {CCC[95% confidence interval (CI)] 0.70 (0.52, 0.81)}; air volumes measured by manual (88 ± 6 ml) and semiautomated (84 ± 6 ml) techniques were also correlated [CCC (95% CI) 0.89 (0.82, 0.94)]. Findings were similar at subsequent time points. The Bland Altman test was not significant. The time required for semiautomated segmentation ranged from an average of 204 s for the 5-min images to 233 s for the 20-min images. These times were appreciably smaller than the typical times of many tens of minutes, even hours, required for manual segmentation. To conclude, a semiautomated process can measure gastric food and air volume using MRI with comparable accuracy and far better efficiency than a manual process.

Improved CT-based estimate of pulmonary gas trapping accounting for scanner and lung-volume variations in a multicenter asthmatic study

Lung air trapping is estimated via quantitative computed tomography (CT) using density threshold-based measures on an expiration scan. However, the effects of scanner differences and imaging protocol adherence on quantitative assessment are known to be problematic. This study investigates the effects of protocol differences, such as using different CT scanners and breath-hold coaches in a multicenter asthmatic study, and proposes new methods that can adjust intersite and intersubject variations. CT images of 50 healthy subjects and 42 nonsevere and 52 severe asthmatics at total lung capacity (TLC) and functional residual capacity (FRC) were acquired using three different scanners and two different coaching methods at three institutions. A fraction threshold-based approach based on the corrected Hounsfield unit of air with tracheal density was applied to quantify air trapping at FRC. The new air-trapping method was enhanced by adding a lung-shaped metric at TLC and the lobar ratio of air-volume change between TLC and FRC. The fraction-based air-trapping method is able to collapse air-trapping data of respective populations into distinct regression lines. Relative to a constant value-based clustering scheme, the slope-based clustering scheme shows the improved performance and reduced misclassification rate of healthy subjects. Furthermore, both lung shape and air-volume change are found to be discriminant variables for differentiating among three populations of healthy subjects and nonsevere and severe asthmatics. In conjunction with the lung shape and air-volume change, the fraction-based measure of air trapping enables differentiation of severe asthmatics from nonsevere asthmatics and nonsevere asthmatics from healthy subjects, critical for the development and evaluation of new therapeutic interventions.

Computer-aided detection and quantification of cavitary tuberculosis from CT scans

PURPOSE

To present a computer-aided detection tool for identifying, quantifying, and evaluating tuberculosis (TB) cavities in the infected lungs from computed tomography (CT) scans.

METHODS

The authors' proposed method is based on a novel shape-based automated detection algorithm on CT scans followed by a fuzzy connectedness (FC) delineation procedure. In order to assess interaction between cavities and airways, the authors first roughly identified air-filled structures (airway, cavities, esophagus, etc.) by thresholding over Hounsfield unit of CT image. Then, airway and cavity structure detection was conducted within the support vector machine classification algorithm. Once airway and cavities were detected automatically, the authors extracted airway tree using a hybrid multiscale approach based on novel affinity relations within the FC framework and segmented cavities using intensity-based FC algorithm. At final step, the authors refined airway structures within the local regions of FC with finer control. Cavity segmentation results were compared to the reference truths provided by expert radiologists and cavity formation was tracked longitudinally from serial CT scans through shape and volume information automatically determined through the authors' proposed system. Morphological evolution of the cavitary TB were analyzed accordingly with this process. Finally, the authors computed the minimum distance between cavity surface and nearby airway structures by using the linear time distance transform algorithm to explore potential role of airways in cavity formation and morphological evolution.

RESULTS

The proposed methodology was qualitatively and quantitatively evaluated on pulmonary CT images of rabbits experimentally infected with TB, and multiple markers such as cavity volume, cavity surface area, minimum distance from cavity surface to the nearest bronchial-tree, and longitudinal change of these markers (namely, morphological evolution of cavities) were determined precisely. While accuracy of the authors' cavity detection algorithm was 94.61%, airway detection part of the proposed methodology showed even higher performance by 99.8%. Dice similarity coefficients for cavitary segmentation experiments were found to be approximately 99.0% with respect to the reference truths provided by two expert radiologists (blinded to their evaluations). Moreover, the authors noted that volume derived from the authors' segmentation method was highly correlated with those provided by the expert radiologists (R(2) = 0.99757 and R(2) = 0.99496, p < 0.001, with respect to the observer 1 and observer 2) with an interobserver agreement of 98%. The authors quantitatively confirmed that cavity formation was positioned by the nearby bronchial-tree after exploring the respective spatial positions based on the minimum distance measurement. In terms of efficiency, the core algorithms take less than 2 min on a linux machine with 3.47 GHz CPU and 24 GB memory.

CONCLUSION

The authors presented a fully automatic method for cavitary TB detection, quantification, and evaluation. The performance of every step of the algorithm was qualitatively and quantitatively assessed. With the proposed method, airways and cavities were automatically detected and subsequently delineated in high accuracy with heightened efficiency. Furthermore, not only morphological information of cavities were obtained through the authors' proposed framework, but their spatial relation to airways, and longitudinal analysis was also provided to get further insight on cavity formation in tuberculosis disease. To the authors' best of knowledge, this is the first study in computerized analysis of cavitary tuberculosis from CT scans.

Pulmonary lobe segmentation based on ridge surface sampling and shape model fitting

PURPOSE

Performing lobe-based quantitative analysis of the lung in computed tomography (CT) scans can assist in efforts to better characterize complex diseases such as chronic obstructive pulmonary disease (COPD). While airways and vessels can help to indicate the location of lobe boundaries, segmentations of these structures are not always available, so methods to define the lobes in the absence of these structures are desirable.

METHODS

The authors present a fully automatic lung lobe segmentation algorithm that is effective in volumetric inspiratory and expiratory computed tomography (CT) datasets. The authors rely on ridge surface image features indicating fissure locations and a novel approach to modeling shape variation in the surfaces defining the lobe boundaries. The authors employ a particle system that efficiently samples ridge surfaces in the image domain and provides a set of candidate fissure locations based on the Hessian matrix. Following this, lobe boundary shape models generated from principal component analysis (PCA) are fit to the particles data to discriminate between fissure and nonfissure candidates. The resulting set of particle points are used to fit thin plate spline (TPS) interpolating surfaces to form the final boundaries between the lung lobes.

RESULTS

The authors tested algorithm performance on 50 inspiratory and 50 expiratory CT scans taken from the COPDGene study. Results indicate that the authors' algorithm performs comparably to pulmonologist-generated lung lobe segmentations and can produce good results in cases with accessory fissures, incomplete fissures, advanced emphysema, and low dose acquisition protocols. Dice scores indicate that only 29 out of 500 (5.85%) lobes showed Dice scores lower than 0.9. Two different approaches for evaluating lobe boundary surface discrepancies were applied and indicate that algorithm boundary identification is most accurate in the vicinity of fissures detectable on CT.

CONCLUSIONS

The proposed algorithm is effective for lung lobe segmentation in absence of auxiliary structures such as vessels and airways. The most challenging cases are those with mostly incomplete, absent, or near-absent fissures and in cases with poorly revealed fissures due to high image noise. However, the authors observe good performance even in the majority of these cases.

Longitudinal changes in airway remodeling and air trapping in severe asthma

RATIONALE AND OBJECTIVES

Previous cross-sectional studies have demonstrated that airway wall thickness and air trapping are greater in subjects with severe asthma than in those with mild-to-moderate asthma. However, a better understanding of how airway remodeling and lung density change over time is needed. This study aimed to evaluate predictors of airway wall remodeling and change in lung function and lung density over time in severe asthma.

MATERIALS AND METHODS

Phenotypic characterization and quantitative multidetector-row computed tomography (MDCT) of the chest were performed at baseline and ∼2.6 years later in 38 participants with asthma (severe n = 24 and mild-to-moderate n = 14) and nine normal controls from the Severe Asthma Research Program.

RESULTS

Subjects with severe asthma had a significant decline in postbronchodilator forced expiratory volume in 1 second percent (FEV1%) predicted over time (P < .001). Airway wall thickness measured by MDCT was increased at multiple airway generations in severe asthma compared to mild-to-moderate asthma (wall area percent [WA%]: P < .05) and normals (P < .05) at baseline and year 2. Over time, there was an increase in WA% and wall thickness percent (WT%) in all subjects (P = .030 and .009, respectively) with no change in emphysema-like lung or air trapping. Baseline prebronchodilator FEV1% inversely correlated with WA% and WT% (both P < .05). In a multivariable regression model, baseline WA%, race, and health care utilization were predictors of subsequent airway remodeling.

CONCLUSIONS

Severe asthma subjects have a greater decline in lung function over time than normal subjects or those with mild-to-moderate asthma. MDCT provides a noninvasive measure of airway wall thickness that may predict subsequent airway remodeling.

Determination of regional lung air volume distribution at mid-tidal breathing from computed tomography: a retrospective study of normal variability and reproducibility

Background

Determination of regional lung air volume has several clinical applications. This study investigates the use of mid-tidal breathing CT scans to provide regional lung volume data.

Methods

Low resolution CT scans of the thorax were obtained during tidal breathing in 11 healthy control male subjects, each on two separate occasions. A 3D map of air volume was derived, and total lung volume calculated. The regional distribution of air volume from centre to periphery of the lung was analysed using a radial transform and also using one dimensional profiles in three orthogonal directions.

Results

The total air volumes for the right and left lungs were 1035 +/− 280 ml and 864 +/− 315 ml, respectively (mean and SD). The corresponding fractional air volume concentrations (FAVC) were 0.680 +/− 0.044 and 0.658 +/− 0.062. All differences between the right and left lung were highly significant (p < 0.0001). The coefficients of variation of repeated measurement of right and left lung air volumes and FAVC were 6.5% and 6.9% and 2.5% and 3.6%, respectively. FAVC correlated significantly with lung space volume (r = 0.78) (p < 0.005). FAVC increased from the centre towards the periphery of the lung. Central to peripheral ratios were significantly higher for the right (0.100 +/− 0.007 SD) than the left (0.089 +/− 0.013 SD) (p < 0.0001).

Conclusion

A technique for measuring the distribution of air volume in the lung at mid-tidal breathing is described. Mean values and reproducibility are described for healthy male control subjects. Fractional air volume concentration is shown to increase with lung size.

Adaptive quantification and longitudinal analysis of pulmonary emphysema with a hidden Markov measure field model

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.

Assessment of regional lung function with multivolume (1)H MR imaging in health and obstructive lung disease: comparison with (3)He MR imaging

PURPOSE

To introduce a method based on multivolume proton (hydrogen [(1)H]) magnetic resonance (MR) imaging for the regional assessment of lung ventilatory function, investigating its use in healthy volunteers and patients with obstructive lung disease and comparing the outcome with the outcome of the research standard helium 3 ((3)He) MR imaging.

MATERIALS AND METHODS

The institutional review board approved the HIPAA-compliant protocol, and informed written consent was obtained from each subject. Twenty-six subjects, including healthy volunteers (n = 6) and patients with severe asthma (n = 11) and mild (n = 6) and severe (n = 3) emphysema, were imaged with a 1.5-T whole-body MR unit at four lung volumes (residual volume [ RV residual volume ], functional residual capacity [ FRC functional residual capacity ], 1 L above FRC functional residual capacity [ FRC+1 L 1 L above FRC ], total lung capacity [ TLC total lung capacity ]) with breath holds of 10-11 seconds, by using volumetric interpolated breath-hold examination. Each pair of volumes were registered, resulting in maps of (1)H signal change between the two lung volumes. (3)He MR imaging was performed at FRC+1 L 1 L above FRC by using a two-dimensional gradient-echo sequence. (1)H signal change and (3)He signal were measured and compared in corresponding regions of interest selected in ventral, intermediate, and dorsal areas.

RESULTS

In all volunteers and patients combined, proton signal difference between TLC total lung capacity and RV residual volume correlated positively with (3)He signal (correlation coefficient R(2) = 0.64, P < .001). Lower (P < .001) but positive correlation results from (1)H signal difference between FRC functional residual capacity and FRC+1 L 1 L above FRC (R(2) = 0.44, P < .001). In healthy volunteers, (1)H signal changes show a higher median and interquartile range compared with patients with obstructive disease and significant differences between nondependent and dependent regions.

CONCLUSION

Findings in this study demonstrate that multivolume (1)H MR imaging, without contrast material, can be used as a biomarker for regional ventilation, both in healthy volunteers and patients with obstructive lung disease.

Airway wall area derived from 3-dimensional computed tomography analysis differs among lung lobes in male smokers

Background

It is time-consuming to obtain the square root of airway wall area of the hypothetical airway with an internal perimeter of 10 mm (√Aaw at Pi10), a comparable index of airway dimensions in chronic obstructive pulmonary disease (COPD), from all airways of the whole lungs using 3-dimensional computed tomography (CT) analysis. We hypothesized that √Aaw at Pi10 differs among the five lung lobes and √Aaw at Pi10 derived from one certain lung lobe has a high level of agreement with that derived from the whole lungs in smokers.

Methods

Pulmonary function tests and chest volumetric CTs were performed in 157 male smokers (102 COPD, 55 non-COPD). All visible bronchial segments from the 3^rd to 5^th generations were segmented and measured using commercially available 3-dimensional CT analysis software. √Aaw at Pi10 of each lung lobe was estimated from all measurable bronchial segments of that lobe.

Results

Using a mixed-effects model, √Aaw at Pi10 differed significantly among the five lung lobes (R² = 0.78, P<0.0001). The Bland-Altman plots show that √Aaw at Pi10 derived from the right or left upper lobe had a high level of agreement with that derived from the whole lungs, while √Aaw at Pi10 derived from the right or left lower lobe did not.

Conclusion

In male smokers, CT-derived airway wall area differs among the five lung lobes, and airway wall area derived from the right or left upper lobe is representative of the whole lungs.

Automatic pulmonary fissure detection and lobe segmentation in CT chest images

BACKGROUND

Multi-detector Computed Tomography has become an invaluable tool for the diagnosis of chronic respiratory diseases. Based on CT images, the automatic algorithm to detect the fissures and divide the lung into five lobes will help regionally quantify, amongst others, the lung density, texture, airway and, blood vessel structures, ventilation and perfusion.

METHODS

Sagittal adaptive fissure scanning based on the sparseness of the vessels and bronchi is employed to localize the potential fissure region. Following a Hessian matrix based line enhancement filter in the coronal slice, the shortest path is determined by means of Uniform Cost Search. Implicit surface fitting based on Radial Basis Functions is used to extract the fissure surface for lobe segmentation. By three implicit fissure surface functions, the lung is divided into five lobes. The proposed algorithm is tested by 14 datasets. The accuracy is evaluated by the mean (±S.D.), root mean square, and the maximum of the shortest Euclidian distance from the manually-defined fissure surface to that extracted by the algorithm.

RESULTS

Averaged over all datasets, the mean (±S.D.), root mean square, and the maximum of the shortest Euclidian distance are 2.05 ± 1.80, 2.46 and 7.34 mm for the right oblique fissure. The measures are 2.77 ± 2.12, 3.13 and 7.75 mm for the right horizontal fissure, 2.31 ± 1.76, 3.25 and 6.83 mm for the left oblique fissure. The fissure detection works for the data with a small lung nodule nearby the fissure and a small lung subpleural nodule. The volume and emphysema index of each lobe can be calculated. The algorithm is very fast, e.g., to finish the fissure detection and fissure extension for the dataset with 320 slices only takes around 50 seconds.

CONCLUSIONS

The sagittal adaptive fissure scanning can localize the potential fissure regions quickly. After the potential region is enhanced by a Hessian based line enhancement filter, Uniform Cost Search can extract the fissures successfully in 2D. Surface fitting is able to obtain three implicit surface functions for each dataset. The current algorithm shows good accuracy, robustness and speed, may help locate the lesions into each lobe and analyze them regionally.

Computer-aided detection system for lung cancer in computed tomography scans: review and future prospects

Introduction

The goal of this paper is to present a critical review of major Computer-Aided Detection systems (CADe) for lung cancer in order to identify challenges for future research. CADe systems must meet the following requirements: improve the performance of radiologists providing high sensitivity in the diagnosis, a low number of false positives (FP), have high processing speed, present high level of automation, low cost (of implementation, training, support and maintenance), the ability to detect different types and shapes of nodules, and software security assurance.

Methods

The relevant literature related to “CADe for lung cancer” was obtained from PubMed, IEEEXplore and Science Direct database. Articles published from 2009 to 2013, and some articles previously published, were used. A systemic analysis was made on these articles and the results were summarized.

Discussion

Based on literature search, it was observed that many if not all systems described in this survey have the potential to be important in clinical practice. However, no significant improvement was observed in sensitivity, number of false positives, level of automation and ability to detect different types and shapes of nodules in the studied period. Challenges were presented for future research.

Conclusions

Further research is needed to improve existing systems and propose new solutions. For this, we believe that collaborative efforts through the creation of open source software communities are necessary to develop a CADe system with all the requirements mentioned and with a short development cycle. In addition, future CADe systems should improve the level of automation, through integration with picture archiving and communication systems (PACS) and the electronic record of the patient, decrease the number of false positives, measure the evolution of tumors, evaluate the evolution of the oncological treatment, and its possible prognosis.

Automated segmentation of pulmonary structures in thoracic computed tomography scans: a review

Computed tomography (CT) is the modality of choice for imaging the lungs in vivo. Sub-millimeter isotropic images of the lungs can be obtained within seconds, allowing the detection of small lesions and detailed analysis of disease processes. The high resolution of thoracic CT and the high prevalence of lung diseases require a high degree of automation in the analysis pipeline. The automated segmentation of pulmonary structures in thoracic CT has been an important research topic for over a decade now. This systematic review provides an overview of current literature. We discuss segmentation methods for the lungs, the pulmonary vasculature, the airways, including airway tree construction and airway wall segmentation, the fissures, the lobes and the pulmonary segments. For each topic, the current state of the art is summarized, and topics for future research are identified.

CURVATURE-BASED CORRECTION ALGORITHM FOR AUTOMATIC LUNG SEGMENTATION ON CHEST CT IMAGES

In order to ameliorate the lung defects caused by missed juxtapleural nodules in lung segmentation on chest computed tomography (CT) images, we develop a Newton–Cotes-based smoothing algorithm (NCBS) which is used as a preliminary step to remove noises as many as possible. Next considering the crescent outline features of the lung, we propose a curvature-based correction algorithm (CBC) for the determination of the correction threshold. The application of the proposed algorithms is demonstrated in the process of lung segmentation and the experimental results on 25 real datasets are illustrated. Furthermore, some experiments are conducted to investigate the effects of the key parameters in CBC on the performances of lung segmentation so as to decide their optimal values. In addition, the CBC is compared with other methods analytically and experimentally. The overall results show that our proposed algorithm in lung segmentation excels the related methods on the capability of automatic selection of the correction threshold, as well as the performances of accuracy, efficiency and feasibility.

Optimization of dual-energy xenon-computed tomography for quantitative assessment of regional pulmonary ventilation

OBJECTIVE

Dual-energy x-ray computed tomography (DECT) offers visualization of the airways and quantitation of regional pulmonary ventilation using a single breath of inhaled xenon gas. In this study, we sought to optimize scanning protocols for DECT xenon gas ventilation imaging of the airways and lung parenchyma and to characterize the quantitative nature of the developed protocols through a series of test-object and animal studies.

MATERIALS AND METHODS

The Institutional Animal Care and Use Committee approved all animal studies reported here. A range of xenon/oxygen gas mixtures (0%, 20%, 25%, 33%, 50%, 66%, 100%; balance oxygen) were scanned in syringes and balloon test-objects to optimize the delivered gas mixture for assessment of regional ventilation while allowing for the development of improved 3-material decomposition calibration parameters. In addition, to alleviate gravitational effects on xenon gas distribution, we replaced a portion of the oxygen in the xenon/oxygen gas mixture with helium and compared gas distributions in a rapid-prototyped human central-airway test-object. Additional syringe tests were performed to determine if the introduction of helium had any effect on xenon quantitation. Xenon gas mixtures were delivered to anesthetized swine to assess airway and lung parenchymal opacification while evaluating various DECT scan acquisition settings.

RESULTS

Attenuation curves for xenon were obtained from the syringe test-objects and were used to develop improved 3-material decomposition parameters (Hounsfield unit enhancement per percentage xenon: within the chest phantom, 2.25 at 80 kVp, 1.7 at 100 kVp, and 0.76 at 140 kVp with tin filtration; in open air, 2.5 at 80 kVp, 1.95 at 100 kVp, and 0.81 at 140 kVp with tin filtration). The addition of helium improved the distribution of xenon gas to the gravitationally nondependent portion of the airway tree test-object, while not affecting the quantitation of xenon in the 3-material decomposition DECT. The mixture 40% Xe/40% He/20% O2 provided good signal-to-noise ratio (SNR), greater than the Rose criterion (SNR > 5), while avoiding gravitational effects of similar concentrations of xenon in a 60% O2 mixture. Compared with 100/140 Sn kVp, 80/140 Sn kVp (Sn = tin filtered) provided improved SNR in a swine with an equivalent thoracic transverse density to a human subject with a body mass index of 33 kg/m. Airways were brighter in the 80/140 Sn kVp scan (80/140 Sn, 31.6%; 100/140 Sn, 25.1%) with considerably lower noise (80/140 Sn, coefficient of variation of 0.140; 100/140 Sn, coefficient of variation of 0.216).

CONCLUSION

To provide a truly quantitative measure of regional lung function with xenon-DECT, the basic protocols and parameter calibrations need to be better understood and quantified. It is critically important to understand the fundamentals of new techniques to allow for proper implementation and interpretation of their results before widespread usage. With the use of an in-house derived xenon calibration curve for 3-material decomposition rather than the scanner supplied calibration and a xenon/helium/oxygen mixture, we demonstrate highly accurate quantitation of xenon gas volumes and avoid gravitational effects on gas distribution. This study provides a foundation for other researchers to use and test these methods with the goal of clinical translation.

DIRBoost-an algorithm for boosting deformable image registration: application to lung CT intra-subject registration

We introduce a boosting algorithm to improve on existing methods for deformable image registration (DIR). The proposed DIRBoost algorithm is inspired by the theory on hypothesis boosting, well known in the field of machine learning. DIRBoost utilizes a method for automatic registration error detection to obtain estimates of local registration quality. All areas detected as erroneously registered are subjected to boosting, i.e. undergo iterative registrations by employing boosting masks on both the fixed and moving image. We validated the DIRBoost algorithm on three different DIR methods (ANTS gSyn, NiftyReg, and DROP) on three independent reference datasets of pulmonary image scan pairs. DIRBoost reduced registration errors significantly and consistently on all reference datasets for each DIR algorithm, yielding an improvement of the registration accuracy by 5-34% depending on the dataset and the registration algorithm employed.

A novel supervised approach for segmentation of lung parenchyma from chest CT for computer-aided diagnosis

Segmentation of lung parenchyma from the chest computed tomography is an important task in analysis of chest computed tomography for diagnosis of lung disorders. It is a challenging task especially in the presence of peripherally placed pathology bearing regions. In this work, we propose a segmentation approach to segment lung parenchyma from chest. The first step is to segment the lungs using iterative thresholding followed by morphological operations. If the two lungs are not separated, the lung junction and its neighborhood are identified and local thresholding is applied. The second step is to extract shape features of the two lungs. The third step is to use a multilayer feed forward neural network to determine if the segmented lung parenchyma is complete, based on the extracted features. The final step is to reconstruct the two lungs in case of incomplete segmentation, by exploiting the fact that in majority of the cases, at least one of the two lungs would have been segmented correctly by the first step. Hence, the complete lung is determined based on the shape and region properties and the incomplete lung is reconstructed by applying graphical methods, namely, reflection and translation. The proposed approach has been tested in a computer-aided diagnosis system for diagnosis of lung disorders, namely, bronchiectasis, tuberculosis, and pneumonia. An accuracy of 97.37 % has been achieved by the proposed approach whereas the conventional thresholding approach was unable to detect peripheral pathology-bearing regions. The results obtained prove to be better than that achieved using conventional thresholding and morphological operations.