Lung Density Measurements Research Articles

Utility of block-matching and 3D filter for reproducibility of lung density and denoising in low-dose chest CT: A pilot study

PurposeThis study aimed to acquire an image quality consistent with that of full-dose chest computed tomography (CT) when obtaining low-dose chest CT images and to analyze the effects of block-matching and 3D (BM3D) filters on lung density measurements and noise reduction in lung parenchyma. MethodsUsing full-dose chest CT images, we evaluated lung density measurements and noise reduction in lung parenchyma images for low-dose chest CT. Three filters (median, Wiener, and the proposed BM3D) were applied to low-dose chest CT images for comparison and analysis with images from full-dose chest CT. To evaluate lung density measurements, we measured CT attenuation at the 15th percentile of the lung CT histogram. The coefficient of variation (COV) and contrast-to-noise ratio (CNR) were used to evaluate the noise level. ResultsThe 15th percentile of the lung CT histogram showed the smallest difference between full- and low-dose CT when applying the BM3D filter, and the highest difference between full- and low-dose CT without filters (full-dose = − 926.28 ± 0.32, BM3D = − 926.65 ± 0.32, and low-dose = − 959.43 ± 0.95) (p < 0.05). The COV was smallest when applying the BM3D filter, whereas the CNR was the highest (p < 0.05). ConclusionsThe results of the study prove that the BM3D filter can reduce image noise while increasing the reproducibility of the lung density, even for low-dose chest CT.

A systematic assessment and optimization of photon-counting CT for lung density quantifications.

Photon-counting computed tomography (PCCT) has recently emerged into clinical use; however, its optimum imaging protocols and added benefits remains unknown in terms of providing more accurate lung density quantification compared to energy-integrating computed tomography (EICT) scanners. To systematically assess the performance of a clinical PCCT scanner for lung density quantifications and compare it against EICT. This cross-sectional study involved a retrospective analysis of subjects scanned (August-December 2021) using a clinical PCCT system. The influence of altering reconstruction parameters was studied (reconstruction kernel, pixel size, slice thickness). A virtual CT dataset of anthropomorphic virtual subjects was acquired to demonstrate the correspondence of findings to clinical dataset, and to perform systematic imaging experiments, not possible using human subjects. The virtual subjects were imaged using a validated, scanner-specific CT simulator of a PCCT and two EICT (defined as EICT A and B) scanners. The images were evaluated using mean absolute error (MAE) of lung and emphysema density against their corresponding ground truth. Clinical and virtual PCCT datasets showed similar trends, with sharper kernels and smaller voxel sizes increasing percentage of low-attenuation areas below -950 HU (LAA-950) by up to 15.7±6.9% and 11.8±5.5%, respectively. Under the conditions studied, higher doses, thinner slices, smaller pixel sizes, iterative reconstructions, and quantitative kernels with medium sharpness resulted in lower lung MAE values. While using these settings for PCCT, changes in the dose level (13 to 1.3 mGy), slice thickness (0.4 to 1.5mm), pixel size (0.49 to 0.98mm), reconstruction technique (70keV-VMI to wFBP), and kernel (Qr48 to Qr60) increased lung MAE by 15.3±2.0, 1.4±0.6, 2.2±0.3, 4.2±0.8, and 9.1±1.6 HU, respectively. At the optimum settings identified per scanner, PCCT images exhibited lower lung and emphysema MAE than those of EICT scanners (by 2.6±1.0 and 9.6±3.4 HU, compared to EICT A, and by 4.8±0.8 and 7.4±2.3 HU, compared to EICT B). The accuracy of lung density measurements was correlated with subjects' mean lung density (p<0.05), measured by PCCT at optimum setting under the conditions studied. Photon-counting CT demonstrated superior performance in density quantifications, with its influences of imaging parameters in line with energy-integrating CT scanners. The technology offers improvement in lung quantifications, thus demonstrating potential toward more objective assessment of respiratory conditions.

Histomorphometric lung density evaluation of Immulina treatment using a murine influenza pneumonia model.

Histomorphometric lung density measurements were used to evaluate the effects of Immulina on mouse pneumonia. Mice were intra-nasally exposed to H1N1 influenza virus at a dose of 5 × 104 PFU/50 μL/mouse. Lung density was measured using the NIH ImageJ software program. Density values were compared to semiquantitative pneumonia severity scores. Lung photomicrographs were evaluated at 25-×, 40-× and 400-× magnification. The study included viral inoculated controls (IC) and non-inoculated controls (NC) and mice either treated or not treated with Immulina. Three doses of Immulina were included (25, 50 or 100 mg/kg) and administered using 3 protocols: prophylactic treatment (P), prodromal treatment (PD) and therapeutic treatment (TH) (note that in most of the evaluations of the data for the three treatment protocols were combined). Groups of mice were evaluated on days 3, 5, 7, 10 and 15 following exposure. The occurrence of "digital pneumonia" (DP) was defined as a density measurement above the 95% confidence limit of the corresponding NC values. A significant reduction in the occurrence of DP with Immulina treatment at the higher doses compared to IC was seen as early as day 3 and persisted up to day 15. There were also statistically significant dose-variable reductions in lung density in response to Immulina. The study suggests early administration of Immulina (P or PD protocols) may enhance resistance against influenza-induced viral pneumonia. A moderate correlation between pneumonia severity scores and lung density was observed for the 25-× and 40-× images (R = 0.56 and 0.53 respectively), and a strong correlation (R = 0.68) for 400-× images.

Re-IMAGinING the Pathway for Clinical Decision Making in Rare Lung Diseases: Moving Towards a United Vision

This industry-supported symposium was held during the European Respiratory Society (ERS) International Congress and included presentations from several internationally renowned experts in rare lung diseases. The panel discussed the need to improve clinical decision making to expedite disease recognition, prognostic prediction, and early treatment in interstitial lung disease (ILD) and alpha 1 antitrypsin (AAT) deficiency-related chronic obstructive pulmonary disease (COPD). Daiana Stolz, Clinic for Pneumology, University Hospital Freiburg, Switzerland, and Marlies S. Wijsenbeek, Erasmus University Medical Centre, Rotterdam, the Netherlands, explained that although high-resolution CT (HRCT) scans may appear similar, ILD from different causes results in significantly different patient outcomes. Therefore, image analysis and the identification of sensitive and specific biomarkers are critical to improving diagnosis and monitoring treatment response and disease progression in ILD. Charlie Strange, Medical University of South Carolina, Charleston, USA, and Gerry McElvaney, Irish Centre for Genetic Lung Disease, Dublin, Ireland, described the variability in CT-based lung density measurements used to assess the progression of emphysema in patients with AAT deficiency. Clinical trial data indicate that accurate CT lung density measurements are superior to lung function measurements and other endpoints to detect disease progression. However, Strange presented data that showed the considerable impact of acute exacerbations of COPD on CT imaging measurements. One organisation working to improve the accuracy and value of imaging data in lung diseases is the Open Source Imaging Consortium (OSIC), Saugatuck, Michigan, USA. Elizabeth Estes, who works for OSIC, explained that OSIC aims to build a large, global database of anonymised patient data and CT images in ILD, with plans for future expansion into other rare lung diseases. The ultimate goals of this effort are to encourage collaboration, and to develop machine learning algorithms to improve clinical decision making in rare lung diseases.

A Bayesian approach to simultaneous adjustment of misclassification and missingness in categorical covariates.

This study considers concurrent adjustment of misclassification and missingness in categorical covariates in regression models. Under various misclassification and missingness mechanisms, we derive a general mixture regression structure for regression models that can incorporate multiple surrogates of categorical covariates that are subject to misclassification and missingness. In simulation studies, we demonstrate that including observations with missingness and/or multiple surrogates of the covariate helps alleviate the efficiency loss caused by misclassification. In addition, we study the efficacy of misclassification adjustment when the number of categories increases for the covariate of interest. Using data from the Longitudinal Studies of HIV-Associated Lung Infections and Complications, we perform simultaneous adjustment of misclassification and missingness in the self-reported cocaine and heroin use variable when assessing its association with lung density measures.

The Application Value of Spiral CT Lung Densitometry Software in the Diagnosis of Radiation-Induced Lung Injury.

In order to study the application value of spiral CT lung density measurement software in the diagnosis of radioactive lung injury, the average CT values of lung apex, hilum, and diaphragm were measured by Pulmo automatic evaluation software of 16-slice spiral CT in 96 patients with different types of radiation lung injury diagnosed by conventional CT and 80 healthy subjects. The radiation lung injury on CT slices was classified, and the lung density was measured. In 96 patients with different types of radiation lung injury, 56 patients had different degrees of increase in average lung density, which was most obvious in the type of air insufficiency and chronic fibrosis. CT values of lung density in the ground glass stage and patch stage of acute radiation pneumonia had little influence due to the range and time of exposure. The lung density of 35 patients with radiation injury was measured in the normal range. There was a significant difference between normal lung density and abnormal lung density in different types of radiation lung injury (X2 = 56.718, P < 0.001). The mean lung density of 68 cases was normal and that of 12 cases was abnormal. There was a significant difference in lung density between the lung injury group and the normal group (X2 = 18.027, P < 0.001). Spiral CT lung density measurement can accurately evaluate the lung density values of different types of radiation lung injury and judge the correlation between lung density and different types of radiation lung injury. It is of great value to diagnose, locate, and master the radiation dose of different types of radiation lung injury.

Deep-inspirational breath-hold (DIBH) technique in left-sided breast cancer: various aspects of clinical utility

BackgroundStudying the clinical utility of deep-inspirational breath-hold (DIBH) in left breast cancer radiotherapy (RT) was aimed at focusing on dosimetry and feasibility aspects.MethodsIn this prospective trial all enrolled patients went through planning CT in supine position under both DIBH and free breathing (FB); in whole breast irradiation (WBI) cases prone CT was also taken. In 3-dimensional conformal radiotherapy (3DCRT) plans heart, left anterior descending coronary artery (LAD), ipsilateral lung and contralateral breast doses were analyzed. The acceptance of DIBH technique as reported by the patients and the staff was analyzed; post-RT side-effects including radiation lung changes (visual scores and lung density measurements) were collected.ResultsAmong 130 enrolled patients 26 were not suitable for the technique while in 16, heart or LAD dose constraints were not met in the DIBH plans. Among 54 and 34 patients receiving WBI and postmastectomy/nodal RT, respectively with DIBH, mean heart dose (MHD) was reduced to < 50%, the heart V25 Gy to < 20%, the LAD mean dose to < 40% and the LAD maximum dose to about 50% as compared to that under FB; the magnitude of benefit was related to the relative increase of the ipsilateral lung volume at DIBH. Nevertheless, heart and LAD dose differences (DIBH vs. FB) individually varied. Among the WBI cases at least one heart/LAD dose parameter was more favorable in the prone or in the supine FB plan in 15 and 4 cases, respectively; differences were numerically small. All DIBH patients completed the RT, inter-fraction repositioning accuracy and radiation side-effects were similar to that of other breast RT techniques. Both the patients and radiographers were satisfied with the technique.ConclusionsDIBH is an excellent heart sparing technique in breast RT, but about one-third of the patients do not benefit from that otherwise laborious procedure or benefit less than from an alternative method.Trial registration: retrospectively registered under ISRCTN14360721 (February 12, 2021)

Comparison of CT Lung Density Measurements between Standard Full-Dose and Reduced-Dose Protocols.

To evaluate the reproducibility and predicted clinical outcomes of CT-based quantitative lung density measurements using standard fixed-dose (FD) and reduced-dose (RD) scans. In this retrospective analysis of prospectively acquired data, 1205 participants (mean age, 65 years ± 9 [standard deviation]; 618 men) enrolled in the COPDGene study who underwent FD and RD CT image acquisition protocols between November 2014 and July 2017 were included. Of these, the RD scans of 640 participants were also reconstructed using iterative reconstruction (IR). Median filtering was applied to the RD scans (RD-MF) to investigate an alternative noise reduction strategy. CT attenuation at the 15th percentile of the lung CT histogram (Perc15) was computed for all image types (FD, RD, RD-MF, and RD-IR). Reproducibility coefficients were calculated to quantify the measurement differences between FD and RD scans. The ability of Perc15 to predict chronic obstructive pulmonary disease (COPD) diagnosis and exacerbation frequency was investigated using receiver operating characteristic analysis. The Perc15 reproducibility coefficients with and without volume adjustment were as follows: RD, 29.43 HU ± 0.62 versus 32.81 HU ± 1.70; RD-MF, 7.42 HU ± 0.42 versus 19.40 HU ± 2.65; and RD-IR, 7.10 HU ± 0.52 versus 22.46 HU ± 3.91. Receiver operating characteristic curve analysis indicated that Perc15 on volume-adjusted FD and RD scans were both predictive for COPD diagnosis (area under the receiver operating characteristic curve [AUC]: FD, 0.724 ± 0.045; RD, 0.739 ± 0.045) and for having one or more exacerbation per year (AUCs: FD, 0.593 ± 0.068; RD, 0.589 ± 0.066). Similar trends were observed when volume adjustment was not applied. A combination of volume adjustment and noise reduction filtering improved the reproducibility of lung density measurements computed using serial FD and RD CT scans.Supplemental material is available for this article.© RSNA, 2021.

Association of quantitative CT lung density measurements and lung function decline in World Trade Center workers.

BackgroundOccupational exposures at the WTC site after 11 September 2001 have been associated with presumably inflammatory chronic lower airway diseases.AimsIn this study, we describe the trajectories of expiratory air flow decline, identify subgroups with adverse progression, and investigate the association of those trajectories with quantitative computed tomography (QCT) imaging measurement of increased and decreased lung density.MethodsWe examined the trajectories of expiratory air flow decline in a group of 1,321 former WTC workers and volunteers with at least three periodic spirometries, and using QCT‐measured low (LAV%, −950 HU) and high (HAV%, from −600 to −250 HU) attenuation volume percent. We calculated the individual regression line slopes for first‐second forced expiratory volume (FEV1slope), identified subjects with rapidly declining (“accelerated decliners”) and increasing (“improved”), and compared them to subjects with “intermediate” (0 to −66.5 mL/year) FEV1slope. We then used multinomial logistic regression to model those three trajectories, and the two lung attenuation metrics.ResultsThe mean longitudinal FEV1 slopes for the entire study population, and its intermediate, decliner, and improved subgroups were, respectively, −40.4, −34.3, −106.5, and 37.6 mL/year. In unadjusted and adjusted analyses, LAV% and HAV% were both associated with “accelerated decliner” status (ORadj, 95% CI 2.37, 1.41–3.97, and 1.77, 1.08–2.89, respectively), compared to the intermediate decline.ConclusionsLongitudinal FEV1 decline in this cohort, known to be associated with QCT proximal airway inflammation metric, is also associated with QCT indicators of increased and decreased lung density. The improved FEV1 trajectory did not seem to be associated with lung density metrics.

Inter- and intra-software reproducibility of computed tomography lung density measurements.

Multiple commercial, open-source, and academic software tools exist for objective quantification of lung density in computed tomography (CT) images. The purpose of this study was to evaluate the intersoftware reproducibility of CT lung density measurements. Computed tomography images from 50 participants from the COPDGeneTM cohort study were randomly selected for analysis; n=10 participants across each global initiative for chronic obstructive lung disease (GOLD) grade (GOLD 0-IV). Academic-based groups (n=4) and commercial vendors (n=4) participated anonymously to generate CT lung density measurements using their software tools. Computed tomography total lung volume (TLV), percentage of the low attenuation areas in the lung with Hounsfield unit (HU) values below -950HU (LAA950 ), and the HU value corresponding to the 15th percentile on the parenchymal density histogram (Perc15) were included in the analysis. The intersoftware bias and reproducibility coefficient (RDC) was generated with and without quality assurance (QA) for manual correction of the lung segmentation; intrasoftware bias and RDC was also generated by repeated measurements on the same images. Intersoftware mean bias was within ±0.22mL, ±0.46%, and ±0.97HU for TLV, LAA950 and Perc15, respectively. The RDC was 0.35L, 1.2% and 1.8HU for TLV, LAA950 and Perc15, respectively. Intersoftware RDC remained unchanged following QA: 0.35L, 1.2% and 1.8HU for TLV, LAA950 and Perc15, respectively. All software investigated had an intrasoftware RDC of 0. The RDC was comparable for TLV, LAA950 and Perc15 measurements, respectively, for academic-based groups/commercial vendor-based software tools: 0.39L/0.32L, 1.2%/1.2%, and 1.7HU/1.6HU. Multivariable regression analysis showed that academic-based software tools had greater within-subject standard deviation of TLV than commercial vendors, but no significant differences between academic and commercial groups were found for LAA950 or Perc15 measurements. Computed tomography total lung volume and lung density measurement bias and reproducibility was reported across eight different software tools. Bias was negligible across vendors, reproducibility was comparable for software tools generated by academic-based groups and commercial vendors, and segmentation QA had negligible impact on measurement variability between software tools. In summary, results from this study report the amount of additional measurement variability that should be accounted for when using different software tools to measure lung density longitudinally with well-standardized image acquisition protocols. However, intrasoftware reproducibility was deterministic for all cases so use of the same software tool to reduce variability for serial studies is highly recommended.

Is lung density associated with severity of COVID-19?

PurposeEmphysema and chronic obstructive lung disease were previously identified as major risk factors for severe disease progression in COVID-19. Computed tomography (CT)-based lung-density analysis offers a fast, reliable, and quantitative assessment of lung density. Therefore, we aimed to assess the benefit of CT-based lung density measurements to predict possible severe disease progression in COVID-19.Material and methodsThirty COVID-19-positive patients were included in this retrospective study. Lung density was quantified based on routinely acquired chest CTs. Presence of COVID-19 was confirmed by reverse transcription polymerase chain reaction (RT-PCR). Wilcoxon test was used to compare two groups of patients. A multivariate regression analysis, adjusted for age and sex, was employed to model the relative increase of risk for severe disease, depending on the measured densities.ResultsIntensive care unit (ICU) patients or patients requiring mechanical ventilation showed a lower proportion of medium- and low-density lung volume compared to patients on the normal ward, but a significantly larger volume of high-density lung volume (12.26 dl IQR 4.65 dl vs. 7.51 dl vs. IQR 5.39 dl, p = 0.039). In multivariate regression analysis, high-density lung volume was identified as a significant predictor of severe disease.ConclusionsThe amount of high-density lung tissue showed a significant association with severe COVID-19, with odds ratios of 1.42 (95% CI: 1.09-2.00) and 1.37 (95% CI: 1.03-2.11) for requiring intensive care and mechanical ventilation, respectively. Acknowledging our small sample size as an important limitation; our study might thus suggest that high-density lung tissue could serve as a possible predictor of severe COVID-19.

Lung density measurement in postmortem computed tomography: a new tool to assess immediate neonatal breath in suspected neonaticides.

Evidence of breath after birth is one of the main forensic issues in suspected neonaticide. Hydrostatic test (HT) and pathological examination are currently used to assess it, but they are not entirely reliable or immediately available. To determine the performance of postmortem computed tomography (PMCT) to assess neonatal breath in suspected neonaticide, by comparing lung CT attenuation values between live birth and stillbirth cases, in correlation with HT and pathology. Cases of suspected neonaticides who underwent a PMCT and complete forensic autopsy with an HT were retrospectively selected from the databases of four French Forensic Medicine Departments. The diagnosis of vitality (i.e., stillbirth or live birth) was based on the pathological examination and/or a combination of arguments, including HT result. Lung density on CT was measured in Hounsfield units (HU) by ROIs drawn in both pulmonary parenchymas. Eleven patients were included, six live birth and five stillbirth cases. The result of HT was concordant with pathological examination when available (seven cases). Mean lung densities in live birth cases (- 173 HU [- 255; - 91 CI 95%]) were significantly lower than in stillbirth cases (40 HU [28; 52 CI 95%]) (p < 0.05), with a very high degree of interobserver reproducibility (ICC = 0.998 (CI 95% 0.991-0.999; p < 0.001). PMCT and especially lung CT attenuation measurement is a reliable and easy-to-use method for assessing neonatal breath in suspected neonaticides.

Deep learning-enabled accurate normalization of reconstruction kernel effects on emphysema quantification in low-dose CT

Lung densitometry is being frequently adopted in CT-based emphysema quantification, yet known to be affected by the choice of reconstruction kernel. This study presents a two-step deep learning architecture that enables accurate normalization of reconstruction kernel effects on emphysema quantification in low-dose CT. Deep learning is used to convert a CT image of a sharp kernel to that of a standard kernel with restoration of truncation artifacts and smoothing-free pixel size normalization. We selected 353 scans reconstructed by both standard and sharp kernels from four different CT scanners from the United States National Lung Screening Trial program database. A truncation artifact correction model was constructed with a combination of histogram extrapolation and a deep learning model trained with truncated and non-truncated image sets. Then, we performed frequency domain zero-padding to normalize reconstruction field of view effects while preventing image smoothing effects. The kernel normalization model has a U-Net based architecture trained for each CT scanner dataset. Three lung density measurements including relative lung area under 950 HU (RA950), lower 15th percentile threshold (perc15), and mean lung density were obtained in the datasets from standard, sharp, and normalized kernels. The effect of kernel normalization was evaluated with pair-wise differences in lung density metrics. The mean of pair-wise differences in RA950 between standard and sharp kernel reconstructions was reduced from 10.75% to −0.07% using kernel normalization. The difference for perc15 decreased from −31.03 HU to −0.30 HU after kernel normalization. Our study demonstrated the feasibility of applying deep learning techniques for normalizing CT kernel effects, thereby reducing the kernel-induced variability in lung density measurements. The deep learning model could increase the accuracy of emphysema quantification, thereby allowing reliable surveillance of emphysema in lung cancer screening even when follow-up CT scans are acquired with different reconstruction kernels.

Lobe-wise assessment of lung volume and density distribution in lung transplant patients and value for early detection of bronchiolitis obliterans syndrome

PurposeTo evaluate quantitative computed tomography (CT) measurements of the lung parenchyma in lung transplant (LTx) patients for early detection of the bronchiolitis obliterans syndrome (BOS). Materials and Methods359 CT scans of 122 lung transplant patients were evaluated. Measurements of lung volume and density were performed for the whole lung and separately for each lobe. For longitudinal analysis the difference between the baseline at 6 months after LTx and follow-up examinations was calculated. Patients with and without BOS (matched 1:2) were compared at two different time points, the last examination before the BOS onset and the first examination within one year after BOS onset. Results30 patients developed BOS during the follow-up period. Longitudinal changes in the lung volume and lung density measured on CT differed significantly between those patients with and without early BOS, in particular the difference of the inspiratory and expiratory lung volume (p < 0.001), the ratio of the expiratory and inspiratory lung volume (p < 0.001-p = 0.001) and MLD (p < 0.001-p = 0.001), the volume on expiration (p < 0.001-p = 0.007), the MLD on expiration (p < 0.001-p = 0.007), and the percentiles on expiration (p < 0.001-p = 0.002) with an increase of lung volume and a decrease of lung density. Changes were pronounced in the lower lobes. Before BOS onset, patients with and without future development of BOS showed no significant differences. ConclusionLongitudinal changes of lung volume and lung density measured on CT start markedly at BOS onset with increased lung volume and decreased lung density indicating increased inflation levels. Even though this method may help to diagnose BOS at onset it is not useful as a predictor for BOS before disease onset.

Impact of advanced detector technology and iterative reconstruction on low-dose quantitative assessment of lung computed tomography density in a biological lung model.

Quantitative computed tomography (QCT)-derived measures of lung density are valued methods for objectively characterizing lung parenchymal and peripheral airways disease and are being used in a growing number of lung disease focused trials. Detector and reconstruction improvements in CT technology have allowed for significant radiation dose reduction in image acquisition with comparable qualitative image quality. We report the impact of detector type and reconstruction type on QCT lung density measures in relation to decreasing dose indices. Two sets of studies were completed in an in vivo pig model with a SOMATOM Definition Flash CT system: (a) prior to system upgrade with conventional detectors (UFC) and filtered back projection (FBP), and (b) post system upgrade with integrated electronic detectors (STELLAR) and iterative reconstruction (SAFIRE). CT data were acquired across estimated CT volume dose indices (CTDIvol ) ranging from 0.75 to 15 mGy at both inspiratory and expiratory breath holds. Semiautomated lung segmentations allowed calculation of histogram median, kurtosis, and 15th percentile. Percentage of voxels below -910 HU and -950 HU (inspiratory), and -856 HU (expiratory) were also examined. The changes in these QCT metrics from dose reduction (15 mGy down to 0.75 mGy) were calculated relative to paired reference values (15 mGy). Results were compared based on detector and reconstruction type. In this study, STELLAR detectors improved concordance with 15 mGy values down to 3 mGy for inspiratory scans and 6 mGy for expiratory scans. The addition of SAFIRE reconstruction in all acquired measurements resulted in minimal deviation from reference values at 0.75 mGy. The use of STELLAR integrated electronic detectors and SAFIRE iterative reconstruction may allow for comparable lung density measures with CT dose indices down to 0.75 mGy.

Lung densitometry: why, how and when.

Lung densitometry assesses with computed tomography (CT) the X-ray attenuation of the pulmonary tissue which reflects both the degree of inflation and the structural lung abnormalities implying decreased attenuation, as in emphysema and cystic diseases, or increased attenuation, as in fibrosis. Five reasons justify replacement with lung densitometry of semi-quantitative visual scales used to measure extent and severity of diffuse lung diseases: (I) improved reproducibility; (II) complete vs. discrete assessment of the lung tissue; (III) shorter computation times; (IV) better correlation with pathology quantification of pulmonary emphysema; (V) better or equal correlation with pulmonary function tests (PFT). Commercially and open platform software are available for lung densitometry. It requires attention to technical and methodological issues including CT scanner calibration, radiation dose, and selection of thickness and filter to be applied to sections reconstructed from whole-lung CT acquisition. Critical is also the lung volume reached by the subject at scanning that can be measured in post-processing and represent valuable information per se. The measurements of lung density include mean and standard deviation, relative area (RA) at -970, -960 or -950 Hounsfield units (HU) and 1st and 15th percentile for emphysema in inspiratory scans, and RA at -856 HU for air trapping in expiratory scans. Kurtosis and skewness are used for evaluating pulmonary fibrosis in inspiratory scans. The main indication for lung densitometry is assessment of emphysema component in the single patient with chronic obstructive pulmonary diseases (COPD). Additional emerging applications include the evaluation of air trapping in COPD patients and in subjects at risk of emphysema and the staging in patients with lymphangioleiomyomatosis (LAM) and with pulmonary fibrosis. It has also been applied to assess prevalence of smoking-related emphysema and to monitor progression of smoking-related emphysema, alpha1 antitrypsin deficiency emphysema, and pulmonary fibrosis. Finally, it is recommended as end-point in pharmacological trials of emphysema and lung fibrosis.

Asynchrony in respiratory movements between the pulmonary lobes in patients with COPD: continuous measurement of lung density by 4-dimensional dynamic-ventilation CT.

PurposeFour-dimensional dynamic-ventilation CT imaging demonstrates continuous movement of the lung. The aim of this study was to assess the correlation between interlobar synchrony in lung density and spirometric values in COPD patients and smokers, by measuring the continuous changes in lung density during respiration on the dynamic-ventilation CT.Materials and methodsThirty-two smokers, including ten with COPD, underwent dynamic-ventilation CT during free breathing. CT data were continuously reconstructed every 0.5 sec. Mean lung density (MLD) of the five lobes (right upper [RU], right middle [RM], right lower [RL], left upper [LU], and left lower [LL]) was continuously measured by commercially available software using a fixed volume of volume of interest which was placed and tracked on a single designated point in each lobe. Concordance between the MLD time curves of six pairs of lung lobes (RU-RL, RU-RM, RM-RL, LU-LL, RU-LU, and RL-LL lobes) was expressed by cross-correlation coefficients. The relationship between these cross-correlation coefficients and the forced expiratory volume in one second/forced vital capacity (FEV1.0/FVC) values was assessed by Spearman rank correlation analysis.ResultsIn all six pairs of the pulmonary lobes, the cross-correlation coefficients of the two MLD curves were significantly positively correlated with FEV1.0/FVC (ρ =0.60–0.73, P<0.001). The mean value of the six coefficients strongly correlated with FEV1.0/FVC (ρ =0.80, P<0.0001).ConclusionThe synchrony of respiratory movements between the pulmonary lobes is limited or lost in patients with more severe airflow limitation.

Standardizing CT lung density measure across scanner manufacturers.

Computed Tomography (CT) imaging of the lung, reported in Hounsfield Units (HU), can be parameterized as a quantitative image biomarker for the diagnosis and monitoring of lung density changes due to emphysema, a type of chronic obstructive pulmonary disease (COPD). CT lung density metrics are global measurements based on lung CT number histograms, and are typically a quantity specifying either the percentage of voxels with CT numbers below a threshold, or a single CT number below which a fixed relative lung volume, nth percentile, falls. To reduce variability in the density metrics specified by CT attenuation, the Quantitative Imaging Biomarkers Alliance (QIBA) Lung Density Committee has organized efforts to conduct phantom studies in a variety of scanner models to establish a baseline for assessing the variations in patient studies that can be attributed to scanner calibration and measurement uncertainty. Data were obtained from a phantom study on CT scanners from four manufacturers with several protocols at various tube potential voltage (kVp) and exposure settings. Free from biological variation, these phantom studies provide an assessment of the accuracy and precision of the density metrics across platforms solely due to machine calibration and uncertainty of the reference materials. The phantom used in this study has three foam density references in the lung density region, which, after calibration against a suite of Standard Reference Materials (SRM) foams with certified physical density, establishes a HU-electron density relationship for each machine-protocol. We devised a 5-step calibration procedure combined with a simplified physical model that enabled the standardization of the CT numbers reported across a total of 22 scanner-protocol settings to a single energy (chosen at 80 keV). A standard deviation was calculated for overall CT numbers for each density, as well as by scanner and other variables, as a measure of the variability, before and after the standardization. In addition, a linear mixed-effects model was used to assess the heterogeneity across scanners, and the 95% confidence interval of the mean CT number was evaluated before and after the standardization. We show that after applying the standardization procedures to the phantom data, the instrumental reproducibility of the CT density measurement of the reference foams improved by more than 65%, as measured by the standard deviation of the overall mean CT number. Using the lung foam that did not participate in the calibration as a test case, a mixed effects model analysis shows that the 95% confidence intervals are [-862.0 HU, -851.3 HU] before standardization, and [-859.0 HU, -853.7 HU] after standardization to 80 keV. This is in general agreement with the expected CT number value at 80 keV of -855.9 HU with 95% CI of [-857.4 HU, -854.5 HU] based on the calibration and the uncertainty in the SRM certified density. This study provides a quantitative assessment of the variations expected in CT lung density measures attributed to non-biological sources such as scanner calibration and scanner x-ray spectrum and filtration. By removing scanner-protocol dependence from the measured CT numbers, higher accuracy and reproducibility of quantitative CT measures were attainable. The standardization procedures developed in study may be explored for possible application in CT lung density clinical data.

HIV Infection Is Independently Associated with Increased CT Scan Lung Density

Noninfectious pulmonary complications are common among HIV-infected individuals and may be detected early by quantitative computed tomography (CT) scanning. The association of HIV disease markers with CT lung density measurement remains poorly understood. One hundred twenty-five participants free of spirometry-defined lung disease were recruited from a longitudinal cohort study of HIV-infected and HIV-uninfected individuals to undergo standardized CT scan of the chest. Parenchymal density for the entire lung volume was calculated using computerized software. Qualitative assessment of CT scans was conducted by two radiologists masked to HIV status. Linear regression models were developed to determine the independent association of markers of HIV infection on inspiratory scan mean lung density (MLD). HIV-infected participants had a significantly higher MLD (denser lung) compared to HIV-uninfected participants (-815 Hounsfield unit [HU] vs -837 HU; P = 0.002). After adjusting for relevant covariates, HIV infection was independently associated with 19.9 HU higher MLD (95% CI 6.04 to 33.7 HU; P = 0.005). In qualitative assessment, only ground glass attenuation and cysts were noted more commonly among HIV-infected individuals compared to HIV-uninfected individuals (34% vs 17% [P = 0.045] and 27% vs 10% [P = 0.03], respectively). No qualitative radiographic abnormalities attenuated the association between HIV infection and increased MLD. HIV infection is independently associated with increased lung density. Although qualitative CT abnormalities were common in this cohort, only ground glass attenuation and cysts were noted more frequently in HIV-infected participants, suggesting that the increased lung density observed among HIV-infected individuals may be associated with subclinical inflammatory lung changes.

TH‐CD‐207B‐11: Multi‐Vendor Phantom Study of CT Lung Density Metrics: Is a Reproducibility of Less Than 1 HU Achievable?

Purpose:To standardize the calibration procedures of CT lung density measurements using low‐density reference foams in a phantom, and to demonstrate a reproducibility of less than 1 HU for lung equivalent foam densities measured across CT vendor platforms and protocols.Methods:A phantom study was conducted on CT scanner models from 4 vendors at 100, 120, and 135/140 kVp and 1.5, 3, and 6 mGy dose settings, using a lung density phantom containing air, water, and 3 reference foams (indirectly calibrated) with discrete densities simulating a 5‐cm slice of the human chest. Customized segmentation software was used to analyze the images and generate a mean HU and variance for each of the density for the 22 vendor/protocols. A 3‐step calibration process was devised to remove a scanner‐dependent parameter using linear regression of the HU value vs the relative electron density. The results were mapped to a single energy (80 keV) for final comparison.Results:The heterogeneity across vendor platforms for each density assessed by a random effects model was reduced by 50% after re‐calibration, while the standard deviation of the mean HU values also improved by about the same amount. The 95% CI of the final HU value was within +/−1 HU for all 3 reference foam densities. For the backing lung foam in the phantom (served as an “unknown”), this CI is +/− 1.6 HU. The kVp and dose settings did not appear to have significant contributions to the variability.Conclusion:With the proposed calibration procedures, the inter‐scanner reproducibility of better than 1 HU is demonstrated in the current phantom study for the reference foam densities, but not yet achieved for a test density. The sources of error are being investigated in the next round of scanning with a certified Standard Reference Material for direct calibration.Fain: research funding from GE Healthcare to develop pulmonary MRI techniques. Hoppel: employee of Toshiba Medical Research Institute USA/financial interest with GE Healthcare. M. Fuld: employee of Siemens Healthcare for medical device equipment and software. This project is supported partially by RSNA QIBA Concept Award (Fain), NIH/NIBIB, HHSN268201300071C (Y).