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

Impact of Simulated Reduced-Dose Chest CT on Diagnosing Pulmonary T1 Tumors and Patient Management

To determine the diagnostic performance of simulated reduced-dose chest CT scans regarding pulmonary T1 tumors and assess the potential impact on patient management, a repository of 218 patients with histologically proven pulmonary T1 tumors was used. Virtual reduced-dose images were simulated at 25%- and 5%-dose levels. Tumor size, attenuation, and localization were scored by two experienced chest radiologists. The impact on patient management was assessed by comparing hypothetical LungRADS scores. The study included 210 patients (41% females, mean age 64.5 ± 9.2 years) with 250 eligible T1 tumors. There were differences between the original and the 5%-but not the 25%-dose simulations, and LungRADS scores varied between the dose levels with no clear trend. Sensitivity of Reader 1 was significantly lower using the 5%-dose vs. 25%-dose vs. original dose for size categorization (0.80 vs. 0.85 vs. 0.84; p = 0.007) and segmental localization (0.81 vs. 0.86 vs. 0.83; p = 0.018). Sensitivities of Reader 2 were unaffected by a dose reduction. A CT dose reduction may affect the correct categorization and localization of pulmonary T1 tumors and potentially affect patient management.

Improving Image Quality and Nodule Characterization in Ultra-low-dose Lung CT with Deep Learning Image Reconstruction

RATIONALE AND OBJECTIVE

To investigate the influence of the deep learning image reconstruction (DLIR) on the image quality and quantitative analysis of pulmonary nodules under ultra-low dose lung CT conditions.

MATERIALS AND METHODS

This was a prospective study with patient consent and included 56 patients with suspected pulmonary nodules. Patients were examined by both standard-dose CT (SDCT) and ultra-low-dose CT (ULDCT). SDCT images were reconstructed with adaptive statistical iterative reconstruction-V 40% (ASIR-V40%) (group A), while ULDCT images were reconstructed using ASIR-V40% (group B) and high-strength DLIR (DLIR-H) (group C). The three image sets were analyzed using a commercial computer aided diagnosis (CAD) software. Parameters such as nodule length, width, density, volume, risk, and classification were measured. The CAD quantitative data of different nodule types (solid, calcified, and subsolid nodules) and nodule image quality scores evaluated by two physicians on a 5-point scale were compared.

RESULT

The radiation dose in ULDCT was 0.25 ± 0.08mSv, 7.2% that of the 3.48 ± 1.08mSv in SDCT (P < 0.001). 104 pulmonary nodules were detected (51/53 solid, 26/24 calcified and 27/27 subsolid in Groups A and (B&C), respectively). Group B had lower density for solid, calcified nodules, and lower volume and risk for subsolid nodules than Group A, while Group C had lower density for calcified nodules (P < 0.05), There were no significant differences in other parameters among the three groups (P > 0.05). Group A and C had similar image quality for nodules and were higher than Group B (P < 0.05).

CONCLUSION

DLIR-H significantly improves image quality than ASIR-V40% and maintains similar nodule detection and characterization with CAD in ULDCT compared to SDCT.

Influence of CT dose reduction on AI-driven malignancy estimation of incidental pulmonary nodules

OBJECTIVES

The purpose of this study was to determine the influence of dose reduction on a commercially available lung cancer prediction convolutional neuronal network (LCP-CNN).

METHODS

CT scans from a cohort provided by the local lung cancer center (n = 218) with confirmed pulmonary malignancies and their corresponding reduced dose simulations (25% and 5% dose) were subjected to the LCP-CNN. The resulting LCP scores (scale 1-10, increasing malignancy risk) and the proportion of correctly classified nodules were compared. The cohort was divided into a low-, medium-, and high-risk group based on the respective LCP scores; shifts between the groups were studied to evaluate the potential impact on nodule management. Two different malignancy risk score thresholds were analyzed: a higher threshold of ≥ 9 ("rule-in" approach) and a lower threshold of > 4 ("rule-out" approach).

RESULTS

In total, 169 patients with 196 nodules could be included (mean age ± SD, 64.5 ± 9.2 year; 49% females). Mean LCP scores for original, 25% and 5% dose levels were 8.5 ± 1.7, 8.4 ± 1.7 (p > 0.05 vs. original dose) and 8.2 ± 1.9 (p < 0.05 vs. original dose), respectively. The proportion of correctly classified nodules with the "rule-in" approach decreased with simulated dose reduction from 58.2 to 56.1% (p = 0.34) and to 52.0% for the respective dose levels (p = 0.01). For the "rule-out" approach the respective values were 95.9%, 96.4%, and 94.4% (p = 0.12). When reducing the original dose to 25%/5%, eight/twenty-two nodules shifted to a lower, five/seven nodules to a higher malignancy risk group.

CONCLUSION

CT dose reduction may affect the analyzed LCP-CNN regarding the classification of pulmonary malignancies and potentially alter pulmonary nodule management.

CLINICAL RELEVANCE STATEMENT

Utilization of a "rule-out" approach with a lower malignancy risk threshold prevents underestimation of the nodule malignancy risk for the analyzed software, especially in high-risk cohorts.

KEY POINTS

• LCP-CNN may be affected by CT image parameters such as noise resulting from low-dose CT acquisitions. • CT dose reduction can alter pulmonary nodule management recommendations by affecting the outcome of the LCP-CNN. • Utilization of a lower malignancy risk threshold prevents underestimation of pulmonary malignancies in high-risk cohorts.

Body composition assessment with limited field-of-view computed tomography: A semantic image extension perspective

Field-of-view (FOV) tissue truncation beyond the lungs is common in routine lung screening computed tomography (CT). This poses limitations for opportunistic CT-based body composition (BC) assessment as key anatomical structures are missing. Traditionally, extending the FOV of CT is considered as a CT reconstruction problem using limited data. However, this approach relies on the projection domain data which might not be available in application. In this work, we formulate the problem from the semantic image extension perspective which only requires image data as inputs. The proposed two-stage method identifies a new FOV border based on the estimated extent of the complete body and imputes missing tissues in the truncated region. The training samples are simulated using CT slices with complete body in FOV, making the model development self-supervised. We evaluate the validity of the proposed method in automatic BC assessment using lung screening CT with limited FOV. The proposed method effectively restores the missing tissues and reduces BC assessment error introduced by FOV tissue truncation. In the BC assessment for large-scale lung screening CT datasets, this correction improves both the intra-subject consistency and the correlation with anthropometric approximations. The developed method is available at https://github.com/MASILab/S-EFOV.

Correlation of lung function with ultra-low-dose CT-detected lung parenchymal abnormalities: a cohort study of 1344 asbestos exposed individuals

INTRODUCTION

Deliberate exposure to medical ionising radiation should be as low as reasonably practicable but the reduction of radiation from CT should be balanced against diagnostic image quality. The ability of ultra-low-dose CT (uLDCT: similar radiation to chest X-ray) to demonstrate low contrast abnormalities (emphysema and interstitial lung abnormality (ILA)) is unclear.The aim of this cross-sectional study was to analyse the lung parenchymal findings from uLDCT scans against physiological measures of respiratory function.

METHODS

WA Asbestos Review Programme participants were eligible if they had an uLDCT scan and lung function assessment between Janary and December 2018. All scans were performed using a single CT machine and reported using a standardised, semiquantitative synoptic report which includes emphysema and linear fibrosis (ILA) scores.

RESULTS

Of 1344 participants, median (IQR) age was 72.0 (65.0-78.0) years, the majority were males (84.9%) with mixed occupational asbestos exposure (68.1%). There were 721 (53.6%) with no abnormality, 158 (11.8%) with emphysema, 465 (34.6%) with ILA. Mean radiation dose was 0.12 mSv. There was statistically significant between group differences for all physiological parameters of lung function compared with controls. For instance, the emphysema score significantly correlated with obstructive forced expiratory volume in 1 s (FEV₁)/forced vital capacity ratio (r=0.512), per cent predicted FEV₁ (r=0.24) and lower diffusion of carbon monoxide (DLCO) (r=0.337). Multivariate modelling demonstrated that increasing age, emphysema and fibrosis scores predicted reduced DLCO (adjusted R²=0.30).

DISCUSSION

uLDCT-detected parenchymal lung abnormalities correlate strongly with significant changes on lung function testing suggesting the observed CT abnormalities are of physiological and clinical significance.

Chest CT scan for the screening of air anomalies at risk of pulmonary barotrauma for the initial medical assessment of fitness to dive in a military population

Introduction: The presence of intra-pulmonary air lesions such as cysts, blebs and emphysema bullae, predisposes to pulmonary barotrauma during pressure variations, especially during underwater diving activities. These rare accidents can have dramatic consequences. Chest radiography has long been the baseline examination for the detection of respiratory pathologies in occupational medicine. It has been replaced since 2018 by the thoracic CT scan for military diving fitness in France. The objective of this work was to evaluate the prevalence of the pulmonary abnormalities of the thoracic CT scan, and to relate them to the characteristics of this population and the results of the spirometry.

Methods: 330 records of military diving candidates who underwent an initial assessment between October 2018 and March 2021 were analyzed, in a single-center retrospective analysis. The following data were collected: sex, age, BMI, history of respiratory pathologies and smoking, treatments, allergies, diving practice, results of spirometry, reports of thoracic CT scans, as well as fitness decision.

Results: The study included 307 candidates, mostly male, with a median age of 25 years. 19% of the subjects had abnormal spirometry. We identified 25% of divers with CT scan abnormalities. 76% of the abnormal scans were benign nodules, 26% of which measured 6 mm or more. Abnormalities with an aerial component accounted for 13% of the abnormal scans with six emphysema bullae, three bronchial dilatations and one cystic lesion. No association was found between the presence of nodules and the general characteristics of the population, whereas in six subjects emphysema bullae were found statistically associated with active smoking or abnormal spirometry results.

Conclusion: The systematic performance of thoracic CT scan in a young population free of pulmonary pathology revealed a majority of benign nodules. Abnormalities with an aerial component are much less frequent, but their presence generally leads to a decision of unfitness. These results argue in favor of a systematic screening of aeric pleuro-pulmonary lesions during the initial assessment for professional divers.

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.

Airspace Dimension Assessment with Nanoparticles (AiDA) in Comparison to Established Pulmonary Function Tests

Background

Airspace Dimensions Assessment with nanoparticles (AiDA) is a new method for non-invasive measurement of pulmonary distal airspaces. The aim of this study was to compare AiDA measurements with other pulmonary function variables to better understand the potential of AiDA in a clinical context.

Methods

AiDA measurements and pulmonary function tests were performed in 695 subjects as part of the Swedish CArdioPulmonary bioImage Study. The measurement protocol included spirometry, measurement of diffusing capacity of carbon monoxide, oscillometry and pulmonary computed tomography. AiDA indices were compared to all other pulmonary examination measurements using multivariate statistical analysis.

Results

Our results show that AiDA measurements were significantly correlated with other pulmonary function examination indices, although covariance was low. We found that AiDA variables explained variance in the data that other lung function variables only influenced to a minor extent.

Conclusion

We conclude that the AiDA method provides information about the lung that is inaccessible with more conventional lung function techniques.

First Performance Evaluation of an Artificial Intelligence-Based Computer-Aided Detection System for Pulmonary Nodule Evaluation in Dual-Source Photon-Counting Detector CT at Different Low-Dose Levels

OBJECTIVE

The aim of this study was to evaluate the image quality (IQ) and performance of an artificial intelligence (AI)-based computer-aided detection (CAD) system in photon-counting detector computed tomography (PCD-CT) for pulmonary nodule evaluation at different low-dose levels.

MATERIALS AND METHODS

An anthropomorphic chest-phantom containing 14 pulmonary nodules of different sizes (range, 3-12 mm) was imaged on a PCD-CT and on a conventional energy-integrating detector CT (EID-CT). Scans were performed with each of the 3 vendor-specific scanning modes (QuantumPlus [Q+], Quantum [Q], and High Resolution [HR]) at decreasing matched radiation dose levels (volume computed tomography dose index ranging from 1.79 to 0.31 mGy) by adapting IQ levels from 30 to 5. Image noise was measured manually in the chest wall at 8 different locations. Subjective IQ was evaluated by 2 readers in consensus. Nodule detection and volumetry were performed using a commercially available AI-CAD system.

RESULTS

Subjective IQ was superior in PCD-CT compared with EID-CT (P < 0.001), and objective image noise was similar in the Q+ and Q-mode (P > 0.05) and superior in the HR-mode (PCD 55.8 ± 11.7 HU vs EID 74.8 ± 5.4 HU; P = 0.01). High resolution showed the lowest image noise values among PCD modes (P = 0.01). Overall, the AI-CAD system delivered comparable results for lung nodule detection and volumetry between PCD- and dose-matched EID-CT (P = 0.08-1.00), with a mean sensitivity of 95% for PCD-CT and of 86% for dose-matched EID-CT in the lowest evaluated dose level (IQ5). Q+ and Q-mode showed higher false-positive rates than EID-CT at lower-dose levels (IQ10 and IQ5). The HR-mode showed a sensitivity of 100% with a false-positive rate of 1 even at the lowest evaluated dose level (IQ5; CDTIvol, 0.41 mGy).

CONCLUSIONS

Photon-counting detector CT was superior to dose-matched EID-CT in subjective IQ while showing comparable to lower objective image noise. Fully automatized AI-aided nodule detection and volumetry are feasible in PCD-CT, but attention has to be paid to false-positive findings.

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.

Diagnostic accuracy of low-dose and ultra-low-dose CT in detection of chest pathology: a systematic review

PURPOSE

Studies have evaluated imaging modalities with a lower radiation dose than standard-dose CT (SD-CT) for chest examination. This systematic review aimed to summarize evidence on diagnostic accuracy of these modalities - low-dose and ultra-low-dose CT (LD- and ULD-CT) - for chest pathology.

METHOD

Ovid-MEDLINE, Ovid-EMBASE and the Cochrane Library were systematically searched April 29th-30th, 2019 and screened by two reviewers. Studies on diagnostic accuracy were included if they defined their index tests as 'LD-CT', 'Reduced-dose CT' or 'ULD-CT' and had SD-CT as reference standard. Risk of bias was evaluated on study level using the Quality Assessment of Diagnostic Accuracy Studies-2. A narrative synthesis was conducted to compare the diagnostic accuracy measurements.

RESULTS

Of the 4257 studies identified, 18 were eligible for inclusion. SD-CT (3.17 ± 1.47 mSv) was used as reference standard in all studies to evaluate diagnostic accuracy of LD- (1.22 ± 0.34 mSv) and ULD-CT (0.22 ± 0.05 mSv), respectively. LD-CT had high sensitivities for detection of bronchiectasis (82-96%), honeycomb (75-100%), and varying sensitivities for nodules (63-99%) and ground glass opacities (GGO) (77-91%). ULD-CT had high sensitivities for GGO (93-100%), pneumothorax (100%), consolidations (90-100%), and varying sensitivities for nodules (60-100%) and emphysema (65-90%).

CONCLUSION

The included studies found LD-CT to have high diagnostic accuracy in detection of honeycombing and bronchiectasis and ULD-CT to have high diagnostic accuracy for pneumothorax, consolidations and GGO. Summarizing evidence on diagnostic accuracy of LD- and ULD-CT for other chest pathology was not possible due to varying outcome measures, lack of precision estimates and heterogeneous study design and methodology.

Respiratory surveillance for coal mine dust and artificial stone exposed workers in Australia and New Zealand: A position statement from the Thoracic Society of Australia and New Zealand

Coal mine lung dust disease (CMDLD) and artificial stone (AS) silicosis are preventable diseases which have occurred in serious outbreaks in Australia recently. This has prompted a TSANZ review of Australia's approach to respiratory periodic health surveillance. While regulating respirable dust exposure remains the foundation of primary and secondary prevention, identification of workers with early disease assists with control of further exposure, and with the aims of preserving lung function and decreasing respiratory morbidity in those affected. Prompt detection of an abnormality also allows for ongoing respiratory specialist clinical management. This review outlines a medical framework for improvements in respiratory surveillance to detect CMDLD and AS silicosis in Australia. This includes appropriate referral, improved data collection and interpretation, enhanced surveillance, the establishment of a nationwide Occupational Lung Disease Registry and an independent advisory group. These measures are designed to improve health outcomes for workers in the coal mining, AS and other dust-exposed and mining industries.

Combination of Deep Learning-Based Denoising and Iterative Reconstruction for Ultra-Low-Dose CT of the Chest: Image Quality and Lung-RADS Evaluation

OBJECTIVE. The objective of our study was to assess the effect of the combination of deep learning-based denoising (DLD) and iterative reconstruction (IR) on image quality and Lung Imaging Reporting and Data System (Lung-RADS) evaluation on chest ultra-low-dose CT (ULDCT). MATERIALS AND METHODS. Forty-one patients with 252 nodules were evaluated retrospectively. All patients underwent ULDCT (mean ± SD, 0.19 ± 0.01 mSv) and standard-dose CT (SDCT) (6.46 ± 2.28 mSv). ULDCT images were reconstructed using hybrid iterative reconstruction (HIR) and model-based iterative reconstruction (MBIR), and they were postprocessed using DLD (i.e., HIR-DLD and MBIR-DLD). SDCT images were reconstructed using filtered back projection. Three independent radiologists subjectively evaluated HIR, HIR-DLD, MBIR, and MBIR-DLD images on a 5-point scale in terms of noise, streak artifact, nodule edge, clarity of small vessels, homogeneity of the normal lung parenchyma, and overall image quality. Two radiologists independently evaluated the nodules according to Lung-RADS using HIR, MBIR, HIR-DLD, and MBIR-DLD ULDCT images and SDCT images. The median scores for subjective analysis were analyzed using Wilcoxon signed rank test with Bonferroni correction. Intraobserver agreement for Lung-RADS category between ULDCT and SDCT was evaluated using the weighted kappa coefficient. RESULTS. In the subjective analysis, ULDCT with DLD showed significantly better scores than did ULDCT without DLD (p < 0.001), and MBIR-DLD showed the best scores among the ULDCT images (p < 0.001) for all items. In the Lung-RADS evaluation, HIR showed fair or moderate agreement (reader 1 and reader 2: κw = 0.46 and 0.32, respectively); MBIR, moderate or good agreement (κw = 0.68 and 0.57); HIR-DLD, moderate agreement (κw = 0.53 and 0.48); and MBIR-DLD, good agreement (κw = 0.70 and 0.72). CONCLUSION. DLD improved the image quality of both HIR and MBIR on ULDCT. MBIR-DLD was superior to HIR_DLD for image quality and for Lung-RADS evaluation.

Emphysema quantification using low-dose computed tomography with deep learning-based kernel conversion comparison

OBJECTIVE

This study determined the effect of dose reduction and kernel selection on quantifying emphysema using low-dose computed tomography (LDCT) and evaluated the efficiency of a deep learning-based kernel conversion technique in normalizing kernels for emphysema quantification.

METHODS

A sample of 131 participants underwent LDCT and standard-dose computed tomography (SDCT) at 1- to 2-year intervals. LDCT images were reconstructed with B31f and B50f kernels, and SDCT images were reconstructed with B30f kernels. A deep learning model was used to convert the LDCT image from a B50f kernel to a B31f kernel. Emphysema indices (EIs), lung attenuation at 15th percentile (perc15), and mean lung density (MLD) were calculated. Comparisons among the different kernel types for both LDCT and SDCT were performed using Friedman's test and Bland-Altman plots.

RESULTS

All values of LDCT B50f were significantly different compared with the values of LDCT B31f and SDCT B30f (p < 0.05). Although there was a statistical difference, the variation of the values of LDCT B50f significantly decreased after kernel normalization. The 95% limits of agreement between the SDCT and LDCT kernels (B31f and converted B50f) ranged from - 2.9 to 4.3% and from - 3.2 to 4.4%, respectively. However, there were no significant differences in EIs and perc15 between SDCT and LDCT converted B50f in the non-chronic obstructive pulmonary disease (COPD) participants (p > 0.05).

CONCLUSION

The deep learning-based CT kernel conversion of sharp kernel in LDCT significantly reduced variation in emphysema quantification, and could be used for emphysema quantification.

KEY POINTS

• Low-dose computed tomography with smooth kernel showed adequate performance in quantifying emphysema compared with standard-dose CT. • Emphysema quantification is affected by kernel selection and the application of a sharp kernel resulted in a significant overestimation of emphysema. • Deep learning-based kernel normalization of sharp kernel significantly reduced variation in emphysema quantification.

Wide-volume versus helical acquisition in unenhanced chest CT: prospective intra-patient comparison of diagnostic accuracy and radiation dose in an ultra-low-dose setting

OBJECTIVES

Diagnostic performance and potential radiation dose reduction of wide-area detector CT sequential acquisition ("wide-volume" acquisition (WV)) in unenhanced chest examination are unknown. This study aims to assess the image quality, the diagnostic performance, and the radiation dose reduction of WV mode compared with the classical helical acquisition for lung parenchyma analysis in an ultra-low-dose (ULD) protocol.

METHODS

After Institutional Review Board Approval and written informed consent, 64 patients (72% men; 67.6 ± 9.7 years old; BMI 26.1 ± 5.3 kg/m²) referred for a clinically indicated unenhanced chest CT were prospectively included. All patients underwent, in addition to a standard helical acquisition (120 kV, automatic tube current modulation), two ULD acquisitions (135 kV, fixed tube current at 10 mA): one in helical mode and one in WV mode. Image noise, subjective image quality (5-level Likert scale), and diagnostic performance for the detection of 9 predetermined parenchymal abnormalities were assessed by two radiologists and compared using the chi-square or Fisher non-parametric tests.

RESULTS

Subjective image quality (4.2 ± 0.7 versus 4.2 ± 0.8, p = 0.56), image noise (41.7 ± 8 versus 40.9 ± 8.7, p = 0.3), and diagnostic performance were equivalent between ULD WV and ULD helical. Radiation dose was significantly lower for the ULD WV acquisition (mean dose-length product 14.1 ± 1.3 mGy cm versus 15.8 ± 1.3, p < 0.0001).

CONCLUSION

An additional 11% dose reduction is achieved with the WV mode in ULD chest CT with fixed tube current, with equivalent image quality and diagnostic performance when compared with the helical acquisition.

KEY POINTS

• Image quality and diagnostic performance of ultra-low-dose unenhanced chest CT are identical between wide-volume mode and the reference helical acquisition. • Wide-volume mode allows an additional radiation dose reduction of 11% (mean dose-length product 14.1 ± 1.3 mGy cm versus 15.8 ± 1.3, p < 0.0001).

Ultra-low-dose chest computed tomography for interstitial lung disease using model-based iterative reconstruction with or without the lung setting

The aim of this study was to assess the effects of reconstruction on the image quality and quantitative analysis for interstitial lung disease (ILD) using filtered back projection (FBP) and model-based iterative reconstruction (MBIR) with the lung setting and the conventional setting on ultra-low-dose computed tomography (CT).Fifty-two patients with known ILD were prospectively enrolled and underwent CT at an ultra-low dose (0.18 ± 0.02 mSv) and a standard dose (7.01 ± 2.66 mSv). Ultra-low-dose CT was reconstructed using FBP (uFBP) and MBIR with the lung setting (uMBIR-Lung) and the conventional setting (uMBIR-Stnd). Standard-dose CT was reconstructed using FBP (sFBP). Three radiologists subjectively evaluated the images on a 3-point scale (1 = worst, 3 = best). For objective image quality analysis, regions of interest were placed in the lung parenchyma and the axillary fat, and standard deviation (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were evaluated. For 32 patients with clinically diagnosed idiopathic interstitial pneumonia, quantitative measurements including total lung volume (TLV) and the percentage of ILD volume (%ILDV) were obtained. The medians of 3 radiologists' scores were analyzed using the Wilcoxon signed-rank test and the objective noise was analyzed using the paired t test. The Bonferroni correction was used for multiple comparisons. The quantitative measurements were analyzed using the Bland-Altman method.uMBIR-Lung scored better than uMBIR-Stnd and worse than sFBP (P < .001), except for noise and streak artifact in subjective analysis. The SD decreased significantly in the order of uMBIR-Stnd, uMBIR-Lung, sFBP, and uFBP (P < .001). The SNR and CNR increased significantly in the order of uMBIR-Stnd, uMBIR-Lung, sFBP, and uFBP (P < .001). For TLV, there was no significant bias between ultra-low-dose MBIRs and sFBP (P > .3). For %ILDV, there was no significant bias between uMBIR-Lung and sFBP (p = 0.8), but uMBIR-Stnd showed significantly lower %ILDV than sFBP (P = .013).uMBIR-Lung provided more appropriate image quality than uMBIR-Stnd. Although inferior to standard-dose CT for image quality, uMBIR-Lung showed equivalent CT quantitative measurements to standard-dose CT.

The Use of Model-based Iterative Reconstruction to Optimize Chest CT Examinations for Diagnosing Lung Metastases in Patients with Sarcoma: A Phantom Study

RATIONALE AND OBJECTIVES

This phantom study aimed to evaluate low-dose (LD) chest computed tomography (CT) protocols using model-based iterative reconstruction (MBIR) for diagnosing lung metastases in patients with sarcoma.

MATERIALS AND METHODS

An adult female anthropomorphic phantom was scanned with a 64-slice CT using four LD protocols and a standard-dose protocol. Absorbed organ doses were measured with 10 metal-oxide-semiconductor field-effect transistor dosimeters. Furthermore, Monte Carlo simulations were performed to estimate organ and effective doses. Image quality in terms of image noise, contrast, and resolution was measured from the CT images reconstructed with conventional filtered back projection, adaptive statistical iterative reconstruction, and MBIR algorithms. All the results were compared to the performance of the standard-dose protocol.

RESULTS

Mean absorbed organ and effective doses were reduced by approximately 95% with the LD protocol (100-kVp tube voltage and a fixed 10-mA tube current) compared to the standard-dose protocol (120-kVp tube voltage and tube current modulation) while yielding an acceptable image quality for diagnosing round-shaped lung metastases. The effective doses ranged from 0.16 to 2.83 mSv in the studied protocols. The image noise, contrast, and resolution were maintained or improved when comparing the image quality of LD protocols using MBIR to the performance of the standard-dose chest CT protocol using filtered back projection. The small round-shaped lung metastases were delineated at levels comparable to the used protocols.

CONCLUSIONS

Radiation exposure in patients can be reduced significantly by using LD chest CT protocols and MBIR algorithm while maintaining image quality for detecting round-shaped lung metastases.

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.

Chest CT Scan at Radiation Dose of a Posteroanterior and Lateral Chest Radiograph Series: A Proof of Principle in Lymphangioleiomyomatosis

BACKGROUND

Given the rising utilization of medical imaging and the risks of radiation, there is increased interest in reducing radiation exposure. The objective of this study was to evaluate, as a proof of principle, CT scans performed at radiation doses equivalent to that of a posteroanterior and lateral chest radiograph series in the cystic lung disease lymphangioleiomyomatosis (LAM).

METHODS

From November 2016 to May 2018, 105 consecutive subjects with LAM received chest CT scans at standard and ultra-low radiation doses. Standard and ultra-low-dose images, respectively, were reconstructed with routine iterative and newer model-based iterative reconstruction. LAM severity can be quantified as cyst score (percentage of lung occupied by cysts), an ideal benchmark for validating CT scans performed at a reduced dose compared with a standard dose. Cyst scores were quantified using semi-automated software and evaluated by linear correlation and Bland-Altman analysis.

RESULTS

Overall, ultra-low-dose CT scans represented a 96% dose reduction, with a median dose equivalent to 1 vs 22 posteroanterior and lateral chest radiograph series (0.14 mSv; 5th-95th percentile, 0.10-0.20 vs standard dose 3.4 mSv; 5th-95th percentile, 1.5-7.4; P < .0001). The mean difference in cyst scores between ultra-low- and standard-dose CT scans was 1.1% ± 2.0%, with a relative difference in cyst score of 11%. Linear correlation coefficient was excellent at 0.97 (P < .0001).

CONCLUSIONS

In LAM chest CT scan at substantial radiation reduction to doses equivalent to that of a posteroanterior and lateral chest radiograph series provides cyst score quantification similar to that of standard-dose CT scan.

TRIAL REGISTRY

ClinicalTrials.gov; Nos.: NCT00001465 and NCT00001532; URL: www.clinicaltrials.gov.

Pulmonary Emphysema Quantification on Ultra-Low-Dose Computed Tomography Using Model-Based Iterative Reconstruction With or Without Lung Setting

OBJECTIVES

To evaluate the influence of model-based iterative reconstruction (MBIR) with lung setting and conventional setting on pulmonary emphysema quantification by ultra-low-dose computed tomography (ULDCT) compared with standard-dose CT (SDCT).

METHODS

Forty-five patients who underwent ULDCT (0.18 ± 0.02 mSv) and SDCT (6.66 ± 2.69 mSv) were analyzed in this retrospective study. Images were reconstructed using filtered back projection (FBP) with smooth and sharp kernels and MBIR with conventional and lung settings. Extent of emphysema was evaluated using fully automated software. Correlation between ULDCT and SDCT was assessed by interclass correlation coefficiency (ICC) and Bland-Altman analysis.

RESULTS

Excellent correlation was seen between MBIR with conventional setting on ULDCT and FBP with smooth kernel on SDCT (ICC, 0.97; bias, -0.31%) and between MBIR with lung setting on ULDCT and FBP with sharp kernel on SDCT (ICC, 0.82; bias, -2.10%).

CONCLUSION

Model-based iterative reconstruction improved the agreement between ULDCT and SDCT on emphysema quantification.

Ultra-low-dose lung screening CT with model-based iterative reconstruction: an assessment of image quality and lesion conspicuity

Background Reducing radiation dose inevitably increases image noise, and thus, it is important in low-dose computed tomography (CT) to maintain image quality and lesion detection performance. Purpose To assess image quality and lesion conspicuity of ultra-low-dose CT with model-based iterative reconstruction (MBIR) and to determine a suitable protocol for lung screening CT. Material and Methods A total of 120 heavy smokers underwent lung screening CT and were randomly and equally assigned to one of five groups: group 1 = 120 kVp, 25 mAs, with FBP reconstruction; group 2 = 120 kVp, 10 mAs, with MBIR; group 3 = 100 kVp, 15 mAs, with MBIR; group 4 = 100 kVp, 10 mAs, with MBIR; and group 5 = 100 kVp, 5 mAs, with MBIR. Two radiologists evaluated intergroup differences with respect to radiation dose, image noise, image quality, and lesion conspicuity using the Kruskal-Wallis test and the Chi-square test. Results Effective doses were 61-87% lower in groups 2-5 than in group 1. Image noises in groups 1 and 5 were significantly higher than in the other groups ( P < 0.001). Overall image quality was best in group 1, but diagnostic acceptability of overall image qualities in groups 1-3 was not significantly different (all P values > 0.05). Lesion conspicuities were similar in groups 1-4, but were significantly poorer in group 5. Conclusion Lung screening CT with MBIR obtained at 100 kVp and 15 mAs enables a ∼60% reduction in radiation dose versus low-dose CT, while maintaining image quality and lesion conspicuity.

Pulmonary emphysema is a predictor of pneumothorax after CT-guided transthoracic pulmonary biopsies of pulmonary nodules

Purpose

Pneumothoraces are the most frequently occurring complications of CT-guided percutaneous transthoracic pulmonary biopsies (PTPB). The aim of this study was to evaluate the influence of pre-diagnostic lung emphysema on the incidence and extent of pneumothoraces and to establish a risk stratification for the evaluation of the pre-procedure complication probability.

Material and methods

CT-guided PTPB of 100 pre-selected patients (mean age 67.1±12.8 years) were retrospectively enrolled from a single center database of 235 PTPB performed between 2012–2014. Patients were grouped according to pneumothorax appearance directly after PTPB (group I: without pneumothorax, n = 50; group II: with pneumothorax, n = 50). Group II was further divided according to post-interventional treatment (group IIa: chest tube placement, n = 24; group IIb: conservative therapy, n = 26). For each patient pre-diagnostic percentage of emphysema was quantified using CT density analysis. Emphysema stages were compared between groups using bivariate analyses and multinomial logistic regression analyses.

Results

Emphysema percentage was significantly associated with the occurrence of post-interventional pneumothorax (p = 0.006). Adjusted for potential confounders (age, gender, lesion size and length of interventional pathway) the study yielded an OR of 1.07 (p = 0.042). Absolute risk of pneumothorax increased from 43.4% at an emphysema rate of 5% to 73.8% at 25%. No differences could be seen in patients with pneumothorax between percentage of emphysema and mode of therapy (p = 0.721).

Conclusion

The rate of lung emphysema is proportionally related to the incidence of pneumothorax after CT-guided PTPB and allows pre-interventional risk stratification. There is no association between stage of emphysema and post-interventional requirement of chest tube placement.

Low-voltage chest CT: another way to reduce the radiation dose in asbestos-exposed patients

AIM

To assess whether low voltage chest computed tomography (CT) can be used to successfully diagnose disease in patients with asbestos exposure.

MATERIALS AND METHODS

Fifty-six former employees of the shipbuilding industry, who were candidates to receive a standard-dose chest CT due to their occupational exposure to asbestos, underwent a routine CT. Immediately after this initial CT, they underwent a second acquisition using low-dose chest CT parameters, based on a low potential (80 kV) and limited tube current. The findings of the two CT protocols were compared based on typical diseases associated with asbestos exposure. The kappa coefficient for each parameter and for an overall rating (grouping them based on mediastinal, pleural, and pulmonary findings) were calculated in order to test for correlations between the two protocols.

RESULTS

A good correlation between routine and low-dose CT was demonstrated for most parameters with a mean radiation dose reduction of up to 83% of the effective dose based on the dose-length product between protocols.

CONCLUSIONS

Low-dose chest CT, based on a limited tube potential, is useful for patients with an asbestos exposure background. Low-dose chest CT can be successfully used to minimise the radiation dose received by patients, as this protocol produced an estimated mean effective dose similar to that of an abdominal or pelvis plain film.

Quantitative assessment of Pulmonary Alveolar Proteinosis (PAP) with ultra-dose CT and correlation with Pulmonary Function Tests (PFTs)

Background

The purpose of this study was to investigate whether ultra-low-dose chest computed tomography (CT) can be used for visual assessment of CT features in patients with pulmonary alveolar proteinosis (PAP) and to evaluate the relationship between the quantitative analysis of the ultra-low-dose CT scans and the pulmonary function tests (PFTs).

Methods

Thirty-eight patients (mean [SD] age, 44.47 [12.28] years; 29 males, 9 females) with PAP were enrolled and subjected to two scans each with low-dose CT (reference parameters: 120 kV and 50 mAs) and ultra-low-dose CT (reference parameters, 80 kV, 25 mAs). Images were reconstructed via filtered back projection (FBP) for low-dose CT and iterative reconstruction (IR) for ultra-low-dose CT. All patients underwent PFT. The Visual analysis for ground glass opacity (GGO) is performed. The quantitative CT and PFT results were analyzed by canonical correlations.

Results

The mean body mass index (BMI) was 25.37±3.26 kg/m². The effective radiation doses were 2.30±0.46 and 0.24±0.05 mSv for low-dose and ultra-low-dose CT, respectively. The size-specific dose estimates were 5.81±0.81 and 0.62±0.09 mSv for low-dose and ultra-low-dose CT. GGOs and interlobular septal thickening were observed bilaterally in all patients. The average visual GGO score was lower in the upper field (2.67±1.24) but higher in the middle and lower fields (3.08±1.32 and 3.08±0.97, respectively). The average score for the whole lung was 2.94±1.19. There is a significant correlation between PFTs and quantitative of ultra-low-dose CT (canonical loading = 0.78).

Conclusions

Ultra-low-dose CT has the potential to quantify the lung parenchyma changes of PAP. This technique could provide a sensitive and objective assessment of PAP and has good relation with PFTs. In addition, the radiation dose of ultra-low-dose CT was very low.

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.

Lung cancer screening with ultra-low dose CT using full iterative reconstruction

PURPOSE

To investigate the diagnostic capability of ultra-low-dose CT (ULDCT) with full iterative reconstruction (f-IR) for lung cancer screening.

MATERIALS AND METHODS

All underwent ULDCT and/or low-dose CT (LD-CT) on a 320-detector scanner. ULDCT images were reconstructed with f-IR. We qualitatively and quantitatively studied 95 nodules in 69 subjects. Two radiologists classified the nodules on ULDCT images as solid-, part-solid-, and pure ground-glass (PGG) and recorded their mean size. Their findings were compared with the reference standard. The observer performance study included 7 other radiologists and 35 subjects with- and 15 without nodules. The results were analyzed by AFROC analysis.

RESULTS

In the qualitative study, the kappa values between observers 1 and 2, respectively, and the reference standard were 0.70 and 0.83; the intra-class correlation coefficients for the nodule diameter between the reference standard and their measurements were 0.84 and 0.90. The 95% confidence interval (CI) for the area under the curve (AUC) difference for nodule detection on LDCT and ULDCT was -0.03 to 0.07. The 95% CI crossed the 0 difference in the AUC but not the pre-defined non-inferiority margin of -0.08.

CONCLUSION

The diagnostic ability of ULDCT using f-IR is comparable to LDCT.

[What future for chest x-ray against ultra-low-dose computed tomography?]

Technological improvements, with iterative reconstruction at the foreground, have lowered the radiation dose of a chest CT close to that of a PA and lateral chest x-ray. This ultra-low dose chest CT (ULD-CT) has an image quality that is degraded on purpose, yet remains diagnostic in many clinical indications. Thus, its effectiveness is already validated for the detection and the monitoring of solid parenchymal nodules, for the diagnosis and monitoring of infectious lung diseases and for the screening of pleural lesions secondary to asbestos exposure. Its limitations are the analysis of the mediastinal structures, the severe obesity (BMI>35) and the detection of interstitial lesions. If it can replace the standard chest CT in these indications, all the more in situations where radiation dose is a major problem (young patients, repeated exams, screening), it progressively emerges as a first line alternative for chest radiograph, providing more data at a similar radiation cost.

Risk factors assessment and risk prediction models in lung cancer screening candidates

From February 2015, low-dose computed tomography (LDCT) screening entered the armamentarium of diagnostic tools broadly available to individuals at high-risk of developing lung cancer. While a huge number of pulmonary nodules are identified, only a small fraction turns out to be early lung cancers. The majority of them constitute a variety of benign lesions. Although it entails a burden of the diagnostic work-up, the undisputable benefit emerges from: (I) lung cancer diagnosis at earlier stages (stage shift); (II) additional findings enabling the implementation of a preventive action beyond the realm of thoracic oncology. This review presents how to utilize the risk factors from distinct categories such as epidemiology, radiology and biomarkers to target the fraction of population, which may benefit most from the introduced screening modality.

[Ultra-low dose chest CT: The end of chest radiograph?]

Ultra-low dose chest CT (ULD-CT) is acquired at a radiation dose lowered to that of a PA and lateral chest X-ray. Its image quality is degraded, yet remains diagnostic in many clinical indications. Technological improvements, with iterative reconstruction at the foreground, allowed a strong increase in the image quality obtained with this examination, which is achievable on most recent (<5 years) scanner. Established clinical indications of ULD-CT are increasing, and its non-inferiority compared to the reference "full dose" chest CT are currently demonstrated for the detection of solid nodules, for asbestos-related pleural diseases screening and for the monitoring of infectious pneumonia. Its current limitations are the obese patients (BMI>35) and the interstitial pneumonia, situations in which their performances are insufficient.

Association of incidental emphysema with annual lung function decline and future development of airflow limitation

Objectives

Emphysema is one of the prognostic factors for rapid lung function decline in patients with COPD, but the impact of incidentally detected emphysema on population without spirometric abnormalities has not been evaluated. This study aimed to determine whether emphysema detected upon computed tomography (CT) screening would accelerate the rate of lung function decline and influence the possibility of future development of airflow limitation in a population without spirometric abnormalities.

Materials and methods

Subjects who participated in a routine screening for health checkup and follow-up pulmonary function tests for at least 3 years between 2004 and 2010 were retrospectively enrolled. The percentage of low-attenuation area below −950 Hounsfield units (%LAA₋₉₅₀) was calculated automatically. A calculated value of %LAA₋₉₅₀ that exceeded 10% was defined as emphysema. Adjusted annual lung function decline was analyzed using random-slope, random-intercept mixed linear regression models.

Results

A total of 628 healthy subjects within the normal range of spriometric values were included. Multivariable analysis showed that the emphysema group exhibited a faster decline in forced vital capacity (−33.9 versus −18.8 mL/year; P=0.02). Emphysema was not associated with the development of airflow limitation during follow-up.

Conclusion

Incidental emphysema quantified using CT scan was significantly associated with a more rapid decline in forced vital capacity in the population with normative spirometric values. However, an association between emphysema and future development of airflow limitation was not observed.