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Background It is increasingly recognized that interstitial lung abnormalities (ILAs) detected at CT have potential clinical implications, but automated identification of ILAs has not yet been fully established. Purpose To develop and test automated ILA probability prediction models using machine learning techniques on CT images. Materials and Methods This secondary analysis of a retrospective study included CT scans from patients in the Boston Lung Cancer Study collected between February 2004 and June 2017. Visual assessment of ILAs by two radiologists and a pulmonologist served as the ground truth. Automated ILA probability prediction models were developed that used a stepwise approach involving section inference and case inference models. The section inference model produced an ILA probability for each CT section, and the case inference model integrated these probabilities to generate the case-level ILA probability. For indeterminate sections and cases, both two- and three-label methods were evaluated. For the case inference model, we tested three machine learning classifiers (support vector machine [SVM], random forest [RF], and convolutional neural network [CNN]). Receiver operating characteristic analysis was performed to calculate the area under the receiver operating characteristic curve (AUC). Results A total of 1382 CT scans (mean patient age, 67 years ± 11 [SD]; 759 women) were included. Of the 1382 CT scans, 104 (8%) were assessed as having ILA, 492 (36%) as indeterminate for ILA, and 786 (57%) as without ILA according to ground-truth labeling. The cohort was divided into a training set ( n = 96; ILA, n = 48), a validation set ( n = 24; ILA, n = 12), and a test set ( n = 1262; ILA, n = 44). Among the models evaluated (two- and three-label section inference models; two- and three-label SVM, RF, and CNN case inference models), the model using the three-label method in the section inference model and the two-label method and RF in the case inference model achieved the highest AUC, at 0.87. Conclusion The model demonstrated substantial performance in estimating ILA probability, indicating its potential utility in clinical settings. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Zagurovskaya in this issue.

Purpose: To directly compare the utility for therapeutic outcome prediction of dynamic first-pass contrast-enhanced (CE)-perfusion area-detector computed tomography (ADCT), MR imaging assessed with the same mathematical method and 2-[fluorine-18]-fluoro-2-deoxy-d-glucose-positron emission tomography combined with CT (PET/CT) for non-small cell lung cancer (NSCLC) patients treated with chemoradiotherapy., Materials and Methods: Forty-three consecutive stage IIIB NSCLC patients, consisting of 25 males (mean age ± standard deviation: 66.6 ± 8.7 years) and 18 females (66.4 ± 8.2 years) underwent PET/CT, dynamic CE-perfusion ADCT and MR imaging, chemoradiotherapy, and follow-up examination. In each patient, total, pulmonary arterial, and systemic arterial perfusions were calculated from both perfusion data and SUV max on PET/CT, assessed for each targeted lesion, and averaged to determine final values. Receiver operating characteristics analyses were performed to compare the utility for distinguishing responders from non-responders using Response Evaluation Criteria in Solid Tumor (RECIST) 1.1 criteria. Overall survival (OS) assessed with each index were compared between two groups by means of the Kaplan-Meier method followed by the log-rank test., Results: Area under the curve (Az) for total perfusion on ADCT was significantly larger than that of pulmonary arterial perfusion (P < 0.05). Az of total perfusion on MR imaging was significantly larger than that of pulmonary arterial perfusion (P < 0.05). Mean OS of responder and non-responder groups were significantly different for total and systemic arterial (P < 0.05) perfusion., Conclusion: Dynamic first-pass CE-perfusion ADCT and MR imaging as well as PET/CT are useful for early prediction of treatment response by NSCLC patients treated with chemoradiotherapy.

Purpose: To compare the effect of different acquisition and reconstruction methods on the radiation dose and accuracy of CT number measurements, using a 320-detector row CT and a Quantitative Imaging Biomarker Alliance (QIBA) recommended phantom., Materials and Methods: Acquisitions were performed on a 320-detector row CT, as 64- and 80-detector row helical and wide detector step-and-shoot (i.e., wide volume) acquisitions with tube currents of 400 mA, 100 mA, 50 mA, 20 mA, and 10 mA. Image was reconstructed with the filtered back projection (FBP), adaptive iterative dose reduction using 3D processing (AIDR 3D), and forward projected model-based iterative reconstruction (FIRST) methods. The difference between measured CT numbers and the actual -856HU value of the phantom insert was determined by each CT acquisition protocol. Differences in actual and measured CT numbers were compared among acquisitions and among reconstruction methods by means of Tukey's HSD test., Results: The CT number obtained with 64-detector row helical acquisition was significantly larger than that obtained with others (p < 0.0001). At each tube current, the CT number reconstructed with FIRST was significantly smaller than that with others (p < 0.0001)., Conclusion: Acquisition and reconstruction methods are significantly affecting radiation dose reduction and accuracy of CT number measurements on a phantom study.

Purpose: To directly compare the effect of a reconstruction algorithm on nodule detection capability of the computer-aided detection (CADe) system using standard-dose, reduced-dose and ultra-low dose chest CTs with and without adaptive iterative dose reduction 3D (AIDR 3D)., Materials and Methods: Our institutional review board approved this study, and written informed consent was obtained from each patient. Standard-, reduced- and ultra-low-dose chest CTs (250 mA, 50 mA and 10 mA) were used to examine 40 patients, 21 males (mean age ± standard deviation: 63.1 ± 11.0 years) and 19 females (mean age, 65.1 ± 12.7 years), and reconstructed as 1 mm-thick sections. Detection of nodule equal to more than 4 mm in dimeter was automatically performed by our proprietary CADe software. The utility of iterative reconstruction method for improving nodule detection capability, sensitivity and false positive rate (/case) of the CADe system using all protocols were compared by means of McNemar's test or signed rank test., Results: Sensitivity (SE: 0.43) and false-positive rate (FPR: 7.88) of ultra-low-dose CT without AIDR 3D was significantly inferior to those of standard-dose CTs (with AIDR 3D: SE, 0.78, p < .0001, FPR, 3.05, p < .0001; and without AIDR 3D: SE, 0.80, p < .0001, FPR: 2.63, p < .0001), reduced-dose CTs (with AIDR 3D: SE, 0.81, p < .0001, FPR, 3.05, p < .0001; and without AIDR 3D: SE, 0.62, p < .0001, FPR: 2.95, p < .0001) and ultra-low-dose CT with AIDR 3D (SE, 0.79, p < .0001, FPR, 4.88, p = .0001)., Conclusion: The AIDR 3D has a significant positive effect on nodule detection capability of the CADe system even when radiation dose is reduced., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Objectives: To determine the value of a raw data-based metal artifact reduction (SEMAR) algorithm for image quality improvement in abdominal CT for patients with small metal implants., Methods: Fifty-eight patients with small metal implants (3-15 mm in size) who underwent treatment for hepatocellular carcinoma were imaged with CT. CT data were reconstructed by filtered back projection with and without SEMAR algorithm in axial and coronal planes. To evaluate metal artefact reduction, mean CT number (HU and SD) and artefact index (AI) values within the liver were calculated. Two readers independently evaluated image quality of the liver and pancreas and visualization of vasculature using a 5-point visual score. HU and AI values and image quality on images with and without SEMAR were compared using the paired Student's t-test and Wilcoxon signed rank test. Interobserver agreement was evaluated using linear-weighted κ test., Results: Mean HU and AI on images with SEMAR was significantly lower than those without SEMAR (P < 0.0001). Liver and pancreas image qualities and visualizations of vasculature were significantly improved on CT with SEMAR (P < 0.0001) with substantial or almost perfect agreement (0.62 ≤ κ ≤ 0.83)., Conclusions: SEMAR can improve image quality in abdominal CT in patients with small metal implants by reducing metallic artefacts., Key Points: • SEMAR algorithm significantly reduces metallic artefacts from small implants in abdominal CT. • SEMAR can improve image quality of the liver in dynamic CECT. • Confidence visualization of hepatic vascular anatomies can also be improved by SEMAR.

Purpose: To prospectively and directly compare the capability for assessments of regional ventilation and pulmonary functional loss in smokers of xenon-ventilation CT obtained with the dual-energy CT (DE-CT) and subtraction CT (Sub-CT) MATERIALS AND METHODS: Twenty-three consecutive smokers (15 men and 8 women, mean age: 69.7±8.7years) underwent prospective unenhanced and xenon-enhanced CTs, the latter by Sub-CT and DE-CT methods, ventilation SPECT and pulmonary function tests. Sub-CT was generated from unenhanced and xenon-enhanced CT, and all co-registered SPECT/CT data were produced from SPECT and unenhanced CT data. For each method, regional ventilation was assessed by using a 11-point scoring system on a per-lobe basis. To determine the functional lung volume by each method, it was also calculated for individual sublets with a previously reported method. To determine inter-observer agreement for each method, ventilation defect assessment was evaluated by using the χ2 test with weighted kappa statistics. For evaluation of the efficacy of each method for pulmonary functional loss assessment, functional lung volume was correlated with%FEV 1 ., Results: Each inter-observer agreement was rated as substantial (Sub-CT: κ=0.69, p<0.0001; DE-CT: κ=0.64, p<0.0001; SPECT/CT: κ=0.64, p<0.0001). Functional lung volume for each method showed significant to good correlation with%FEV 1 (Sub-CT: r=0.72, p=0.0001; DE-CT: r=0.74, p<0.0001; SPECT/CT: r=0.66, p=0.0006)., Conclusion: Xenon-enhanced CT obtained by Sub-CT can be considered at least as efficacious as that obtained by DE-CT and SPECT/CT for assessment of ventilation abnormality and pulmonary functional loss in smokers., (Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.)

Purpose: To directly compare the capability of three reconstruction methods using, respectively, forward projected model-based iterative reconstruction (FIRST), adaptive iterative dose reduction using three dimensional processing (AIDR 3D) and filter back projection (FBP) for radiation dose reduction and accuracy of computer-aided volumetry (CADv) measurements on chest CT examination in a phantom study., Materials and Methods: An anthropomorphic thoracic phantom with 30 simulated nodules of three density types (100, -630, and -800 HU) and five different diameters was scanned with an area-detector CT at tube currents of 270, 200, 120, 80, 40, 20, and 10mA. Each scanned data set was reconstructed as thin-section CT with three methods, and all simulated nodules were measured with CADv software. For comparison of the capability for CADv at each tube current, Tukey's HSD test was used to compare the percentage of absolute measurement errors for all three reconstruction methods. Absolute percentage measurement errors were then compared by means of Dunett's test for each tube current at 270mA (standard tube current)., Results: Mean absolute measurement errors of AIDR 3D and FIRST methods for each nodule type were significantly lower than those of the FBP method at 20mA and 10mA (p<0.05). In addition, absolute measurement errors of the FBP method at 20mA and 10mA was significantly higher than that at 270mA for all nodule types (p<0.05)., Conclusion: The FIRST and AIDR 3D methods are more effective than the FBP method for radiation dose reduction, while yielding better measurement accuracy of CADv for chest CT examination., (Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.)

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., (© The Foundation Acta Radiologica 2015.)

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., (Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.)

Purpose: To determine the capability and influence of the mathematical method on dynamic contrast-enhanced (CE-) perfusion area detector CT (ADCT) for early prediction of treatment response as well as progression free and overall survival (PFS and OS) of non-small cell lung cancer (NSCLC) patients treated with chemoradiotherapy., Materials and Methods: Sixty-six consecutive stage III NSCLC patients underwent dynamic CE-perfusion ADCT examinations, chemoradiotherapy and follow-up examinations. Response Evaluation Criteria in Solid Tumors (RECIST) criteria were used to divide all patients into responders and non-responders. Differences in each of the indices for all targeted lesions between measurements obtained 2 weeks prior to the first and the third course of chemotherapy were determined for all patients. ROC analyses were employed to determine the capability of perfusion indices as markers for distinguishing RECIST responders from non-responders. To evaluate their capability for early prediction of therapeutic effect, OS of perfusion index-based responders and non-responders were compared by using the Kaplan-Meier method followed by log-rank test., Results: Area under the curve (Az) for total perfusion by means of the dual-input maximum slope method was significantly larger than that of pulmonary arterial perfusion using the same method (p=0.007) and of perfusion with the single-input maximum slope method (p=0.007). Mean OS demonstrated significantly difference between responder- and non-responder groups for total perfusion (p=0.02)., Conclusion: Mathematical models have significant influence on assessment for early prediction of treatment response, disease progression and overall survival using dynamic CE-perfusion ADCT for NSCLC patients treated with chemoradiotherapy., (Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.)

Purpose: To directly compare the capability of hybrid-type iterative reconstruction (i.e., adaptive iterative dose reduction using 3D processing: AIDR 3D) and filter back projection (FBP) for radiation dose reduction during dynamic contrast-enhanced (CE-) perfusion area-detector CT (ADCT) for lung and nodule perfusion assessment., Materials and Methods: Thirty-six patients with lung cancers who underwent perfusion ADCT (SD-ADCT) at 120 mA and were enrolled in this study. ADCT data at 80 mA (reduced-dose ADCT: RD-ADCT), 60 mA (low-dose ADCT: LD-ADCT) and 40 mA (very low-dose ADCT: VLD-ADCT) were computationally simulated using SD-ADCT data, and reconstructed with and without AIDR 3D. Image noise and lung and nodule perfusion parameters were evaluated using ROI measurements. To determine the utility of AIDR 3D for dose reduction, image noise was compared between each protocol with and without AIDR 3D by means of the t-test. Correlations and limits of agreement for parameters obtained with SD-ADCT and other protocols were also evaluated., Results: Image noise of all protocols with AIDR 3D was significantly lower than that of LD-ADCT and VLD-ADCT without AIDR 3D (p<0.05). Significant correlations for image noise between SD-ADCT and all protocols with AIDR 3D (0.45 ≤ r ≤ 0.99, p<0.0001) were equal to or better than that without AIDR 3D (0.28 ≤ r ≤ 0.99, p<0.0001). The limits of agreement for perfusion parameters with AIDR 3D were smaller than those without AIDR 3D for each tube current., Conclusion: AIDR 3D is more effective than FBP for dose reduction of perfusion ADCT while maintaining image quality and reducing measurement errors., (Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.)

Purpose: The image noise and image quality of a prototype ultra-high-resolution computed tomography (U-HRCT) scanner was evaluated and compared with those of conventional high-resolution CT (C-HRCT) scanners., Materials and Methods: This study was approved by the institutional review board. A U-HRCT scanner prototype with 0.25 mm x 4 rows and operating at 120 mAs was used. The C-HRCT images were obtained using a 0.5 mm x 16 or 0.5 mm x 64 detector-row CT scanner operating at 150 mAs. Images from both scanners were reconstructed at 0.1-mm intervals; the slice thickness was 0.25 mm for the U-HRCT scanner and 0.5 mm for the C-HRCT scanners. For both scanners, the display field of view was 80 mm. The image noise of each scanner was evaluated using a phantom. U-HRCT and C-HRCT images of 53 images selected from 37 lung nodules were then observed and graded using a 5-point score by 10 board-certified thoracic radiologists. The images were presented to the observers randomly and in a blinded manner., Results: The image noise for U-HRCT (100.87 ± 0.51 Hounsfield units [HU]) was greater than that for C-HRCT (40.41 ± 0.52 HU; P < .0001). The image quality of U-HRCT was graded as superior to that of C-HRCT (P < .0001) for all of the following parameters that were examined: margins of subsolid and solid nodules, edges of solid components and pulmonary vessels in subsolid nodules, air bronchograms, pleural indentations, margins of pulmonary vessels, edges of bronchi, and interlobar fissures., Conclusion: Despite a larger image noise, the prototype U-HRCT scanner had a significantly better image quality than the C-HRCT scanners.

Purpose: To evaluate the effects of adaptive iterative dose reduction using 3D processing (AIDR 3D) for quantification of two measures of emphysema: percentage of low-attenuation volume (LAV%) and size distribution of low-attenuation lung regions., Method and Materials: Fifty-two patients who underwent standard-dose (SDCT) and low-dose CT (LDCT)were included. SDCT without AIDR 3D, LDCT without AIDR 3D, and LDCT with AIDR 3D were used for emphysema quantification. First, LAV% was computed at 10 thresholds from −990 to −900 HU. Next, at the same thresholds, linear regression on a log–log plot was used to compute the power law exponent (D)for the cumulative frequency-size distribution of low-attenuation lung regions. Bland–Altman analysis was used to assess whether AIDR 3D improved agreement between LDCT and SDCT for emphysema quantification of LAV% and D., Results: The mean relative differences in LAV% between LDCT without AIDR 3D and SDCT were 3.73%–88.18% and between LDCT with AIDR 3D and SDCT were −6.61% to 0.406%. The mean relative differences in D between LDCT without AIDR 3D and SDCT were 8.22%–19.11% and between LDCT with AIDR3D and SDCT were 1.82%–4.79%. AIDR 3D improved agreement between LDCT and SDCT at thresholds from −930 to −990 HU for LAV% and at all thresholds for D., Conclusion: AIDR 3D improved the consistency between LDCT and SDCT for emphysema quantification of LAV% and D., (Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.)

Objective: The purpose of this study was to investigate efficacy of two types of emphysema quantification: percentage of low-attenuation lung regions (%LA); and size distribution of these regions. On a log-log plot, cumulative frequency-size distribution of low-attenuation lung regions can be fitted by a straight line whose slope (D) has been reported to reflect diffusing capacity. In this study, %LA and D were compared with pulmonary function test (PFT) parameters, especially with ratio of diffusing capacity of carbon monoxide to effective alveolar ventilation (i.e., DLCO/VA)., Materials and Methods: Thin-section unenhanced CT images were acquired from 30 patients (25 men, five women; mean [SD] age, 70.1 ± 12.1 years), of whom 25 had received diagnosis of COPD, and %LA and D were calculated at 20 thresholds, ranging from -995 to -900 HU. To determine utility of %LA and D, we used Pearson's correlation for emphysema quantification and PFT. Significance of the coefficients was determined with Bonferroni correction (p < 0.0025). Finally, the relationships between emphysema quantification and DLCO/VA were examined by linear models and Akaike information criterion (AIC)., Results: The correlation coefficients for %LA and DLCO/VA were statistically significant at all the thresholds (optimal coefficient, -0.761). The correlation coefficients for D and DLCO/VA were statistically significant at the thresholds from -945 to -900 HU (optimal coefficient, -0.646). AIC values showed that the most accurate prediction of DLCO/VA was obtained by the model incorporating both %LA and D., Conclusion: Both %LA and D showed significant correlation with DLCO/VA. Combining %LA and D resulted in more accurate evaluation of DLCO/VA than did using %LA or D alone.

Objectives: To assess the effects of image reconstruction method on hepatic CT perfusion (CTP) values using two CT protocols with different radiation doses., Materials and Methods: Sixty patients underwent hepatic CTP and were randomly divided into two groups. Tube currents of 210 or 250 mA were used for the standard dose group and 120 or 140 mA for the low dose group. The higher currents were selected for large patients. Demographic features of the groups were compared. CT images were reconstructed by using filtered back projection (FBP), image filter (quantum de-noising, QDS), and adaptive iterative dose reduction (AIDR). Hepatic arterial and portal perfusion (HAP and HPP, ml/min/100ml) and arterial perfusion fraction (APF, %) were calculated using the dual-input maximum slope method. ROIs were placed on each hepatic segment. Perfusion and Hounsfield unit (HU) values, and image noises (standard deviations of HU value, SD) were measured and compared between the groups and among the methods., Results: There were no significant differences in the demographic features of the groups, nor were there any significant differences in mean perfusion and HU values for either the groups or the image reconstruction methods. Mean SDs of each of the image reconstruction methods were significantly lower (p<0.0001) for the standard dose group than the low dose group, while mean SDs for AIDR were significantly lower than those for FBP for both groups (p=0.0006 and 0.013). Radiation dose reductions were approximately 45%., Conclusions: Image reconstruction method did not affect hepatic perfusion values calculated by dual-input maximum slope method with or without radiation dose reductions. AIDR significantly reduced images noises., (Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.)

Objective: The purpose of this study was to determine the utility of adaptive iterative dose reduction using 3D processing (AIDR 3D) for image noise reduction and assessment of radiologic findings obtained with reduced- and low-dose chest CT in patients with various pulmonary diseases., Subjects and Methods: Chest CT examinations at three different tube current settings and using 16- and 64-MDCT scanners were performed for 37 patients. Standard-dose (150 mAs) data were reconstructed as thin-section CT without AIDR 3D, and low-dose (25 mAs) and reduced-dose (50 mAs) data were reconstructed as thin-section CT without and with AIDR 3D. To compare image quality, image noises at all CT doses were quantitatively assessed by region of interest measurements. For comparison of radiologic finding assessments, likelihoods of occurrence of emphysema, ground-glass opacity, reticular opacity, bronchiectasis, honeycomb pattern, and nodules were evaluated on a 5-point scale. Then, image noise and agreements of radiologic findings between standard-dose CT and others were statistically evaluated., Results: The image quality scores of reduced- and low-dose CT without AIDR 3D were significantly lower than those of both protocols with AIDR 3D and standard-dose CT (p<0.05). All intermethod agreements for emphysema, ground-glass opacity, bronchiectasis, honeycomb pattern, and nodules, except for those observed on low-dose CT without AIDR 3D, were almost perfect (κ>0.81)., Conclusion: AIDR 3D is useful for image noise reduction and assessment of radiologic findings obtained with reduced- and low-dose CT for patients with various pulmonary diseases.

Objective: The purpose of this study is to investigate the effect of a novel reconstruction algorithm, adaptive iterative dose reduction using 3D processing, on emphysema quantification by low-dose CT., Materials and Methods: Twenty-six patients who had undergone standard-dose (150 mAs) and low-dose (25 mAs) CT scans were included in this retrospective study. Emphysema was quantified by several quantitative measures, including emphysema index given by the percentage of lung region with low attenuation (lower than -950 HU), the 15th percentile of lung density, and size distribution of low-attenuation lung regions, on standard-dose CT images reconstructed without adaptive iterative dose reduction using 3D processing and on low-dose CT images reconstructed both without and with adaptive iterative dose reduction using 3D processing. The Bland-Altman analysis was used to assess whether the agreement between emphysema quantifications on low-dose CT and on standard-dose CT was improved by the use of adaptive iterative dose reduction using 3D processing., Results: For the emphysema index, the mean differences between measurements on low-dose CT and on standard-dose CT were 1.98% without and -0.946% with the use of adaptive iterative dose reduction using 3D processing. For 15th percentile of lung density, the mean differences without and with adaptive iterative dose reduction using 3D processing were -6.67 and 1.28 HU, respectively. For the size distribution of low-attenuation lung regions, the ranges of the mean relative differences without and with adaptive iterative dose reduction using 3D processing were 21.4-85.5% and -14.1% to 11.2%, respectively. For 15th percentile of lung density and the size distribution of low-attenuation lung regions, the agreement was thus improved by the use of adaptive iterative dose reduction using 3D processing., Conclusion: The use of adaptive iterative dose reduction using 3D processing resulted in greater consistency of emphysema quantification by low-dose CT, with quantification by standard-dose CT.