Emphysema quantification by low-dose CT: potential impact of adaptive iterative dose reduction using 3D processing

Deep Learning-Based Kernel Adaptation Enhances Quantification of Emphysema on Low-Dose Chest CT for Predicting Long-Term Mortality

OBJECTIVES

The aim of this study was to ascertain the predictive value of quantifying emphysema using low-dose computed tomography (LDCT) post deep learning-based kernel adaptation on long-term mortality.

MATERIALS AND METHODS

This retrospective study investigated LDCTs obtained from asymptomatic individuals aged 60 years or older during health checkups between February 2009 and December 2016. These LDCTs were reconstructed using a 1- or 1.25-mm slice thickness alongside high-frequency kernels. A deep learning algorithm, capable of generating CT images that resemble standard-dose and low-frequency kernel images, was applied to these LDCTs. To quantify emphysema, the lung volume percentage with an attenuation value less than or equal to -950 Hounsfield units (LAA-950) was gauged before and after kernel adaptation. Low-dose chest CTs with LAA-950 exceeding 6% were deemed emphysema-positive according to the Fleischner Society statement. Survival data were sourced from the National Registry Database at the close of 2021. The risk of nonaccidental death, excluding causes such as injury or poisoning, was explored according to the emphysema quantification results using multivariate Cox proportional hazards models.

RESULTS

The study comprised 5178 participants (mean age ± SD, 66 ± 3 years; 3110 males). The median LAA-950 (18.2% vs 2.6%) and the proportion of LDCTs with LAA-950 exceeding 6% (96.3% vs 39.3%) saw a significant decline after kernel adaptation. There was no association between emphysema quantification before kernel adaptation and the risk of nonaccidental death. Nevertheless, after kernel adaptation, higher LAA-950 (hazards ratio for 1% increase, 1.01; P = 0.045) and LAA-950 exceeding 6% (hazards ratio, 1.36; P = 0.008) emerged as independent predictors of nonaccidental death, upon adjusting for age, sex, and smoking status.

CONCLUSIONS

The application of deep learning for kernel adaptation proves instrumental in quantifying pulmonary emphysema on LDCTs, establishing itself as a potential predictive tool for long-term nonaccidental mortality in asymptomatic individuals.

Area-Detector Computed Tomography for Pulmonary Functional Imaging

An area-detector CT (ADCT) has a 320-detector row and can obtain isotropic volume data without helical scanning within an area of nearly 160 mm. The actual-perfusion CT data within this area can, thus, be obtained by means of continuous dynamic scanning for the qualitative or quantitative evaluation of regional perfusion within nodules, lymph nodes, or tumors. Moreover, this system can obtain CT data with not only helical but also step-and-shoot or wide-volume scanning for body CT imaging. ADCT also has the potential to use dual-energy CT and subtraction CT to enable contrast-enhanced visualization by means of not only iodine but also xenon or krypton for functional evaluations. Therefore, systems using ADCT may be able to function as a pulmonary functional imaging tool. This review is intended to help the reader understand, with study results published during the last a few decades, the basic or clinical evidence about (1) newly applied reconstruction methods for radiation dose reduction for functional ADCT, (2) morphology-based pulmonary functional imaging, (3) pulmonary perfusion evaluation, (4) ventilation assessment, and (5) biomechanical evaluation.

Emphysema Quantifications With CT Scan: Assessing the Effects of Acquisition Protocols and Imaging Parameters Using Virtual Imaging Trials

BACKGROUND

CT scan has notable potential to quantify the severity and progression of emphysema in patients. Such quantification should ideally reflect the true attributes and pathologic conditions of subjects, not scanner parameters. To achieve such an objective, the effects of the scanner conditions need to be understood so the influence can be mitigated.

RESEARCH QUESTION

How do CT scan imaging parameters affect the accuracy of emphysema-based quantifications and biomarkers?

STUDY DESIGN AND METHODS

Twenty anthropomorphic digital phantoms were developed with diverse anatomic attributes and emphysema abnormalities informed by a real COPD cohort. The phantoms were input to a validated CT scan simulator (DukeSim), modeling a commercial scanner (Siemens Flash). Virtual images were acquired under various clinical conditions of dose levels, tube current modulations (TCM), and reconstruction techniques and kernels. The images were analyzed to evaluate the effects of imaging parameters on the accuracy of density-based quantifications (percent of lung voxels with HU < -950 [LAA-950] and 15th percentile of lung histogram HU [Perc15]) across varied subjects. Paired t tests were performed to explore statistical differences between any two imaging conditions.

RESULTS

The most accurate imaging condition corresponded to the highest acquired dose (100 mAs) and iterative reconstruction (SAFIRE) with the smooth kernel of I31, where the measurement errors (difference between measurement and ground truth) were 35 ± 3 Hounsfield Units (HU), -4% ± 5%, and 26 ± 10 HU (average ± SD), for the mean lung HU, LAA-950, and Perc15, respectively. Without TCM and at the I31 kernel, increase of dose (20 to 100 mAs) improved the lung mean absolute error (MAE) by 4.2 ± 2.3 HU (average ± SD). TCM did not contribute to a systematic improvement of lung MAE.

INTERPRETATION

The results highlight that although CT scan quantification is possible, its reliability is impacted by the choice of imaging parameters. The developed virtual imaging trial platform in this study enables comprehensive evaluation of CT scan methods in reliable quantifications, an effort that cannot be readily made with patient images or simplistic physical phantoms.

Emphysema Quantification Using Ultra-Low-Dose Chest CT: Efficacy of Deep Learning-Based Image Reconstruction

Background and Objectives: Although reducing the radiation dose level is important during diagnostic computed tomography (CT) applications, effective image quality enhancement strategies are crucial to compensate for the degradation that is caused by a dose reduction. We performed this prospective study to quantify emphysema on ultra-low-dose CT images that were reconstructed using deep learning-based image reconstruction (DLIR) algorithms, and compared and evaluated the accuracies of DLIR algorithms versus standard-dose CT. Materials and Methods: A total of 32 patients were prospectively enrolled, and all underwent standard-dose and ultra-low-dose (120 kVp; CTDIvol < 0.7 mGy) chest CT scans at the same time in a single examination. A total of six image datasets (filtered back projection (FBP) for standard-dose CT, and FBP, adaptive statistical iterative reconstruction (ASIR-V) 50%, DLIR-low, DLIR-medium, DLIR-high for ultra-low-dose CT) were reconstructed for each patient. Image noise values, emphysema indices, total lung volumes, and mean lung attenuations were measured in the six image datasets and compared (one-way repeated measures ANOVA). Results: The mean effective doses for standard-dose and ultra-low-dose CT scans were 3.43 ± 0.57 mSv and 0.39 ± 0.03 mSv, respectively (p < 0.001). The total lung volume and mean lung attenuation of five image datasets of ultra-low-dose CT scans, emphysema indices of ultra-low-dose CT scans reconstructed using ASIR-V 50 or DLIR-low, and the image noise of ultra-low-dose CT scans that were reconstructed using DLIR-low were not different from those of standard-dose CT scans. Conclusions: Ultra-low-dose CT images that were reconstructed using DLIR-low were found to be useful for emphysema quantification at a radiation dose of only 11% of that required for standard-dose CT.

A Challenge for Emphysema Quantification Using a Deep Learning Algorithm With Low-dose Chest Computed Tomography

PURPOSE

We aimed to identify clinically relevant deep learning algorithms for emphysema quantification using low-dose chest computed tomography (LDCT) through an invitation-based competition.

MATERIALS AND METHODS

The Korean Society of Imaging Informatics in Medicine (KSIIM) organized a challenge for emphysema quantification between November 24, 2020 and January 26, 2021. Seven invited research teams participated in this challenge. In total, 558 pairs of computed tomography (CT) scans (468 pairs for the training set, and 90 pairs for the test set) from 9 hospitals were collected retrospectively or prospectively. CT acquisition followed the hospitals' protocols to reflect the real-world clinical setting. Using the training set, each team developed an algorithm that generated converted LDCT by changing the pixel values of LDCT to simulate those of standard-dose CT (SDCT). The agreement between SDCT and LDCT was evaluated using the intraclass correlation coefficient (ICC; 2-way random effects, absolute agreement, and single rater) for the percentage of low-attenuated area below -950 HU (LAA-950 HU), κ value for emphysema categorization (LAA-950 HU, <5%, 5% to 10%, and ≥10%) and cosine similarity of LAA-950 HU.

RESULTS

The mean LAA-950 HU of the test set was 14.2%±10.5% for SDCT, 25.4%±10.2% for unconverted LDCT, and 12.9%±10.4%, 11.7%±10.8%, and 12.4%±10.5% for converted LDCT (top 3 teams). The agreement between the SDCT and converted LDCT of the first-place team was 0.94 (95% confidence interval: 0.90, 0.97) for ICC, 0.71 (95% confidence interval: 0.58, 0.84) for categorical agreement, and 0.97 (interquartile range: 0.94 to 0.99) for cosine similarity.

CONCLUSIONS

Emphysema quantification with LDCT was feasible through deep learning-based CT conversion strategies.

Ultra-low-dose CT combined with noise reduction techniques for quantification of emphysema in COPD patients: An intra-individual comparison study with standard-dose CT

PURPOSE

Phantom studies in CT emphysema quantification show that iterative reconstruction and deep learning-based noise reduction (DLNR) allow lower radiation dose. We compared emphysema quantification on ultra-low-dose CT (ULDCT) with and without noise reduction, to standard-dose CT (SDCT) in chronic obstructive pulmonary disease (COPD).

METHOD

Forty-nine COPD patients underwent ULDCT (third generation dual-source CT; 70ref-mAs, Sn-filter 100kVp; median CTDIvol 0.38 mGy) and SDCT (64-multidetector CT; 40mAs, 120kVp; CTDIvol 3.04 mGy). Scans were reconstructed with filtered backprojection (FBP) and soft kernel. For ULDCT, we also applied advanced modelled iterative reconstruction (ADMIRE), levels 1/3/5, and DLNR, levels 1/3/5/9. Emphysema was quantified as Low Attenuation Value percentage (LAV%, ≤-950HU). ULDCT measures were compared to SDCT as reference standard.

RESULTS

For ULDCT, the median radiation dose was 84 % lower than for SDCT. Median extent of emphysema was 18.6 % for ULD-FBP and 15.4 % for SDCT (inter-quartile range: 11.8-28.4 % and 9.2 %-28.7 %, p = 0.002). Compared to SDCT, the range in limits of agreement of emphysema quantification as measure of variability was 14.4 for ULD-FBP, 11.0-13.1 for ULD-ADMIRE levels and 10.1-13.9 for ULD-DLNR levels. Optimal settings were ADMIRE 3 and DLNR 3, reducing variability of emphysema quantification by 24 % and 27 %, at slight underestimation of emphysema extent (-1.5 % and -2.9 %, respectively).

CONCLUSIONS

Ultra-low-dose CT in COPD patients allows dose reduction by 84 %. State-of-the-art noise reduction methods in ULDCT resulted in slight underestimation of emphysema compared to SDCT. Noise reduction methods (especially ADMIRE 3 and DLNR 3) reduced variability of emphysema quantification in ULDCT by up to 27 % compared to FBP.

Potential for dose reduction in CT emphysema densitometry with post-scan noise reduction: a phantom study

OBJECTIVE

The aim of this phantom study was to investigate the effect of scan parameters and noise suppression techniques on the minimum radiation dose for acceptable image quality for CT emphysema densitometry.

METHODS

The COPDGene phantom was scanned on a third generation dual-source CT system with 16 scan setups (CTDI_vol 0.035-10.680 mGy). Images were reconstructed at 1.0/0.7 mm slice thickness/increment, with three kernels (one soft, two hard), filtered backprojection and three grades of third-generation iterative reconstruction (IR). Additionally, deep learning-based noise suppression software was applied. Main outcomes: overlap in area of the normalized histograms of CT density for the emphysema insert and lung material, and the radiation dose required for a maximum of 4.3% overlap (defined as acceptable image quality).

RESULTS

In total, 384 scan reconstructions were analyzed. Decreasing radiation dose resulted in an exponential increase of the overlap in normalized histograms of CT density. The overlap was 11-91% for the lowest dose setting (CTDI_vol 0.035mGy). The soft kernel reconstruction showed less histogram overlap than hard filter kernels. IR and noise suppression also reduced overlap. Using intermediate grade IR plus noise suppression software allowed for 85% radiation dose reduction while maintaining acceptable image quality.

CONCLUSION

CT density histogram overlap can quantify the degree of discernibility of emphysema and healthy lung tissue. Noise suppression software, IR, and soft reconstruction kernels substantially decrease the dose required for acceptable image quality.

ADVANCES IN KNOWLEDGE

Noise suppression software, IR, and soft reconstruction kernels allow radiation dose reduction by 85% while still allowing differentiation between emphysema and normal lung tissue.

Technical Note: Design and implementation of a high-throughput pipeline for reconstruction and quantitative analysis of CT image data

PURPOSE

With recent substantial improvements in modern computing, interest in quantitative imaging with CT has seen a dramatic increase. As a result, the need to both create and analyze large, high-quality datasets of clinical studies has increased as well. At present, no efficient, widely available method exists to accomplish this. The purpose of this technical note is to describe an open-source high-throughput computational pipeline framework for the reconstruction and analysis of diagnostic CT imaging data to conduct large-scale quantitative imaging studies and to accelerate and improve quantitative imaging research.

METHODS

The pipeline consists of two, primary "blocks": reconstruction and analysis. Reconstruction is carried out via a graphics processing unit (GPU) queuing framework developed specifically for the pipeline that allows a dataset to be reconstructed using a variety of different parameter configurations such as slice thickness, reconstruction kernel, and simulated acquisition dose. The analysis portion then automatically analyzes the output of the reconstruction using "modules" that can be combined in various ways to conduct different experiments. Acceleration of analysis is achieved using cluster processing. Efficiency and performance of the pipeline are demonstrated using an example 142 subject lung screening cohort reconstructed 36 different ways and analyzed using quantitative emphysema scoring techniques.

RESULTS

The pipeline reconstructed and analyzed the 5112 reconstructed datasets in approximately 10 days, a roughly 72× speedup over previous efforts using the scanner for reconstructions. Tightly coupled pipeline quality assurance software ensured proper performance of analysis modules with regard to segmentation and emphysema scoring.

CONCLUSIONS

The pipeline greatly reduced the time from experiment conception to quantitative results. The modular design of the pipeline allows the high-throughput framework to be utilized for other future experiments into different quantitative imaging techniques. Future applications of the pipeline being explored are robustness testing of quantitative imaging metrics, data generation for deep learning, and use as a test platform for image-processing techniques to improve clinical quantitative imaging.

Radiation dose reduction techniques for chest CT: Principles and clinical results

Computer tomography plays a major role in the evaluation of thoracic diseases, especially since the advent of the multidetector-row CT (MDCT) technology. However, the increase use of this technique has raised some concerns about the resulting radiation dose. In this review, we will present the various methods allowing limiting the radiation dose exposure resulting from chest CT acquisitions, including the options of image filtering and iterative reconstruction (IR) algorithms. The clinical applications of reduced dose protocols will be reviewed, especially for lung nodule detection and diagnosis of pulmonary thromboembolism. The performance of reduced dose protocols for infiltrative lung disease assessment will also be discussed. Lastly, the influence of using IR algorithms on computer-aided detection and volumetry of lung nodules, as well as on quantitative and functional assessment of chest diseases will be presented and discussed.

Emphysema quantification using chest CT: influence of radiation dose reduction and reconstruction technique

BACKGROUND

Computed tomography (CT) emphysema quantification is affected by both radiation dose (i.e. image noise) and reconstruction technique. At reduced dose, filtered back projection (FBP) results in an overestimation of the amount of emphysema due to higher noise levels, while the use of iterative reconstruction (IR) can result in an underestimation due to reduced noise. The objective of this study was to determine the influence of dose reduction and hybrid IR (HIR) or model-based IR (MIR) on CT emphysema quantification.

METHODS

Twenty-two patients underwent inspiratory chest CT scan at routine radiation dose and at 45%, 60% and 75% reduced radiation dose. Acquisitions were reconstructed with FBP, HIR and MIR. Emphysema was quantified using the 15th percentile of the attenuation curve and the percentage of voxels below -950 HU. To determine whether the use of a different percentile or HU threshold is more accurate at reduced dose levels and with IR, additional measurements were performed using different percentiles and HU thresholds to determine the optimal combination.

RESULTS

Dose reduction resulted in a significant overestimation of emphysema, while HIR and MIR resulted in an underestimation. Lower HU thresholds with FBP at reduced dose and higher HU thresholds with HIR and MIR resulted in emphysema percentages comparable to the reference. The 15th percentile quantification method showed similar results as the HU threshold method.

CONCLUSIONS

This within-patients study showed that CT emphysema quantification is significantly affected by dose reduction and IR. This can potentially be solved by adapting commonly used thresholds.

Impact of Model-Based Iterative Reconstruction on the Correlation between Computed Tomography Quantification of a Low Lung Attenuation Area and Airway Measurements and Pulmonary Function Test Results in Normal Subjects

Objective

To compare correlations between pulmonary function test (PFT) results and different reconstruction algorithms and to suggest the optimal reconstruction protocol for computed tomography (CT) quantification of low lung attenuation areas and airways in healthy individuals.

Materials and Methods

A total of 259 subjects with normal PFT and chest CT results were included. CT scans were reconstructed using filtered back projection, hybrid-iterative reconstruction, and model-based IR (MIR). For quantitative analysis, the emphysema index (EI) and wall area percentage (WA%) were determined. Subgroup analysis according to smoking history was also performed.

Results

The EIs of all the reconstruction algorithms correlated significantly with the forced expiratory volume in one second (FEV1)/forced vital capacity (FVC) (all p < 0.001). The EI of MIR showed the strongest correlation with FEV1/FVC (r = -0.437). WA% showed a significant correlation with FEV1 in all the reconstruction algorithms (all p < 0.05) correlated significantly with FEV1/FVC for MIR only (p < 0.001). The WA% of MIR showed the strongest correlations with FEV1 (r = -0.205) and FEV1/FVC (r = -0.250). In subgroup analysis, the EI of MIR had the strongest correlation with PFT in both ever-smoker and never-smoker subgroups, although there was no significant difference in the EI between the reconstruction algorithms. WA% of MIR showed a significantly thinner airway thickness than the other algorithms (49.7 ± 7.6 in ever-smokers and 49.5 ± 7.5 in never-smokers, all p < 0.001), and also showed the strongest correlation with PFT in both ever-smoker and never-smoker subgroups.

Conclusion

CT quantification of low lung attenuation areas and airways by means of MIR showed the strongest correlation with PFT results among the algorithms used, in normal subjects.

Functional respiratory imaging (FRI) for optimizing therapy development and patient care

Functional imaging techniques offer the possibility of improved visualization of anatomical structures such as; airways, lobe volumes and blood vessels. Computer-based flow simulations with a three-dimensional element add functionality to the images. By providing valuable detailed information about airway geometry, internal airflow distribution and inhalation profile, functional respiratory imaging can be of use routinely in the clinic. Three dimensional visualization allows for highly detailed follow-up in terms of disease progression or in assessing effects of interventions. Here, we explore the usefulness of functional respiratory imaging in different respiratory diseases. In patients with asthma and COPD, functional respiratory imaging has been used for phenotyping these patients, to predict the responder and non-responder phenotype and to evaluate different innovative therapeutic interventions.

Comparison of Filtered Back Projection, Hybrid Iterative Reconstruction, Model-Based Iterative Reconstruction, and Virtual Monoenergetic Reconstruction Images at Both Low- and Standard-Dose Settings in Measurement of Emphysema Volume and Airway Wall Thickness: A CT Phantom Study

Objective

To evaluate the accuracy of emphysema volume (EV) and airway measurements (AMs) produced by various iterative reconstruction (IR) algorithms and virtual monoenergetic images (VME) at both low- and standard-dose settings.

Materials and Methods

Computed tomography (CT) images were obtained on phantom at both low- (30 mAs at 120 kVp) and standard-doses (100 mAs at 120 kVp). Each CT scan was reconstructed using filtered back projection, hybrid IR (iDose⁴; Philips Healthcare), model-based IR (IMR-R1, IMR-ST1, IMR-SP1; Philips Healthcare), and VME at 70 keV (VME70). The EV of each air column and wall area percentage (WA%) of each airway tube were measured in all algorithms. Absolute percentage measurement errors of EV (APE_vol) and AM (APE_WA%) were then calculated.

Results

Emphysema volume was most accurately measured in IMR-R1 (APE_vol in low-dose, 0.053 ± 0.002; APE_vol in standard-dose, 0.047 ± 0.003; all p < 0.001) and AM was the most accurate in IMR-SP1 on both low- and standard-doses CT (APE_WA% in low-dose, 0.067 ± 0.002; APE_WA% in standard-dose, 0.06 ± 0.003; all p < 0.001). There were no significant differences in the APE_vol of IMR-R1 between low- and standard-doses (all p > 0.05). VME70 showed a significantly higher APE_vol than iDose⁴, IMR-R1, and IMR-ST1 (all p < 0.004). VME70 also showed a significantly higher APE_WA% compared with the other algorithms (all p < 0.001).

Conclusion

IMR was the most accurate technique for measurement of both EV and airway wall thickness. However, VME70 did not show a significantly better accuracy compared with other algorithms.

Quantitative Computed Tomography (CT) Assessment of Emphysema in Patients with Severe Chronic Obstructive Pulmonary Disease (COPD) and its Correlation with Age, Sex, Pulmonary Function Tests, BMI, Smoking, and Biomass Exposure

Background

To evaluate the role of HRCT in quantifying emphysema in severe COPD patients and to study the variations in the pattern of emphysema in relation to age, sex, FEV1, smoking index, biomass exposure, and BMI.

Material/Methods

Automatic lung segmentation of HRCT scans in 41 severe COPD patients (GOLD stage III or more) was done using an emphysema protocol. The extent of emphysema was assessed using the density mask method with a threshold of –950 HU (%LAA-950). The percentage of emphysema in each lung lobe and both lungs was correlated with 6 parameters – age, sex, BMI, smoking index, biomass exposure, and FEV₁.

Results

Smoking resulted in homogenously distributed emphysema regardless of the severity of smoking. BMI was inversely correlated with the extent of emphysema. A significant association was found between the percentage of emphysema in the right lower lobe and BMI (P=0.015), between biomass exposure and the percentage of emphysema in RUL, RLL, and both lungs (P values of 0.024, 0.016, and 0.036, respectively). The extent of emphysema was disproportionately low compared to the amount of obstruction on PFTs, indicating an airway predominant variety of COPD with significant biomass exposure.

Conclusions

Smoking is associated with a relatively homogenous distribution of emphysema with no regional predilection. Biomass exposure produces predominantly right-sided emphysema. BMI decreases with increasing levels of emphysema in the right lower lobe. These risk factors of emphysema patterns are helpful in deciding on the management, including surgical options.

Emphysema quantification and lung volumetry in chest X-ray equivalent ultralow dose CT - Intra-individual comparison with standard dose CT

OBJECTIVES

To determine whether ultralow dose chest CT with tin filtration can be used for emphysema quantification and lung volumetry and to assess differences in emphysema measurements and lung volume between standard dose and ultralow dose CT scans using advanced modeled iterative reconstruction (ADMIRE).

METHODS

84 consecutive patients from a prospective, IRB-approved single-center study were included and underwent clinically indicated standard dose chest CT (1.7±0.6mSv) and additional single-energy ultralow dose CT (0.14±0.01mSv) at 100kV and fixed tube current at 70mAs with tin filtration in the same session. Forty of the 84 patients (48%) had no emphysema, 44 (52%) had emphysema. One radiologist performed fully automated software-based pulmonary emphysema quantification and lung volumetry of standard and ultralow dose CT with different levels of ADMIRE. Friedman test and Wilcoxon rank sum test were used for multiple comparison of emphysema and lung volume. Lung volumes were compared using the concordance correlation coefficient.

RESULTS

The median low-attenuation areas (LAA) using filtered back projection (FBP) in standard dose was 4.4% and decreased to 2.6%, 2.1% and 1.8% using ADMIRE 3, 4, and 5, respectively. The median values of LAA in ultralow dose CT were 5.7%, 4.1% and 2.4% for ADMIRE 3, 4, and 5, respectively. There was no statistically significant difference between LAA in standard dose CT using FBP and ultralow dose using ADMIRE 4 (p=0.358) as well as in standard dose CT using ADMIRE 3 and ultralow dose using ADMIRE 5 (p=0.966). In comparison with standard dose FBP the concordance correlation coefficients of lung volumetry were 1.000, 0.999, and 0.999 for ADMIRE 3, 4, and 5 in standard dose, and 0.972 for ADMIRE 3, 4 and 5 in ultralow dose CT.

CONCLUSIONS

Ultralow dose CT at chest X-ray equivalent dose levels allows for lung volumetry as well as detection and quantification of emphysema. However, longitudinal emphysema analyses should be performed with the same scan protocol and reconstruction algorithms for reproducibility.

Adaptive iterative dose reduction 3D (AIDR 3D) vs. filtered back projection: radiation dose reduction capabilities of wide volume and helical scanning techniques on area-detector CT in a chest phantom study

BACKGROUND

Computed tomography (CT) has important roles for lung cancer screening, and therefore radiation dose reduction by using iterative reconstruction technique and scanning methods receive widespread attention.

PURPOSE

To evaluate the effect of two reconstruction techniques (filtered back projection [FBP] and adaptive iterative dose reduction using three-dimensional processing [AIDR 3D]) and two acquisition techniques (wide-volume scan [WVS] and helical scan as 64-detector-row CT [64HS]) on the lung nodule identifications of using a chest phantom.

MATERIAL AND METHODS

A chest CT phantom including lung nodules was scanned using WVS and 64HS at nine different tube currents (TCs; range, 270-10 mA). All CT datasets were reconstructed with AIDR 3D and FBP. Standard deviation (SD) measurements by region of interest placement and qualitative nodule identifications were statistically compared. 64HS and WVS were evaluated separately, and FBP images acquired with 270 mA was defined as the standard reference.

RESULTS

SDs of all datasets with AIDR 3D showed no significant differences (P > 0.05) with standard reference. When comparing nodule identifications, area under the curve on WVS with AIDR 3D with TC <30 mA, on 64HS with AIDR 3D with TC <40 mA, and on reconstructions with FBP and each scan method with TC <60 mA was significantly lower than with standard reference (P < 0.05). With the same TC and reconstruction, SDs and nodule identifications of WVS were not significantly different from 64HS (P > 0.05).

CONCLUSION

In term of SD of lung parenchyma and nodule identification, AIDR 3D can achieve more radiation dose reduction than FBP and there is no significant different between WVS and 64HS.

Emphysema Quantification Using Ultralow-Dose CT With Iterative Reconstruction and Filtered Back Projection

OBJECTIVE

The purpose of this study was to evaluate agreement between standard-dose CT (SDCT) and ultralow-dose CT (ULDCT) findings with respect to emphysema quantification. ULDCT images were reconstructed with and without iterative reconstruction (IR). Adaptive iterative dose reduction with 3D processing was used for IR.

MATERIALS AND METHODS

Fifty patients who underwent SDCT and ULDCT were included. The tube current for SDCT was 250 mA, and that for ULDCT was 10 mA. SDCT, ULDCT without IR, and ULDCT with IR were used for emphysema quantification. The low-attenuation volume percentage (LAV%) in the lungs at four thresholds (-970, -950, -930, and -910 HU), mean lung attenuation, and total lung volume were computed. Concordance correlation coefficients (CCC) were used to assess the agreement of emphysema quantification between SDCT and ULDCT.

RESULTS

The LAV% CCC values were 0.310-0.789 between SDCT and ULDCT without IR and 0.934-0.966 between SDCT and ULDCT with IR. The agreement of LAV% improved when IR was used for ULDCT. The mean lung attenuation CCC value between SDCT and ULDCT without IR was substantial (0.957), whereas that between SDCT and ULDCT with IR was poor (0.890). The total lung volume CCC values were substantial (0.982 with IR, 0.983 without IR).

CONCLUSION

ULDCT with and without IR can substitute for SDCT in emphysema quantification.

3D lung motion assessments on inspiratory/expiratory thin-section CT: Capability for pulmonary functional loss of smoking-related COPD in comparison with lung destruction and air trapping

PURPOSE

To evaluate the utility of three-dimensional (3D) lung motion on inspiratory and expiratory CT for pulmonary functional loss in smoking-related COPD in comparison with lung destruction and air trapping assessments.

METHOD AND MATERIALS

Forty-four consecutive smokers and COPD patients prospectively underwent inspiratory and expiratory CT. A 3D motion vector map was generated from these CTs, and regional motion magnitudes were measured at the horizontal axis (X-axis), the ventrodorsal axis (Y-axis), and the craniocaudal axis (Z-axis). All mean magnitudes within the entire lung (MMLX, MMLY, and MMLZ) were normalized by expiratory CT lung volume. Moreover, CT-based functional lung volume (FLV) on inspiratory CT and air trapping lung volume (ATLV) on expiratory CT were assessed quantitatively. To evaluate the capability for pulmonary function loss assessment, all MMLs were correlated with pulmonary function tests. Then, discrimination analysis was performed to determine the concordance capability for clinical stage, and correct classification capabilities were compared by means of McNemar's test.

RESULTS

Multiple regression analysis showed MMLY (β=0.657, p<0.001) and FLV (β=0.375, p=0.019) were correlated with percentage of predicted forced expiratory volume in 1 second. Correct classification capabilities using patient characteristics and MMLs (68.2 (30/44)%) were significantly higher than those obtained by patient characteristics, FLV, and ATLV (54.5 (24/44)%), p=0.031).

CONCLUSION

3D lung motion parameter assessment is useful for smoking-related COPD assessment as well as lung parenchymal destruction and/or air trapping evaluations.

Achievable dose reduction using iterative reconstruction for chest computed tomography: A systematic review

OBJECTIVES

Iterative reconstruction (IR) allows for dose reduction with maintained image quality in CT imaging. In this systematic review the reported effective dose reductions for chest CT and the effects on image quality are investigated.

METHODS

A systematic search in PubMed and EMBASE was performed. Primary outcome was the reported local reference and reduced effective dose and secondary outcome was the image quality with IR. Both non contrast-enhanced and enhanced studies comparing reference dose with reduced dose were included.

RESULTS

24 studies were included. The median number of patients per study was 66 (range 23-200) with in total 1806 patients. The median reported local reference dose of contrast-enhanced chest CT with FBP was 2.6 (range 1.5-21.8) mSv. This decreased to 1.4 (range 0.4-7.3) mSv at reduced dose levels using IR. With non contrast-enhanced chest CT the dose decreased from 3.4 (range 0.7-7.8) mSv to 0.9 (range 0.1-4.5) mSv. Objective mage quality and diagnostic confidence and acceptability remained the same or improved with IR compared to FBP in most studies while data on diagnostic accuracy was limited.

CONCLUSION

Radiation dose can be reduced to less than 2 mSv for contrast-enhanced chest CT and non contrast-enhanced chest CT is possible at a submillisievert dose using IR algorithms.

Low-dose CT screening using hybrid iterative reconstruction: confidence ratings of diagnoses of simulated lesions other than lung cancer

OBJECTIVE

To evaluate the confidence ratings of diagnoses of simulated lesions other than lung cancer on low-dose screening CT with hybrid iterative reconstruction (IR).

METHODS

Simulated lesions (emphysema, mediastinal masses and interstitial pneumonia) in a chest phantom were scanned by a 320-row area detector CT. The scans were performed by 64-row and 160-row helical scans at various dose levels and were reconstructed by filtered back projection (FBP) and IR. Emphysema, honeycombing and reticular opacity were visually scored on a four-point scale by six thoracic radiologists. The ground-glass opacity as a percentage of total lung volume (%GGO), CT value and contrast-to-noise ratio (CNR) of mediastinal masses were calculated. These scores and values were compared between FBP and IR. Wilcoxon's signed-rank test was used (p < 0.05). Interobserver agreements were evaluated by κ statistics.

RESULTS

There were no significant differences in visual assessment. Interobserver agreement was almost perfect. CT values were almost equivalent between FBP and IR, whereas CNR with IR was significantly higher than that with FBP. %GGO significantly increased at low-dose levels with FBP; however, IR suppressed the elevation.

CONCLUSION

The confidence ratings of diagnoses of simulated lesions other than lung cancer on low-dose CT screening were not degraded with hybrid IR compared with FBP.

ADVANCES IN KNOWLEDGE

Hybrid IR did not degrade the confidence ratings of diagnoses on visual assessment and differential diagnoses based on CT value of mediastinal masses, and it showed the advantage of higher GGO conspicuity at low-dose level. Radiologists can analyse images of hybrid IR alone on low-dose CT screening for lung cancer.

Ultralow-radiation-dose chest CT: accuracy for lung densitometry and emphysema detection

OBJECTIVE

The purpose of this study was to determine whether ultralow-radiation-dose chest CT can be used for quantification of lung density and for emphysema detection in participants undergoing lung cancer screening.

SUBJECTS AND METHODS

Fifty-two patients were prospectively enrolled and underwent scanning twice with low-dose CT (reference parameters, 120 kV, 50 effective mAs) and ultralow-dose CT (reference parameters, 80 kV, 4-5 effective mAs). Images were reconstructed by filtered back projection (FBP) for low-dose CT and FBP and iterative reconstruction (IR) for ultralow-dose CT. Radiation dose was recorded. Image noise, mean lung attenuation, 15th percentile of lung attenuation, and emphysema index were measured in each image series and compared. Test characteristics of ultralow-dose CT in detecting more than subtle emphysema (emphysema index≥3%) were calculated.

RESULTS

The effective dose of low-dose CT was 2.1±0.5 mSv, and that of ultralow-dose CT was 0.13±0.04 mSv. Compared with the findings for low-dose CT, absolute overestimation of emphysema index was 7% on ultralow-dose CT images reconstructed with FBP and 2% on those processed with IR. The 15th percentile of lung attenuation was underestimated by 21.3 HU on ultralow-dose FBP images and by 5.8 HU on IR images. No relevant bias was observed for mean lung attenuation. Four patients (8%) had more than subtle emphysema. The emphysema index measured at ultralow-dose CT with FBP and IR had 100% and 100% sensitivity and 92% and 96% specificity in identifying patients with more than subtle emphysema at a cutoff of greater than 12.1% for FBP and greater than 6.7% for IR.

CONCLUSION

Ultralow-dose chest CT performed for lung cancer screening can be used for quantification of lung density and for emphysema detection. IR improves the accuracy of ultralow-dose CT in this setting.

CT-Definable Subtypes of Chronic Obstructive Pulmonary Disease: A Statement of the Fleischner Society

The purpose of this statement is to describe and define the phenotypic abnormalities that can be identified on visual and quantitative evaluation of computed tomographic (CT) images in subjects with chronic obstructive pulmonary disease (COPD), with the goal of contributing to a personalized approach to the treatment of patients with COPD. Quantitative CT is useful for identifying and sequentially evaluating the extent of emphysematous lung destruction, changes in airway walls, and expiratory air trapping. However, visual assessment of CT scans remains important to describe patterns of altered lung structure in COPD. The classification system proposed and illustrated in this article provides a structured approach to visual and quantitative assessment of COPD. Emphysema is classified as centrilobular (subclassified as trace, mild, moderate, confluent, and advanced destructive emphysema), panlobular, and paraseptal (subclassified as mild or substantial). Additional important visual features include airway wall thickening, inflammatory small airways disease, tracheal abnormalities, interstitial lung abnormalities, pulmonary arterial enlargement, and bronchiectasis.

Iterative reconstruction for quantitative computed tomography analysis of emphysema: consistent results using different tube currents

Purpose

To assess the advantages of iterative reconstruction for quantitative computed tomography (CT) analysis of pulmonary emphysema.

Materials and methods

Twenty-two patients with pulmonary emphysema underwent chest CT imaging using identical scanners with three different tube currents: 240, 120, and 60 mA. Scan data were converted to CT images using Adaptive Iterative Dose Reduction using Three Dimensional Processing (AIDR3D) and a conventional filtered-back projection mode. Thus, six scans with and without AIDR3D were generated per patient. All other scanning and reconstruction settings were fixed. The percent low attenuation area (LAA%; < −950 Hounsfield units) and the lung density 15th percentile were automatically measured using a commercial workstation. Comparisons of LAA% and 15th percentile results between scans with and without using AIDR3D were made by Wilcoxon signed-rank tests. Associations between body weight and measurement errors among these scans were evaluated by Spearman rank correlation analysis.

Results

Overall, scan series without AIDR3D had higher LAA% and lower 15th percentile values than those with AIDR3D at each tube current (P<0.0001). For scan series without AIDR3D, lower tube currents resulted in higher LAA% values and lower 15th percentiles. The extent of emphysema was significantly different between each pair among scans when not using AIDR3D (LAA%, P<0.0001; 15th percentile, P<0.01), but was not significantly different between each pair among scans when using AIDR3D. On scans without using AIDR3D, measurement errors between different tube current settings were significantly correlated with patients’ body weights (P<0.05), whereas these errors between scans when using AIDR3D were insignificantly or minimally correlated with body weight.

Conclusion

The extent of emphysema was more consistent across different tube currents when CT scans were converted to CT images using AIDR3D than using a conventional filtered-back projection method.

Airflow limitation in chronic obstructive pulmonary disease: ratio and difference of percentage of low-attenuation lung regions in paired inspiratory/expiratory computed tomography

RATIONALE AND OBJECTIVES

The purpose of this study was to analyze the relationship between airflow limitation and two types of computed tomography (CT) measurements: expiratory/inspiratory (E/I) ratio and E/I difference of percentage of low-attenuation lung regions (LAA%).

MATERIALS AND METHODS

Thirty patients who underwent inspiratory and expiratory CT scans were included in this study. The CT data were used to calculate the LAA% E/I ratio and E/I difference. Other types of CT measurements were also obtained, including the E/I ratio and E/I difference of lung volume, mean lung density, standard deviation, skewness, and kurtosis. LAA% was calculated at 20 thresholds (-990 to -800 HU). Pearson's correlation between the measurements and forced expiratory flow in 1 second was used to determine the efficacy of LAA% E/I ratio and E/I difference. P values of <5.88 × 10⁻⁵ were considered statistically significant with Bonferroni correction.

RESULTS

The LAA% E/I ratio and E/I difference significantly correlated with forced expiratory flow in 1 second. The best correlation coefficient for the LAA% E/I ratio was -0.699 (P = 1.75 × 10⁻⁵) and for the LAA% E/I difference was -0.723 (P = 6.53 × 10⁻⁶). The best correlation coefficient for the LAA% E/I difference was stronger than that for the other types of CT measurements.

CONCLUSIONS

The LAA% E/I ratio and E/I difference significantly correlated with airflow limitation in chronic obstructive pulmonary disease.

The effects of dynamic hyperinflation on CT emphysema measurements in patients with COPD

OBJECTIVES

Dynamic hyperinflation (DH) significantly affects dyspnea and intolerance to exercise in patients with chronic obstructive pulmonary disease (COPD). Quantitative computed tomography (QCT) of the chest is the modality of choice for quantification of the extent of anatomical lung damage in patients with COPD. The purpose of this article is to assess the effects of DH on QCT measurements.

METHODS

The study sample comprised patients with Global initiative for Chronic Obstructive Lung Disease (GOLD) stages III and IV COPD referred for chest CT. We examined differences in total lung volume (TLV), emphysema volume (EV), and emphysema index (EI) determined by QCT before and after DH induction by metronome-paced tachypnea (MPT). Initial (resting) and post-MPT CT examinations were performed with the same parameters.

RESULTS

Images from 66 CT scans (33 patients) were evaluated. EV and EI, but not TLV, increased significantly (p<0.0001) after DH induction.

CONCLUSION

QCT showed significant increases in EV and EI after MPT-induced DH in patients with GOLD stages III and IV COPD. For longitudinal assessment of patients with COPD using QCT, we recommend the application of a pre-examination rest period, as DH could mimic disease progression. QCT studies of the effects of DH-preventive therapy before exercise could expand our knowledge of effective measures to delay DH-related progression of COPD.

Adaptive Iterative Dose Reduction Using Three Dimensional Processing (AIDR3D) improves chest CT image quality and reduces radiation exposure

Objective

To assess the advantages of Adaptive Iterative Dose Reduction using Three Dimensional Processing (AIDR3D) for image quality improvement and dose reduction for chest computed tomography (CT).

Methods

Institutional Review Boards approved this study and informed consent was obtained. Eighty-eight subjects underwent chest CT at five institutions using identical scanners and protocols. During a single visit, each subject was scanned using different tube currents: 240, 120, and 60 mA. Scan data were converted to images using AIDR3D and a conventional reconstruction mode (without AIDR3D). Using a 5-point scale from 1 (non-diagnostic) to 5 (excellent), three blinded observers independently evaluated image quality for three lung zones, four patterns of lung disease (nodule/mass, emphysema, bronchiolitis, and diffuse lung disease), and three mediastinal measurements (small structure visibility, streak artifacts, and shoulder artifacts). Differences in these scores were assessed by Scheffe's test.

Results

At each tube current, scans using AIDR3D had higher scores than those without AIDR3D, which were significant for lung zones (p<0.0001) and all mediastinal measurements (p<0.01). For lung diseases, significant improvements with AIDR3D were frequently observed at 120 and 60 mA. Scans with AIDR3D at 120 mA had significantly higher scores than those without AIDR3D at 240 mA for lung zones and mediastinal streak artifacts (p<0.0001), and slightly higher or equal scores for all other measurements. Scans with AIDR3D at 60 mA were also judged superior or equivalent to those without AIDR3D at 120 mA.

Conclusion

For chest CT, AIDR3D provides better image quality and can reduce radiation exposure by 50%.

Evaluation of dose reduction and image quality in CT colonography: comparison of low-dose CT with iterative reconstruction and routine-dose CT with filtered back projection

OBJECTIVE

To prospectively evaluate the radiation dose and image quality comparing low-dose CT colonography (CTC) reconstructed using different levels of iterative reconstruction techniques with routine-dose CTC reconstructed with filtered back projection.

METHODS

Following institutional ethics clearance and informed consent procedures, 210 patients underwent screening CTC using automatic tube current modulation for dual positions. Examinations were performed in the supine position with a routine-dose protocol and in the prone position, randomly applying four different low-dose protocols. Supine images were reconstructed with filtered back projection and prone images with iterative reconstruction. Two blinded observers assessed the image quality of endoluminal images. Image noise was quantitatively assessed by region-of-interest measurements.

RESULTS

The mean effective dose in the supine series was 1.88 mSv using routine-dose CTC, compared to 0.92, 0.69, 0.57, and 0.46 mSv at four different low doses in the prone series (p < 0.01). Overall image quality and noise of low-dose CTC with iterative reconstruction were significantly improved compared to routine-dose CTC using filtered back projection. The lowest dose group had image quality comparable to routine-dose images.

CONCLUSIONS

Low-dose CTC with iterative reconstruction reduces the radiation dose by 48.5 to 75.1% without image quality degradation compared to routine-dose CTC with filtered back projection.

KEY POINTS

• Low-dose CTC reduces radiation dose ≥ 48.5% compared to routine-dose CTC. • Iterative reconstruction improves overall CTC image quality compared with FBP. • Iterative reconstruction reduces overall CTC image noise compared with FBP. • Automated exposure control with iterative reconstruction is useful for low-dose CTC.

Iterative reconstruction technique vs filter back projection: utility for quantitative bronchial assessment on low-dose thin-section MDCT in patients with/without chronic obstructive pulmonary disease

OBJECTIVES

The aim of this study was to evaluate the utility of the iterative reconstruction (IR) technique for quantitative bronchial assessment during low-dose computed tomography (CT) as a substitute for standard-dose CT in patients with/without chronic obstructive pulmonary disease.

METHODS

Fifty patients (mean age, 69.2; mean % predicted FEV1, 79.4) underwent standard-dose CT (150mAs) and low-dose CT (25mAs). Except for tube current, the imaging parameters were identical for both protocols. Standard-dose CT was reconstructed using filtered back-projection (FBP), and low-dose CT was reconstructed using IR and FBP. For quantitative bronchial assessment, the wall area percentage (WA%) of the sub-segmental bronchi and the airway luminal volume percentage (LV%) from the main bronchus to the peripheral bronchi were acquired in each dataset. The correlation and agreement of WA% and LV% between standard-dose CT and both low-dose CTs were statistically evaluated.

RESULTS

WA% and LV% between standard-dose CT and both low-dose CTs were significant correlated (r > 0.77, p < 0.00001); however, only the LV% agreement between SD-CT and low-dose CT reconstructed with IR was moderate (concordance correlation coefficient = 0.93); the other agreement was poor (concordance correlation coefficient <0.90).

CONCLUSIONS

Quantitative bronchial assessment via low-dose CT has potential as a substitute for standard-dose CT by using IR and airway luminal volumetry techniques.

KEY POINTS

• Quantitative bronchial assessment of COPD using low-dose CT is possible. • Airway luminal volumetry with iterative reconstruction is insusceptible to dose reduction. • Filtered back-projection is susceptible to the effect of dose reduction. • Wall area percentage assessment is easily influenced by dose reduction.

Progress in Imaging COPD, 2004 - 2014

Computed tomography (CT) has contributed substantially to our understanding of COPD over the past decade. Visual and quantitative assessments of CT in COPD are complementary. Visual assessment should provide assessment of centrilobular, panlobular and paraseptal emphysema, airway wall thickening, bronchiectasis, findings of respiratory bronchiolitis, and enlargement of the pulmonary artery. Quantitative CT permits evaluation of severity of emphysema, airway wall thickening, and expiratory air trapping, and is now being used for longitudinal evaluation of the progression of COPD. Innovative techniques are being developed to use CT to characterize the pattern of emphysema and smoking- related respiratory bronchiolitis. Magnetic resonance imaging (MRI) and positron emission tomography PET-CT are useful research tools in the evaluation of COPD.

Recent advances in thoracic x-ray computed tomography for pulmonary imaging

The present article reviews recent advances in pulmonary computed tomography (CT) imaging, focusing on the application of dual-energy CT and the use of iterative reconstruction. Dual-energy CT has proven to be useful in the characterization of pulmonary blood pool in the setting of pulmonary embolism, characterization of diffuse lung parenchymal diseases, evaluation of thoracic malignancies and in imaging of lung ventilation using inhaled xenon. The benefits of iterative reconstruction have been largely derived from reduction of image noise compared with filtered backprojection reconstructions which, in turn, enables the use of lower radiation dose CT acquisition protocols without sacrificing image quality. Potential clinical applications of iterative reconstruction include imaging for pulmonary nodules and high-resolution pulmonary CT.

Quantitative analysis of emphysema and airway measurements according to iterative reconstruction algorithms: comparison of filtered back projection, adaptive statistical iterative reconstruction and model-based iterative reconstruction

OBJECTIVES

To evaluate filtered back projection (FBP) and two iterative reconstruction (IR) algorithms and their effects on the quantitative analysis of lung parenchyma and airway measurements on computed tomography (CT) images.

METHODS

Low-dose chest CT obtained in 281 adult patients were reconstructed using three algorithms: FBP, adaptive statistical IR (ASIR) and model-based IR (MBIR). Measurements of each dataset were compared: total lung volume, emphysema index (EI), airway measurements of the lumen and wall area as well as average wall thickness. Accuracy of airway measurements of each algorithm was also evaluated using an airway phantom.

RESULTS

EI using a threshold of -950 HU was significantly different among the three algorithms in decreasing order of FBP (2.30 %), ASIR (1.49 %) and MBIR (1.20 %) (P < 0.01). Wall thickness was also significantly different among the three algorithms with FBP (2.09 mm) demonstrating thicker walls than ASIR (2.00 mm) and MBIR (1.88 mm) (P < 0.01). Airway phantom analysis revealed that MBIR showed the most accurate value for airway measurements.

CONCLUSION

The three algorithms presented different EIs and wall thicknesses, decreasing in the order of FBP, ASIR and MBIR. Thus, care should be taken in selecting the appropriate IR algorithm on quantitative analysis of the lung.

KEY POINTS

• Computed tomography is increasingly used to provide objective measurements of intra-thoracic structures. • Iterative reconstruction algorithms can affect quantitative measurements of lung and airways. • Care should be taken in selecting reconstruction algorithms in longitudinal analysis. • Model-based iterative reconstruction seems to provide the most accurate airway measurements.

Low-dose CT for quantitative analysis in acute respiratory distress syndrome

INTRODUCTION

The clinical use of serial quantitative computed tomography (CT) to characterize lung disease and guide the optimization of mechanical ventilation in patients with acute respiratory distress syndrome (ARDS) is limited by the risk of cumulative radiation exposure and by the difficulties and risks related to transferring patients to the CT room. We evaluated the effects of tube current-time product (mAs) variations on quantitative results in healthy lungs and in experimental ARDS in order to support the use of low-dose CT for quantitative analysis.

METHODS

In 14 sheep chest CT was performed at baseline and after the induction of ARDS via intravenous oleic acid injection. For each CT session, two consecutive scans were obtained applying two different mAs: 60 mAs was paired with 140, 15 or 7.5 mAs. All other CT parameters were kept unaltered (tube voltage 120 kVp, collimation 32 × 0.5 mm, pitch 0.85, matrix 512 × 512, pixel size 0.625 × 0.625 mm). Quantitative results obtained at different mAs were compared via Bland-Altman analysis.

RESULTS

Good agreement was observed between 60 mAs and 140 mAs and between 60 mAs and 15 mAs (all biases less than 1%). A further reduction of mAs to 7.5 mAs caused an increase in the bias of poorly aerated and nonaerated tissue (-2.9% and 2.4%, respectively) and determined a significant widening of the limits of agreement for the same compartments (-10.5% to 4.8% for poorly aerated tissue and -5.9% to 10.8% for nonaerated tissue). Estimated mean effective dose at 140, 60, 15 and 7.5 mAs corresponded to 17.8, 7.4, 2.0 and 0.9 mSv, respectively. Image noise of scans performed at 140, 60, 15 and 7.5 mAs corresponded to 10, 16, 38 and 74 Hounsfield units, respectively.

CONCLUSIONS

A reduction of effective dose up to 70% has been achieved with minimal effects on lung quantitative results. Low-dose computed tomography provides accurate quantitative results and could be used to characterize lung compartment distribution and possibly monitor time-course of ARDS with a lower risk of exposure to ionizing radiation. A further radiation dose reduction is associated with lower accuracy in quantitative results.