Improved image quality and dose reduction in abdominal CT with deep-learning reconstruction algorithm: a phantom study.

OBJECTIVES: To assess the impact of a new artificial intelligence deep-learning reconstruction (Precise Image; AI-DLR) algorithm on image quality against a hybrid iterative reconstruction (IR) algorithm in abdominal CT for different clinical indications. METHODS: Acquisitions on phantoms were performed at 5 dose levels (CTDIvol: 13/11/9/6/1.8 mGy). Raw data were reconstructed using level 4 of iDose4 (i4) and 3 levels of AI-DLR (Smoother/Smooth/Standard). Noise power spectrum (NPS), task-based transfer function (TTF) and detectability index (d') were computed: d' modelled detection of a liver metastasis (LM) and hepatocellular carcinoma at portal (HCCp) and arterial (HCCa) phases. Image quality was subjectively assessed on an anthropomorphic phantom by 2 radiologists. RESULTS: From Standard to Smoother levels, noise magnitude and average NPS spatial frequency decreased and the detectability (d') of all simulated lesions increased. For both inserts, TTF values were similar for all three AI-DLR levels from 13 to 6 mGy but decreased from Standard to Smoother levels at 1.8 mGy. Compared to the i4 used in clinical practice, d' values were higher using the Smoother and Smooth levels and close for the Standard level. For all dose levels, except at 1.8 mGy, radiologists considered images satisfactory for clinical use for the 3 levels of AI-DLR, but rated images too smooth using the Smoother level. CONCLUSION: Use of the Smooth and Smoother levels of AI-DLR reduces the image noise and improves the detectability of lesions and spatial resolution for standard and low-dose levels. Using the Smooth level is apparently the best compromise between the lowest dose level and adequate image quality. KEY POINTS: • Evaluation of the impact of a new artificial intelligence deep-learning reconstruction (AI-DLR) on image quality and dose compared to a hybrid iterative reconstruction (IR) algorithm. • The Smooth and Smoother levels of AI-DLR reduced the image noise and improved the detectability of lesions and spatial resolution for standard and low-dose levels. • The Smooth level seems the best compromise between the lowest dose level and adequate image quality.

S100B serum level: A relevant biomarker for the management of non-traumatic headaches in emergency care?

BACKGROUND: The diagnostic of primary or secondary headaches in emergency units is mostly based on brain imaging, which is expensive and sometimes hardly accessible. An increase in serum S100B protein has already been found in several neurological conditions inducing brain damage. The objective of this study was to assess the diagnostic performance of S100B serum assay to distinguish primary and secondary headaches among patients with non-traumatic headaches in the emergency department. METHODS: This was a phase 2, prospective, monocentric diagnostic study. Eighty-one adult patients with non-traumatic headaches in the emergency department were included. In addition to the usual management, a blood assay of the S100B protein was performed in the emergency department, as well as a brain MRI between 48 and 96 h if not performed during the initial management. The primary or secondary headache diagnosis was made at one month by an expert committee, blindly of the results of the S100B assay. The primary outcome was the blood assay of the S100B protein. RESULTS: There was 63 patients for analysis in the primary headache group and 17 in the secondary headache group. The S100B protein assay was significantly higher in secondary headaches than primary headaches, with an AUC of the ROC curve of 0.67. The optimal threshold of 0.06 µg.L-1 allowed to obtain those diagnostic characteristics: sensitivity 75% [48; 93], specificity 62% [48; 74], PPV 35% [20; 54] and NPV 90% [76; 97]. The association between the S100B protein level and the onset of pain was significantly higher for patients with headaches <3 h. CONCLUSION: The assay of the S100B protein could be useful in the management of this pathology in emergencies. Future studies taking into account dosing time and etiologies could be conducted in order to refine its use in practice.

Abdominal image quality and dose reduction with energy-integrating or photon-counting detectors dual-source CT: A phantom study.

PURPOSE: The purpose of this study was to assess image-quality and dose reduction potential using a photon-counting computed tomography (PCCT) system by comparison with two different dual-source CT (DSCT) systems using two phantoms. MATERIALS AND METHODS: Acquisitions on phantoms were performed using two DSCT systems (DSCT1 [Somatom Force] and DSCT2 [Somatom Pro.Pulse]) and one PCCT system (Naeotom Alpha) at four dose levels (13/6/3.4/1.8 mGy). Noise power spectrum (NPS) and task-based transfer function (TTF) were computed to assess noise magnitude and noise texture and spatial resolution (f50), respectively. Detectability indexes (d') were computed to model the detection of abdominal lesions: one unenhanced high-contrast task, one contrast-enhanced high-contrast task and one unenhanced low-contrast task. Image quality was subjectively assessed on an anthropomorphic phantom by two radiologists. RESULTS: For all dose levels, noise magnitude values were lower with PCCT than with DSCTs. For all CT systems, similar noise texture values were found at 13 and 6 mGy, but the greatest noise texture values were found for DSCT2 and the lowest for PCCT at 3.4 and 1.8 mGy. For high-contrast inserts, similar or lower f50 values were found with PCCT than with DSCT1 and the opposite pattern was found for the low-contrast insert. For the three simulated lesions, d' values were greater with PCCT than with DSCTs. Abdominal images were rated satisfactory for clinical use by the radiologists for all dose levels with PCCT and for 13 and 6 mGy with DSCTs. CONCLUSION: By comparison with DSCTs, PCCT reduces image-noise and improves detectability of simulated abdominal lesions without altering the spatial resolution and image texture. Image-quality obtained with PCCT seem to indicate greater potential for dose optimization than those obtained with DSCTs.

Evaluation of left ventricular ejection fraction by a new automatic tool on a pocket ultrasound device: Concordance study with cardiac magnetic resonance imaging.

INTRODUCTION: Assessment of left ventricular ejection fraction (LVEF) is one of the primary objectives of echocardiography. The gold standard assessment technique in emergency medicine is eyeballing. A new tool is now available on pocket ultrasound devices (PUD): automatic LVEF. The primary aim of this study was to evaluate the concordance between LVEF values estimated by automatic LVEF with PUD and by cardiac magnetic resonance imaging (MRI). MATERIALS: This was a prospective, monocentric, and observational study. All adult patients with an indication for cardiac MRI underwent a point-of-care ultrasound. Blinded to the MRI results, the emergency physician assessed LVEF using the automatic PUD tool and by visual evaluation. RESULTS: Sixty patients were included and analyzed. Visual estimation of LVEF was feasible for all patients and automatic evaluation for 52 (87%) patients. Lin's concordance correlation coefficient between automatic ejection fraction with PUD and by cardiac MRI was 0.23 (95% CI, 0.03-0.40). CONCLUSION: Concordance between LVEF estimated by the automatic ejection fraction with PUD and LVEF estimated by MRI was non-existent.

Ultra-low dose chest CT for the diagnosis of pulmonary arteriovenous malformation in patients with hereditary hemorrhagic telangiectasia.

PURPOSE: The purpose of this study was to compare ultra-low dose (ULD) and standard low-dose (SLD) chest computed tomography (CT) in terms of radiation exposure, image quality and diagnostic value for diagnosing pulmonary arteriovenous malformation (AVM) in patients with hereditary hemorrhagic telangiectasia (HHT). MATERIALS AND METHODS: In this prospective board-approved study consecutive patients with HHT referred to a reference center for screening and/or follow-up chest CT examination were prospectively included from December 2020 to January 2022. Patients underwent two consecutive non-contrast chest CTs without dose modulation (i.e., one ULD protocol [80 kVp or 100 kVp, CTDIvol of 0.3 mGy or 0.6 mGy] and one SLD protocol [140 kVp, CTDIvol of 1.3 mGy]). Objective image noises measured at the level of tracheal carina were compared between the two protocols. Overall image quality and diagnostic confidence were scored on a 4-point Likert scale (1 = insufficient to 4 = excellent). Sensitivity, specificity, positive predictive value and negative predictive value of ULD CT for diagnosing pulmonary AVM with a feeding artery of over 2 mm in diameter were calculated along with their 95% confidence intervals (CI) using SLD images as the standard of reference. RESULTS: A total of 44 consecutive patients with HHT (31 women; mean age, 42 ± 16 [standard deviation (SD)] years; body mass index, 23.2 ± 4.5 [SD] kg/m2) were included. Thirty-four pulmonary AVMs with a feeding artery of over 2 mm in diameter were found with SLD images versus 35 with ULD images. Sensitivity, specificity, predictive positive value, and predictive negative value of ULD CT for the diagnosis of PAVM were 100% (34/34; 95% CI: 90-100), 96% (18/19; 95% CI: 74-100), 97% (34/35; 95% CI: 85-100) and 100% (18/18; 95% CI: 81-100), respectively. A significant difference in diagnostic confidence scores was found between ULD (3.8 ± 0.4 [SD]) and SLD (3.9 ± 0.1 [SD]) CT images (P = 0.03). No differences in overall image quality scores were found between ULD CT examinations (3.9 ± 0.2 [SD]) and SLD (4 ± 0 [SD]) CT examinations (P = 0.77). Effective radiation dose decreased significantly by 78.8% with ULD protocol, with no significant differences in noise values between ULD CT images (16.7 ± 5.0 [SD] HU) and SLD images (17.7 ± 6.6 [SD] HU) (P = 0.07). CONCLUSION: ULD chest CT provides 100% sensitivity and 96% specificity for the diagnosis of treatable pulmonary AVM with a feeding artery of over 2 mm in diameter, leading to a 78.8% dose-saving compared with a standard low-dose protocol.

Impact of the automatic tube current modulation (ATCM) system on virtual monoenergetic image quality for dual-source CT: A phantom study.

PURPOSE: To assess the impact of the automatic tube current modulation (ATCM) on virtual monoenergetic images (VMIs) quality in dual-source CT(DSCT). MATERIALS AND METHODS: Acquisitions were performed on DSCT using the Mercury phantom. The acquisition parameters for an abdomen-pelvic examination with single-energy CT(SECT) and dual-energy CT(DECT) imaging were used. Acquisitions were performed for each imaging mode using fixed mAs and ATCM. The mAs value was set to obtain a volume CT dose index of 11 mGy in fixed mAs acquisitions. This value was used as the reference mAs in ATCM acquisitions. The noise power spectrum and task-based transfer function at 40,50,60 and 70 keV levels were computed on VMIs and SECT images. The detectability index (d') was calculated for a lesion with an iodine concentration of 10 mg/mL. RESULTS: The noise magnitude on VMIs was higher with the ATCM system than with fixed mAs for all energy levels and section diameters of 21,26 and 31 cm. The noise texture and spatial resolution were similar between the fixed mAs and ATCM acquisitions for both imaging modes. The d' values were lower for all energy levels with ATCM than with fixed mAs acquisitions for 21 and 26 cm diameters by -39.82 ± 9.32%, similar at 31 cm diameter -4.13 ± 0.24% and higher at 36 cm diameter 10.40 ± 6.69%. It was higher on VMIs at all energy levels compared to SECT images. CONCLUSIONS: The ATCM system could be used with DECT imaging to optimize patient exposure without changing the noise texture and spatial resolution of VMIs compared to fixed mAs and SECT.

First Results of a New Deep Learning Reconstruction Algorithm on Image Quality and Liver Metastasis Conspicuity for Abdominal Low-Dose CT.

The study's aim was to assess the impact of a deep learning image reconstruction algorithm (Precise Image; DLR) on image quality and liver metastasis conspicuity compared with an iterative reconstruction algorithm (IR). This retrospective study included all consecutive patients with at least one liver metastasis having been diagnosed between December 2021 and February 2022. Images were reconstructed using level 4 of the IR algorithm (i4) and the Standard/Smooth/Smoother levels of the DLR algorithm. Mean attenuation and standard deviation were measured by placing the ROIs in the fat, muscle, healthy liver, and liver tumor. Two radiologists assessed the image noise and image smoothing, overall image quality, and lesion conspicuity using Likert scales. The study included 30 patients (mean age 70.4 ± 9.8 years, 17 men). The mean CTDIvol was 6.3 ± 2.1 mGy, and the mean dose-length product 314.7 ± 105.7 mGy.cm. Compared with i4, the HU values were similar in the DLR algorithm at all levels for all tissues studied. For each tissue, the image noise significantly decreased with DLR compared with i4 (p < 0.01) and significantly decreased from Standard to Smooth (-26 ± 10%; p < 0.01) and from Smooth to Smoother (-37 ± 8%; p < 0.01). The subjective image assessment confirmed that the image noise significantly decreased between i4 and DLR (p < 0.01) and from the Standard to Smoother levels (p < 0.01), but the opposite occurred for the image smoothing. The highest scores for overall image quality and conspicuity were found for the Smooth and Smoother levels.

Comparison of image quality of two versions of deep-learning image reconstruction algorithm on a rapid kV-switching CT: a phantom study.

BACKGROUND: To assess the impact of the new version of a deep learning (DL) spectral reconstruction on image quality of virtual monoenergetic images (VMIs) for contrast-enhanced abdominal computed tomography in the rapid kV-switching platform. METHODS: Two phantoms were scanned with a rapid kV-switching CT using abdomen-pelvic CT examination parameters at dose of 12.6 mGy. Images were reconstructed using two versions of DL spectral reconstruction algorithms (DLSR V1 and V2) for three reconstruction levels. The noise power spectrum (NSP) and task-based transfer function at 50% (TTF50) were computed at 40/50/60/70 keV. A detectability index (d') was calculated for enhanced lesions at low iodine concentrations: 2, 1, and 0.5 mg/mL. RESULTS: The noise magnitude was significantly lower with DLSR V2 compared to DLSR V1 for energy levels between 40 and 60 keV by -36.5% ± 1.4% (mean ± standard deviation) for the standard level. The average NPS frequencies increased significantly with DLSR V2 by 23.7% ± 4.2% for the standard level. The highest difference in TTF50 was observed at the mild level with a significant increase of 61.7% ± 11.8% over 40-60 keV energy with DLSR V2. The d' values were significantly higher for DLSR V2 versus DLSR V1. CONCLUSIONS: The DLSR V2 improves image quality and detectability of low iodine concentrations in VMIs compared to DLSR V1. This suggests a great potential of DLSR V2 to reduce iodined contrast doses.

Determining the Optimal Energy Level of Virtual Monoenergetic Images in Dual-Source CT for Diagnosis of Bowel Obstruction and Colitis.

Images from 64 patients undergoing an enhanced abdominal-pelvis scan at portal phase in dual-energy CT mode for the diagnosis of colitis or bowel obstruction were retrospectively analyzed. Acquisitions were performed on a third-generation dual-source CT (DSCT) 100/Sn150 kVp. Mixed images were generated, as well as virtual monoenergetic images (VMIs) at 40/50/60/70 keV. Objective image quality was assessed on VMIs and mixed images by measuring contrast, noise and contrast-to-noise ratio (CNR). Noise, smoothing and overall image quality were subjectively analyzed by two radiologists using Likert scales. For both patient groups, the noise decreased significantly according to the energy level from 40 to 60 keV by -47.2 ± 24.0% for bowel obstruction and -50.4 ± 18.2% for colitis. It was similar between 60 and 70 keV (p = 0.475 and 0.059, respectively). Noise values were significantly higher in VMIs than in mixed images, except for 70 keV (p = 0.53 and 0.071, respectively). Similar results were observed for contrast values, with a decrease between 40 and 70 keV of -56.3 ± 7.9% for bowel obstruction -56.2 ± 10.9% for colitis. The maximum CNR value was found at 60 keV compared to other energy levels and mixed images, but there was no significant difference with the other energy levels apart from 70 keV (-9.7 ± 9.8%) for bowel obstruction and 40 keV (-6.6 ± 8.2%) and 70 keV (-5.8 ± 9.2%) for colitis. The VMIs at 60 keV presented higher scores for all criteria for bowel obstruction and colitis, with no significant difference in smoothing score compared to mixed images (p = 0.119 and p = 0.888, respectively).

Dose variations for biopsy, puncture and drainage under CT guidance: A national survey in 1709 patients.

Background: A nation-wide study recently published the dose reference levels for the main CT-guided interventional procedures in 5001 patients. We assessed the impact of patient's age, sex and targeted organ on the patient dose during thoracic and abdominopelvic biopsies and punctures/drainages. Patients and methods: Data were extracted from the previous nationwide study. All biopsies, punctures and drainages for thoracic or abdominopelvic locations performed between January 2017 and June 2019 in all participating centers were included in the study. Multivariable analyses were carried out using a linear regression of the dose-length product (DLP) log, adjusted to age, sex, anatomical location, number of helical acquisitions and inclusion center. Results: Of the 5001 patients of the initial study, 2383 benefited from thoracic or abdominopelvic procedures, including 674 percutaneous destructions excluded. 1709 patients (44 centers), 1045 men, 664 women, median age 64.4 ± 14.0 years were included. The mean DLP was 751.2 ± 642.7mGy.cm. It was significantly higher in men than women (p = 0.0005) and higher for abdominopelvic procedures than for thoracic locations (p<0.0001). Conclusion: Doses delivered to patients for abdominal and thoracic biopsies and punctures/drainages performed under CT guidance depend on gender and location. Furthers studies taking into account the patient's morphology and anatomical location of the procedure would allow proposing finer dose reference levels.

Retrospective analysis of dose delivered to the uterus during CT examination in pregnant women.

PURPOSE: The purpose of this study was to analyze the dose to the uterus (UD) calculated for pregnant women per computed tomography (CT) acquisition and per CT examination in our Institution. MATERIALS AND METHODS: Consecutive pregnant women who underwent CT examination from June 2014 to February 2022 and for whom UD calculation was performed by a medical physicist were retrospectively included. UDs were computed per CT acquisition using the CT Expo 2.4 software and were summed up to obtain the total UD per CT examination. The CTDIvol and dose-length product (DLP) values were retrieved from the dose report and compared with those calculated by the software. RESULTS: A total of 256 pregnant women with a mean age of 29.4 ± 5.5 (SD) years (range: 18-48) at 24.5 ± 10.4 (SD) weeks of amenorrhea (range: 1-40) were included. UDs were computed for 339 CT acquisitions. The CTDIvol and DLP computed by the software were significantly greater than those retrieved from the dose reports (P < 0.05). The greatest UDs were reported for the abdomen-pelvis (10.93 ± 5.74 [SD] mGy; range: 1.2-24.1), chest-abdomen-pelvis (9.79 ± 7.09 [SD] mGy; range: 3.9-22.1), pelvis (18.50 ± 17.96 [SD] mGy; range: 5.8-31.2) and lumbar spine (10.24 ± 11.38 [SD] mGy; range: 2.3-29.6) CT examinations. The total UDs per CT examination were > 20 mGy for 10 pregnant women and the maximum total UD was 52.3 mGy. CONCLUSION: Greatest UDs during CT examinations are observed when the pelvis is directly exposed to X-rays. With current dose levels and in optimized practices, UDs per CT acquisition and CT examination are always below 100 mGy. UD calculations cannot be performed for CT examinations that do not directly expose the pelvis (i.e., those < 1 mGy).

Phantom task-based image quality assessment of three generations of rapid kV-switching dual-energy CT systems on virtual monoenergetic images.

PURPOSE: To compare the spectral performance of three rapid kV switching dual-energy CT (DECT) systems on virtual monoenergetic images (VMIs) at low-energy levels on abdominal imaging. METHODS: A multi-energy phantom was scanned on three DECT systems equipped with three different gemstone spectral imaging (GSI) platforms: GSI (1st generation, GSI-1st), GSI-Pro (2nd generation, GSI-2nd ), and GSI-Xtream (3rd generation, GSI-3rd). Acquisitions on the phantom were performed with a CTDIvol close to 11mGy. For all platforms, raw data were reconstructed using filtered-back projection (FBP) and a hybrid iterative reconstruction algorithm (ASIR-V at 50%; AV50). A deep-learning image reconstruction (DLR) algorithm (TrueFidelity) was used only for the GSI-3rd. Noise power spectrum (NPS) and task-based transfer function (TTF) were evaluated from 40 to 80 keV of VMIs. A detectability index (d') was computed to assess the detection of two contrast-enhanced lesions according to the keV level used. RESULTS: For all GSI platforms, the noise magnitude decreased from 40 to 70 keV, and using AV50 compared to FBP. The average NPS spatial frequency (fav ) and spatial resolution (TTF50% ) were similar from 40 to 70 keV and decreased with AV50 compared to FBP. Compared to AV50, using DLR reduced the noise magnitude (-27% ± 3%) and improved fav values (10% ± 0%) and altering spatial resolution (2% ± 5%). For the two lesions, d' values peaked at 70 keV for GSI-1st and GSI-2nd platforms and at 40/50 keV for GSI-3rd, for all reconstruction algorithms. The highest d' values were found for the GSI-3rd with DLR. CONCLUSION: Differences in image quality were found between the GSI platforms for VMIs at low keV. The new DLR algorithm on the GSI-3rd platform reduced noise and improved spatial resolution and detectability without changing the noise texture for VMIs at low keV. The choice of the best energy level in VMIs depends on the platform and the reconstruction algorithm.

Impact of an artificial intelligence deep-learning reconstruction algorithm for CT on image quality and potential dose reduction: A phantom study.

BACKGROUND: Recently, computed tomography (CT) manufacturers have developed deep-learning-based reconstruction algorithms to compensate for the limitations of iterative reconstruction (IR) algorithms, such as image smoothing and the spatial resolution's dependence on contrast and dose levels. PURPOSE: To assess the impact of an artificial intelligence deep-learning reconstruction (AI-DLR) algorithm on image quality and dose reduction compared with a hybrid IR algorithm in chest CT for different clinical indications. METHODS: Acquisitions on the CT American College of Radiology (ACR) 464 and CT Torso CTU-41 phantoms were performed at five dose levels (CTDIvol : 9.5/7.5/6/2.5/0.4 mGy) used for chest CT conditions. Raw data were reconstructed using filtered backprojection, two levels of IR (iDose4 levels 4 (i4) and 7 (i7)), and five levels of AI-DLR (Precise Image; Smoother, Smooth, Standard, Sharp, Sharper). Noise power spectrum (NPS), task-based transfer function, and detectability index (d') were computed: d'-modeled detection of a soft tissue mediastinal nodule (low-contrast soft tissue chest nodule within the mediastinum [LCN]), ground-glass opacity (GGO), or high-contrast pulmonary (HCP) lesion. The subjective image quality of chest anthropomorphic phantom images was independently evaluated by two radiologists. They assessed image noise, image smoothing, contrast between vessels and fat in the mediastinum for mediastinal images, visual border detection between bronchus and lung parenchyma for parenchymal images, and overall image quality using a commonly used four- or five-point scale. RESULTS: From Standard to Smoother levels, on average, the noise magnitude decreased (for all dose levels: -66.3% ± 0.5% for mediastinal images and -63.1% ± 0.1% for parenchymal images), the average NPS spatial frequency decreased (for all dose levels: -35.3% ± 2.2% for mediastinal images and -13.3% ± 2.2% for parenchymal images), and the detectability (d') of the three lesions increased. The opposite pattern was found from Standard to Sharper levels. From Smoother to Sharper levels, the spatial resolution increased for the low-contrast polyethylene insert and the opposite for the high-contrast air insert. Compared to the i4 used in clinical practice, d' values were higher using Smoother (mean for all dose levels: 338.7% ± 29.4%), Smooth (103.4% ± 11.2%), and Standard (34.1% ± 6.6%) levels for the LCN on mediastinal images and Smoother (169.5% ± 53.2% for GGO and 136.9% ± 1.6% for HCP) and Smooth (36.4% ± 22.1% and 24.1% ± 0.9%, respectively) levels for parenchymal images. Radiologists considered the images satisfactory for clinical use at these levels, but adaptation to the dose level of the protocol is required. CONCLUSION: With AI-DLR, the smoothest levels reduced the noise and improved the detectability of chest lesions but increased the image smoothing. The opposite was found with the sharpest levels. The choice of level depends on the dose level and type of image: mediastinal or parenchymal.

Comparison of virtual monoenergetic imaging between a rapid kilovoltage switching dual-energy computed tomography with deep-learning and four dual-energy CTs with iterative reconstruction.

BACKGROUND: To assess the spectral performance of rapid kV switching dual-energy CT (KVSCT-Canon) equipped with a Deep-Learning spectral reconstruction algorithm on virtual-monoenergetic images at low-energy levels and to compare its performances with four other dual-energy CT (DECT) platforms equipped with iterative reconstruction algorithms. METHODS: Two CT phantoms were scanned on five DECT platforms: KVSCT-Canon, fast kV-switching CT (KVSCT-GE), split filter CT, dual-source CT (DSCT), and dual-layer CT (DLCT). The classical parameters of abdomen-pelvic examinations were used for all phantom acquisitions, and a CTDIvol close to 10 mGy. For KVSCT-Canon, virtual-monoenergetic images were reconstructed with a clinical slice thickness of 0.5 and 1.5 mm to be close to other platforms. Noise power spectrum (NPS) and task-based transfer function (TTF) were evaluated from 40 to 80 keV of virtual-monoenergetic images. A detectability index (d') was computed to model the detection task of two contrast-enhanced lesions as function of keV. RESULTS: For KVSCT-Canon, the noise magnitude and average NPS spatial frequency (fav) decreased from 40 to 70 keV and increased thereafter. Similar noise magnitude outcomes were found for KVSCT-GE but the opposite for fav. For the other DECT platforms, the noise magnitude decreased as the keV increased. For split filter CT, DSCT and DLCT, the fav values increased from 40 to 80 keV. For all DECT platforms, TTF at 50% (f50) decreased as the keV increased, decreasing spatial resolution. For KVSCT-Canon, d' values peaked at 60 and 70 keV for both simulated lesions and from 50 to 70 keV for KVSCT-GE. d' decreased between 40 and 70 keV for DSCT, DLCT and split filter CT. For KVSCT-Canon, the increase in slice thickness decreases noise magnitude, fav and f50 and increases d' values. The highest d' values were found for DLCT at 40 and 50 keV and for KVSCT-Canon at 1.5 mm for other keV. CONCLUSIONS: For KVSCT-Canon, the detectability of contrast-enhanced lesions was highest at 60 keV. The highest d' values were found for DLCT at 40 and 50 keV and for KVSCT-Canon at 1.5 mm for other keV.

Ultra-low-dose chest CT performance for the detection of viral pneumonia patterns during the COVID-19 outbreak period: a monocentric experience.

BACKGROUND: Ultra low dose chest computed tomography (CT) acquisitions have been used for selected emergency room patients with acute dyspnea or minor thoracic trauma. The purpose of this study was to evaluate the diagnostic performance of ultra-low-dose (ULD) chest CT for detecting viral pneumonia patterns compared to standard (STD) dose chest CT. METHODS: All consecutive adult patients with two non-enhanced chest CT acquisitions, one STD and one ULD, for suspicion of viral pneumonia between March 5th and April 2nd 2020 were included. CT results were divided into two groups: non-viral pneumonia CT or compatible with viral pneumonia CT based on viral pneumonia CT patterns: ground-glass opacity (GGO), consolidation, crazy paving, air bronchogram signs and fibrous stripes. The diagnostic performance of ULD CT for suspicion of viral pneumonia was evaluated. For CTs compatible with viral pneumonia, CT pattern detection on ULD CT was assessed and STD CT was used as a reference. RESULTS: The study included 380 patients with 97 CTs (25.5%) compatible with viral pneumonia. The mean effective doses (EDs) were 1.66 (1.29; 2.18) mSv for STD and 0.20 (0.18; 0.22) mSv for ULD CT (P<0.001). The sensitivity and specificity of ULD CT for viral pneumonia detection were 98.9% and 99.0%, respectively. GGO, consolidation and fibrous stripes were equally visible in STD and ULD in 100% (n=97), 36% (n=35) and 23% (n=22) of compatible viral pneumonia-CT patients, respectively. Air bronchogram sign detection was equivalent, concerning 23% (n=22) of patients in STD and 22% (n=21) in ULD. Crazy paving was visible in 24% (n=23) of patients in STD and only 8% (n=8) in ULD (P=0.003). CONCLUSIONS: In comparison to STD dose chest CT, ULD chest CT, with a mean reduction dose of 88.0%, has comparable diagnostic performance for detecting viral pneumonia on CT.

Assessment of new-generation high-power electronic nicotine delivery system as thermal aerosol generation device for inhaled bronchodilators.

PURPOSE: A need remains for alternative devices for aerosol drug delivery that are low cost, convenient and easy to use for the patient, but also capable of producing small-sized aerosol particles. This study investigated the potential of recent high power electronic nicotine delivery systems (ENDS) as aerosol generation devices for inhaled bronchodilators. METHODS: The particle size distribution was measured using a cascade impactor. The delivery of terbutaline sulfate, a current bronchodilator used for asthma or COPD therapy by inhalation, was studied. This drug was quantified by liquid chromatography coupled with tandem mass spectrometry. RESULTS: The particle size distribution in terms of mass frequency (in two ways, gravimetrically and quantitatively through drug assay on each stage) and the terbutaline sulfate concentration in the aerosol were elucidated. The mass median aerodynamic diameter (MMAD) and the drug delivery rose when the power level increased, to reach 5.6±0.4µg/puff with a MMAD of 0.78±0.03µm at 25W. CONCLUSION: New generation high-power ENDS are very efficient to generate carrier-droplets in the submicron range containing drug molecules with a constant drug concentration whatever the size-fractions. ENDS appear to be highly patient-adaptive.

Nicotine delivery from the refill liquid to the aerosol via high-power e-cigarette device.

To offer an enhanced and well-controlled nicotine delivery from the refill liquid to the aerosol is a key point to adequately satisfy nicotine cravings using electronic nicotine delivery systems (ENDS). A recent high-power ENDS, exhibiting higher aerosol nicotine delivery than older technologies, was used. The particle size distribution was measured using a cascade impactor. The effects of the refill liquid composition on the nicotine content of each size-fraction in the submicron range were investigated. Nicotine was quantified by liquid chromatography coupled with tandem mass spectrometry. Particle size distribution of the airborne refill liquid and the aerosol nicotine demonstrated that the nicotine is equally distributed in droplets regardless of their size. Results also proved that the nicotine concentration in aerosol was significantly lower compared to un-puffed refill liquid. A part of the nicotine may be left in the ENDS upon depletion, and consequently a portion of the nicotine may not be transferred to the user. Thus, new generation high-power ENDS associated with propylene glycol/vegetable glycerin (PG/VG) based solvent were very efficient to generate carrier-droplets containing nicotine molecules with a constant concentration. Findings highlighted that a portion of the nicotine in the refill liquid may not be transferred to the user.