Youbing Yin

Mass preserving nonrigid registration of CT lung images using cubic B-spline

The authors propose a nonrigid image registration approach to align two computed-tomography (CT)-derived lung datasets acquired during breath-holds at two inspiratory levels when the image distortion between the two volumes is large. The goal is to derive a three-dimensional warping function that can be used in association with computational fluid dynamics studies. In contrast to the sum of squared intensity difference (SSD), a new similarity criterion, the sum of squared tissue volume difference (SSTVD), is introduced to take into account changes in reconstructed Hounsfield units (scaled attenuation coefficient, HU) with inflation. This new criterion aims to minimize the local tissue volume difference within the lungs between matched regions, thus preserving the tissue mass of the lungs if the tissue density is assumed to be relatively constant. The local tissue volume difference is contributed by two factors: Change in the regional volume due to the deformation and change in the fractional tissue content in a region due to inflation. The change in the regional volume is calculated from the Jacobian value derived from the warping function and the change in the fractional tissue content is estimated from reconstructed HU based on quantitative CT measures. A composite of multilevel B-spline is adopted to deform images and a sufficient condition is imposed to ensure a one-to-one mapping even for a registration pair with large volume difference. Parameters of the transformation model are optimized by a limited-memory quasi-Newton minimization approach in a multiresolution framework. To evaluate the effectiveness of the new similarity measure, the authors performed registrations for six lung volume pairs. Over 100 annotated landmarks located at vessel bifurcations were generated using a semiautomatic system. The results show that the SSTVD method yields smaller average landmark errors than the SSD method across all six registration pairs.

Simulation of pulmonary air flow with a subject-specific boundary condition

We present a novel image-based technique to estimate a subject-specific boundary condition (BC) for computational fluid dynamics (CFD) simulation of pulmonary air flow. The information of regional ventilation for an individual is derived by registering two computed tomography (CT) lung datasets and then passed to the CT-resolved airways as the flow BC. The CFD simulations show that the proposed method predicts lobar volume changes consistent with direct image-measured metrics, whereas the other two traditional BCs (uniform velocity or uniform pressure) yield lobar volume changes and regional pressure differences inconsistent with observed physiology.

A multiscale MDCT image-based breathing lung model with time-varying regional ventilation

A novel algorithm is presented that links local structural variables (regional ventilation and deforming central airways) to global function (total lung volume) in the lung over three imaged lung volumes, to derive a breathing lung model for computational fluid dynamics simulation. The algorithm constitutes the core of an integrative, image-based computational framework for subject-specific simulation of the breathing lung. For the first time, the algorithm is applied to three multi-detector row computed tomography (MDCT) volumetric lung images of the same individual. A key technique in linking global and local variables over multiple images is an in-house mass-preserving image registration method. Throughout breathing cycles, cubic interpolation is employed to ensure C1 continuity in constructing time-varying regional ventilation at the whole lung level, flow rate fractions exiting the terminal airways, and airway deformation. The imaged exit airway flow rate fractions are derived from regional ventilation with the aid of a three-dimensional (3D) and one-dimensional (1D) coupled airway tree that connects the airways to the alveolar tissue. An in-house parallel large-eddy simulation (LES) technique is adopted to capture turbulent-transitional-laminar flows in both normal and deep breathing conditions. The results obtained by the proposed algorithm when using three lung volume images are compared with those using only one or two volume images. The three-volume-based lung model produces physiologically-consistent time-varying pressure and ventilation distribution. The one-volume-based lung model under-predicts pressure drop and yields un-physiological lobar ventilation. The two-volume-based model can account for airway deformation and non-uniform regional ventilation to some extent, but does not capture the non-linear features of the lung.

Computed Tomography Predictors of Response to Endobronchial Valve Lung Reduction Treatment. Comparison with Chartis

RATIONALE Chartis Pulmonary Assessment System (Pulmonx Inc., Redwood, CA) is an invasive procedure used to assess collateral ventilation and select candidates for valve-based lung volume reduction (LVR) therapy. Quantitative computed tomography (QCT) is a potential alternative to Chartis and today consists primarily of assessing fissure integrity (FI). OBJECTIVES In this retrospective analysis, we aimed to determine QCT predictors of LVR outcome and compare the QCT model with Chartis in selecting likely responders to valve-based LVR treatment. METHODS Baseline CT scans of 146 subjects with severe emphysema who underwent endobronchial valve LVR were analyzed retrospectively using dedicated lung quantitative imaging software (Apollo; VIDA Diagnostics, Coralville, IA). A lobar volume reduction greater than 350 ml at 3 months was considered to be indicative of positive response to treatment. Thirty-four CT baseline variables, including quantitative measurements of FI, density, and vessel volumetry, were used to feed a multiple logistic regression analysis to find significant predictors of LVR outcome. The primary predictors were then used in 33 datasets with Chartis results to evaluate the relative performance of QCT versus Chartis. MEASUREMENTS AND MAIN RESULTS FI (P < 0.0001) and low attenuation clusters (P = 0.01) measured in the treated lobe and vascular volumetric percentage of patients detected smallest vessels (P = 0.02) were identified as the primary QCT predictors of LVR outcome. Accuracy for QCT patient selection based on these primary predictors was comparable to Chartis (78.8 vs. 75.8%). CONCLUSIONS Quantitative CT led to comparable results to Chartis for classifying LVR and is a promising tool to effectively select patients for valve-based LVR procedures.

Pulmonary Microvascular Blood Flow in Mild Chronic Obstructive Pulmonary Disease and Emphysema. The MESA COPD Study

RATIONALE Smoking-related microvascular loss causes end-organ damage in the kidneys, heart, and brain. Basic research suggests a similar process in the lungs, but no large studies have assessed pulmonary microvascular blood flow (PMBF) in early chronic lung disease. OBJECTIVES To investigate whether PMBF is reduced in mild as well as more severe chronic obstructive pulmonary disease (COPD) and emphysema. METHODS PMBF was measured using gadolinium-enhanced magnetic resonance imaging (MRI) among smokers with COPD and control subjects age 50 to 79 years without clinical cardiovascular disease. COPD severity was defined by standard criteria. Emphysema on computed tomography (CT) was defined by the percentage of lung regions below -950 Hounsfield units (-950 HU) and by radiologists using a standard protocol. We adjusted for potential confounders, including smoking, oxygenation, and left ventricular cardiac output. MEASUREMENTS AND MAIN RESULTS Among 144 participants, PMBF was reduced by 30% in mild COPD, by 29% in moderate COPD, and by 52% in severe COPD (all P < 0.01 vs. control subjects). PMBF was reduced with greater percentage emphysema-950HU and radiologist-defined emphysema, particularly panlobular and centrilobular emphysema (all P ≤ 0.01). Registration of MRI and CT images revealed that PMBF was reduced in mild COPD in both nonemphysematous and emphysematous lung regions. Associations for PMBF were independent of measures of small airways disease on CT and gas trapping largely because emphysema and small airways disease occurred in different smokers. CONCLUSIONS PMBF was reduced in mild COPD, including in regions of lung without frank emphysema, and may represent a distinct pathological process from small airways disease. PMBF may provide an imaging biomarker for therapeutic strategies targeting the pulmonary microvasculature.

A cubic B-spline-based hybrid registration of lung CT images for a dynamic airway geometric model with large deformation.

The goal of this study is to develop a matching algorithm that can handle large geometric changes in x-ray computed tomography (CT)-derived lung geometry occurring during deep breath maneuvers. These geometric relationships are further utilized to build a dynamic lung airway model for computational fluid dynamics (CFD) studies of pulmonary air flow. The proposed algorithm is based on a cubic B-spline-based hybrid registration framework that incorporates anatomic landmark information with intensity patterns. A sequence of invertible B-splines is composed in a multiresolution framework to ensure local invertibility of the large deformation transformation and a physiologically meaningful similarity measure is adopted to compensate for changes in voxel intensity due to inflation. Registrations are performed using the proposed approach to match six pairs of 3D CT human lung datasets. Results show that the proposed approach has the ability to match the intensity pattern and the anatomical landmarks, and ensure local invertibility for large deformation transformations. Statistical results also show that the proposed hybrid approach yields significantly improved results as compared with approaches using either landmarks or intensity alone.

Registration-based assessment of regional lung function via volumetric CT images of normal subjects vs. severe asthmatics

The purpose of this work was to explore the use of image registration-derived variables associated with computed tomographic (CT) imaging of the lung acquired at multiple volumes. As an evaluation of the utility of such an imaging approach, we explored two groups at the extremes of population ranging from normal subjects to severe asthmatics. A mass-preserving image registration technique was employed to match CT images at total lung capacity (TLC) and functional residual capacity (FRC) for assessment of regional air volume change and lung deformation between the two states. Fourteen normal subjects and thirty severe asthmatics were analyzed via image registration-derived metrics together with their pulmonary function test (PFT) and CT-based air-trapping. Relative to the normal group, the severely asthmatic group demonstrated reduced air volume change (consistent with air trapping) and more isotropic deformation in the basal lung regions while demonstrating increased air volume change associated with increased anisotropic deformation in the apical lung regions. These differences were found despite the fact that both PFT-derived TLC and FRC in the two groups were nearly 100% of predicted values. Data suggest that reduced basal-lung air volume change in severe asthmatics was compensated by increased apical-lung air volume change and that relative increase in apical-lung air volume change in severe asthmatics was accompanied by enhanced anisotropic deformation. These data suggest that CT-based deformation, assessed via inspiration vs. expiration scans, provides a tool for distinguishing differences in lung mechanics when applied to the extreme ends of a population range.

Evaluation of Lobar Biomechanics during Respiration Using Image Registration

The human lungs are divided into five independent compartments called lobes. The lobar fissures separate the lung lobes. It is hypothesized that the lobar surfaces slide against each other during respiration. We propose a method to evaluate the sliding motion of the lobar surfaces during respiration using lobe-by-lobe mass-preserving non-rigid image registration. We measure lobar sliding by evaluating the relative displacement on both sides of the fissure. The results show a superior-inferior gradient in the magnitude of lobar sliding. We compare whole-lung-based registration accuracy to lobe-by-lobe registration accuracy using vessel bifurcation landmarks.

Local tissue-weight-based nonrigid registration of lung images with application to regional ventilation

In this paper, a new nonrigid image registration method is presented to align two volumetric lung CT datasets with an application to estimate regional ventilation. Instead of the sum of squared intensity difference (SSD), we introduce the sum of squared tissue volume difference (SSTVD) as the similarity criterion to take into account the variation of intensity due to respiration. This new criterion aims to minimize the local difference of tissue volume inside the lungs between two images scanned in the same session or over short periods of time, thus preserving the tissue weight of the lungs. Our approach is tested using a pair of volumetric lung datasets acquired at 15% and 85% of vital capacity (VC) in a single scanning session. The results show that the new SSTVD predicts a smaller registration error and also yields a better alignment of structures within the lungs than the normal SSD similarity measure. In addition, the regional ventilation derived from the new method exhibits a much more improved physiological pattern than that of SSD.

Assessment of regional non-linear tissue deformation and air volume change of human lungs via image registration

We evaluate the non-linear characteristics of the human lung via image registration-derived local variables based on volumetric multi-detector-row computed tomographic (MDCT) lung image data of six normal human subjects acquired at three inflation levels: 20% of vital capacity (VC), 60% VC and 80% VC. Local variables include Jacobian (ratio of volume change) and maximum shear strain for assessment of lung deformation, and air volume change for assessment of air distribution. First, the variables linearly interpolated between 20% and 80% VC images to reflect deformation from 20% to 60% VC are compared with those of direct registration of 20% and 60% VC images. The result shows that the linearly-interpolated variables agree only qualitatively with those of registration (P<0.05). Then, a quadratic (or linear) interpolation is introduced to link local variables to global air volumes of three images (or 20% and 80% VC images). A sinusoidal breathing waveform is assumed for assessing the time rate of change of these variables. The results show significant differences between two-image and three-image results (P<0.05). The three-image results for the whole lung indicate that the peak of the maximum shear rate occurs at about 37% of the maximum volume difference between 20% and 80% VC, while the peaks for the Jacobian and flow rate occur at 50%. This is in agreement with accepted physiology whereby lung tissues deform more at lower lung volumes due to lower elasticity and greater compliance. Furthermore, the three-image results show that the upper and middle lobes, even in the recumbent, supine posture, reach full expansion earlier than the lower lobes.