Ultralow-dose Chest CT Research Articles

A Multidisciplinary Assessment of Standard Low Dose Versus Ultra-Low Dose Chest CT Scans For Pectus Excavatum Imaging

AimEvaluation of ultra-low dose chest CT imaging for the assessment of pectus excavatum severity as determined by pediatric radiologists and pediatric surgeons using Haller (HI) and Correction indices (CI). MethodsA single institution, prospective evaluation of patients being evaluated for pectus excavatum were scanned with a standard low-dose chest CT protocol (CARE) followed by a consecutive ultra-low dose CT scan (ULTRA). 3 surgeons and 4 radiologists were instructed to determine HI and CI in each series. The Intraclass Correlation Coefficient (ICC) was used to calculate the agreement level between CARE and ULTRA. Bland-Altman (BA) and scatter plots were also performed to determine bias of each approach. Results32 patients had CARE and ULTRA consecutively. The ICC for HI demonstrated good reliability with a value of 0.89 and excellent reliability for CI with a value of 0.91. The reliability for HI was greater in the surgeon group (0.89) compared to the radiologist group (0.88). The reliability for CI was greater in the radiologist group (0.92) compared to the surgeon group (0.90). The Bland Altman plots for the HI and CI demonstrate no consistent bias for CARE or ULTRA approach when evaluating HI and CI. ConclusionUltra-low dose CT scan imaging compared to standard low-dose CT appears to be a reliable alternative for evaluating PE severity as assessed by HI and CI. This work supports the evaluation and potential development of a standardized CT imaging protocol capable of reducing radiation exposure without sacrificing imaging for PE patients. Level of evidence2

Effects of tube voltage, radiation dose and adaptive statistical iterative reconstruction strength level on the detection and characterization of pulmonary nodules in ultra-low-dose chest CT

ObjectiveTo explore the effects of tube voltage, radiation dose and adaptive statistical iterative reconstruction (ASiR-V) strength level on the detection and characterization of pulmonary nodules by an artificial intelligence (AI) software in ultra-low-dose chest CT (ULDCT).Materials and methodsAn anthropomorphic thorax phantom containing 12 spherical simulated nodules (Diameter: 12 mm, 10 mm, 8 mm, 5 mm; CT value: -800HU, -630HU, 100HU) was scanned with three ULDCT protocols: Dose-1 (70kVp:0.11mSv, 100kVp:0.10mSv), Dose-2 (70kVp:0.34mSv, 100kVp:0.32mSv), Dose-3 (70kVp:0.53mSv, 100kVp:0.51mSv). All scanning protocols were repeated five times. CT images were reconstructed using four different strength levels of ASiR-V (0%=FBP, 30%, 50%, 70%ASiR-V) with a slice thickness of 1.25 mm. The characteristics of the physical nodules were used as reference standards. All images were analyzed using a commercially available AI software to identify nodules for calculating nodule detection rate (DR) and to obtain their long diameter and short diameter, which were used to calculate the deformation coefficient (DC) and size measurement deviation percentage (SP) of nodules. DR, DC and SP of different imaging groups were statistically compared.ResultsImage noise decreased with the increase of ASiR-V strength level, and the 70 kV images had lower noise under the same strength level (mean-value 70 kV: 40.14 ± 7.05 (dose 1), 27.55 ± 7.38 (dose 2), 23.88 ± 6.98 (dose 3); 100 kV: 42.36 ± 7.62 (dose 1); 30.78 ± 6.87 (dose 2); 26.49 ± 6.61 (dose 3)). Under the same dose level, there were no differences in DR between 70 kV and 100 kV (dose 1: 58.76% vs. 58.33%; dose 2: 73.33% vs. 70.83%; dose 3: 75.42% vs. 75.42%, all p > 0.05). The DR of GGNs increased significantly at dose 2 and higher (70 kV: 38.12% (dose 1), 60.63% (dose 2), 64.38% (dose 3); 100 kV: 37.50% (dose 1), 59.38% (dose 2), 66.25% (dose 3)). In general, the use of ASiR-V at higher strength levels (> 50%) and 100 kV provided better (lower) DC and SP.ConclusionDetection rates are similar between 70 kV and 100 kV scans. The 70 kV images have better noise performance under the same ASiR-V level, while images of 100 kV and higher ASiR-V levels are better in preserving the nodule morphology (lower DC and SP); the dose levels above 0.33mSv provide high sensitivity for nodules detection, especially the simulated ground glass nodules.

Prospective evaluation of deep learning image reconstruction for Lung-RADS and automatic nodule volumetry on ultralow-dose chest CT.

To prospectively evaluate whether Lung-RADS classification and volumetric nodule assessment were feasible with ultralow-dose (ULD) chest CT scans with deep learning image reconstruction (DLIR). The institutional review board approved this prospective study. This study included 40 patients (mean age, 66±12 years; 21 women). Participants sequentially underwent LDCT and ULDCT (CTDIvol, 0.96±0.15 mGy and 0.12±0.01 mGy) scans reconstructed with the adaptive statistical iterative reconstruction-V 50% (ASIR-V50) and DLIR. CT image quality was compared subjectively and objectively. The pulmonary nodules were assessed visually by two readers using the Lung-RADS 1.1 and automatically using a computerized assisted tool. DLIR provided a significantly higher signal-to-noise ratio for LDCT and ULDCT images than ASIR-V50 (all P < .001). In general, DLIR showed superior subjective image quality for ULDCT images (P < .001) and comparable quality for LDCT images compared to ASIR-V50 (P = .01-1). The per-nodule sensitivities of observers for Lung-RADS category 3-4 nodules were 70.6-88.2% and 64.7-82.4% for DLIR-LDCT and DLIR-ULDCT images (P = 1) and categories were mostly concordant within observers. The per-nodule sensitivities of the computer-assisted detection for nodules ≥4 mm were 72.1% and 67.4% on DLIR-LDCT and ULDCT images (P = .50). The 95% limits of agreement for nodule volume differences between DLIR-LDCT and ULDCT images (-85.6 to 78.7 mm3) was similar to the within-scan nodule volume differences between DLIR- and ASIR-V50-LDCT images (-63.9 to 78.5 mm3), with volume differences smaller than 25% in 88.5% and 92.3% of nodules, respectively (P = .65). DLIR enabled comparable Lung-RADS and volumetric nodule assessments on ULDCT images to LDCT images.

Ultra-low dose chest CT with silver filter and deep learning reconstruction significantly reduces radiation dose and retains quantitative information in the investigation and monitoring of lymphangioleiomyomatosis (LAM)

PurposeFrequent CT scans to quantify lung involvement in cystic lung disease increases radiation exposure. Beam shaping energy filters can optimize imaging properties at lower radiation dosages. The aim of this study is to investigate whether use of SilverBeam filter and deep learning reconstruction algorithm allows for reduced radiation dose chest CT scanning in patients with lymphangioleiomyomatosis (LAM).Material and methodsIn a single-center prospective study, 60 consecutive patients with LAM underwent chest CT at standard and ultra-low radiation doses. Standard dose scan was performed with standard copper filter and ultra-low dose scan was performed with SilverBeam filter. Scans were reconstructed using a soft tissue kernel with deep learning reconstruction (AiCE) technique and using a soft tissue kernel with hybrid iterative reconstruction (AIDR3D). Cyst scores were quantified by semi-automated software. Signal-to-noise ratio (SNR) was calculated for each reconstruction. Data were analyzed by linear correlation, paired t-test, and Bland–Altman plots.ResultsPatients averaged 49.4 years and 100% were female with mean BMI 26.6 ± 6.1 kg/m2. Cyst score measured by AiCE reconstruction with SilverBeam filter correlated well with that of AIDR3D reconstruction with standard filter, with a 1.5% difference, and allowed for an 85.5% median radiation dosage reduction (0.33 mSv vs. 2.27 mSv, respectively, p < 0.001). Compared to standard filter with AIDR3D, SNR for SilverBeam AiCE images was slightly lower (3.2 vs. 3.1, respectively, p = 0.005).ConclusionSilverBeam filter with deep learning reconstruction reduces radiation dosage of chest CT, while maintaining accuracy of cyst quantification as well as image quality in cystic lung disease.Clinical relevance statementRadiation dosage from chest CT can be significantly reduced without sacrificing image quality by using silver filter in combination with a deep learning reconstructive algorithm.Key Points• Deep learning reconstruction in chest CT had no significant effect on cyst quantification when compared to conventional hybrid iterative reconstruction.• SilverBeam filter reduced radiation dosage by 85.5% compared to standard dose chest CT.• SilverBeam filter in coordination with deep learning reconstruction maintained image quality and diagnostic accuracy for cyst quantification when compared to standard dose CT with hybrid iterative reconstruction.Graphical abstract

Deep Learning-Based Reconstruction Algorithm With Lung Enhancement Filter for Chest CT: Effect on Image Quality and Ground Glass Nodule Sharpness.

To assess the effect of a new lung enhancement filter combined with deep learning image reconstruction (DLIR) algorithm on image quality and ground-glass nodule (GGN) sharpness compared to hybrid iterative reconstruction or DLIR alone. Five artificial spherical GGNs with various densities (-250, -350, -450, -550, and -630 Hounsfield units) and 10 mm in diameter were placed in a thorax anthropomorphic phantom. Four scans at four different radiation dose levels were performed using a 256-slice CT (Revolution Apex CT, GE Healthcare). Each scan was reconstructed using three different reconstruction algorithms: adaptive statistical iterative reconstruction-V at a level of 50% (AR50), Truefidelity (TF), which is a DLIR method, and TF with a lung enhancement filter (TF + Lu). Thus, 12 sets of reconstructed images were obtained and analyzed. Image noise, signal-to-noise ratio, and contrast-to-noise ratio were compared among the three reconstruction algorithms. Nodule sharpness was compared among the three reconstruction algorithms using the full-width at half-maximum value. Furthermore, subjective image quality analysis was performed. AR50 demonstrated the highest level of noise, which was decreased by using TF + Lu and TF alone (P = 0.001). TF + Lu significantly improved nodule sharpness at all radiation doses compared to TF alone (P = 0.001). The nodule sharpness of TF + Lu was similar to that of AR50. Using TF alone resulted in the lowest nodule sharpness. Adding a lung enhancement filter to DLIR (TF + Lu) significantly improved the nodule sharpness compared to DLIR alone (TF). TF + Lu can be an effective reconstruction technique to enhance image quality and GGN evaluation in ultralow-dose chest CT scans.

Pneumonia pattern recognition on ultra-low-dose CT does not allow for a reliable differentiation between viral and bacterial pneumonia: A multicentre observer study

PurposeWhile a reliable differentiation between viral and bacterial pneumonia is not possible with chest X-ray, this study investigates whether ultra-low-dose chest-CT (ULDCT) could be used for this purpose. MethodsIn the OPTIMACT trial 281 patients had a final diagnosis of pneumonia, and 96/281 (34%) had one or more positive microbiology results: 60 patients viral pathogens, 48 patients bacterial pathogens. These 96 ULDCT’s were blindly and independently evaluated by two chest radiologists, who reported CT findings, pneumonia pattern, and most likely type of pathogen. Differences between groups were analysed for each radiologist separately, diagnostic accuracy was evaluated by calculating sensitivity. ResultsThe dominant CT finding significantly differed between the viral and bacterial pathogen groups (p = 0.04; p = 0.04). Consolidation was the most frequent dominant CT finding in both patients with viral and bacterial pathogens, but was observed significantly more often in those with a bacterial pathogen: 32/60 and 22/60 versus 38/48 and 31/48 (p = 0.005; p = 0.004).The lobar pneumonia pattern was more frequently observed in patients with a bacterial pathogen: 23/48 and 18/48, versus 10/60 and 8/60 for viral pathogens (p < 0.001; p = 0.004). For the bronchopneumonia and interstitial pneumonia patterns the proportions of viral and bacterial pathogens were not significantly different.Both radiologists suggested a viral pathogen correctly (sensitivity) in 6/60 (10%), for a bacterial pathogen this was 34/48 (71%). ConclusionReliable differentiation between viral and bacterial pneumonia could not be made by pattern recognition on ULDCT, although a lobar pneumonia pattern was significantly more often observed in bacterial infection.

Simultaneous assessment of heart and lungs with gated high-pitch ultra-low dose chest CT using artificial intelligence-based calcium scoring

PurposeThe combined testing for coronary artery and pulmonary diseases is of clinical interest as risk factors are shared. In this study, a novel ECG-gated tin-filtered ultra-low dose chest CT protocol (GCCT) for integrated heart and lung acquisition and the applicability of artificial intelligence (AI)-based coronary artery calcium scoring were assessed. MethodsIn a clinical registry of 10481 patients undergoing heart and lung CT, GCCT was applied in 44 patients on a dual-source CT. Coronary calcium scans (CCS) with 120 kVp, 100 kVp, and tin-filtered 100 kVp (Sn100) of controls, matched with regard to age, sex, and body-mass index, were retrieved from the registry (ntotal=176, 66.5 (59.4–74.0) years, 52 men). Automatic tube current modulation was used in all scans. In 20 patients undergoing GCCT and Sn100 CCS, Agatston scores were measured both semi-automatically by experts and by AI, and classified into six groups (0, <10, <100, <400, <1000, ≥1000). ResultsEffective dose decreased significantly from 120 kVp CCS (0.50 (0.41–0.61) mSv) to 100 kVp CCS (0.34 (0.26–0.37) mSv) to Sn100 CCS (0.14 (0.11–0.17) mSv). GCCT showed higher values (0.28 (0.21–0.32) mSv) than Sn100 CCS but lower than 120 kVp and 100 kVp CCS (all p < 0.05) despite greater scan length. Agatston scores correlated strongly between GCCT and Sn100 CCS in semi-automatic and AI-based measurements (both ρ = 0.98, p < 0.001) resulting in high agreement in Agatston score classification (κ = 0.97, 95% CI 0.92–1.00; κ = 0.89, 95% CI 0.79–0.99). Regarding chest findings, further diagnostic steps were recommended in 28 patients. ConclusionsGCCT allows for reliable coronary artery disease and lung cancer screening with ultra-low radiation exposure. GCCT-derived Agatston score shows excellent agreement with standard CCS, resulting in equivalent risk stratification.

Semi-automated volumetry of pulmonary nodules: Intra-individual comparison of standard dose and chest X-ray equivalent ultralow dose chest CT scans

PurposeTo assess the performance of semi-automated volumetry of solid pulmonary nodules on single-energy tin-filtered ultralow dose (ULD) chest CT scans at a radiation dose equivalent to chest X-ray relative to standard dose (SD) chest CT scans and assess the impact of kernel and iterative reconstruction selection. MethodsNinety-four consecutive patients from a prospective single-center study were included and underwent clinically indicated SD chest CT (1.9 ± 0.8 mSv) and additional ULD chest CT (0.13 ± 0.01 mSv) in the same session. All scans were reconstructed with a soft tissue (Br40) and lung (Bl64) kernel as well as with Filtered Back Projection (FBP) and Iterative Reconstruction (ADMIRE-3 and ADMIRE-5). One hundred and forty-eight solid pulmonary nodules were identified and analysed by semi-automated volumetry on all reconstructions. Nodule volumes were compared amongst all reconstructions thereby focusing on the agreement between SD and ULD scans. ResultsNodule volumes ranged from 58.5 (28.8–126) mm3 for ADMIRE-5 Br40 ULD reconstructions to 72.5 (39–134) mm3 for FBP Bl64 SD reconstructions with significant differences between reconstructions (p < 0.001). Interscan agreement of volumes between two given reconstructions ranged from ICC = 0.605 to ICC = 0.999. Between SD and ULD scans, agreement of nodule volumes was highest for FBP Br40 (ICC = 0.995), FBP Bl64 (ICC = 0.939) and ADMIRE-5 Bl64 (ICC = 0.994) reconstructions. ADMIRE-3 reconstructions exhibited reduced interscan agreement of nodule volumes (ICCs from 0.788 − 0.882). ConclusionsThe interscan agreement of node volumes between SD and ULD is high depending on the choice of kernel and reconstruction algorithm. However, caution should be exercised when comparing two image series that were not identically reconstructed.

Ultra-low-dose CT versus chest X-ray for patients suspected of pulmonary disease at the emergency department: a multicentre randomised clinical trial

BackgroundChest CT displays chest pathology better than chest X-ray (CXR). We evaluated the effects on health outcomes of replacing CXR by ultra-low-dose chest-CT (ULDCT) in the diagnostic work-up of patients...

A deep-learning method for the denoising of ultra-low dose chest CT in coronary artery calcium score evaluation

To evaluate a novel deep-learning denoising method for ultra-low dose CT (ULDCT) in the assessment of coronary artery calcium score (CACS). Sixty adult patients who underwent two unenhanced chest CT examinations, a normal dose CT (NDCT) and an ULDCT, were enrolled prospectively between September 2017 to December 201. A special training set was created to learn the characteristics of the real noise affecting the ULDCT implementing a fully convolutional neural network with batch normalisation. Subsequently, the 60 ULDCTs of the evaluation set were denoised. Two blinded radiologists assessed the NDCT, ULDCT, and denoised-ULDCT (DULDCT), assigning a CACS and categorised each scan as having a score above or below 100 and presence of calcifications (score 0 versus >0). Statistical analysis was used to evaluate the agreement between the readers and differences in CACSs between each imaging method. After excluding one patient, the cohort included 59 patients (median age 67 years, 58% men). The ULDCT median effective radiation dose (ERD) was 0.172 mSv, which was 2.8% of the NDCT median ERD. Denoising improved the signal-to-noise ratio by 27.7% (p<0.001). Interobserver agreement was almost perfect between readers (intraclass correlation coefficient >0.993). CACSs were lower for ULDCT and DULDCT as compared to the NDCT (p≤0.001). In differentiating between the presence and absence of coronary artery calcifications, DULDCT showed greater accuracy (98-100%) and positive likelihood ratio (14.29->99) compared to ULDCT (92% and 2.78, respectively). DULCT significantly reduced the image noise and better identified patients with no coronary artery calcifications than native ULDCT.

Quantitative analysis of image quality and radiation dose of ultra low dose chest CT using AI-based deep learning reconstruction algorithm ‘AiCE’

초저선량 흉부 CT(ultra low dose chest CT; ULDCT)는 영상 재구성 기술의 발전으로 기존의 저선량 흉부 CT(low dose chest CT; LDCT)보다 낮은 선량을 사용하더라도 적절한 품질의 영상 획득이 가능하다. 최근 인공지능 기반의 영상 재구성 알고리즘인 AiCE(Advanced Intelligent Clear-IQ Engine)가 상용화가 되었다. 본 연구는 ULDCT에 최신 기술인 AiCE를 적용하여 팬텀 실험과 실제 환자 실험을 실시하여 영상 화질 및 방사선 선량을 정량적으로 분석해 보고자 하였다. Lung man 팬텀에 LDCT, ULDCT프로토콜로 검사하고, Noise, SNR, DLP 값을 구해 기존의 영상 재구성 방식과 이미지 품질 차이를 정량적으로 분석했다. 또한 60명의 인원을 30명씩 두 그룹으로 나누어 AiCE가 적용된 LDCT와 ULDCT 프로토콜을 사용해 검사했다. 팬텀 연구에서 방사선량은 49% 감소했고, AiCE가 적용된 ULDCT 영상이 제일 SNR값이 높고 Noise가 적었다. 환자 실험에서 방사선량은 55% 감소했다. AiCE가 적용된 ULDCT 영상은 체적이 두꺼운 upper lobe 부분에서 약 2%의 SNR 저하와 0.5%의 Noise가 증가했다. 적절한 환자의 체적 범위 내에서 선별하여 사용하는 ULDCT는 피폭 위험도를 절반 이하로 낮춰 주고 환자의 불안감을 해소하면서 최적의 영상을 획득하는 좋은 방안이 될 것으로 판단된다.

Impact of Morphotype on Image Quality and Diagnostic Performance of Ultra-Low-Dose Chest CT.

Objectives: The image quality of an Ultra-Low-Dose (ULD) chest CT depends on the patient’s morphotype. We hypothesize that there is a threshold beyond which the diagnostic performance of a ULD chest CT is too degraded. This work assesses the influence of morphotype (Body Mass Index BMI, Maximum Transverse Chest Diameter MTCD and gender) on image quality and the diagnostic performance of a ULD chest CT. Methods: A total of 170 patients from three prior prospective monocentric studies were retrospectively included. Renewal of consent was waived by our IRB. All the patients underwent two consecutive unenhanced chest CT acquisitions with a full dose (120 kV, automated tube current modulation) and a ULD (135 kV, fixed tube current at 10 mA). Image noise, subjective image quality and diagnostic performance for nine predefined lung parenchyma lesions were assessed by two independent readers, and correlations with the patient’s morphotype were sought. Results: The mean BMI was 26.6 ± 5.3; 20.6% of patients had a BMI > 30. There was a statistically significant negative correlation of the BMI with the image quality (ρ = −0.32; IC95% = (−0.468; −0.18)). The per-patient diagnostic performance of ULD was sensitivity, 77%; specificity, 99%; PPV, 94% and NPV, 65%. There was no statistically significant influence of the BMI, the MTCD nor the gender on the per-patient and per-lesion diagnostic performance of a ULD chest CT, apart from a significant negative correlation for the detection of emphysema. Conclusions: Despite a negative correlation between the BMI and the image quality of a ULD chest CT, we did not find a correlation between the BMI and the diagnostic performance of the examination, suggesting a possible use of the ULD protocol in obese patients.

Radiomics in the evaluation of lung nodules: Intrapatient concordance between full-dose and ultra-low-dose chest computed tomography

PurposeThe purpose of this study was to retrospectively evaluate the quantitative and qualitative intrapatient concordance of pulmonary nodule risk assessment by commercially available radiomics software between full-dose (FD) chest-CT and ultra-low-dose (ULD) chest CT. Materials and methodsBetween July 2013 and September 2015, 68 patients (52 men and16 women; mean age, 65.5±10.6 [SD] years; range: 35–87 years) with lung nodules≥5mm and<30mm who underwent the same day FD chest CT (helical acquisition; 120kV; automated tube current modulation) and ULD chest CT (helical acquisition; 135kV; 10mA fixed) were retrospectively included. Each nodule on each acquisition was assessed by a commercial radiomics software providing a similarity malignancy index (mSI), classifying it as “benign-like” (mSI<0.1); “malignant-like” (mSI>0.9) or “undetermined” (0.1≤mSI≤0.9). Intrapatient qualitative agreement was evaluated with weighted Cohen–Kappa test and quantitative agreement with intraclass correlation coefficient (ICC). ResultsNinety-nine lung nodules with a mean size of 9.14±4.3 (SD) mm (range: 5–25mm) in 68 patients (mean 1.46 nodule per patient; range: 1–5) were assessed; mean mSI was 0.429±0.331 (SD) (range: 0.001–1) with FD chest CT (22/99 [22%] “benign-like”, 67/99 [68%] “undetermined” and 10/99 [10%] “malignant-like”) and mean mSI was 0.487±0.344 (SD) (range: 0.002–1) with ULD chest CT (20/99 [20%] “benign-like”, 59/99 [60%] “undetermined” and 20/99 [20%] “malignant-like”). Qualitative and quantitative agreement of FD chest CT with ULD chest CT were “good” with Kappa value of 0.60 (95% CI: 0.46–0.74) and ICC of 0.82 (95% CI: 0.73–0.87), respectively. ConclusionA good agreement in malignancy similarity index can be obtained between ULD chest CT and FD chest CT using radiomics software. However, further studies must be done with more case material to confirm our results and elucidate the diagnostic capabilities of radiomics software using ULD chest CT for lung nodule characterization by comparison with FD chest CT.

Image quality of ultralow-dose chest CT using deep learning techniques: potential superiority of vendor-agnostic post-processing over vendor-specific techniques.

To compare the image quality between the vendor-agnostic and vendor-specific algorithms on ultralow-dose chest CT. Vendor-agnostic deep learning post-processing model (DLM), vendor-specific deep learning image reconstruction (DLIR, high level), and adaptive statistical iterative reconstruction (ASiR, 70%) algorithms were employed. One hundred consecutive ultralow-dose noncontrast CT scans (CTDIvol; mean, 0.33 ± 0.056 mGy) were reconstructed with five algorithms: DLM-stnd (standard kernel), DLM-shrp (sharp kernel), DLIR, ASiR-stnd, and ASiR-shrp. Three thoracic radiologists blinded to the reconstruction algorithms reviewed five sets of 100 images and assessed subjective noise, spatial resolution, distortion artifact, and overall image quality. They selected the most preferred algorithm among five image sets for each case. Image noise and signal-to-noise ratio were measured. Edge-rise-distance was measured at a pulmonary vessel, i.e., the distance between two points where attenuation was 10% and 90% of maximal intravascular intensity. The skewness of attenuation was calculated in homogeneous areas. DLM-stnd, followed by DLIR, showed the best subjective noise on both lung and mediastinal windows, while DLIR yielded the least measured noise (ps < .0001). Compared to DLM-stnd, DLIR showed inferior subjective spatial resolution on lung window and higher edge-rise-distance (ps < .0001). Additionally, DLIR showed the most frequent distortion artifacts and deviated skewness (ps < .0001). DLM-stnd scored the best overall image quality, followed by DLM-shrp and DLIR (mean score 3.89 ± 0.19, 3.68 ± 0.24, and 3.53 ± 0.33; ps < .001). Two among three readers preferred DLM-stnd on both windows. Although DLIR provided the best quantitative noise profile, DLM-stnd showed the best overall image quality with fewer artifacts and was preferred by two among three readers. • A vendor-agnostic deep learning post-processing algorithm applied to ultralow-dose chest CT exhibited the best image quality compared to vendor-specific deep learning algorithm and ASiR techniques. • Two out of three readers preferred a vendor-agnostic deep learning post-processing algorithm in comparison to vendor-specific deep learning algorithm and ASiR techniques. • A vendor-specific deep learning reconstruction algorithm yielded the least image noise, but showed significantly more frequent specific distortion artifacts and increased skewness of attenuation compared to a vendor-agnostic algorithm.

Detection of pulmonary nodules: a clinical study protocol to compare ultra-low dose chest CT and standard low-dose CT using ASIR-V

IntroductionLung cancer screening in individuals at risk has been recommended by various scientific institutions. One of the main concerns for CT screening is repeated radiation exposure, with the risk of...

Coronary risk assessment at X-ray dose equivalent ungated chest CT: Results of a multi-reader study

ObjectivesTo determine the value of ultra-low dose chest CT with tin filtration for ordinal coronary artery calcium (CAC) risk scoring. Methods50 patients were prospectively included and underwent clinical standard dose chest CT (1.8±0.7mSv) and ultra-low dose CT (0.13±0.01mSv). Four radiologists estimated presence and extent of CAC. ResultsWeighted kappa values for CAC were 0.76–0.97 in standard dose and 0.75–0.95 in ultra-low dose CT (p<0.001). Good to excellent agreement was observed for CAC ordinal risk assessment, with readers reporting identical risk in 81% of cases. ConclusionCAC risk can be qualitatively assessed from X-ray dose equivalent ungated chest CT.

Diagnostic accuracy of chest X-ray dose-equivalent CT for assessing calcified atherosclerotic burden of the thoracic aorta.

To determine the value of ultralow-dose chest CT for estimating the calcified atherosclerotic burden of the thoracic aorta using tin-filter CT and compare its diagnostic accuracy with chest direct radiography. A total of 106 patients from a prospective, IRB-approved single-centre study were included and underwent standard dose chest CT (1.7 ± 0.7 mSv) by clinical indication followed by ultralow-dose CT with 100 kV and spectral shaping by a tin filter (0.13 ± 0.01 mSv) to achieve chest X-ray equivalent dose in the same session. Two independent radiologists reviewed the CT images, rated image quality and estimated presence and extent of calcification of aortic valve, ascending aorta and aortic arch. Conventional radiographs were also reviewed for presence of aortic calcifications. The sensitivity of ultralow-dose CT for the detection of calcifications of the aortic valve, ascending aorta and aortic arch was 93.5, 96.2 and 96.2%, respectively, compared with standard dose CT. The sensitivity for the detection of thoracic aortic calcification was significantly lower on chest X-ray (52.3%) compared with ultralow-dose CT (p < 0.001). A reliable estimation of calcified atherosclerotic burden of the thoracic aorta can be achieved with modern tin-filter CT at dose values comparable to chest direct radiography. Advances in knowledge: Our findings suggest that ultralow-dose CT is an excellent tool for assessing the calcified atherosclerotic burden of the thoracic aorta with higher diagnostic accuracy than conventional chest radiography and importantly without the additional cost of increased radiation dose.

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

ObjectivesTo 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). Methods84 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. ResultsThe 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. ConclusionsUltralow 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.

Correlation of ultra-low dose chest CT findings with physiologic measures of asbestosis.

The correlation between ultra low dose computed tomography (ULDCT)-detected parenchymal lung changes and pulmonary function abnormalities is not well described. This study aimed to determine the relationship between ULDCT-detected interstitial lung disease (ILD) and measures of pulmonary function in an asbestos-exposed population. Two thoracic radiologists independently categorised prone ULDCT scans from 143 participants for ILD appearances as absent (score 0), probable (1) or definite (2) without knowledge of asbestos exposure or lung function. Pulmonary function measures included spirometry and diffusing capacity to carbon monoxide (DLCO). Participants were 92% male with a median age of 73.0years. CT dose index volume was between 0.6 and 1.8mGy. Probable or definite ILD was reported in 63 (44.1%) participants. Inter-observer agreement was good (k = 0.613, p < 0.001). There was a statistically significant correlation between the ILD score and both forced expiratory volume in 1second (FEV1) and forced vital capacity (FVC) (r = -0.17, p = 0.04 and r = -0.20, p = 0.02). There was a strong correlation between ILD score and DLCO (r = -0.34, p < 0.0001). Changes consistent with ILD on ULDCT correlate well with corresponding reductions in gas transfer, similar to standard CT. In asbestos-exposed populations, ULDCT may be adequate to detect radiological changes consistent with asbestosis. • Interobserver agreement for the ILD score using prone ULDCT is good. • Prone ULDCT appearances of ILD correlate with changes in spirometric observations. • Prone ULDCT appearances of ILD correlate strongly with changes in gas transfer. • Prone ULDCT may provide sufficient radiological evidence to inform the diagnosis of asbestosis.

Ultra low-dose chest ct with iterative reconstructions as an alternative to conventional chest x-ray prior to heart surgery (CRICKET study): Rationale and design of a multicenter randomized trial

BackgroundStroke after cardiac surgery is a severe complication with a persistently high incidence of 1.4 – 9.7%. Postoperative strokes are mainly embolic and can be provoked by manipulation and clamping of the aorta during cardiac surgery, resulting in the mobilization of atherothrombotic material and calcifications from the aortic wall. Computed tomography (CT) can offer preoperative visualization of aortic calcifications with low radiation exposure. We hypothesize that preoperative knowledge regarding the location and extent of aortic calcifications can be used to optimize surgical strategy and decrease postoperative stroke rate. Methods/designThe CRICKET study (ultra low-dose chest CT with iterative reconstructions as an alternative to conventional chest x-ray prior to heart surgery) is a prospective multicenter randomized clinical trial to evaluate whether non-contrast chest CT before cardiac surgery can decrease postoperative stroke rate by optimizing surgical strategy. Patients scheduled to undergo cardiac surgery aged 18 years and older are eligible for inclusion. Exclusion criteria are pregnancy, a chest/cardiac CT in the past three months, emergency surgery, concomitant or prior participation in a study with ionizing radiation and unwillingness to be informed about incidental findings. Subjects (n = 1.724) are randomized between routine care, including a chest x-ray, or routine care with an additional low dose chest CT. The primary objective is to investigate whether the postoperative in-hospital stroke rate is reduced in the CT arm compared to the routine care arm of the randomized trial. The secondary outcome measures are altered surgical approach based on CT findings and cost-effectiveness.