CT findings and patterns of e-cigarette or vaping product use-associated lung injury: A multicenter cohort of 160 cases

BACKGROUND: e-Cigarette or vaping-induced lung injury (EVALI) causes a spectrum of CT lung injury patterns. Relative frequencies and associations with vaping behavior are unknown. RESEARCH QUESTION: What are the frequencies of imaging findings and CT patterns in EVALI and what is the relationship to vaping behavior? STUDY DESIGN AND METHODS: CT scans of 160 subjects with EVALI from 15 institutions were retrospectively reviewed. CT findings and patterns were defined and agreed on via consensus. The parenchymal organizing pneumonia (OP) pattern was defined as regional or diffuse ground-glass opacity (GGO) ± consolidation without centrilobular nodules (CNs). An airway-centered OP pattern was defined as diffuse CNs with little or no GGO, whereas a mixed OP pattern was a combination of the two. Other patterns included diffuse alveolar damage (DAD), acute eosinophilic-like pneumonia, and pulmonary hemorrhage. Cases were classified as atypical if they did not fit into a pattern. Imaging findings, pattern frequencies, and injury severity were correlated with substance vaped (marijuana derives [tetrahydrocannabinol] [THC] only, nicotine derivates only, and both), vaping frequency, regional geography, and state recreational THC legality. One-way analysis of variance, χ RESULTS: A total of 160 patients (79.4% men) with a mean age of 28.2 years (range, 15-68 years) with EVALI underwent CT scan. Seventy-seven (48.1%), 15 (9.4%), and 68 (42.5%) patients admitted to vaping THC, nicotine, or both, respectively. Common findings included diffuse or lower lobe GGO with subpleural (78.1%), lobular (59.4%), or peribronchovascular (PBV) sparing (40%). Septal thickening (50.6%), lymphadenopathy (63.1%), and CNs (36.3%) were common. PBV sparing was associated with younger age (P = .02). Of 160 subjects, 156 (97.5%) had one of six defined patterns. Parenchymal, airway-centered, and mixed OP patterns were seen in 89 (55.6%), 14 (8.8%), and 32 (20%) patients, respectively. Acute eosinophilic-like pneumonia (six of 160, 3.8%), DAD (nine of 160, 5.6%), pulmonary hemorrhage (six of 160, 3.8%), and atypical (four of 160, 2.5%) patterns were less common. Increased vaping frequency was associated with more severe injury (P = .008). Multivariable analysis showed a negative association between vaping for \u3e 6 months and DAD pattern (P = .03). Two subjects (1.25%) with DAD pattern died. There was no relation between pattern and injury severity, geographic location, and state legality of recreational use of THC. INTERPRETATION: EVALI typically causes an OP pattern but exists on a spectrum of acute lung injury. Vaping habits do not correlate with CT patterns except for negative correlation between vaping \u3e 6 months and DAD pattern. PBV sparing, not previously described in acute lung injury, is a common finding

Anomalous Systemic Arterial Supply to Normal Basal Segments of the Lung.

Supplemental material is available for this article

Three-Dimensional Pressure Profile of the Lower Esophageal Sphincter and Crural Diaphragm in Patients with Achalasia Esophagus

Background & aimsSmooth muscles of the lower esophageal sphincter (LES) and skeletal muscle of the crural diaphragm (esophagus hiatus) provide the sphincter mechanisms at the esophagogastric junction (EGJ). We investigated differences in the 3-dimensional (3D) pressure profile of the LES and hiatal contraction between healthy subjects and patients with achalasia esophagus.MethodsWe performed a prospective study of 10 healthy subjects (controls; 7 male; mean age, 60 ± 15 years; mean body mass index, 25 ± 2) and 12 patients with a diagnosis of achalasia (7 male; mean age, 63 ± 13 years; mean body mass index, 26 ± 1), enrolled at a gastroenterology clinic. Participants underwent 3D high-resolution manometry (3DHRM) with a catheter equipped with 96 transducers (for the EGJ pressure recording). A 0.5-mm metal ball was taped close to the transducer number 1 of the 3DHRM catheter. EGJ pressure was recorded at end-expiration (LES pressure) and at the peak of forced inspiration (hiatal contraction). Computed tomography (CT) scans were performed to localize the circumferential location of the metal ball on the catheter. Esophagus, LES, stomach, right and left crus of the diaphragm, and spine were segmented in each CT scan slice images to construct the 3D morphology of the region.ResultsThe metal ball was located at the 7 o'clock position in all controls. The circumferential orientation of metal ball was displaced 45 to 90 degrees in patients with achalasia compared with controls. The 3D-pressure profile of the EGJ at end-expiration and forced inspiration revealed marked differences between the groups. The LES turns to the left as it entered from the chest into the abdomen, forming an angle between the spine and LES. The spine-LES angle was smaller in patients with achalasia (104°) compared with controls (124°). Five of the 10 subjects with achalasia had physical breaks in the left crus of the diaphragm CONCLUSIONS: Besides LES, the 3D pressure profile of the EGJ can indicate anatomic and functional abnormalities of the crural diaphragm muscle in patients with achalasia esophagus. Further studies are needed to define the nature of hiatal and crural diaphragm dysfunction in patients with achalasia of the esophagus

4D Flow Vorticity Visualization Predicts Regions of Quantitative Flow Inconsistency for Optimal Blood Flow Measurement.

PurposeTo evaluate whether automated vorticity mapping four-dimensional (4D) flow MRI can identify regions of quantitative flow inconsistency.Materials and methodsIn this retrospective study, 35 consecutive patients who underwent MR angiography with 4D flow MRI at 3.0 T from December 2017 to October 2018 were analyzed using a λ 2-based technique for vorticity visualization and quantification. The patients were aged 58.6 years ± 14.4 (standard deviation), 12 were women, 18 had ascending aortic aneurysms (maximal diameter > 4.0 cm), and 10 had bicuspid aortic valves. Flow measurements were made in the ascending aorta (aAo), mid-descending aorta, main pulmonary artery, and superior vena cava. Statistical tests included t tests and F tests with a type I error threshold (α) of .05.ResultsThe 35 patients were visually classified as having no (n = 9), mild (n = 8), moderate (n = 11), or severe vorticity (n = 7). Across all patients, standard deviation of cardiac output in the aAo (0.58 L/min ± 0.45) was significantly (P < .001) higher than in the pulmonary arteries (0.15 L/min ± 0.10) and descending aorta and superior vena cava (0.14 L/min ± 0.12). The standard deviation of cardiac output observed in the aAo was significantly greater (P < .01) in patients with moderate or severe vorticity (0.73 L/min ± 0.55) than in those with none or mild vorticity (0.44 L/min ± 0.26).ConclusionCardiac output and blood flow are essential MRI measurements in the evaluation of structural heart disease. Vorticity visualization may be used to help guide optimal location for flow quantification.© RSNA, 2020See also the commentary by Markl in this issue

Automated Deep Learning Analysis for Quality Improvement of CT Pulmonary Angiography.

CT pulmonary angiography (CTPA) is the first-line imaging test for evaluation of acute pulmonary emboli. However, diagnostic quality is heterogeneous across institutions and is frequently limited by suboptimal pulmonary artery (PA) contrast enhancement. In this retrospective study, a deep learning algorithm for measuring enhancement of the central PAs was developed and assessed for feasibility of its use in quality improvement of CTPA. In a convenience sample of 450 patients, automated measurement of CTPA enhancement showed high agreement with manual radiologist measurement (r = 0.996). Using a threshold of less than 250 HU for suboptimal enhancement, the sensitivity and specificity of the automated classification were 100% and 99.5%, respectively. The algorithm was further evaluated in a random sampling of 3195 CTPA examinations from January 2019 through May 2021. Beginning in January 2021, the scanning protocol was transitioned from bolus tracking to a timing bolus strategy. Automated analysis of these examinations showed that most suboptimal examinations following the change in protocol were performed using one scanner, highlighting the potential value of deep learning algorithms for quality improvement in the radiology department. Keywords: CT Angiography, Pulmonary Arteries © RSNA, 2022

Deep learning automates detection of wall motion abnormalities via measurement of longitudinal strain from ECG-gated CT images

Introduction4D cardiac CT (cineCT) is increasingly used to evaluate cardiac dynamics. While echocardiography and CMR have demonstrated the utility of longitudinal strain (LS) measures, measuring LS from cineCT currently requires reformatting the 4D dataset into long-axis imaging planes and delineating the endocardial boundary across time. In this work, we demonstrate the ability of a recently published deep learning framework to automatically and accurately measure LS for detection of wall motion abnormalities (WMA).MethodsOne hundred clinical cineCT studies were evaluated by three experienced cardiac CT readers to identify whether each AHA segment had a WMA. Fifty cases were used for method development and an independent group of 50 were used for testing. A previously developed convolutional neural network was used to automatically segment the LV bloodpool and to define the 2, 3, and 4 CH long-axis imaging planes. LS was measured as the perimeter of the bloodpool for each long-axis plane. Two smoothing approaches were developed to avoid artifacts due to papillary muscle insertion and texture of the endocardial surface. The impact of the smoothing was evaluated by comparison of LS estimates to LV ejection fraction and the fractional area change of the corresponding view.ResultsThe automated, DL approach successfully analyzed 48/50 patients in the training cohort and 47/50 in the testing cohort. The optimal LS cutoff for identification of WMA was -21.8, -15.4, and -16.6% for the 2-, 3-, and 4-CH views in the training cohort. This led to correct labeling of 85, 85, and 83% of 2-, 3-, and 4-CH views, respectively, in the testing cohort. Per-study accuracy was 83% (84% sensitivity and 82% specificity). Smoothing significantly improved agreement between LS and fractional area change (R 2: 2 CH = 0.38 vs. 0.89 vs. 0.92).ConclusionAutomated LV blood pool segmentation and long-axis plane delineation via deep learning enables automatic LS assessment. LS values accurately identify regional wall motion abnormalities and may be used to complement standard visual assessments

4D Flow MRI Quantification of Congenital Shunts: Comparison to Invasive Catheterization

PurposeTo compare invasive right heart catheterization with four-dimensional (4D) flow MRI for estimating shunt fraction in patients with intracardiac and extracardiac shunts.Materials and methodsIn this retrospective study, patients who underwent 4D flow MRI and invasive right heart catheterization with a shunt run between August 2015 and November 2018 were included. The primary objective was comparison of estimated shunt fraction (ratio of pulmonary-to-systemic flow, Qp/Qs) at 4D flow and catheterization. Secondary objectives included comparison of the right ventricular-to-left ventricular stroke volume ratio (RVSV/LVSV) to shunt fraction (for those with applicable shunts) and comparison of cardiac output between 4D flow and catheterization. Statistical analysis included Pearson correlation and Bland-Altman plots.ResultsA total of 33 patients met inclusion criteria (mean age, 49 years ± 16 [standard deviation]; 24 women). 4D flow measurements of Qp/Qs strongly correlated with those at catheterization (r = 0.938), and there was no bias. RVSV/LVSV correlated strongly with Qp/Qs from 4D flow (r = 0.852) and catheterization (r = 0.842). Measurements of left ventricle (Qs) and right ventricle (QP) cardiac output from 4D flow and catheterization (Fick) correlated moderately overall (r = 0.673 [Qp] and r = 0.750 [Qs]).ConclusionShunt fraction measurement using 4D flow MRI compares well with that using invasive cardiac catheterization.Supplemental material is available for this article.© RSNA, 2021

Automated CT Staging of Chronic Obstructive Pulmonary Disease Severity for Predicting Disease Progression and Mortality with a Deep Learning Convolutional Neural Network

PurposeTo develop a deep learning-based algorithm to stage the severity of chronic obstructive pulmonary disease (COPD) through quantification of emphysema and air trapping on CT images and to assess the ability of the proposed stages to prognosticate 5-year progression and mortality.Materials and methodsIn this retrospective study, an algorithm using co-registration and lung segmentation was developed in-house to automate quantification of emphysema and air trapping from inspiratory and expiratory CT images. The algorithm was then tested in a separate group of 8951 patients from the COPD Genetic Epidemiology study (date range, 2007-2017). With measurements of emphysema and air trapping, bivariable thresholds were determined to define CT stages of severity (mild, moderate, severe, and very severe) and were evaluated for their ability to prognosticate disease progression and mortality using logistic regression and Cox regression.ResultsOn the basis of CT stages, the odds of disease progression were greatest among patients with very severe disease (odds ratio [OR], 2.67; 95% CI: 2.02, 3.53; P < .001) and were elevated in patients with moderate disease (OR, 1.50; 95% CI: 1.22, 1.84; P = .001). The hazard ratio of mortality for very severe disease at CT was 2.23 times the normal ratio (95% CI: 1.93, 2.58; P < .001). When combined with Global Initiative for Chronic Obstructive Lung Disease (GOLD) staging, patients with GOLD stage 2 disease had the greatest odds of disease progression when the CT stage was severe (OR, 4.48; 95% CI: 3.18, 6.31; P < .001) or very severe (OR, 4.72; 95% CI: 3.13, 7.13; P < .001).ConclusionAutomated CT algorithms can facilitate staging of COPD severity, have diagnostic performance comparable with that of spirometric GOLD staging, and provide further prognostic value when used in conjunction with GOLD staging.Supplemental material is available for this article.© RSNA, 2021See also commentary by Kalra and Ebrahimian in this issue