Using Transitional Changes on High-Resolution Computed Tomography to Monitor the Impact of Cyclophosphamide or Mycophenolate Mofetil on Systemic Sclerosis-Related Interstitial Lung Disease.

OBJECTIVE: To examine changes in the extent of specific patterns of interstitial lung disease (ILD) as they transition from one pattern to another in response to immunosuppressive therapy in systemic sclerosis-related ILD (SSc-ILD). METHODS: We evaluated changes in the quantitative extent of specific lung patterns of ILD using volumetric high-resolution computed tomography (HRCT) scans obtained at baseline and after 2 years of therapy in patients treated with either cyclophosphamide (CYC) for 1 year or mycophenolate mofetil (MMF) for 2 years in Scleroderma Lung Study II. ILD patterns included lung fibrosis, ground glass, honeycombing, and normal lung. Net change was calculated as the difference in the probability of change from one ILD pattern to another. Wilcoxon's signed rank test was used to compare the changes. RESULTS: Forty-seven and 50 patients had baseline and follow-up scans in the CYC and MMF groups, respectively. Mean net improvements reflecting favorable changes from one ILD pattern to another in the whole lung in the CYC and MMF groups, respectively, were as follows: from lung fibrosis to a normal lung pattern, 21% and 19%; from a ground-glass pattern to a normal lung pattern, 30% and 28%; and from lung fibrosis to a ground-glass pattern, 5% and 0.5%. The mean overall improvement in transitioning from a ground-glass pattern or lung fibrosis to a normal lung pattern was significant for both treatments (all P < 0.001). CONCLUSION: Significantly favorable transitions from both ground-glass and lung fibrosis ILD patterns to a normal lung pattern were observed in patients undergoing immunosuppressive treatment for SSc-ILD, suggesting the usefulness of examining these transitions for insights into the underlying pathobiology of treatment response.

Correction to: Toward clinically usable CAD for lung cancer screening with computed tomography.

The original version of this article, published on 24 July 2014, unfortunately contained a mistake. In section "Discussion," a sentence was worded incorrectly.

Building a high-resolution T2-weighted MR-based probabilistic model of tumor occurrence in the prostate.

PURPOSE: We present a method for generating a T2 MR-based probabilistic model of tumor occurrence in the prostate to guide the selection of anatomical sites for targeted biopsies and serve as a diagnostic tool to aid radiological evaluation of prostate cancer. MATERIALS AND METHODS: In our study, the prostate and any radiological findings within were segmented retrospectively on 3D T2-weighted MR images of 266 subjects who underwent radical prostatectomy. Subsequent histopathological analysis determined both the ground truth and the Gleason grade of the tumors. A randomly chosen subset of 19 subjects was used to generate a multi-subject-derived prostate template. Subsequently, a cascading registration algorithm involving both affine and non-rigid B-spline transforms was used to register the prostate of every subject to the template. Corresponding transformation of radiological findings yielded a population-based probabilistic model of tumor occurrence. The quality of our probabilistic model building approach was statistically evaluated by measuring the proportion of correct placements of tumors in the prostate template, i.e., the number of tumors that maintained their anatomical location within the prostate after their transformation into the prostate template space. RESULTS: Probabilistic model built with tumors deemed clinically significant demonstrated a heterogeneous distribution of tumors, with higher likelihood of tumor occurrence at the mid-gland anterior transition zone and the base-to-mid-gland posterior peripheral zones. Of 250 MR lesions analyzed, 248 maintained their original anatomical location with respect to the prostate zones after transformation to the prostate. CONCLUSION: We present a robust method for generating a probabilistic model of tumor occurrence in the prostate that could aid clinical decision making, such as selection of anatomical sites for MR-guided prostate biopsies.

Clear Cell Renal Cell Carcinoma: Identifying the Loss of the Y Chromosome on Multiphasic MDCT.

OBJECTIVE: The objective of our study was to investigate whether multiphasic MDCT enhancement can help identify clear cell renal cell carcinomas (RCCs) with the loss of the Y chromosome. MATERIALS AND METHODS: We derived a cohort of 43 clear cell RCCs in men who underwent preoperative four-phase renal mass MDCT from October 2000 to August 2013. Each lesion was segmented in its entirety on axial images. A computer-assisted detection algorithm selected a 0.5-cm-diameter region of maximal attenuation within each lesion in each phase. A 0.5-cm-diameter ROI was manually placed on uninvolved renal cortex in each phase. The relative attenuation of each lesion was calculated as follows: [(maximal lesion attenuation - cortex attenuation) / cortex attenuation] × 100. Absolute attenuation and relative attenuation in each phase were compared using t tests. RESULTS: Both clear cell RCCs with the loss of the Y chromosome and clear cell RCCs without the loss of the Y chromosome exhibited peak enhancement in the corticomedullary phase. However, relative nephrographic attenuation of clear cell RCCs with the loss of Y was significantly less than that of clear cell RCCs without the loss of Y (mean, -8.9 vs 8.4 respectively; p = 0.013). A relative nephrographic attenuation threshold of -1.6 identified the loss of Y with an accuracy of 70% (30/43), sensitivity of 73% (16/22), and specificity of 67% (14/21). CONCLUSION: Multiphasic MDCT enhancement may assist in identifying the loss of the Y chromosome in clear cell RCCs; this result should be validated in a large prospective trial.

Type 1 papillary renal cell carcinoma: differentiation from Type 2 papillary RCC on multiphasic MDCT.

PURPOSE: To investigate whether multiphasic MDCT enhancement can help differentiate type 1 papillary renal cell carcinoma (RCC) from type 2 papillary RCC. METHODS: With IRB approval for this HIPAA-compliant retrospective study, we derived a cohort of 36 type 1 papillary RCCs and 33 type 2 papillary RCCs with preoperative multiphasic MDCT with up to four phases (unenhanced, corticomedullary, nephrographic, and excretory) from 2000 to 2013. Following segmentation, a computer-assisted detection (CAD) algorithm selected a 0.5 cm-diameter region of maximal attenuation within each lesion in each phase; a 0.5 cm-diameter region of interest was manually placed on uninvolved renal cortex in each phase. The relative attenuation of each lesion was calculated as [(Lesion attenuation-cortex attenuation)/cortex attenuation] × 100. Absolute and relative attenuation values were compared using Mann-Whitney tests with Bonferroni correction for multiple comparisons. RESULTS: Relative excretory phase attenuation of type 2 papillary RCCs was significantly greater than that of type 1 papillary RCCs (2.0 vs. -18.3, p = 0.005). Relative excretory phase attenuation differentiated type 1 papillary RCCs from type 2 papillary RCCs with an accuracy of 73% (36/49), sensitivity of 87% (26/30), positive predictive value of 74% (26/35), and negative predictive value of 71% (10/14). CONCLUSION: Multiphasic MDCT enhancement may assist in differentiating type 1 papillary RCCs from type 2 papillary RCCs, if prospectively validated.

Performance of Relative Enhancement on Multiphasic MRI for the Differentiation of Clear Cell Renal Cell Carcinoma (RCC) From Papillary and Chromophobe RCC Subtypes and Oncocytoma.

OBJECTIVE: The objective of our study was to investigate the performance of relative enhancement on multiphasic MRI to differentiate clear cell renal cell carcinoma (RCC) from other RCC subtypes (papillary and chromophobe) and oncocytoma. MATERIALS AND METHODS: For this study, we derived a cohort of 34 clear cell RCCs, nine oncocytomas, 12 papillary RCCs, and 10 chromophobe RCCs with a preoperative multiphasic dynamic contrast-enhanced MRI study with up to four phases (i.e., unenhanced, corticomedullary, nephrographic, excretory) from 2005 to 2016. These groups were evaluated for multiphasic enhancement and were compared using Kruskal-Wallis and Mann-Whitney tests. ROC curves were constructed and logistic regression analyses were performed to evaluate the performance of multiphasic enhancement in differentiating clear cell RCCs from the other three groups. RESULTS: Clear cell RCCs exhibited significantly greater relative signal intensity compared with uninvolved renal cortex in the corticomedullary phase (mean, 2.9) than oncocytomas (-21.7, p = 0.001), papillary RCCs (-53.0, p < 0.001), and chromophobe RCCs (-21.0, p < 0.001). Relative signal intensity in the corticomedullary phase differentiated clear cell RCCs from oncocytomas with an AUC of 0.90 and with an accuracy of 84% (32/38), sensitivity of 90% (27/30), and specificity of 63% (5/8) after controlling for lesion size, patient age, and patient sex. Relative corticomedullary signal intensity differentiated clear cell RCCs from oncocytomas and other RCC subtypes with an AUC of 0.93 and with an accuracy of 90% (53/59), sensitivity of 90% (27/30), and specificity of 90% (26/29) after controlling for lesion size, patient age, and patient sex. CONCLUSION: Multiphasic MRI enhancement may assist in differentiating clear cell RCC from oncocytomas and other RCC subtypes, if validated in prospective studies.

The effect of radiation dose reduction on computer-aided detection (CAD) performance in a low-dose lung cancer screening population.

PURPOSE: Lung cancer screening with low-dose CT has recently been approved for reimbursement, heralding the arrival of such screening services worldwide. Computer-aided detection (CAD) tools offer the potential to assist radiologists in detecting nodules in these screening exams. In lung screening, as in all CT exams, there is interest in further reducing radiation dose. However, the effects of continued dose reduction on CAD performance are not fully understood. In this work, we investigated the effect of reducing radiation dose on CAD lung nodule detection performance in a screening population. METHODS: The raw projection data files were collected from 481 patients who underwent low-dose screening CT exams at our institution as part of the National Lung Screening Trial (NLST). All scans were performed on a multidetector scanner (Sensation 64, Siemens Healthcare, Forchheim Germany) according to the NLST protocol, which called for a fixed tube current scan of 25 effective mAs for standard-sized patients and 40 effective mAs for larger patients. The raw projection data were input to a reduced-dose simulation software to create simulated reduced-dose scans corresponding to 50% and 25% of the original protocols. All raw data files were reconstructed at the scanner with 1 mm slice thickness and B50 kernel. The lungs were segmented semi-automatically, and all images and segmentations were input to an in-house CAD algorithm trained on higher dose scans (75-300 mAs). CAD findings were compared to a reference standard generated by an experienced reader. Nodule- and patient-level sensitivities were calculated along with false positives per scan, all of which were evaluated in terms of the relative change with respect to dose. Nodules were subdivided based on size and solidity into categories analogous to the LungRADS assessment categories, and sub-analyses were performed. RESULTS: From the 481 patients in this study, 82 had at least one nodule (prevalence of 17%) and 399 did not (83%). A total of 118 nodules were identified. Twenty-seven nodules (23%) corresponded to LungRADS category 4 based on size and composition, while 18 (15%) corresponded to LungRADS category 3 and 73 (61%) corresponded to LungRADS category 2. For solid nodules ≥8 mm, patient-level median sensitivities were 100% at all three dose levels, and mean sensitivities were 72%, 63%, and 63% at original, 50%, and 25% dose, respectively. Overall mean patient-level sensitivities for nodules ranging from 3 to 45 mm were 38%, 37%, and 38% at original, 50%, and 25% dose due to the prevalence of smaller nodules and nonsolid nodules in our reference standard. The mean false-positive rates were 3, 5, and 13 per case. CONCLUSIONS: CAD sensitivity decreased very slightly for larger nodules as dose was reduced, indicating that reducing the dose to 50% of original levels may be investigated further for use in CT screening. However, the effect of dose was small relative to the effect of the nodule size and solidity characteristics. The number of false positives per scan increased substantially at 25% dose, illustrating the importance of tuning CAD algorithms to very challenging, high-noise screening exams.

Clear cell renal cell carcinoma: identifying the gain of chromosome 12 on multiphasic MDCT.

PURPOSE: To determine whether multiphasic MDCT enhancement can help identify the gain of chromosome 12 in clear cell renal cell carcinomas (RCCs). METHODS: With IRB approval for this HIPAA-compliant case control study, we derived a cohort of 65 clear cell RCCs with preoperative four-phase renal mass MDCT from October 2000 to August 2013. Each lesion was segmented in its entirety on axial images in all phases. A computer-assisted detection (CAD) algorithm selected a 0.5-cm-diameter region of maximal attenuation within each lesion in each phase. Attenuation in each phase between clear cell RCCs with and without the gain of 12 was compared using t-tests. RESULTS: While the entire cohort of clear cell RCCs exhibited peak enhancement in the corticomedullary phase, the subcohort of lesions with the gain of 12 exhibited significantly greater enhancement in the nephrographic (179 vs. 145 HU, p = 0.004) and excretory phases (147 vs. 118 HU, p = 0.004) than the subcohort of lesions without the gain of 12. A nephrographic threshold of 186 HU identified the gain of 12 with an accuracy of 86% (56/65), specificity of 93% (51/55), and negative predictive value of 91% (51/56). CONCLUSION: Multiphasic MDCT enhancement, specifically enhancement in the nephrographic and excretory phases, may potentially assist in identifying the gain of 12 in clear cell RCCs.

A Novel Quantitative Computed Tomographic Analysis Suggests How Sirolimus Stabilizes Progressive Air Trapping in Lymphangioleiomyomatosis.

RATIONALE: The Multicenter International Lymphangioleiomyomatosis Efficacy and Safety of Sirolimus (MILES) trial demonstrated that sirolimus stabilized lung function and improved measures of functional performance and quality of life in patients with lymphangioleiomyomatosis. The physiologic mechanisms of these beneficial actions of sirolimus are incompletely understood. OBJECTIVES: To prospectively determine the longitudinal computed tomographic lung imaging correlates of lung function change in MILES patients treated with placebo or sirolimus. METHODS: We determined the baseline to 12-month change in computed tomographic image-derived lung volumes and the volume of the lung occupied by cysts in the 31 MILES participants (17 in sirolimus group, 14 in placebo group) with baseline and 12-month scans. MEASUREMENTS AND MAIN RESULTS: There was a trend toward an increase in median expiratory cyst volume percentage in the placebo group and a reduction in the sirolimus group (+2.68% vs. +0.97%, respectively; P = 0.10). The computed tomographic image-derived residual volume and the ratio of residual volume to total lung capacity increased more in the placebo group than in the sirolimus group (+214.4 ml vs. +2.9 ml [P = 0.054] and +0.05 ml vs. -0.01 ml [P = 0.0498], respectively). A Markov transition chain analysis of respiratory cycle cyst volume changes revealed greater dynamic variation in the sirolimus group than in the placebo group at the 12-month time point. CONCLUSIONS: Collectively, these data suggest that sirolimus attenuates progressive gas trapping in lymphangioleiomyomatosis, consistent with a beneficial effect of the drug on airflow obstruction. We speculate that a reduction in lymphangioleiomyomatosis cell burden around small airways and cyst walls alleviates progressive airflow limitation and facilitates cyst emptying.

Radiomics of Lung Nodules: A Multi-Institutional Study of Robustness and Agreement of Quantitative Imaging Features.

Radiomics is to provide quantitative descriptors of normal and abnormal tissues during classification and prediction tasks in radiology and oncology. Quantitative Imaging Network members are developing radiomic "feature" sets to characterize tumors, in general, the size, shape, texture, intensity, margin, and other aspects of the imaging features of nodules and lesions. Efforts are ongoing for developing an ontology to describe radiomic features for lung nodules, with the main classes consisting of size, local and global shape descriptors, margin, intensity, and texture-based features, which are based on wavelets, Laplacian of Gaussians, Law's features, gray-level co-occurrence matrices, and run-length features. The purpose of this study is to investigate the sensitivity of quantitative descriptors of pulmonary nodules to segmentations and to illustrate comparisons across different feature types and features computed by different implementations of feature extraction algorithms. We calculated the concordance correlation coefficients of the features as a measure of their stability with the underlying segmentation; 68% of the 830 features in this study had a concordance CC of ≥0.75. Pairwise correlation coefficients between pairs of features were used to uncover associations between features, particularly as measured by different participants. A graphical model approach was used to enumerate the number of uncorrelated feature groups at given thresholds of correlation. At a threshold of 0.75 and 0.95, there were 75 and 246 subgroups, respectively, providing a measure for the features' redundancy.

Robustness-Driven Feature Selection in Classification of Fibrotic Interstitial Lung Disease Patterns in Computed Tomography Using 3D Texture Features.

Lack of classifier robustness is a barrier to widespread adoption of computer-aided diagnosis systems for computed tomography (CT). We propose a novel Robustness-Driven Feature Selection (RDFS) algorithm that preferentially selects features robust to variations in CT technical factors. We evaluated RDFS in CT classification of fibrotic interstitial lung disease using 3D texture features. CTs were collected for 99 adult subjects separated into three datasets: training, multi-reconstruction, testing. Two thoracic radiologists provided cubic volumes of interest corresponding to six classes: pulmonary fibrosis, ground-glass opacity, honeycombing, normal lung parenchyma, airway, vessel. The multi-reconstruction dataset consisted of CT raw sinogram data reconstructed by systematically varying slice thickness, reconstruction kernel, and tube current (using a synthetic reduced-tube-current algorithm). Two support vector machine classifiers were created, one using RDFS ("with-RDFS") and one not ("without-RDFS"). Classifier robustness was compared on the multi-reconstruction dataset, using Cohen's kappa to assess classification agreement against a reference reconstruction. Classifier performance was compared on the testing dataset using the extended g-mean (EGM) measure. With-RDFS exhibited superior robustness (kappa 0.899-0.989) compared to without-RDFS (kappa 0.827-0.968). Both classifiers demonstrated similar performance on the testing dataset (EGM 0.778 for with-RDFS; 0.785 for without-RDFS), indicating that RDFS does not compromise classifier performance when discarding nonrobust features. RDFS is highly effective at improving classifier robustness against slice thickness, reconstruction kernel, and tube current without sacrificing performance, a result that has implications for multicenter clinical trials that rely on accurate and reproducible quantitative analysis of CT images collected under varied conditions across multiple sites, scanners, and timepoints.

Bone tumor segmentation on bone scans using context information and random forests.

Bone tumor segmentation on bone scans has recently been adopted as a basis for objective tumor assessment in several phase II and III clinical drug trials. Interpretation can be difficult due to the highly sensitive but non-specific nature of bone tumor appearance on bone scans. In this paper we present a machine learning approach to segmenting tumors on bone scans, using intensity and context features aimed at addressing areas prone to false positives. We computed the context features using landmark points, identified by a modified active shape model. We trained a random forest classifier on 100 and evaluated on 73 prostate cancer subjects from a multi-center clinical trial. A reference segmentation was provided by a board certified radiologist. We evaluated our learning based method using the Jaccard index and compared against the state of the art, rule based method. Results showed an improvement from 0.50 +/- 0.31 to 0.57 +/- 0.27. We found that the context features played a significant role in the random forest classifier, helping to correctly classify regions prone to false positives.

Toward clinically usable CAD for lung cancer screening with computed tomography.

OBJECTIVES: The purpose of this study was to define clinically appropriate, computer-aided lung nodule detection (CAD) requirements and protocols based on recent screening trials. In the following paper, we describe a CAD evaluation methodology based on a publically available, annotated computed tomography (CT) image data set, and demonstrate the evaluation of a new CAD system with the functionality and performance required for adoption in clinical practice. METHODS: A new automated lung nodule detection and measurement system was developed that incorporates intensity thresholding, a Euclidean Distance Transformation, and segmentation based on watersheds. System performance was evaluated against the Lung Imaging Database Consortium (LIDC) CT reference data set. RESULTS: The test set comprised thin-section CT scans from 108 LIDC subjects. The median (±IQR) sensitivity per subject was 100 (±37.5) for nodules ≥ 4 mm and 100 (±8.33) for nodules ≥ 8 mm. The corresponding false positive rates were 0 (±2.0) and 0 (±1.0), respectively. The concordance correlation coefficient between the CAD nodule diameter and the LIDC reference was 0.91, and for volume it was 0.90. CONCLUSIONS: The new CAD system shows high nodule sensitivity with a low false positive rate. Automated volume measurements have strong agreement with the reference standard. Thus, it provides comprehensive, clinically-usable lung nodule detection and assessment functionality. KEY POINTS: • CAD requirements can be based on lung cancer screening trial results. • CAD systems can be evaluated using publically available annotated CT image databases. • A new CAD system was developed with a low false positive rate. • The CAD system has reliable measurement tools needed for clinical use.

Optimal surface segmentation using flow lines to quantify airway abnormalities in chronic obstructive pulmonary disease.

This paper introduces a graph construction method for multi-dimensional and multi-surface segmentation problems. Such problems can be solved by searching for the optimal separating surfaces given the space of graph columns defined by an initial coarse surface. Conventional straight graph columns are not well suited for surfaces with high curvature, we therefore propose to derive columns from properly generated, non-intersecting flow lines. This guarantees solutions that do not self-intersect. The method is applied to segment human airway walls in computed tomography images in three-dimensions. Phantom measurements show that the inner and outer radii are estimated with sub-voxel accuracy. Two-dimensional manually annotated cross-sectional images were used to compare the results with those of another recently published graph based method. The proposed approach had an average overlap of 89.3±5.8%, and was on average within 0.096±0.097mm of the manually annotated surfaces, which is significantly better than what the previously published approach achieved. A medical expert visually evaluated 499 randomly extracted cross-sectional images from 499 scans and preferred the proposed approach in 68.5%, the alternative approach in 11.2%, and in 20.3% no method was favoured. Airway abnormality measurements obtained with the method on 490 scan pairs from a lung cancer screening trial correlate significantly with lung function and are reproducible; repeat scan R(2) of measures of the airway lumen diameter and wall area percentage in the airways from generation 0 (trachea) to 5 range from 0.96 to 0.73.

Toward a theory of statistical tree-shape analysis.

To develop statistical methods for shapes with a tree-structure, we construct a shape space framework for tree-shapes and study metrics on the shape space. This shape space has singularities which correspond to topological transitions in the represented trees. We study two closely related metrics on the shape space, TED and QED. QED is a quotient euclidean distance arising naturally from the shape space formulation, while TED is the classical tree edit distance. Using Gromov's metric geometry, we gain new insight into the geometries defined by TED and QED. We show that the new metric QED has nice geometric properties that are needed for statistical analysis: Geodesics always exist and are generically locally unique. Following this, we can also show the existence and generic local uniqueness of average trees for QED. TED, while having some algorithmic advantages, does not share these advantages. Along with the theoretical framework we provide experimental proof-of-concept results on synthetic data trees as well as small airway trees from pulmonary CT scans. This way, we illustrate that our framework has promising theoretical and qualitative properties necessary to build a theory of statistical tree-shape analysis.

Extraction of airways from CT (EXACT'09).

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.

Factors influencing the decline in lung density in a Danish lung cancer screening cohort.

Lung cancer screening trials provide an opportunity to study the natural history of emphysema by using computed tomography (CT) lung density as a surrogate parameter. In the Danish Lung Cancer Screening Trial, 2,052 participants were included. At screening rounds, smoking habits were recorded and spirometry was performed. CT lung density was measured as the volume-adjusted 15th percentile density (PD15). A mixed effects model was used with former smoking males with <30 pack-yrs and without airflow obstruction (AFO) at entry as a reference group. At study entry, 893 (44%) participants had AFO. For the reference group, PD15 was 72.6 g·L(-1) with an annual decline of -0.33 g·L(-1). Female sex and current smoking increased PD15 at baseline, 17.3 g·L(-1) (p<0.001) and 10 g·L(-1) (p<0.001), respectively; and both increased the annual decline in PD15 (female: -0.3 g·L(-1); current smoking: -0.4 g·L(-1)). The presence and severity of AFO was a strong predictor of low PD15 at baseline (Global Initiative for Chronic Obstructive Lung Disease (GOLD) I: -1.4 g·L(-1); GOLD II: -6.3 g·L(-1); GOLD III: -17 g·L(-1)) and of increased annual decline in PD15 (GOLD I: -0.2 g·L(-1); GOLD II: -0.5 g·L(-1); GOLD III: -0.5 g·L(-1)). Female sex, active smoking and the presence of AFO are associated with accelerated decline in lung density.

Toward automatic regional analysis of pulmonary function using inspiration and expiration thoracic CT.

PURPOSE: To analyze pulmonary function using a fully automatic technique which processes pairs of thoracic CT scans acquired at breath-hold inspiration and expiration, respectively. The following research objectives are identified to: (a) describe and systematically analyze the processing pipeline and its results; (b) verify that the quantitative, regional ventilation measurements acquired through CT are meaningful for pulmonary function analysis; (c) identify the most effective of the calculated measurements in predicting pulmonary function; and (d) demonstrate the potential of the system to deliver clinically important information not available through conventional spirometry. METHODS: A pipeline of automatic segmentation and registration techniques is presented and demonstrated on a database of 216 subjects well distributed over the various stages of COPD (chronic obstructive pulmonary disorder). Lungs, fissures, airways, lobes, and vessels are automatically segmented in both scans and the expiration scan is registered with the inspiration scan using a fully automatic nonrigid registration algorithm. Segmentations and registrations are examined and scored by expert observers to analyze the accuracy of the automatic methods. Quantitative measures representing ventilation are computed at every image voxel and analyzed to provide information about pulmonary function, both globally and on a regional basis. These CT derived measurements are correlated with results from spirometry tests and used as features in a kNN classifier to assign COPD global initiative for obstructive lung disease (GOLD) stage. RESULTS: The steps of anatomical segmentation (of lungs, lobes, and vessels) and registration in the workflow were shown to perform very well on an individual basis. All CT-derived measures were found to have good correlation with spirometry results, with several having correlation coefficients, r, in the range of 0.85-0.90. The best performing kNN classifier succeeded in classifying 67% of subjects into the correct COPD GOLD stage, with a further 29% assigned to a class neighboring the correct one. CONCLUSIONS: Pulmonary function information can be obtained from thoracic CT scans using the automatic pipeline described in this work. This preliminary demonstration of the system already highlights a number of points of clinical importance such as the fact that an inspiration scan alone is not optimal for predicting pulmonary function. It also permits measurement of ventilation on a per lobe basis which reveals, for example, that the condition of the lower lobes contributes most to the pulmonary function of the subject. It is expected that this type of regional analysis will be instrumental in advancing the understanding of multiple pulmonary diseases in the future.

Mass preserving image registration for lung CT.

This paper presents a mass preserving image registration algorithm for lung CT images. To account for the local change in lung tissue intensity during the breathing cycle, a tissue appearance model based on the principle of preservation of total lung mass is proposed. This model is incorporated into a standard image registration framework with a composition of a global affine and several free-form B-Spline transformations with increasing grid resolution. The proposed mass preserving registration method is compared to registration using the sum of squared intensity differences as a similarity function on four groups of data: 44 pairs of longitudinal inspiratory chest CT scans with small difference in lung volume; 44 pairs of longitudinal inspiratory chest CT scans with large difference in lung volume; 16 pairs of expiratory and inspiratory CT scans; and 5 pairs of images extracted at end exhale and end inhale phases of 4D-CT images. Registration errors, measured as the average distance between vessel tree centerlines in the matched images, are significantly lower for the proposed mass preserving image registration method in the second, third and fourth group, while there is no statistically significant difference between the two methods in the first group. Target registration error, assessed via a set of manually annotated landmarks in the last group, was significantly smaller for the proposed registration method.

Texture-based analysis of COPD: a data-driven approach.

This study presents a fully automatic, data-driven approach for texture-based quantitative analysis of chronic obstructive pulmonary disease (COPD) in pulmonary computed tomography (CT) images. The approach uses supervised learning where the class labels are, in contrast to previous work, based on measured lung function instead of on manually annotated regions of interest (ROIs). A quantitative measure of COPD is obtained by fusing COPD probabilities computed in ROIs within the lung fields where the individual ROI probabilities are computed using a k nearest neighbor (kNN ) classifier. The distance between two ROIs in the kNN classifier is computed as the textural dissimilarity between the ROIs, where the ROI texture is described by histograms of filter responses from a multi-scale, rotation invariant Gaussian filter bank. The method was trained on 400 images from a lung cancer screening trial and subsequently applied to classify 200 independent images from the same screening trial. The texture-based measure was significantly better at discriminating between subjects with and without COPD than were the two most common quantitative measures of COPD in the literature, which are based on density. The proposed measure achieved an area under the receiver operating characteristic curve (AUC) of 0.713 whereas the best performing density measure achieved an AUC of 0.598. Further, the proposed measure is as reproducible as the density measures, and there were indications that it correlates better with lung function and is less influenced by inspiration level.