Preoperative (3-dimensional) computed tomography lung reconstruction before anatomic segmentectomy or lobectomy for stage I non-small cell lung cancer.

OBJECTIVES: Accurate cancer localization and negative resection margins are necessary for successful segmentectomy. In this study, we evaluate a newly developed software package that permits automated segmentation of the pulmonary parenchyma, allowing 3-dimensional assessment of tumor size, location, and estimates of surgical margins. METHODS: A pilot study using a newly developed 3-dimensional computed tomography analytic software package was performed to retrospectively evaluate preoperative computed tomography images of patients who underwent segmentectomy (n = 36) or lobectomy (n = 15) for stage 1 non-small cell lung cancer. The software accomplishes an automated reconstruction of anatomic pulmonary segments of the lung based on bronchial arborization. Estimates of anticipated surgical margins and pulmonary segmental volume were made on the basis of 3-dimensional reconstruction. RESULTS: Autosegmentation was achieved in 72.7% (32/44) of preoperative computed tomography images with slice thicknesses of 3 mm or less. Reasons for segmentation failure included local severe emphysema or pneumonitis, and lower computed tomography resolution. Tumor segmental localization was achieved in all autosegmented studies. The 3-dimensional computed tomography analysis provided a positive predictive value of 87% in predicting a marginal clearance greater than 1 cm and a 75% positive predictive value in predicting a margin to tumor diameter ratio greater than 1 in relation to the surgical pathology assessment. CONCLUSIONS: This preoperative 3-dimensional computed tomography analysis of segmental anatomy can confirm the tumor location within an anatomic segment and aid in predicting surgical margins. This 3-dimensional computed tomography information may assist in the preoperative assessment regarding the suitability of segmentectomy for peripheral lung cancers.

Impact of emphysema heterogeneity on pulmonary function.

OBJECTIVES: To investigate the association between emphysema heterogeneity in spatial distribution, pulmonary function and disease severity. METHODS AND MATERIALS: We ascertained a dataset of anonymized Computed Tomography (CT) examinations acquired on 565 participants in a COPD study. Subjects with chronic bronchitis (CB) and/or bronchodilator response were excluded resulting in 190 cases without COPD and 160 cases with COPD. Low attenuations areas (LAAs) (≤ 950 Hounsfield Unit (HU)) were identified and quantified at the level of individual lobes. Emphysema heterogeneity was defined in a manner that ranged in value from -100% to 100%. The association between emphysema heterogeneity and pulmonary function measures (e.g., FEV1% predicted, RV/TLC, and DLco% predicted) adjusted for age, sex, and smoking history (pack-years) was assessed using multiple linear regression analysis. RESULTS: The majority (128/160) of the subjects with COPD had a heterogeneity greater than zero. After adjusting for age, gender, smoking history, and extent of emphysema, heterogeneity in depicted disease in upper lobe dominant cases was positively associated with pulmonary function measures, such as FEV1 Predicted (p<.001) and FEV1/FVC (p<.001), as well as disease severity (p<0.05). We found a negative association between HI% , RV/TLC (p<0.001), and DLco% (albeit not a statistically significant one, p = 0.06) in this group of patients. CONCLUSION: Subjects with more homogeneous distribution of emphysema and/or lower lung dominant emphysema tend to have worse pulmonary function.

Automated identification of retinal vessels using a multiscale directional contrast quantification (MDCQ) strategy.

PURPOSE: A novel algorithm is presented to automatically identify the retinal vessels depicted in color fundus photographs. METHODS: The proposed algorithm quantifies the contrast of each pixel in retinal images at multiple scales and fuses the resulting consequent contrast images in a progressive manner by leveraging their spatial difference and continuity. The multiscale strategy is to deal with the variety of retinal vessels in width, intensity, resolution, and orientation; and the progressive fusion is to combine consequent images and meanwhile avoid a sudden fusion of image noise and/or artifacts in space. To quantitatively assess the performance of the algorithm, we tested it on three publicly available databases, namely, DRIVE, STARE, and HRF. The agreement between the computer results and the manual delineation in these databases were quantified by computing their overlapping in both area and length (centerline). The measures include sensitivity, specificity, and accuracy. RESULTS: For the DRIVE database, the sensitivities in identifying vessels in area and length were around 90% and 70%, respectively, the accuracy in pixel classification was around 99%, and the precisions in terms of both area and length were around 94%. For the STARE database, the sensitivities in identifying vessels were around 90% in area and 70% in length, and the accuracy in pixel classification was around 97%. For the HRF database, the sensitivities in identifying vessels were around 92% in area and 83% in length for the healthy subgroup, around 92% in area and 75% in length for the glaucomatous subgroup, around 91% in area and 73% in length for the diabetic retinopathy subgroup. For all three subgroups, the accuracy was around 98%. CONCLUSIONS: The experimental results demonstrate that the developed algorithm is capable of identifying retinal vessels depicted in color fundus photographs in a relatively reliable manner.

Pulmonary fissure integrity and collateral ventilation in COPD patients.

PURPOSE: To investigate whether the integrity (completeness) of pulmonary fissures affects pulmonary function in patients with chronic obstructive pulmonary disease (COPD). MATERIALS AND METHODS: A dataset consisting of 573 CT exams acquired on different subjects was collected from a COPD study. According to the global initiative for chronic obstructive lung disease (GOLD) criteria, these subjects (examinations) were classified into five different subgroups, namely non-COPD (222 subjects), GOLD-I (83 subjects), GOLD-II (141 subjects), GOLD-III (63 subjects), and GOLD-IV (64 subjects), in terms of disease severity. An available computer tool was used to aid in an objective and efficient quantification of fissure integrity. The correlations between fissure integrity, and pulmonary functions (e.g., FEV1, and FEV1/FVC) and COPD severity were assessed using Pearson and Spearman's correlation coefficients, respectively. RESULTS: For the five sub-groups ranging from non-COPD to GOLD-IV, the average integrities of the right oblique fissure (ROF) were 81.8%, 82.4%, 81.8%, 82.8%, and 80.2%, respectively; the average integrities of the right horizontal fissure (RHF) were 62.6%, 61.8%, 62.1%, 62.2%, and 62.3%, respectively; the average integrities of the left oblique fissure (LOF) were 82.0%, 83.2%, 81.7%, 82.0%, and 78.4%, respectively; and the average integrities of all fissures in the entire lung were 78.0%, 78.6%, 78.1%, 78.5%, and 76.4%, respectively. Their Pearson correlation coefficients with FEV1 and FE1/FVC range from 0.027 to 0.248 with p values larger than 0.05. Their Spearman correlation coefficients with COPD severity except GOLD-IV range from -0.013 to -0.073 with p values larger than 0.08. CONCLUSION: There is no significant difference in fissure integrity for patients with different levels of disease severity, suggesting that the development of COPD does not change the completeness of pulmonary fissures and incomplete fissures alone may not contribute to the collateral ventilation.

Direct assessment of lung function in COPD using CT densitometric measures.

To investigate whether lung function in patients with chronic obstructive pulmonary disease (COPD) can be directly predicted using CT densitometric measures and assess the underlying prediction errors as compared with the traditional spirometry-based measures. A total of 600 CT examinations were collected from a COPD study. In addition to the entire lung volume, the extent of emphysema depicted in each CT examination was quantified using density mask analysis (densitometry). The partial least square regression was used for constructing the prediction model, where a repeated random split-sample validation was employed. For each split, we randomly selected 400 CT exams for training (regression) purpose and the remaining 200 exams for assessing performance in prediction of lung function (e.g., FEV1 and FEV1/FVC) and disease severity. The absolute and percentage errors as well as their standard deviations were computed. The averaged percentage errors in prediction of FEV1, FEV1/FVC%, TLC, RV/TLC% and DLco% predicted were 33%, 17%, 9%, 18% and 23%, respectively. When classifying the exams in terms of disease severity grades using the CT measures, 37% of the subjects were correctly classified with no error and 83% of the exams were either correctly classified or classified into immediate neighboring categories. The linear weighted kappa and quadratic weighted kappa were 0.54 (moderate agreement) and 0.72 (substantial agreement), respectively. Despite the existence of certain prediction errors in quantitative assessment of lung function, the CT densitometric measures could be used to relatively reliably classify disease severity grade of COPD patients in terms of GOLD.

Bidirectional elastic image registration using B-spline affine transformation.

A registration scheme termed as B-spline affine transformation (BSAT) is presented in this study to elastically align two images. We define an affine transformation instead of the traditional translation at each control point. Mathematically, BSAT is a generalized form of the affine transformation and the traditional B-spline transformation (BST). In order to improve the performance of the iterative closest point (ICP) method in registering two homologous shapes but with large deformation, a bidirectional instead of the traditional unidirectional objective/cost function is proposed. In implementation, the objective function is formulated as a sparse linear equation problem, and a sub-division strategy is used to achieve a reasonable efficiency in registration. The performance of the developed scheme was assessed using both two-dimensional (2D) synthesized dataset and three-dimensional (3D) volumetric computed tomography (CT) data. Our experiments showed that the proposed B-spline affine model could obtain reasonable registration accuracy.

Assessment of lung volume collapsibility in chronic obstructive lung disease patients using CT.

OBJECTIVE: To investigate the collapsibility of the lung and individual lobes in patients with COPD during inspiration/expiration and assess the association of whole lung and lobar volume changes with pulmonary function tests (PFTs) and disease severity. METHODS: PFT measures used were RV/TLC%, FEV1% predicted, FVC, FEV1/FVC%, DLco% predicted and GOLD category. A total of 360 paired inspiratory and expiratory CT examinations acquired in 180 subjects were analysed. Automated computerised algorithms were used to compute individual lobe and total lung volumes. Lung volume collapsibility was assessed quantitatively using the simple difference between CT computed inspiration (I) and expiration (E) volumes (I-E), and a relative measure of volume changes, (I-E)/I. RESULTS: Mean absolute collapsibility (I-E) decreased in all lung lobes with increasing disease severity defined by GOLD classification. Relative collapsibility (I-E)/I showed a similar trend. Upper lobes had lower volume collapsibility across all GOLD categories and lower lobes collectively had the largest volume collapsibility. Whole lung and left lower lobe collapsibility measures tended to have the highest correlations with PFT measures. Collapsibility of lung lobes and whole lung was also negatively correlated with the degree of air trapping between expiration and inspiration, as measured by mean lung density. All measured associations were statistically significant (P < 0.01). CONCLUSION: Severity of COPD appears associated with increased collapsibility in the upper lobes, but change (decline) in collapsibility is faster in the lower lobes. KEY POINTS: • Inspiratory and expiratory computed tomography allows assessment of lung collapsibility • Lobe volume collapsibility is significantly correlated with measures of lung function. • As COPD severity increases, collapsibility of individual lung lobes decreases. • Upper lobes exhibit more severe disease, while lower lobes decline faster.

Computerized identification of airway wall in CT examinations using a 3D active surface evolution approach.

Airway diseases (e.g., asthma, emphysema, and chronic bronchitis) are extremely common worldwide. Any morphological variations (abnormalities) of airways may physically change airflow and ultimately affect the ability of the lungs in gas exchange. In this study, we describe a novel algorithm aimed to automatically identify airway walls depicted on CT images. The underlying idea is to place a three-dimensional (3D) surface model within airway regions and thereafter allow this model to evolve (deform) under predefined external and internal forces automatically to the location where these forces reach a state of balance. By taking advantage of the geometric and the density characteristics of airway walls, the evolution procedure is performed in a distance gradient field and ultimately stops at regions with the highest contrast. The performance of this scheme was quantitatively evaluated from several perspectives. First, we assessed the accuracy of the developed scheme using a dedicated lung phantom in airway wall estimation and compared it with the traditional full-width at half maximum (FWHM) method. The phantom study shows that the developed scheme has an error ranging from 0.04 mm to 0.36 mm, which is much smaller than the FWHM method with an error ranging from 0.16 mm to 0.84 mm. Second, we compared the results obtained by the developed scheme with those manually delineated by an experienced (>30 years) radiologist on clinical chest CT examinations, showing a mean difference of 0.084 mm. In particular, the sensitivity of the scheme to different reconstruction kernels was evaluated on real chest CT examinations. For the 'lung', 'bone' and 'standard' kernels, the average airway wall thicknesses computed by the developed scheme were 1.302 mm, 1.333 mm and 1.339 mm, respectively. Our preliminary experiments showed that the scheme had a reasonable accuracy in airway wall estimation. For a clinical chest CT examination, it took around 4 min for this scheme to identify the inner and outer airway walls on a modern PC.

Optimal threshold in CT quantification of emphysema.

OBJECTIVES: To determine the optimal threshold by quantitatively assessing the extent of emphysema at the level of the entire lung and at the level of individual lobes using a large, diverse dataset of computed tomography (CT) examinations. METHODS: This study comprises 573 chest CT examinations acquired from subjects with different levels of airway obstruction (222 none, 83 mild, 141 moderate, 63 severe and 64 very severe). The extent of emphysema was quantified using the percentage of the low attenuation area (LAA%) divided by the total lung or lobe volume(s). The correlations between the extent of emphysema, and pulmonary functions and the five-category classification were assessed using Pearson and Spearman's correlation coefficients, respectively. When quantifying emphysema using a density mask, a wide range of thresholds from -850 to -1,000 HU were used. RESULTS: The highest correlations of LAA% with the five-category classification and PFT measures ranged from -925 to -965 HU for each individual lobe and the entire lung. However, the differences between the highest correlations and those obtained at -950 HU are relatively small. CONCLUSION: Although there are variations in the optimal cut-off thresholds for individual lobes, the single threshold of -950 HU is still an acceptable threshold for density-based emphysema quantification.

Automated lobe-based airway labeling.

Regional quantitative analysis of airway morphological abnormalities is of great interest in lung disease investigation. Considering that pulmonary lobes are relatively independent functional unit, we develop and test a novel and efficient computerized scheme in this study to automatically and robustly classify the airways into different categories in terms of pulmonary lobe. Given an airway tree, which could be obtained using any available airway segmentation scheme, the developed approach consists of four basic steps: (1) airway skeletonization or centerline extraction, (2) individual airway branch identification, (3) initial rule-based airway classification/labeling, and (4) self-correction of labeling errors. In order to assess the performance of this approach, we applied it to a dataset consisting of 300 chest CT examinations in a batch manner and asked an image analyst to subjectively examine the labeled results. Our preliminary experiment showed that the labeling accuracy for the right upper lobe, the right middle lobe, the right lower lobe, the left upper lobe, and the left lower lobe is 100%, 99.3%, 99.3%, 100%, and 100%, respectively. Among these, only two cases are incorrectly labeled due to the failures in airway detection. It takes around 2 minutes to label an airway tree using this algorithm.

Identification of pulmonary fissures using a piecewise plane fitting algorithm.

We describe an automated computerized scheme to identify pulmonary fissures depicted in chest computed tomography (CT) examinations from a novel perspective. Whereas CT images can be regarded as a cloud of points, the underlying idea is to search for surface-like structures in the three-dimensional (3D) Euclidean space by using an efficient plane fitting algorithm. The proposed plane fitting operation is performed in a number of small spherical lung sub-volumes to detect small planar patches. Using a simple clustering criterion based on their spatial coherence and surface area, the identified planar patches, assumed to represent fissures, are classified into different types of fissures, namely left oblique, right oblique and right horizontal fissures. The performance of the developed scheme was assessed by comparing with a manually created "reference standard" and the results obtained by a previously developed approach on a dataset of 30 lung CT examinations. The experiments show that the average discrepancy is around 1.0mm in comparison with the reference standard, while the corresponding maximum discrepancy is 20.5mm. In addition, 94% of the fissure voxels identified by the computerized scheme are within 3mm of the fissures in the reference standard. As compared to a previously developed approach, we also found that the newly developed scheme had a smaller discrepancy with the standard reference. In efficiency, it takes approximately 8 min to identify the fissures in a chest CT examination on a typical PC. The developed scheme demonstrates a reasonable performance in terms of accuracy, robustness, and computational efficiency.

CT based computerized identification and analysis of human airways: a review.

As one of the most prevalent chronic disorders, airway disease is a major cause of morbidity and mortality worldwide. In order to understand its underlying mechanisms and to enable assessment of therapeutic efficacy of a variety of possible interventions, noninvasive investigation of the airways in a large number of subjects is of great research interest. Due to its high resolution in temporal and spatial domains, computed tomography (CT) has been widely used in clinical practices for studying the normal and abnormal manifestations of lung diseases, albeit there is a need to clearly demonstrate the benefits in light of the cost and radiation dose associated with CT examinations performed for the purpose of airway analysis. Whereas a single CT examination consists of a large number of images, manually identifying airway morphological characteristics and computing features to enable thorough investigations of airway and other lung diseases is very time-consuming and susceptible to errors. Hence, automated and semiautomated computerized analysis of human airways is becoming an important research area in medical imaging. A number of computerized techniques have been developed to date for the analysis of lung airways. In this review, we present a summary of the primary methods developed for computerized analysis of human airways, including airway segmentation, airway labeling, and airway morphometry, as well as a number of computer-aided clinical applications, such as virtual bronchoscopy. Both successes and underlying limitations of these approaches are discussed, while highlighting areas that may require additional work.

Pulmonary nodule registration: rigid or nonrigid?

PURPOSE: The primary aim of this study is to investigate the performance difference of rigid and nonrigid registration schemes in matching corresponding pulmonary nodules depicted on sequential chest computed tomography (CT) examinations. METHODS: A gradient descent based rigid registration algorithm with scaling was developed and it handled the involved geometric transformations (i.e., translation, rescaling, shearing, and rotation) separately instead of optimizing them in a single pass. Given two lung CT examinations, the scaling and translation parameters were simply estimated from the lung volume dimensions (e.g., size and mass center), while the rotation parameters were optimized progressively using gradient descent. To investigate the performance difference of rigid and nonrigid schemes in pulmonary nodule registration, the well-known nonrigid Demons algorithm was implemented and tested along with the developed schemes against 60 diverse low-dose clinical lung CT examinations with average 2-yr follow-up scans. A verified cancer and its correspondence in the follow-up scan as well as their spatial locations (mass center) were identified in each examination. In addition to the computational efficiency, the accuracy of these registration procedures was assessed by computing the Euclidean distances between the corresponding nodules after the registration. To demonstrate the advantage of the developed algorithm, the authors also implemented a fast iterative closest point (ICP) based rigid algorithm and compared their performance. RESULTS: Our experiments on the collected chest CT examinations showed that the nodule registration errors in 3D Euclidean distance for the developed rigid affine approach, the traditional ICP algorithm, and the refining nonrigid Demons algorithm were 9.6, 9.8, and 10.0 mm, respectively, and the corresponding computational costs in time were 5, 300, and 55 s, respectively. CONCLUSIONS: A rigid solution may be preferred in practice for the pulmonary nodule registration in longitudinal studies because of its relatively high efficiency and sufficient accuracy for the clinical need.