Comparison of low- and ultralow-dose computed tomography protocols for quantitative lung and airway assessment

Dose robustness of deep learning models for anatomic segmentation of computed tomography images

Purpose

The trend towards lower radiation doses and advances in computed tomography (CT) reconstruction may impair the operation of pretrained segmentation models, giving rise to the problem of estimating the dose robustness of existing segmentation models. Previous studies addressing the issue suffer either from a lack of registered low- and full-dose CT images or from simplified simulations.

Approach

We employed raw data from full-dose acquisitions to simulate low-dose CT scans, avoiding the need to rescan a patient. The accuracy of the simulation is validated using a real CT scan of a phantom. We consider down to 20% reduction of radiation dose, for which we measure deviations of several pretrained segmentation models from the full-dose prediction. In addition, compatibility with existing denoising methods is considered.

Results

The results reveal the surprising robustness of the TotalSegmentator approach, showing minimal differences at the pixel level even without denoising. Less robust models show good compatibility with the denoising methods, which help to improve robustness in almost all cases. With denoising based on a convolutional neural network (CNN), the median Dice between low- and full-dose data does not fall below 0.9 (12 for the Hausdorff distance) for all but one model. We observe volatile results for labels with effective radii less than 19 mm and improved results for contrasted CT acquisitions.

Conclusion

The proposed approach facilitates clinically relevant analysis of dose robustness for human organ segmentation models. The results outline the robustness properties of a diverse set of models. Further studies are needed to identify the robustness of approaches for lesion segmentation and to rank the factors contributing to dose robustness.

Direct estimation of regional lung volume change from paired and single CT images using residual regression neural network

BACKGROUND

Chest computed tomography (CT) enables characterization of pulmonary diseases by producing high-resolution and high-contrast images of the intricate lung structures. Deformable image registration is used to align chest CT scans at different lung volumes, yielding estimates of local tissue expansion and contraction.

PURPOSE

We investigated the utility of deep generative models for directly predicting local tissue volume change from lung CT images, bypassing computationally expensive iterative image registration and providing a method that can be utilized in scenarios where either one or two CT scans are available.

METHODS

A residual regression convolutional neural network, called Reg3DNet+, is proposed for directly regressing high-resolution images of local tissue volume change (i.e., Jacobian) from CT images. Image registration was performed between lung volumes at total lung capacity (TLC) and functional residual capacity (FRC) using a tissue mass- and structure-preserving registration algorithm. The Jacobian image was calculated from the registration-derived displacement field and used as the ground truth for local tissue volume change. Four separate Reg3DNet+ models were trained to predict Jacobian images using a multifactorial study design to compare the effects of network input (i.e., single image vs. paired images) and output space (i.e., FRC vs. TLC). The models were trained and evaluated on image datasets from the COPDGene study. Models were evaluated against the registration-derived Jacobian images using local, regional, and global evaluation metrics.

RESULTS

Statistical analysis revealed that both factors - network input and output space - were significant determinants for change in evaluation metrics. Paired-input models performed better than single-input models, and model performance was better in the output space of FRC rather than TLC. Mean structural similarity index for paired-input models was 0.959 and 0.956 for FRC and TLC output spaces, respectively, and for single-input models was 0.951 and 0.937. Global evaluation metrics demonstrated correlation between registration-derived Jacobian mean and predicted Jacobian mean: coefficient of determination (r2 ) for paired-input models was 0.974 and 0.938 for FRC and TLC output spaces, respectively, and for single-input models was 0.598 and 0.346. After correcting for effort, registration-derived lobar volume change was strongly correlated with the predicted lobar volume change: for paired-input models r2 was 0.899 for both FRC and TLC output spaces, and for single-input models r2 was 0.803 and 0.862, respectively.

CONCLUSIONS

Convolutional neural networks can be used to directly predict local tissue mechanics, eliminating the need for computationally expensive image registration. Networks that use paired CT images acquired at TLC and FRC allow for more accurate prediction of local tissue expansion compared to networks that use a single image. Networks that only require a single input image still show promising results, particularly after correcting for effort, and allow for local tissue expansion estimation in cases where multiple CT scans are not available. For single-input networks, the FRC image is more predictive of local tissue volume change compared to the TLC image.

A Computer-based Analysis for Identification and Quantification of Small Airway Disease in Lung Computed Tomography Images: A Comprehensive Review for Radiologists

Computed tomography (CT) imaging is being increasingly used in clinical practice for detailed characterization of lung diseases. Respiratory diseases involve various components of the lung, including the small airways. Evaluation of small airway disease on CT images is challenging as the airways cannot be visualized directly by a CT scanner. Small airway disease can manifest as pulmonary air trapping (AT). Although AT may be sometimes seen as mosaic attenuation on expiratory CT images, it is difficult to identify diffuse AT visually. Computer technology advances over the past decades have provided methods for objective quantification of small airway disease on CT images. Quantitative CT (QCT) methods are being rapidly developed to quantify underlying lung diseases with greater precision than subjective visual assessment of CT images. A growing body of evidence suggests that QCT methods can be practical tools in the clinical setting to identify and quantify abnormal regions of the lung accurately and reproducibly. This review aimed to describe the available methods for the identification and quantification of small airway disease on CT images and to discuss the challenges of implementing QCT metrics in clinical care for patients with small airway disease.

Imaging Regional Airway Involvement of Asthma: Heterogeneity in Ventilation, Mucus Plugs and Remodeling

The imaging of asthma using chest computed tomography (CT) is well-established (Jarjour et al., Am J Respir Crit Care Med 185(4):356-62, 2012; Castro et al., J Allergy Clin Immunol 128:467-78, 2011). Moreover, recent advances in functional imaging of the lungs with advanced computer analysis of both CT and magnetic resonance images (MRI) of the lungs have begun to play a role in quantifying regional obstruction. Specifically, quantitative measurements of the airways for bronchial wall thickening, luminal narrowing and distortion, the amount of mucus plugging, parenchymal density, and ventilation defects that could contribute to the patient's disease course are instructive for the entire care team. In this chapter, we will review common imaging methods and findings that relate to the heterogeneity of asthma. This information can help to guide treatment decisions. We will discuss mucous plugging, quantitative assessment of bronchial wall thickening, delta lumen phenomenon, parenchymal low-density lung on CT, and ventilation defect percentage on MRI as metrics for assessing regional ventilatory dysfunction.

Airway Detection in COPD at Low-Dose CT Using Deep Learning and Multiparametric Freeze and Grow

PURPOSE

To present and validate a fully automated airway detection method at low-dose CT in patients with chronic obstructive pulmonary disease (COPD).

MATERIALS AND METHODS

In this retrospective study, deep learning (DL) and freeze-and-grow (FG) methods were optimized and applied to automatically detect airways at low-dose CT. Four data sets were used: two data sets consisting of matching standard- and low-dose CT scans from the Genetic Epidemiology of COPD (COPDGene) phase II (2014-2017) cohort (n = 2 × 236; mean age ± SD, 70 years ± 9; 123 women); one data set consisting of low-dose CT scans from the COPDGene phase III (2018-2020) cohort (n = 335; mean age ± SD, 73 years ± 8; 173 women); and one data set consisting of low-dose, anonymized CT scans from the 2003 Dutch-Belgian Randomized Lung Cancer Screening trial (n = 55) acquired by using different CT scanners. Performance measures for different methods were computed and compared by using the Wilcoxon signed rank test.

RESULTS

At low-dose CT, 56 294 of 62 480 (90.1%) airways of the reference total airway count (TAC) and 32 109 of 37 864 (84.8%) airways of the peripheral TAC (TAC_p), detected at standard-dose CT, were detected. Significant losses (P < .001) of 14 526 of 76 453 (19.0%) airways and 884 of 6908 (12.8%) airways in the TAC and 12 256 of 43 462 (28.2%) airways and 699 of 3882 (18.0%) airways in the TAC_p were observed, respectively, for the multiprotocol and multiscanner data without retraining. When using the automated low-dose CT method, TAC values of 347, 342, 323, and 266 and TAC_p values of 205, 202, 289, and 141 were observed for those who have never smoked and participants at Global Initiative for Chronic Obstructive Lung Disease stages 0, 1, and 2, respectively, which were superior to the respective values previously reported for matching groups when using a semiautomated method at standard-dose CT.

CONCLUSION

A low-cost, automated CT-based airway detection method was suitable for investigation of airway phenotypes at low-dose CT.Keywords: Airway, Airway Count, Airway Detection, Chronic Obstructive Pulmonary Disease, CT, Deep Learning, Generalizability, Low-Dose CT, Segmentation, Thorax, LungClinical trial registration no. NCT00608764 Supplemental material is available for this article. © RSNA, 2022.

Correlations of Computed Tomography Measurement of Distal Pulmonary Vascular Pruning with Airflow Limitation and Emphysema in COPD Patients

Background

Pulmonary vascular alteration is an important feature of chronic obstructive pulmonary disease (COPD), which is characterized by distal pulmonary vascular pruning in angiography. We aimed to further investigate the clinical relevance of pulmonary vasculature in COPD patients using non-contrast computed tomography (CT).

Methods

Seventy-one control subjects and 216 COPD patients completed the questionnaires, spirometry, and computed tomography (CT) scans within 1 month and were included in the study. Small pulmonary vessels represented by percentage of cross-sectional area of pulmonary vessels smaller than 5 mm² or 5–10 mm² to the total lung fields (%CSA<5 or %CSA5–10, respectively) were measured using ImageJ software. Spearman correlation was used to investigate the relationship between %CSA<5 and airflow limitation. A receiver operating characteristic (ROC) curve was built to evaluate the value of %CSA<5 in discriminating COPD patients from healthy control subjects. Segmented regression was used to analyze the relationship between %CSA<5 and %LAA-950 (percentage of low-attenuation areas less than −950 HU).

Results

We found a significant correlation between %CSA<5 and forced expiratory volume in one second (FEV1) percentage of predicted value (%pred) (r = 0.564, P < 0.001). The area under the ROC curve for the value of %CSA<5 in distinguishing COPD was 0.816, with a cut-off value of 0.537 (Youden index J, 0.501; sensitivity, 78.24%; specificity, 71.83%). Since the relationship between %CSA<5 and %LAA-950 was not constant, performance of segmented regression was better than ordinary linear regression (adjusted R², 0.474 vs 0.332, P < 0.001 and P < 0.001, respectively). As %CSA<5 decreased, %LAA-950 slightly increased until an inflection point (%CSA<5 = 0.524) was reached, after which the %LAA-950 increased apparently with a decrease in %CSA<5.

Conclusion

%CSA<5 was significantly correlated with both airflow limitation and emphysema, and we identified an inflection point for the relationship between %CSA<5 and %LAA-950.

Quantitative Computed Tomography: What Clinical Questions Can it Answer in Chronic Lung Disease?

Quantitative computed tomography (QCT) has recently gained an important role in the functional assessment of chronic lung disease. Its capacity in diagnostic, staging, and prognostic evaluation in this setting is similar to that of traditional pulmonary function testing. Furthermore, it can demonstrate lung injury before the alteration of pulmonary function test parameters, and it enables the classification of disease phenotypes, contributing to the customization of therapy and performance of comparative studies without the intra- and inter-observer variation that occurs with qualitative analysis. In this review, we address technical issues with QCT analysis and demonstrate the ability of this modality to answer clinical questions encountered in daily practice in the management of patients with chronic lung disease.

Menstrual cycle impacts lung structure measures derived from quantitative computed tomography

OBJECTIVE

Quantitative computed tomography (qCT) is being increasingly incorporated in research studies and clinical trials aimed at understanding lung disease risk, progression, exacerbations, and intervention response. Menstrual cycle-based changes in lung function are recognized; however, the impact on qCT measures is currently unknown. We hypothesize that the menstrual cycle impacts qCT-derived measures of lung structure in healthy women and that the degree of measurement change may be mitigated in subjects on cyclic hormonal birth control.

METHODS

Thirty-one non-smoking, healthy women with regular menstrual cycles (16 of which were on cyclic hormonal birth control) underwent pulmonary function testing and qCT imaging at both menses and early luteal phase time points. Data were evaluated to identify lung measurements which changed significantly across the two key time points and to compare degree of change across metrics for the sub-cohort with versus without birth control.

RESULTS

The segmental airway measurements were larger and mean lung density was higher at menses compared to the early luteal phase. The sub-cohort with cyclic hormonal birth control did not have less evidence of measurement difference over the menstrual cycle compared to the sub-cohort without hormonal birth control.

CONCLUSIONS

This study provides evidence that qCT-derived measures from the lung are impacted by the female menstrual cycle. This indicates studies seeking to use qCT as a more sensitive measure of cross-sectional differences or longitudinal changes in these derived lung measurements should consider acquiring data at a consistent time in the menstrual cycle for pre-menopausal women and warrants further exploration.

KEY POINTS

• Lung measurements from chest computed tomography are used in multicenter studies exploring lung disease progression and treatment response. • The menstrual cycle impacts lung structure measurements. • Cyclic variability should be considered when evaluating longitudinal change with CT in menstruating women.

CT-Based Commercial Software Applications: Improving Patient Care Through Accurate COPD Subtyping

Chronic obstructive pulmonary disease (COPD) is heterogenous in its clinical manifestations and disease progression. Patients often have disease courses that are difficult to predict with readily available data, such as lung function testing. The ability to better classify COPD into well-defined groups will allow researchers and clinicians to tailor novel therapies, monitor their effects, and improve patient-centered outcomes. Different modalities of assessing these COPD phenotypes are actively being studied, and an area of great promise includes the use of quantitative computed tomography (QCT) techniques focused on key features such as airway anatomy, lung density, and vascular morphology. Over the last few decades, companies around the world have commercialized automated CT software packages that have proven immensely useful in these endeavors. This article reviews the key features of several commercial platforms, including the technologies they are based on, the metrics they can generate, and their clinical correlations and applications. While such tools are increasingly being used in research and clinical settings, they have yet to be consistently adopted for diagnostic work-up and treatment planning, and their full potential remains to be explored.

Origins of and lessons from quantitative functional X-ray computed tomography of the lung

Functional CT of the lung has emerged from quantitative CT (qCT). Structural details extracted at multiple lung volumes offer indices of function. Additionally, single volumetric images, if acquired at standardized lung volumes and body posture, can be used to model function by employing such engineering techniques as computational fluid dynamics. With the emergence of multispectral CT imaging including dual energy from energy integrating CT scanners and multienergy binning using the newly released photon counting CT technology, function is tagged via use of contrast agents. Lung disease phenotypes have previously been lumped together by the limitations of spirometry and plethysmography. QCT and its functional embodiment have been imbedded into studies seeking to characterize chronic obstructive pulmonary disease, severe asthma, interstitial lung disease and more. Reductions in radiation dose by an order of magnitude or more have been achieved. At the same time, we have seen significant increases in spatial and density resolution along with methodologic validations of extracted metrics. Together, these have allowed attention to turn towards more mild forms of disease and younger populations. In early applications, clinical CT offered anatomic details of the lung. Functional CT offers regional measures of lung mechanics, the assessment of functional small airways disease, as well as regional ventilation-perfusion matching (V/Q) and more. This paper will focus on the use of quantitative/functional CT for the non-invasive exploration of dynamic three-dimensional functioning of the breathing lung and beating heart within the unique negative pressure intrathoracic environment of the closed chest.

Pulmonary Toxicities of Immunotherapy

Immune checkpoint inhibitors are a form of immunotherapy that are increasingly being used in a wide variety of cancers. Immune-related adverse events (irAEs) pose a major challenge in the treatment of cancer patients. Pneumonitis, the most common lung irAE, can cause significant disruptions in the treatment of cancer and may be life-threatening. The goal of this chapter is to instruct readers on the incidence and clinical manifestations of pneumonitis and to offer guidance in the evaluation and treatment of patients with pneumonitis.

Creating a training set for artificial intelligence from initial segmentations of airways

Airways segmentation is important for research about pulmonary disease but require a large amount of time by trained specialists. We used an openly available software to improve airways segmentations obtained from an artificial intelligence (AI) tool and retrained the tool to get a better performance. Fifteen initial airway segmentations from low-dose chest computed tomography scans were obtained with a 3D-Unet AI tool previously trained on Danish Lung Cancer Screening Trial and Erasmus-MC Sophia datasets. Segmentations were manually corrected in 3D Slicer. The corrected airway segmentations were used to retrain the 3D-Unet. Airway measurements were automatically obtained and included count, airway length and luminal diameter per generation from the segmentations. Correcting segmentations required 2–4 h per scan. Manually corrected segmentations had more branches (p < 0.001), longer airways (p < 0.001) and smaller luminal diameters (p = 0.004) than initial segmentations. Segmentations from retrained 3D-Unets trended towards more branches and longer airways compared to the initial segmentations. The largest changes were seen in airways from 6th generation onwards. Manual correction results in significantly improved segmentations and is potentially a useful and time-efficient method to improve the AI tool performance on a specific hospital or research dataset.

Case Studies in Physiology: Temporal variations of the lung parenchyma and vasculature in asymptomatic COVID-19 pneumonia: a multispectral CT assessment

This study reports systematic longitudinal pathophysiology of lung parenchymal and vascular effects of asymptomatic COVID-19 pneumonia in a young, healthy never-smoking male. Inspiratory and expiratory noncontrast along with contrast dual-energy computed tomography (DECT) scans of the chest were performed at baseline on the day of acute COVID-19 diagnosis (day 0), and across a 90-day period. Despite normal vital signs and pulmonary function tests on the day of diagnosis, the CT scans and corresponding quantification metrics detected abnormalities in parenchymal expansion based on image registration, ground-glass (GGO) texture (inflammation) as well as DECT-derived pulmonary blood volume (PBV). Follow-up scans on day 30 showed improvement in the lung parenchymal mechanics as well as reduced GGO and improved PBV distribution. Improvements in lung PBV continued until day 90. However, the heterogeneity of parenchymal mechanics and texture-derived GGO increased on days 60 and 90. We highlight that even asymptomatic COVID-19 infection with unremarkable vital signs and pulmonary function tests can have measurable effects on lung parenchymal mechanics and vascular pathophysiology, which may follow apparently different clinical courses. For this asymptomatic subject, post COVID-19 regional mechanics demonstrated persistent increased heterogeneity concomitant with return of elevated GGOs, despite early improvements in vascular derangement.NEW & NOTEWORTHY We characterized the temporal changes of lung parenchyma and microvascular pathophysiology from COVID-19 infection in an asymptomatic young, healthy nonsmoking male using dual-energy CT. Lung parenchymal mechanics and microvascular disease followed different clinical courses. Heterogeneous perfused blood volume became more uniform on follow-up visits up to 90 days. However, post COVID-19 mechanical heterogeneity of the lung parenchyma increased after apparent improvements in vascular abnormalities, even with normal spirometric indices.

A calibration CT mini-lung-phantom created by 3-D printing and subtractive manufacturing

We describe the creation and characterization of a calibration CT mini‐lung‐phantom incorporating simulated airways and ground‐glass densities. Ten duplicate mini‐lung‐phantoms with Three‐Dimensional (3‐D) printed tubes simulating airways and gradated density polyurethane foam blocks were designed and built. Dimensional accuracy and CT numbers were measured using micro‐CT and clinical CT scanners. Micro‐CT images of airway tubes demonstrated an average dimensional variation of 0.038 mm from nominal values. The five different densities of incorporated foam blocks, simulating ground‐glass, showed mean CT numbers (±standard deviation) of −897.0 ± 1.5, −844.1 ± 1.5, −774.1 ± 2.6, −695.3 ± 1.6, and −351.0 ± 3.7 HU, respectively. Three‐Dimensional printing and subtractive manufacturing enabled rapid, cost‐effective production of ground‐truth calibration mini‐lung‐phantoms with low inter‐sample variation that can be scanned simultaneously with the patient undergoing lung quantitative CT.

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

Purpose

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.

Materials and Methods

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.

Results

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.

Conclusion

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.

Computed tomography based measurements to evaluate lung density and lung growth after congenital diaphragmatic hernia

Emphysema-like-change of lung is one aspect of lung morbidity in children after congenital diaphragmatic hernia (CDH). This study aims to evaluate if the extent of reduced lung density can be quantified through pediatric chest CT examinations, if side differences are present and if emphysema-like tissue is more prominent after CDH than in controls. Thirty-seven chest CT scans of CDH patients (mean age 4.5 ± 4.0 years) were analyzed semi-automatically and compared to an age-matched control group. Emphysema-like-change was defined as areas of lung density lower than - 950 HU in percentage (low attenuating volume, LAV). A p-value lower than 0.05 was regarded as statistically significant. Hypoattenuating lung tissue was more frequently present in the ipsilateral lung than the contralateral side (LAV 12.6% vs. 5.7%; p < 0.0001). While neither ipsilateral nor contralateral lung volume differed between CDH and control (p > 0.05), LAV in ipsilateral (p = 0.0002), but not in contralateral lung (p = 0.54), was higher in CDH than control. It is feasible to quantify emphysema-like-change in pediatric patients after CDH. In the ipsilateral lung, low-density areas are much more frequently present both in comparison to contralateral and to controls. Especially the ratio of LAV ipsilateral/contralateral seems promising as a quantitative parameter in the follow-up after CDH.

QIBA guidance: Computed tomography imaging for COVID-19 quantitative imaging applications

As the COVID-19 pandemic impacts global populations, computed tomography (CT) lung imaging is being used in many countries to help manage patient care as well as to rapidly identify potentially useful quantitative COVID-19 CT imaging biomarkers. Quantitative COVID-19 CT imaging applications, typically based on computer vision modeling and artificial intelligence algorithms, include the potential for better methods to assess COVID-19 extent and severity, assist with differential diagnosis of COVID-19 versus other respiratory conditions, and predict disease trajectory. To help accelerate the development of robust quantitative imaging algorithms and tools, it is critical that CT imaging is obtained following best practices of the quantitative lung CT imaging community. Toward this end, the Radiological Society of North America's (RSNA) Quantitative Imaging Biomarkers Alliance (QIBA) CT Lung Density Profile Committee and CT Small Lung Nodule Profile Committee developed a set of best practices to guide clinical sites using quantitative imaging solutions and to accelerate the international development of quantitative CT algorithms for COVID-19. This guidance document provides quantitative CT lung imaging recommendations for COVID-19 CT imaging, including recommended CT image acquisition settings for contemporary CT scanners. Additional best practice guidance is provided on scientific publication reporting of quantitative CT imaging methods and the importance of contributing COVID-19 CT imaging datasets to open science research databases.

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.

The emerging role of quantitative imaging in asthma

Quantitative imaging of the lung has proved to be a valuable tool that has improved our understanding of asthma. CT, MRI, and positron emission tomography have all been utilized in asthma with each modality having its own distinct advantages and disadvantages. Research has now demonstrated that quantitative imaging plays a valuable role in characterizing asthma phenotypes and endotypes, as well as potentially predicting future asthma morbidity. Nonetheless, future research is needed in order to minimize radiation exposure, standardize reporting, and further delineate how imaging can predict longitudinal outcomes. With future work, quantitative imaging may make its way into the clinical care of asthma and change our practice.

Factors Affecting Radiation Dose in Computed Tomography Angiograms for Pulmonary Embolism: A Retrospective Cohort Study

Objectives

Computed tomography pulmonary angiogram (CTPA) is one of the most commonly ordered and frequently overused tests. The purpose of this study was to evaluate the mean radiation dose to patients getting CTPA and to identify factors that are associated with higher dose.

Material and Methods

This institutionally approved retrospective study included all patients who had a CTPA to rule out acute pulmonary embolism between 2016 and 2018 in a tertiary care center. Patient data (age, sex, body mass index [BMI], and patient location), CT scanner type, image reconstruction methodology, and radiation dose parameters (dose-length product [DLP]) were recorded. Effective dose estimates were obtained by multiplying DLP by conversion coefficient (0.014 mSv•mGy^-1•cm^-1). Multivariate logistic regression analysis was performed to determine the factors affecting the radiation dose.

Results

There were 2342 patients (1099 men and 1243 women) with a mean age of 58.1 years (range 0.2-104.4 years) and BMI of 31.3 kg/m² (range 12-91.5 kg/m²). The mean effective radiation dose was 5.512 mSv (median - 4.27 mSv; range 0.1-43.0 mSv). Patient factors, including BMI >25 kg/m², male sex, age >18 years, and intensive care unit (ICU) location, were associated with significantly higher dose (P < 0.05). CT scanning using third generation dual-source scanner with model-based iterative reconstruction (IR) had significantly lower dose (mean: 4.90 mSv) versus single-source (64-slice) scanner with filtered back projection (mean: 9.29 mSv, P < 0.001).

Conclusion

Patients with high BMI and ICU referrals are associated with high CT radiation dose. They are most likely to benefit by scanning on newer generation scanner using advance model-based IR techniques.

Feasibility of bronchial wall quantification in low- and ultralow-dose third-generation dual-source CT: An ex vivo lung study

PURPOSE

To investigate image quality and bronchial wall quantification in low- and ultralow-dose third-generation dual-source computed tomography (CT).

METHODS

A lung specimen from a formerly healthy male was scanned using third-generation dual-source CT at standard-dose (51 mAs/120 kV, CTDI_vol 3.41 mGy), low-dose (1/4th and 1/10th of standard dose), and ultralow-dose setting (1/20th). Low kV (70, 80, 90, and Sn100 kV) scanning was applied in each low/ultralow-dose setting, combined with adaptive mAs to keep a constant dose. Images were reconstructed at advanced modeled iterative reconstruction (ADMIRE) levels 1, 3, and 5 for each scan. Bronchial wall were semi-automatically measured from the lobar level to subsegmental level. Spearman correlation analysis was performed between bronchial wall quantification (wall thickness and wall area percentage) and protocol settings (dose, kV, and ADMIRE). ANOVA with a post hoc pairwise test was used to compare signal-to-noise ratio (SNR), noise and bronchial wall quantification values among standard- and low/ultralow-dose settings, and among ADMIRE levels.

RESULTS

Bronchial wall quantification had no correlation with dose level, kV, or ADMIRE level (|correlation coefficients| < 0.3). SNR and noise showed no statistically significant differences at different kV in the same ADMIRE level (1, 3, or 5) and in the same dose group (P > 0.05). Generally, there were no significant differences in bronchial wall quantification among the standard- and low/ultralow-dose settings, and among different ADMIRE levels (P > 0.05).

CONCLUSION

The combined use of low/ultralow-dose scanning and ADMIRE does not influence bronchial wall quantification compared to standard-dose CT. This specimen study suggests the potential that an ultralow-dose scan can be used for bronchial wall quantification.

Optimal threshold in low-dose CT quantification of emphysema

OBJECTIVE

Low-dose CT is now widely used in the screening of lung cancer and the detection of pulmonary nodules. There has also been increasing interest in using Low-dose CT for evaluating emphysema. In conventional dose CT, the threshold of -950HU is a common threshold for density-based emphysema quantification for worldwide population. However, the optimal threshold for assessing emphysema at low-dose CT has not been determined. The purpose of this study is to determine the optimal threshold for low-dose CT quantification of emphysema for Chinese population.

MATERIALS AND METHODS

In this study, 548 low-dose chest CT examinations acquired from different subjects (119 none, 49 mild, 163 moderate, 152 severe, and 65 very severe obstruction) are collected. At the level of the entire lung and individual lobes, the extent of emphysema was quantified by the percentage of the low attenuation area (LAA%) at a wide range of thresholds from -850HU to -1000HU. Both Pearson and Spearman's rank correlation coefficients were used to assess the correlations between 1) LAA% and pulmonary functions and 2) LAA% and the five-category classification. The statistical significance of the difference between correlation coefficients were evaluated using Steiger'Z test.

RESULTS

LAA% had a good correlation with both pulmonary function (|r| = 0.1-0.600, p < 0.001) and the five-category classification (r = 0.163-0.602, p < 0.001) in both the entire lung and individual lobes under different thresholds. The highest correlation coefficient is obtained at -940HU instead of -950HU.

CONCLUSION

Low-dose CT can be used for quantitative assessment of emphysema, and the threshold of -940HU is a suitable threshold for quantifying emphysema in low-dose CT images for Chinese population.

Immune-Related Adverse Events: Pneumonitis

Checkpoint inhibitors are part of the family of immunotherapies and are increasingly being used in a wide variety of cancers. Immune-related adverse events pose a major challenge in the treatment of cancer patients. Pneumonitis is a rare immune-related adverse event that presents in distinct patterns. The goal of this chapter is to instruct readers on the incidence and clinical manifestations of pneumonitis and to offer guidance in the evaluation and treatment of patients with pneumonitis.

Influence of Inspiratory/Expiratory CT Registration on Quantitative Air Trapping

RATIONALE AND OBJECTIVES

The aim of this study was to assess variability in quantitative air trapping (QAT) measurements derived from spatially aligned expiration CT scans.

MATERIALS AND METHODS

Sixty-four paired CT examinations, from 16 school-age cystic fibrosis subjects examined at four separate time intervals, were used in this study. For each pair, visually inspected lobe segmentation maps were generated and expiration CT data were registered to the inspiration CT frame. Measurements of QAT, the percentage of voxels on the expiration CT scan below a set threshold were calculated for each lobe and whole-lung from the registered expiration CT and compared to the true values from the unregistered data.

RESULTS

A mathematical model, which simulates the effect of variable regions of lung deformation on QAT values calculated from aligned to those from unaligned data, showed the potential for large bias. Assessment of experimental QAT measurements using Bland-Altman plots corroborated the model simulations, demonstrating biases greater than 5% when QAT was approximately 40% of lung volume. These biases were removed when calculating QAT from aligned expiration CT data using the determinant of the Jacobian matrix. We found, by Dice coefficient analysis, good agreement between aligned expiration and inspiration segmentation maps for the whole-lung and all but one lobe (Dice coefficient > 0.9), with only the lingula generating a value below 0.9 (mean and standard deviation of 0.85 ± 0.06).

CONCLUSION

The subtle and predictable variability in corrected QAT observed in this study suggests that image registration is reliable in preserving the accuracy of the quantitative metrics.

Reproducibility of an airway tapering measurement in computed tomography with application to bronchiectasis

We propose a pipeline to acquire a scalar tapering measurement from the carina to the most distal point of an individual airway visible on computed tomography (CT). We show the applicability of using tapering measurements on clinically acquired data by quantifying the reproducibility of the tapering measure. We generate a spline from the centerline of an airway to measure the area and arclength at contiguous intervals. The tapering measurement is the gradient of the linear regression between area in log space and arclength. The reproducibility of the measure was assessed by analyzing different radiation doses, voxel sizes, and reconstruction kernel on single timepoint and longitudinal CT scans and by evaluating the effect of airway bifurcations. Using 74 airways from 10 CT scans, we show a statistical difference, p = 3.4 × 10 - 4 , in tapering between healthy airways ( n = 35 ) and those affected by bronchiectasis ( n = 39 ). The difference between the mean of the two populations is 0.011    mm - 1 , and the difference between the medians of the two populations was 0.006    mm - 1 . The tapering measurement retained a 95% confidence interval of ± 0.005    mm - 1 in a simulated 25 mAs scan and retained a 95% confidence of ± 0.005    mm - 1 on simulated CTs up to 1.5 times the original voxel size. We have established an estimate of the precision of the tapering measurement and estimated the effect on precision of the simulated voxel size and CT scan dose. We recommend that the scanner calibration be undertaken with the phantoms as described, on the specific CT scanner, radiation dose, and reconstruction algorithm that are to be used in any quantitative studies.

Navigating Goals of Care While Confronting an Urgent, Difficult Intubation: A Case Report

Urgent airway management is challenging because time constraints limit thorough evaluation and planning before endotracheal intubation. In this report, we describe a case in which an airway history review revealed extraordinarily complex airway anatomy that led to a decision not to attempt intubation in a man with end-stage chronic obstructive pulmonary disease. We emphasize the utility of reviewing history and imaging before attempted urgent intubation. We discuss the importance of a multidisciplinary approach that includes the patient, their family, and consultants when high-risk intubation is contemplated. The ethical role of the anesthesiologist is also discussed.

Machine learning approach for distinguishing malignant and benign lung nodules utilizing standardized perinodular parenchymal features from CT

PURPOSE

Computed tomography (CT) is an effective method for detecting and characterizing lung nodules in vivo. With the growing use of chest CT, the detection frequency of lung nodules is increasing. Noninvasive methods to distinguish malignant from benign nodules have the potential to decrease the clinical burden, risk, and cost involved in follow-up procedures on the large number of false-positive lesions detected. This study examined the benefit of including perinodular parenchymal features in machine learning (ML) tools for pulmonary nodule assessment.

METHODS

Lung nodule cases with pathology confirmed diagnosis (74 malignant, 289 benign) were used to extract quantitative imaging characteristics from computed tomography scans of the nodule and perinodular parenchyma tissue. A ML tool development pipeline was employed using k-medoids clustering and information theory to determine efficient predictor sets for different amounts of parenchyma inclusion and build an artificial neural network classifier. The resulting ML tool was validated using an independent cohort (50 malignant, 50 benign).

RESULTS

The inclusion of parenchymal imaging features improved the performance of the ML tool over exclusively nodular features (P < 0.01). The best performing ML tool included features derived from nodule diameter-based surrounding parenchyma tissue quartile bands. We demonstrate similar high-performance values on the independent validation cohort (AUC-ROC = 0.965). A comparison using the independent validation cohort with the Fleischner pulmonary nodule follow-up guidelines demonstrated a theoretical reduction in recommended follow-up imaging and procedures.

CONCLUSIONS

Radiomic features extracted from the parenchyma surrounding lung nodules contain valid signals with spatial relevance for the task of lung cancer risk classification. Through standardization of feature extraction regions from the parenchyma, ML tool validation performance of 100% sensitivity and 96% specificity was achieved.

Measuring lung function in airways diseases: current and emerging techniques

Chronic airways diseases, including asthma, COPD and cystic fibrosis, cause significant morbidity and mortality and are associated with high healthcare expenditure, in the UK and worldwide. For patients with these conditions, improvements in clinical outcomes are likely to depend on the application of precision medicine, that is, the matching of the right treatment to the right patient at the right time. In this context, the identification and targeting of 'treatable traits' is an important priority in airways disease, both to ensure the appropriate use of existing treatments and to facilitate the development of new disease-modifying therapy. This requires not only better understanding of airway pathophysiology but also an enhanced ability to make physiological measurements of disease activity and lung function and, if we are to impact on the natural history of these diseases, reliable measures in early disease. In this article, we outline some of the key challenges faced by the respiratory community in the management of airways diseases, including early diagnosis, disease stratification and monitoring of therapeutic response. In this context, we review the advantages and limitations of routine physiological measurements of respiratory function including spirometry, body plethysmography and diffusing capacity and discuss less widely used methods such as forced oscillometry, inert gas washout and the multiple inert gas elimination technique. Finally, we highlight emerging technologies including imaging methods such as quantitative CT and hyperpolarised gas MRI as well as quantification of lung inhomogeneity using precise in-airway gas analysis and mathematical modelling. These emerging techniques have the potential to enhance existing measures in the assessment of airways diseases, may be particularly valuable in early disease, and should facilitate the efforts to deliver precision respiratory medicine.

Airway quantification using adaptive statistical iterative reconstruction-V on wide-detector low-dose CT: a validation study on lung specimen

PURPOSE

To evaluate the accuracy of airway quantification of adaptive statistical iterative reconstruction (ASIR)-V on low-dose CT using a human lung specimen.

METHOD

A lung specimen was scanned on Revolution CT with low-dose settings (20 mAs, 40 mAs and 60 mAs/100 kV) and standard-dose setting (100 mAs/120 kV). CT images were reconstructed using lung kernel with eleven ASIR-V levels from 0 to 100% with 10% interval. ASIR-V level from 0 to 100% with 10% interval was reconstructed on lung kernel. Wall area percentage (%WA) and wall thickness (WT) were measured.

RESULTS

Radiation dose of 20 mAs, 40 mAs and 60 mAs low-dose settings reduced by 87.6%, 75.2% and 62.8% compared to that on standard dose, respectively. Low-dose settings significantly decreased image SNR (p < 0.05) and increased noise (p < 0.001). ASIR-V level exponentially improved image SNR and linearly decreased image noise (all p < 0.001). The mean airway measurement ratios of low-dose to standard-dose were within 2% variation for %WA and within 3% variation for WT. Most %WA and WT values showed no obvious correlation with ASIR-V levels.

CONCLUSION

ASIR-V showed to improve image quality in low radiation dose. However, low-dose settings and ASIR-V strength did not significantly influence airway quantification values, although variation in measurements slightly increased with dose reduction.

Longitudinal airway remodeling in active and past smokers in a lung cancer screening population

OBJECTIVES

To longitudinally investigate smoking cessation-related changes of quantitative computed tomography (QCT)-based airway metrics in a group of heavy smokers.

METHODS

CT scans were acquired in a lung cancer screening population over 4 years at 12-month intervals in 284 long-term ex-smokers (ES), 405 continuously active smokers (CS), and 31 subjects who quitted smoking within 2 years after baseline CT (recent quitters, RQ). Total diameter (TD), lumen area (LA), and wall percentage (WP) of 1st-8th generation airways were computed using airway analysis software. Inter-group comparison was performed using Mann-Whitney U test or Student's t test (two groups), and ANOVA or ANOVA on ranks with Dunn's multiple comparison test (more than two groups), while Fisher's exact test or chi-squared test was used for categorical data. Multiple linear regression was used for multivariable analysis.

RESULTS

At any time, TD and LA were significantly higher in ES than CS, for example, in 5th-8th generation airways at baseline with 6.24 mm vs. 5.93 mm (p < 0.001) and 15.23 mm² vs. 13.51 mm² (p < 0.001), respectively. RQ showed higher TD (6.15 mm vs. 5.93 mm, n.s.) and significantly higher LA (14.77 mm² vs. 13.51 mm², p < 0.001) than CS after 3 years, and after 4 years. In multivariate analyses, smoking status independently predicted TD, LA, and WP at baseline, at 3 years and 4 years (p < 0.01-0.001), with stronger impact than pack years.

CONCLUSIONS

Bronchial dimensions depend on the smoking status. Smoking-induced airway remodeling can be partially reversible after smoking cessation even in long-term heavy smokers. Therefore, QCT-based airway metrics in clinical trials should consider the current smoking status besides pack years.

KEY POINTS

• Airway lumen and diameter are decreased in active smokers compared to ex-smokers, and there is a trend towards increased airway wall thickness in active smokers. • Smoking-related airway changes improve within 2 years after smoking cessation. • Smoking status is an independent predictor of airway dimensions.

Unilateral Chronic Lung Allograft Dysfunction Assessed by Biphasic Computed Tomographic Volumetry in Bilateral Living-donor Lobar Lung Transplantation

Background

Early diagnosis of unilateral chronic lung allograft dysfunction (CLAD) is difficult because the unaffected contralateral lung functions as a reservoir in bilateral living-donor lobar lung transplantation (LDLLT). We previously reported the usefulness of ¹³³Xe ventilation scintigraphy for detection of unilateral change, but the supply of ¹³³Xe has been stopped globally. The present study aimed to examine the usefulness of inspiratory and expiratory computed tomography (I/E CT) volumetry for detection of unilateral change in CLAD patients.

Methods

This was a retrospective single-center, observational study using prospectively collected data. A total of 58 patients who underwent bilateral LDLLT from August 2008 to February 2017 were analyzed. Respiratory function tests, I/E CT were prospectively conducted. ΔLung volume was defined as the value obtained by subtracting expiratory lung volume from inspiratory lung volume.

Results

Fourteen (24%) cases were clinically diagnosed with CLAD, of which 10 (71%) were diagnosed as unilateral CLAD. ΔLung volume of bilateral lungs strongly correlated with forced vital capacity (r = 0.92, P < 0.01) and forced expiratory volume in 1 second (r = 0.80, P < 0.01). Regardless the phenotypes (bronchiolitis obliterans syndrome or restrictive allograft syndrome) of CLAD, Δlung volume onset/baseline significantly decreased compared with that in the non-CLAD group. Among the 10 unilateral CLAD patients, 3 with clinically suspected unilateral rejection yet did not show a 20% decline in forced expiratory volume in 1 second. In 2 of these, Δlung volume of unilateral lungs on the rejection side decreased by 20% or more.

Conclusions

Our findings suggest that I/E CT volumetry may be useful for assessment and early diagnosis of unilateral CLAD after bilateral LDLLT.

Ultralow dose CT for follow-up of solid pulmonary nodules: A pilot single-center study using Bland-Altman analysis

Solid pulmonary nodules are a common finding requiring serial computed tomography (CT) imaging. We sought to explore the detection and measurement accuracy of an ultralow-dose CT (ULDCT) protocol compared with our standard low-dose CT (LDCT) nodule follow-up protocol.In this pragmatic single-center pilot prospective cohort study, patients scheduled for clinically indicated CT surveillance of 1 or more known solid pulmonary nodules >2 mm underwent ULDCT immediately after routine LDCT. The Bland-Altman 95% limits of agreement for diameter and volumetry were calculated.In all, 57 patients underwent 60 imaging episodes, with 170 evaluable nodules. ULDCT detected all known solid pulmonary nodules >2 mm. Bland-Altman analyses demonstrated clinically agreement for both nodule diameter and volume, both of which fell within prespecified limits.This single-center pilot study suggests that ULDCT may be of use in surveillance of known solid pulmonary nodules >2 mm.

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

PURPOSE

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

Immune-Related Adverse Events: Pneumonitis

Checkpoint inhibitors are part of the family of immunotherapies and are increasingly being used in a wide variety of cancers. Immune-related adverse events pose a major challenge in the treatment of cancer patients. Pneumonitis is a rare immune-related adverse event that presents in distinct patterns. The goal of this chapter is to instruct readers on the incidence and clinical manifestations of pneumonitis and to offer guidance in the evaluation and treatment of patients with pneumonitis.