Assessment of the pulmonary structure-function relationship and clinical outcomes measures: quantitative volumetric CT of the lung

Which airways should we treat? Structure-function relationships and estimation of the singular input modes from the forward model alone

Structure-function relationships occur throughout the sciences. Motivated by optimization of such systems, we develop a framework for estimating the input modes from the singular value decomposition from the action of the forward operator alone. These can then be used to determine the input (structure) changes, which induce the largest output (function) changes. The accuracy of the estimate is determined by reference to the method of snapshots. The proposed method is demonstrated on several example problems, and finally used to approximate the optimal airway treatment set for a problem in respiratory physiology.

A Survey on Artificial Intelligence in Pulmonary Imaging

Over the last decade, deep learning (DL) has contributed a paradigm shift in computer vision and image recognition creating widespread opportunities of using artificial intelligence in research as well as industrial applications. DL has been extensively studied in medical imaging applications, including those related to pulmonary diseases. Chronic obstructive pulmonary disease, asthma, lung cancer, pneumonia, and, more recently, COVID-19 are common lung diseases affecting nearly 7.4% of world population. Pulmonary imaging has been widely investigated toward improving our understanding of disease etiologies and early diagnosis and assessment of disease progression and clinical outcomes. DL has been broadly applied to solve various pulmonary image processing challenges including classification, recognition, registration, and segmentation. This paper presents a survey of pulmonary diseases, roles of imaging in translational and clinical pulmonary research, and applications of different DL architectures and methods in pulmonary imaging with emphasis on DL-based segmentation of major pulmonary anatomies such as lung volumes, lung lobes, pulmonary vessels, and airways as well as thoracic musculoskeletal anatomies related to pulmonary diseases.

Quantitative CT imaging and advanced visualization methods: potential application in novel coronavirus disease 2019 (COVID-19) pneumonia

Increasingly, quantitative lung computed tomography (qCT)-derived metrics are providing novel insights into chronic inflammatory lung diseases, including chronic obstructive pulmonary disease, asthma, interstitial lung disease, and more. Metrics related to parenchymal, airway, and vascular anatomy together with various measures associated with lung function including regional parenchymal mechanics, air trapping associated with functional small airways disease, and dual-energy derived measures of perfused blood volume are offering the ability to characterize disease phenotypes associated with the chronic inflammatory pulmonary diseases. With the emergence of COVID-19, together with its widely varying degrees of severity, its rapid progression in some cases, and the potential for lengthy post-COVID-19 morbidity, there is a new role in applying well-established qCT-based metrics. Based on the utility of qCT tools in other lung diseases, previously validated supervised classical machine learning methods, and emerging unsupervised machine learning and deep-learning approaches, we are now able to provide desperately needed insight into the acute and the chronic phases of this inflammatory lung disease. The potential areas in which qCT imaging can be beneficial include improved accuracy of diagnosis, identification of clinically distinct phenotypes, improvement of disease prognosis, stratification of care, and early objective evaluation of intervention response. There is also a potential role for qCT in evaluating an increasing population of post-COVID-19 lung parenchymal changes such as fibrosis. In this work, we discuss the basis of various lung qCT methods, using case-examples to highlight their potential application as a tool for the exploration and characterization of COVID-19, and offer scanning protocols to serve as templates for imaging the lung such that these established qCT analyses have the best chance at yielding the much needed new insights.

Automatic airway tree segmentation based on multi-scale context information

PURPOSE

Airway tree segmentation plays a pivotal role in chest computed tomography (CT) analysis tasks such as lesion localization, surgical planning, and intra-operative guidance. The remaining challenge is to identify small bronchi correctly, which facilitates further segmentation of the pulmonary anatomies.

METHODS

A three-dimensional (3D) multi-scale feature aggregation network (MFA-Net) is proposed against the scale difference of substructures in airway tree segmentation. In this model, the multi-scale feature aggregation (MFA) block is used to capture the multi-scale context information, which improves the sensitivity of the small bronchi segmentation and addresses the local discontinuities. Meanwhile, the concept of airway tree partition is introduced to evaluate the segmentation performance at a more granular level.

RESULTS

Experiments were conducted on a dataset of 250 CT scans, which were annotated by experienced clinical radiologists. Through the airway partition, we evaluated the segmentation results of the small bronchi compared with the state-of-the-art methods. Experiments show that MFA-Net achieves the best performance in the Dice similarity coefficient (DSC) in the intra-lobar airway and improves the true positive rate (TPR) by 7.59% on average. Besides, in the entire airway, the proposed method achieves the best results in DSC and TPR scores of 86.18% and 79.31%, respectively, with the consequence of higher false positives.

CONCLUSION

The MFA-Net is competitive with the state-of-the-art methods. The experiment results indicate that the MFA block improves the performance of the network by utilizing multi-scale context information. More accurate segmentation results will be more helpful in further clinical analysis.

Regional lung avoidance by CT numbers to reduce radiation-induced lung damage risk in non-small-cell lung cancer: a simulation study

Background: Selective avoidance aims at sparing functional lung regions. Here, we preferentially direct radiation to irreversibly nonfunctional lung areas based on planning CT imaging to reduce functional lung damage.Materials and methods: For 12 stage I-IV NSCLC patients, 5 lung substructures were segmented on the planning CT, combining voxels <-900HU, -900HU to -801HU, -800HU to -701HU, -700HU to -601HU and ≥-600HU (Level 1 to 5). Two VMAT plans were optimized: a reference plan blinded from substructures and a selective avoidance plan (AV) imposing gradually stricter constraints on Level 1-5, based on previously validated associations between lung subvolume baseline density and density increase (ΔHU) after treatment. Characteristics of treatment plans were evaluated, including subvolumes, dose, and predicted ΔHU (with reported 95% CI reflecting prediction model uncertainty).Results: Segmented substructures were on average 477 cc, 1157 cc, 484 cc, 69 cc, and 123 cc (Level 1-5). AV plans could spare Level 3-5, e.g., mean dose decrease of 3.5 Gy (range 0.6 Gy; 6.0 Gy) for Level 5, p<.001. This significantly reduced the average lung mass with predicted ΔHU>20HU by 12.5 g (95% CI: 5.4-16.9) and 27.1 g (95% CI: 10.2-32.9) for a median and upper 10th percentile patient susceptibility for damage simulation, respectively.Conclusions: Lung damage avoidance based on CT density is feasible and easy to implement. A biomarker providing a reliable selection of patients with high susceptibility for lung damage will be crucial to show the clinical relevance of this avoidance planning strategy.

A healthy dose of chaos: Using fractal frameworks for engineering higher-fidelity biomedical systems

Optimal levels of chaos and fractality are distinctly associated with physiological health and function in natural systems. Chaos is a type of nonlinear dynamics that tends to exhibit seemingly random structures, whereas fractality is a measure of the extent of organization underlying such structures. Growing bodies of work are demonstrating both the importance of chaotic dynamics for proper function of natural systems, as well as the suitability of fractal mathematics for characterizing these systems. Here, we review how measures of fractality that quantify the dose of chaos may reflect the state of health across various biological systems, including: brain, skeletal muscle, eyes and vision, lungs, kidneys, tumours, cell regulation, skin and wound repair, bone, vasculature, and the heart. We compare how reports of either too little or too much chaos and fractal complexity can be damaging to normal biological function, and suggest that aiming for the healthy dose of chaos may be an effective strategy for various biomedical applications. We also discuss rising examples of the implementation of fractal theory in designing novel materials, biomedical devices, diagnostics, and clinical therapies. Finally, we explain important mathematical concepts of fractals and chaos, such as fractal dimension, criticality, bifurcation, and iteration, and how they are related to biology. Overall, we promote the effectiveness of fractals in characterizing natural systems, and suggest moving towards using fractal frameworks as a basis for the research and development of better tools for the future of biomedical engineering.

Modified and Optimized Method for Segmenting Pulmonary Parenchyma in CT Lung Images, Based on Fractional Calculus and Natural Selection

Computer-aided diagnosis of lung segmentation is the fundamental requirement to diagnose lung diseases. In this paper, a two-dimensional (2D) Otsu algorithm by Darwinian particle swarm optimization (DPSO) and fractional-order Darwinian particle swarm optimization (FODPSO) is proposed to segment the pulmonary parenchyma from the lung image obtained through computed tomography (CT) scans. The proposed method extracts pulmonary parenchyma from multi-sliced CT. This is a preprocessing step to identify pulmonary diseases such as emphysema, tumor, and lung cancer. Image segmentation plays a significant role in automated pulmonary disease diagnosis. In traditional 2D Otsu, exhaustive search plays an important role in image segmentation. However, the main disadvantage of the 2D Otsu method is its complex computation and processing time. In this paper, the 2D Otsu method optimized by DPSO and FODPSO is developed to reduce complex computations and time. The efficient segmentation is very important in object classification and detection. The particle swarm optimization (PSO) method is widely used to speed up the computation and maintain the same efficiency. In the proposed algorithm, the limitation of PSO of getting trapped in local optimum solutions is overcome. The segmentation technique is assessed and equated with the traditional 2D Otsu method. The test results demonstrate that the proposed strategy gives better results. The algorithm is tested on the Lung Image Database Consortium image collections.

An Exploratory Radiomics Approach to Quantifying Pulmonary Function in CT Images

Contemporary medical imaging is becoming increasingly more quantitative. The emerging field of radiomics is a leading example. By translating unstructured data (i.e., images) into structured data (i.e., imaging features), radiomics can potentially characterize clinically useful imaging phenotypes. In this paper, an exploratory radiomics approach is used to investigate the potential association between quantitative imaging features and pulmonary function in CT images. Thirty-nine radiomic features were extracted from the lungs of 64 patients as potential imaging biomarkers for pulmonary function. Collectively, these features capture the morphology of the lungs, as well as intensity variations, fine-texture, and coarse-texture of the pulmonary tissue. The extracted lung radiomics data was compared to conventional pulmonary function tests. In general, patients with larger lungs of homogeneous, low attenuating pulmonary tissue (as measured via radiomics) were found to be associated with poor spirometry performance and a lower diffusing capacity for carbon monoxide. Unsupervised dynamic data clustering revealed subsets of patients with similar lung radiomic patterns that were found to be associated with similar forced expiratory volume in one second (FEV₁) measurements. This implies that patients with similar radiomic feature vectors also presented with comparable spirometry performance, and were separable by varying degrees of pulmonary function as measured by imaging.

Developing and Implementing Biomarkers and Novel Imaging in COPD

This article serves as a CME available, enduring material summary of the following COPD9_USA presentations: "Computed Tomography and COPD" Presenter: George R. Washko, MD "CT Imaging in Routine Clinical Practice: Are We Ready for Prime Time?" Presenter: Meilan K. Han, MD "Beyond CT: What MRI can Tell Us about COPD" Presenter: R. Graham Barr, MD.

Pulmonary kinematics from image data: a review

The effects of certain lung pathologies include alterations in lung physiology negatively affecting pulmonary compliance. Current approaches to diagnosis and treatment assessment of lung disease commonly rely on pulmonary function testing. Such testing is limited to global measures of lung function, neglecting regional measurements, which are critical for early diagnosis and localization of disease. Increased accessibility to medical image acquisition strategies with high spatiotemporal resolution coupled with the development of sophisticated intensity-based and geometric registration techniques has resulted in the recent exploration of modeling pulmonary motion for calculating local measures of deformation. In this review, the authors provide a broad overview of such research efforts for the estimation of pulmonary deformation. This includes discussion of various techniques, current trends in validation approaches, and the public availability of software and data resources.

A differential geometric approach to automated segmentation of human airway tree

Airway diseases are frequently associated with morphological changes that may affect the physiology of the lungs. Accurate characterization of airways may be useful for quantitatively assessing prognosis and for monitoring therapeutic efficacy. The information gained may also provide insight into the underlying mechanisms of various lung diseases. We developed a computerized scheme to automatically segment the 3-D human airway tree depicted on computed tomography (CT) images. The method takes advantage of both principal curvatures and principal directions in differentiating airways from other tissues in geometric space. A "puzzle game" procedure is used to identify false negative regions and reduce false positive regions that do not meet the shape analysis criteria. The negative impact of partial volume effects on small airway detection is partially alleviated by repeating the developed differential geometric analysis on lung anatomical structures modeled at multiple iso-values (thresholds). In addition to having advantages, such as full automation, easy implementation and relative insensitivity to image noise and/or artifacts, this scheme has virtually no leakage issues and can be easily extended to the extraction or the segmentation of other tubular type structures (e.g., vascular tree). The performance of this scheme was assessed quantitatively using 75 chest CT examinations acquired on 45 subjects with different slice thicknesses and using 20 publicly available test cases that were originally designed for evaluating the performance of different airway tree segmentation algorithms.

A new computationally efficient CAD system for pulmonary nodule detection in CT imagery

Early detection of lung nodules is extremely important for the diagnosis and clinical management of lung cancer. In this paper, a novel computer aided detection (CAD) system for the detection of pulmonary nodules in thoracic computed tomography (CT) imagery is presented. The paper describes the architecture of the CAD system and assesses its performance on a publicly available database to serve as a benchmark for future research efforts. Training and tuning of all modules in our CAD system is done using a separate and independent dataset provided courtesy of the University of Texas Medical Branch (UTMB). The publicly available testing dataset is that created by the Lung Image Database Consortium (LIDC). The LIDC data used here is comprised of 84 CT scans containing 143 nodules ranging from 3 to 30mm in effective size that are manually segmented at least by one of the four radiologists. The CAD system uses a fully automated lung segmentation algorithm to define the boundaries of the lung regions. It combines intensity thresholding with morphological processing to detect and segment nodule candidates simultaneously. A set of 245 features is computed for each segmented nodule candidate. A sequential forward selection process is used to determine the optimum subset of features for two distinct classifiers, a Fisher Linear Discriminant (FLD) classifier and a quadratic classifier. A performance comparison between the two classifiers is presented, and based on this, the FLD classifier is selected for the CAD system. With an average of 517.5 nodule candidates per case/scan (517.5+/-72.9), the proposed front-end detector/segmentor is able to detect 92.8% of all the nodules in the LIDC/testing dataset (based on merged ground truth). The mean overlap between the nodule regions delineated by three or more radiologists and the ones segmented by the proposed segmentation algorithm is approximately 63%. Overall, with a specificity of 3 false positives (FPs) per case/patient on average, the CAD system is able to correctly identify 80.4% of the nodules (115/143) using 40 selected features. A 7-fold cross-validation performance analysis using the LIDC database only shows CAD sensitivity of 82.66% with an average of 3 FPs per CT scan/case.

Update on radiology of emphysema and therapeutic implications

Emerging treatments require appropriate CT targeting of a selected lobe or lobes and target airways to obtain a successful response. CT scan is used in pretreatment planning to select patients and plan treatment strategy and posttreatment to confirm correct deployment of devices and assess treatment response. Increasingly treatments are being developed to treat patients who have emphysema who require accurate quantitation of extent and distribution of the process. Functional assessment can be made by inference of detailed anatomic correlates and by direct measurement of regional function using dynamic scan protocols. This article summarizes the current role of imaging in the assessment of patients who have emphysema.

CT quantification of emphysema in young subjects with no recognizable chest disease

OBJECTIVE

The purpose of this prospective study was to evaluate volumetric CT emphysema quantification (CT densitovolumetry) in a young population with no recognizable lung disease.

SUBJECTS AND METHODS

A cohort of 30 nonsmoking patients with no recognizable lung disease (16 men, 14 women; age range, 19-41 years) underwent inspiratory and expiratory CT, after which the data were postprocessed for volumetric quantification of emphysema (threshold, -950 HU). Correlation was tested for age, weight, height, sex, body surface area (BSA), and physical activity. Normal limits were established by mean +/- 1.96 SD.

RESULTS

No correlation was found between the measured volumes and age or physical activity. Correlation was found between BSA and normal lung volume in inspiration (r = 0.69, p = 0.000), shrink volume (i.e., difference in total lung volume in inspiration and in expiration) (r = 0.66, p = 0.000), and percentage of shrink volume (r = 0.35, p = 0.05). For an alpha error of 5%, the limits of normality based on this sample are percentage of emphysema in inspiration, 0.35%; percentage of emphysema in expiration, 0.12%; and maximum lung volume in expiration, 3.6 L. The maximum predicted percentage of shrink volume can be calculated as %SV = 29.43% + 16.97% x BSA (+/- 1.96 x 7.61%).

CONCLUSION

Young healthy nonsmokers with no recognizable lung disease can also show a small proportion of emphysematous-like changes on CT densitovolumetry when a threshold of -950 HU is used. Reference values should be considered when applying the technique for early detection or grading of emphysema and when studying aging lungs.

Integrated CT/bronchoscopy in the central airways: preliminary results

RATIONALE AND OBJECTIVES

Many imaging modalities and methodologies exist for evaluating the pulmonary airways. Individually, each modality provides insight to the state of the airways; however, alone they do not necessarily provide a comprehensive description. The goal of this work is to integrate complementary medical imaging datasets to form a synergistic description of the airways.

MATERIALS AND METHODS

Two digital bronchoscopic techniques were used to evaluate the pulmonary mucosa. A digital color bronchoscopic system was used to detect mucosal color alterations, and a fluorescence detection system was used to assess the microvasculature of the bronchial mucosa. Study participants were also imaged with a multidetector row computed tomographic (MDCT) scanner. Virtual bronchoscopic and image registration techniques were exploited to combine three-dimensional surface renderings, extracted from the MDCT data, together with the two-dimensional digital bronchoscopic images. Validation of the fusion process was performed on a rubber phantom of an adult airway with 4 embedded metal beads.

RESULTS

The fusion of the MDCT extracted airway tree and the digital bronchoscopic datasets were presented for three study participants. In addition, the detected accuracy of the registration method to reliably align the MDCT and bronchoscopic image datasets was determined to be 1.98 mm in the phantom airway model.

CONCLUSION

We have demonstrated that merging of three distinct digital datasets to provide a single synergistic description of the airways is possible. This is a pilot project in the field of eidomics, the process of combining digital image datasets and image-based processes together. We anticipate that in the future eidomics will provide a universal and predictive imaging language that will change health care delivery.

The porcine lung as a potential model for cystic fibrosis

Airway disease currently causes most of the morbidity and mortality in patients with cystic fibrosis (CF). However, understanding the pathogenesis of CF lung disease and developing novel therapeutic strategies have been hampered by the limitations of current models. Although the gene encoding the cystic fibrosis transmembrane conductance regulator (CFTR) has been targeted in mice, CF mice fail to develop lung or pancreatic disease like that in humans. In many respects, the anatomy, biochemistry, physiology, size, and genetics of pigs resemble those of humans. Thus pigs with a targeted CFTR gene might provide a good model for CF. Here, we review aspects of porcine airways and lung that are relevant to CF.

Oxygen-enhanced magnetic resonance imaging versus computed tomography: multicenter study for clinical stage classification of smoking-related chronic obstructive pulmonary disease

RATIONALE

Oxygen-enhanced magnetic resonance imaging (MRI) has been proposed as a useful tool for assessing regional morphological and functional changes in chronic obstructive pulmonary disease (COPD).

OBJECTIVES

To prospectively and directly compare the efficacy of O(2)-enhanced MRI and quantitative computed tomography (CT) for smoking-related pulmonary functional loss assessment and clinical stage classification of smoking-related COPD.

METHODS

One hundred sixty smokers were classified into four age- and gender-matched groups by using the GOLD criteria for smokers: Smokers without COPD (n = 40), Mild COPD (n = 40), Moderate COPD (n = 40), and Severe or Very Severe COPD (n = 40). All smokers underwent O(2)-enhanced MRI, multidetector-row CT, and pulmonary function test. Mean relative enhancement ratio on O(2)-enhanced MRI and CT-based functional lung volume (FLV) on quantitative CT were calculated. To compare the efficacy of O(2)-enhanced MRI and quantitative CT for pulmonary functional loss assessment, both indexes were correlated with pulmonary functional parameters. To determine the efficacy of two methods for clinical stage classification, the four clinical groups' mean relative enhancement ratio and CT-based FLV were statistically compared.

MEASUREMENTS AND MAIN RESULTS

Correlations of both indexes with pulmonary functional parameters were significant (P < 0.0001). Pulmonary functional parameters and mean relative enhancement ratio for the four clinical groups showed significant differences (P < 0.05). CT-based FLVs of smokers without COPD and mild COPD were significantly different from those for moderate COPD and severe or very severe COPD (P < 0.05).

CONCLUSIONS

O(2)-enhanced MRI is effective for pulmonary functional loss assessment and clinical stage classification of smoking-related COPD and quantitative CT.

Application of an optical flow method to inspiratory and expiratory lung MDCT to assess regional air trapping: a feasibility study

OBJECTIVE

We describe the application of an optical flow method to inspiratory and expiratory high-resolution volumetric lung MDCT for the assessment of regional air trapping.

CONCLUSION

Qualitative and quantitative assessment of regional air trapping is feasible using an optical flow method to align volumetric MDCT data sets.

In vivo micro-CT lung imaging via a computer-controlled intermittent iso-pressure breath hold (IIBH) technique

Respiratory research with mice using micro-computed tomography (micro-CT) has been predominantly hindered by the limited resolution and signal-to-noise ratio (SNR) as a result of respiratory motion artefacts. In this study, we develop a novel technique for capturing the lung microstructure in vivo using micro-CT, through a computer-controlled intermittent iso-pressure breath hold (IIBH), to reduce respiratory motion, increasing resolution and SNR of reconstructed images. We compare four gating techniques, i.e. no gating, late expiratory (LE) gating, late inspiratory (LI) gating and finally intermittent iso-pressure breath hold (IIBH) gating. Quantitatively, we compare several common image analysis methods used to extract valuable physiologic and anatomic information from the respiratory system, and show that the IIBH technique produces the most representative and repeatable results.

Validation of computed tomographic lung densitometry for monitoring emphysema in alpha1-antitrypsin deficiency

BACKGROUND

Lung densitometry derived from computed tomographic images offers an opportunity to quantify emphysema non-invasively, but a pathological standard cannot be applied to validate its use in longitudinal monitoring studies. Consequently, forced expiratory volume in 1 second (FEV1) remains the standard against which new methods must be judged. We related progression of densitometry (15th percentile point and voxel index, threshold -950 Hounsfield units) to disease stage and FEV1 decline in two studies of subjects with alpha1-antitrypsin deficiency (PiZ).

METHODS

Consistency of progression, measured using densitometry and FEV1, was assessed in relation to disease stage in a 2 year study of 74 subjects grouped according to the FEV1 criteria employed in the GOLD guidelines. In the second study of a subgroup of subjects with extended data (n=34), summary statistics were applied to measurements performed annually over 3 years and the rate of progression of densitometry was related to FEV1 decline.

RESULTS

The progression of percentile point was consistent across a wide spectrum of disease severity, but voxel index progression varied in association with disease stage (p=0.004). In the second study, FEV1 decline correlated with progression of lung densitometry (percentile point: rS=0.527, p=0.001; voxel index: rS=-0.398, p=0.012).

CONCLUSIONS

15th percentile point is a more consistent measure of lung density loss across a wide range of physiological impairment than voxel index. However, both methods are valid for use in longitudinal and interventional studies in which emphysema is the major outcome target.

Novel method to calculate pulmonary compliance images in rodents from computed tomography acquired at constant pressures

Our goal was to develop a method for generating high-resolution three-dimensional pulmonary compliance images in rodents from computed tomography (CT) images acquired at a series of constant pressures in ventilated animals. One rat and one mouse were used to demonstrate this technique. A pre-clinical GE flat panel CT scanner (maximum 31 line-pairs cm(-1) resolution) was utilized for image acquisition. The thorax of each animal was imaged with breath-holds at 2, 6, 10, 14 and 18 cm H2O pressure in triplicate. A deformable image registration algorithm was applied to each pair of CT images to map corresponding tissue elements. Pulmonary compliance was calculated on a voxel by voxel basis using adjacent pairs of CT images. Triplicate imaging was used to estimate the measurement error of this technique. The 3D pulmonary compliance images revealed regional heterogeneity of compliance. The maximum total lung compliance measured 0.080 (+/-0.007) ml air per cm H2O per ml of lung and 0.039 (+/-0.004) ml air per cm H2O per ml of lung for the rat and mouse, respectively. In this study, we have demonstrated a unique method of quantifying regional lung compliance from 4 to 16 cm H2O pressure with sub-millimetre spatial resolution in rodents.

Reduction of half-scan shading artifact based on full-scan correction

RATIONALE AND OBJECTIVES

Temporal studies (such as blood perfusion) commonly are used to measure function. Radiation dosage is a primary limiting factor for these scans. Half-scan reconstruction can decrease dosage and improve temporal resolution, but is not viable for quantitative studies because of shading artifact. We propose a method for identifying the artifact and minimizing its effect.

MATERIALS AND METHODS

It is possible to measure the shading artifact by producing both a full-scan and a half-scan reconstruction from the same projection data. A correlation was shown between the subset of data used for reconstruction and per-pixel variation. Furthermore, this variation can be parameterized by only the center angle of the projection data. By performing a single full-scan acquisition, it is possible to generate many half-scan reconstructions and measure the artifact; then future half-scan acquisitions can be corrected.

RESULTS

The artifact is caused by the inhomogeneity in the object being scanned. Before correction, the root mean square error between the half-scan reconstruction and the full-scan is 41.0. After correction, the error is decreased to 10.7, or 26% of the original value.

CONCLUSION

We present a method that can measure and correct for object-dependent half-scan shading artifact. This can enable half-scan reconstruction for use in quantitative temporal studies.

Atlas-driven lung lobe segmentation in volumetric X-ray CT images

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

Static versus prospective gated non-breath hold volumetric MDCT imaging of the lungs

RATIONALE AND OBJECTIVES

The study's aim is to establish lung-imaging methods that provide for the ability to image the lung under dynamic non-breath hold conditions while providing "virtual breath hold" quantifiable volumetric image data sets. Static breath hold images are used as the gold standard for evaluating these virtual breath hold images in both a phantom and sheep.

MATERIALS AND METHODS

Axial methods for gating image acquisition to multiple points in the respiratory cycle interleaved with incremental table stepping during multidetector-row computed tomographic (MDCT) scanning were developed. Data sets are generated over multiple breaths, providing volume images representative of multiple points within a respiratory cycle. To determine the reproducibility and accuracy of the methods, six anesthetized sheep were studied by means of MDCT in nongated and airway-pressure (P(awy))-gated modes in which P(awy) was 0, 7, and 15 cm H2O.

RESULTS

No significant differences were found between coefficients of variation in air volume measured from repeated static scans (1.74% +/- 1.78%), gated scans: inspiratory (1.2% +/- 0.44%) or expiratory gated (1.39% +/- 0.98%), or between static (1.74% +/- 1.78%) and gated (1.39% +/- 0.98%) scanning at similar P(awy) (P > .1). Measured air volumes were larger from static versus gated scans by 5.85% +/- 3.77% at 7 cm H2O and 4.45% +/- 3.6% at 15 cm H2O of P(awy) (P < .05), consistent with hysteresis. Differences between air volumes at 7 and 15 cm H2O measured from either static or gated scans or that delivered by a super syringe were insignificant (P < .05). Visual accuracy of three-dimensional anatomic geometry was achieved, and landmark certainty was within 1 mm across respiratory cycles.

CONCLUSIONS

A method has been shown that provides for accurate gating to respiratory signals during axial scanning. High-resolution volumetric image data sets are achievable while the scanned subject is breathing. Images are quantitatively similar to breath hold images, with differences likely explained by known pressure-volume hysteresis effects.

High-Resolution CT in Diagnosis of Diffuse Infiltrative Lung Disease

The chest radiograph remains the first imaging modality for the approach to diffuse infiltrative lung disease (DILD), but, 23 years after its introduction, high-resolution CT (HRCT) is still considered the best imaging tool for the evaluation of the pulmonary interstitium and to diagnose and assess DILD. The introduction of multidetector computed tomography (MDCT) has provided the thoracic radiologist with a powerful tool with which to image the lung. Moreover MDCT has enabled radiologists to understand better the functional information contained within CT images of DILD. By focusing on the HRCT signs, patterns, and distributions of abnormalities, and mentioning the clinical aspects and the new recent advances in pulmonary imaging, in this article we provide an overview of a practical approach to the interpretation of the DILD.

Macro-optical color assessment of the pulmonary airways with subsequent three-dimensional multidetector-x-ray-computed-tomography assisted display

Bronchial diseases alter the color and structural characteristics of the pulmonary mucosa through changes in blood flow, epithelial thickening, and abnormal cell growth. Current analysis of these subtle changes includes visual interpretation of the airway color and topography through bronchoscopy procedures, and quantitative multidetector-x-ray-computed-tomography (MDCT)-based structural analysis, each affording valuable insights to the health of the lungs. The fusion of the bronchoscopy and MDCT image data promises to provide a synergistic data set exhibiting both mucosal color and topography crucial to fostering an understanding of airway structure and function. A real-time airway color analysis imaging system is developed and utilized to perform pulmonary mucosal color assessment in healthy volunteers with subsequent comparative studies performed in example disease states. Our results indicate that macro-optical digital bronchoscopes with appropriate image analysis may have a significant impact on understanding bronchial diseases. To ensure the correct interpretation of scene content, which is critical in the assessment of airway topography, we are developing methods of extracting 3-D structure from 2-D bronchoscope images utilizing MDCT imaging techniques. The resulting 3-D true-color images of the pulmonary mucosa facilitate the combination of mucosal color and topography analysis as well as region of interest localization within the airway tree.

Quantitative CT of emphysema and the airways

Chronic lung disease is a leading cause of morbidity and mortality in the United States. Quantitative techniques for assessing emphysema and related airway disease have been slow to gain acceptance among radiologists, who have traditionally used description of structural changes to evaluate these diseases. This review provides an overview of these quantitative techniques.

CT-based geometry analysis and finite element models of the human and ovine bronchial tree

The interpretation of experimental results from functional medical imaging is complicated by intersubject and interspecies differences in airway geometry. The application of computational models in understanding the significance of these differences requires methods for generation of subject-specific geometric models of the bronchial airway tree. In the current study, curvilinear airway centerline and diameter models have been fitted to human and ovine bronchial trees using detailed data segmented from multidetector row X-ray-computed tomography scans. The trees have been extended to model the entire conducting airway system by using a volume-filling algorithm to generate airway centerline locations within detailed volume descriptions of the lungs or lobes. Analysis of the geometry of the scan-based and model-based airways has verified their consistency with measures from previous anatomic studies and has provided new anatomic data for the ovine bronchial tree. With the use of an identical parameter set, the volume-filling algorithm has produced airway trees with branching asymmetry appropriate for the human and ovine lung, demonstrating the dependence of the method on the shape of the lung or lobe volume. The modeling approach that has been developed can be applied to any level of detail of the airway tree and into any volume shape for the lung; hence it can be used directly for different individuals or animals and for any number of scan-based airways. The resulting models are subject-specific computational meshes with anatomically consistent geometry, suitable for application in simulation studies.

Evaluation of the human airway with multi-detector x-ray-computed tomography and optical imaging

Defining the healthy human airway is important in enhancing our understanding of pulmonary disease states such as inflammation and cancer. The structure of the human airway, both static and dynamic, can be assessed using multi-detector CT (MDCT) scanning. This modality also allows for the evaluation of structures outside of the airway. The airway wall can be directly visualized using CCD chip high-resolution color optical imaging through endoscopy allowing bronchial wall evaluation by traditional biopsy methods, as well as by newer optically based strategies. We suggest that these two imaging modalities, MDCT and optical imaging, provide complementary information about the normal airway, and the airway in various diseases. Methods for evaluating the human airway using MDCT images are presented facilitating automatic airway segmentation, branchpoint finding and airway dimension analysis. The airway wall color is objectively evaluated as an important surrogate for airway wall inflammation and cancer formation, and the integration of the color endoscopic information into the MDCT scan data set is currently ongoing. The amalgamation of these two digital imaging modalities appears increasingly useful for enabling biopsy techniques, and for relating structure and function of the airway. In addition, these developments may be progressively more useful in understanding the normal airway structure and function, for defining airway diseases patterns and for guiding biopsy and therapeutic procedures.

Segmentation and analysis of the human airway tree from three-dimensional X-ray CT images

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

Pulmonary embolus imaging with multislice CT

Multiple data support the concept that single-slice and multislice CT have fundamentally modified the diagnostic approach of patients with suspected PE. Although the definitive role of spiral CT angiography in the diagnostic algorithm has yet to be determined, it is clear that CT angiography has numerous advantages compared with other diagnostic tests. Bearing in mind that an effective diagnostic strategy should be as flexible as possible to be applied in every clinical setting, the role of spiral CT angiography in the diagnostic algorithm has to be considered among several practical parameters, such as the experience of the attending physician, degree of severity of the patient's clinical condition, the availability of diagnostic equipment, and specific logistics.

Establishing a normative atlas of the human lung: intersubject warping and registration of volumetric CT images

RATIONALE AND OBJECTIVES

To establish the range of normal values for quantitative CT-based measures of lung structure and function, the authors developed a method for matching pulmonary structures across individuals and creating a normative human lung atlas.

MATERIALS AND METHODS

A computerized human lung atlas was synthesized from computed tomographic (CT) images from six subjects by means of three-dimensional image registration. The authors identified a set of reproducible feature points for each CT image and used these points to establish correspondences across subjects, used a landmark- and intensity-based consistent image registration algorithm to register a template image volume from the population to the rest of the pulmonary CT volumes in the population, averaged these transformations, and constructed an atlas by deforming the template with the average transformation.

RESULTS

The effectiveness of the authors' method was evaluated and visualized by means of both gray-level and segmented CT images. The method reduced the average landmark registration error from 10.5 mm to 0.4 mm and the average relative volume overlap error from 0.7 to 0.11 for the six data sets studied.

CONCLUSION

The method, and the computerized human lung atlas constructed and visualized by the authors with this method, provides a basis for establishing regional ranges of normative values for structural and functional measures of the human lung.

Influence of gravity on radiographic contrast material-based measurements of regional blood flow distribution

RATIONALE AND OBJECTIVES

Radiographic measurement of regional blood flow distribution in the lungs is potentially biased because the contrast material used to track flow is denser than blood. The authors performed this study to evaluate the effect of gravity on flow estimates by using an experimental test phantom and numeric simulations.

MATERIALS AND METHODS

Cross-sectionally uniform boluses of radiopaque contrast material were delivered at the upstream end of a horizontal inlet tube connected to a downstream axisymmetric bifuration attached to collecting tubing spirals. The phantom was imaged by using both planar angiography and dynamic multi-detector row computed tomography (CT) during the passage of the bolus through the phantom. The images were analyzed to determine the relative amounts of contrast material traveling through the top and bottom branches of the bifurcation by using varying Reynolds numbers and ratios of inlet tube volume to bolus volume. Numeric simulations of flow within a straight channeL with use of a dispersion operator intended to simulate settling of the bolus due to gravity, were performed under conditions representative of those in the experiments.

RESULTS

When the plane of the bifurcation was vertical and actual flow through the two branches was equal, the fraction of contrast material passing through the downward-directed branch increased with decreasing Reynolds number and increasing inlet tube-bolus volume ratio. This occurred in both the experiments and the simulations.

CONCLUSION

Because in the circulation Reynolds number decreases and pathway length increases with decreasing vessel diameter, the accuracy of regional flow measurements obtained with angiography or CT within the lungs may be limited by density differences between contrast material and blood.