Multiscale drainage dynamics with Haines jumps monitored by stroboscopic 4D X-ray microscopy.

Slow multiphase flow in porous media is intriguing because its underlying dynamics is almost deterministic, yet depends on a hierarchy of spatiotemporal processes. There has been great progress in the experimental study of such multiphase flows, but three-dimensional (3D) microscopy methods probing the pore-scale fluid dynamics with millisecond resolution have been lacking. Yet, it is precisely at these length and time scales that the crucial pore-filling events known as Haines jumps take place. Here, we report four-dimensional (4D) (3D + time) observations of multiphase flow in a consolidated porous medium as captured in situ by stroboscopic X-ray micro-tomography. With a total duration of 6.5 s and 2 kHz frame rate, our experiments provide unprecedented access to the multiscale liquid dynamics. Our tomography strategy relies on the fact that Haines jumps, although irregularly spaced in time, are almost deterministic, and therefore repeatable during imbibition-drainage cycling. We studied the time-dependent flow pattern in a porous medium consisting of sintered glass shards. Exploiting the repeatability, we could combine the radiographic projections recorded under different angles during successive cycles into a 3D movie, allowing us to reconstruct pore-scale events, such as Haines jumps, with a spatiotemporal resolution that is two orders of magnitude higher than was hitherto possible. This high resolution allows us to explore the detailed interfacial dynamics during drainage, including fluid-front displacements and velocities. Our experimental approach opens the way to the study of fast, yet deterministic mesoscopic processes also other than flow in porous media.

A semiparametric Gaussian mixture model for chest CT-based 3D blood vessel reconstruction.

Computed tomography (CT) has been a powerful diagnostic tool since its emergence in the 1970s. Using CT data, 3D structures of human internal organs and tissues, such as blood vessels, can be reconstructed using professional software. This 3D reconstruction is crucial for surgical operations and can serve as a vivid medical teaching example. However, traditional 3D reconstruction heavily relies on manual operations, which are time-consuming, subjective, and require substantial experience. To address this problem, we develop a novel semiparametric Gaussian mixture model tailored for the 3D reconstruction of blood vessels. This model extends the classical Gaussian mixture model by enabling nonparametric variations in the component-wise parameters of interest according to voxel positions. We develop a kernel-based expectation-maximization algorithm for estimating the model parameters, accompanied by a supporting asymptotic theory. Furthermore, we propose a novel regression method for optimal bandwidth selection. Compared to the conventional cross-validation-based (CV) method, the regression method outperforms the CV method in terms of computational and statistical efficiency. In application, this methodology facilitates the fully automated reconstruction of 3D blood vessel structures with remarkable accuracy.

Predicting Obstructive Sleep Apnea Based on Computed Tomography Scans Using Deep Learning Models.

Rationale: The incidence of clinically undiagnosed obstructive sleep apnea (OSA) is high among the general population because of limited access to polysomnography. Computed tomography (CT) of craniofacial regions obtained for other purposes can be beneficial in predicting OSA and its severity. Objectives: To predict OSA and its severity based on paranasal CT using a three-dimensional deep learning algorithm. Methods: One internal dataset (N = 798) and two external datasets (N = 135 and N = 85) were used in this study. In the internal dataset, 92 normal participants and 159 with mild, 201 with moderate, and 346 with severe OSA were enrolled to derive the deep learning model. A multimodal deep learning model was elicited from the connection between a three-dimensional convolutional neural network-based part treating unstructured data (CT images) and a multilayer perceptron-based part treating structured data (age, sex, and body mass index) to predict OSA and its severity. Measurements and Main Results: In a four-class classification for predicting the severity of OSA, the AirwayNet-MM-H model (multimodal model with airway-highlighting preprocessing algorithm) showed an average accuracy of 87.6% (95% confidence interval [CI], 86.8-88.6%) in the internal dataset and 84.0% (95% CI, 83.0-85.1%) and 86.3% (95% CI, 85.3-87.3%) in the two external datasets, respectively. In the two-class classification for predicting significant OSA (moderate to severe OSA), the area under the receiver operating characteristic curve, accuracy, sensitivity, specificity, and F1 score were 0.910 (95% CI, 0.899-0.922), 91.0% (95% CI, 90.1-91.9%), 89.9% (95% CI, 88.8-90.9%), 93.5% (95% CI, 92.7-94.3%), and 93.2% (95% CI, 92.5-93.9%), respectively, in the internal dataset. Furthermore, the diagnostic performance of the Airway Net-MM-H model outperformed that of the other six state-of-the-art deep learning models in terms of accuracy for both four- and two-class classifications and area under the receiver operating characteristic curve for two-class classification (P < 0.001). Conclusions: A novel deep learning model, including a multimodal deep learning model and an airway-highlighting preprocessing algorithm from CT images obtained for other purposes, can provide significantly precise outcomes for OSA diagnosis.

A Novel Air Trapping Segment Score Identifies Opposing Effects of Obesity and Eosinophilia on Air Trapping in Asthma.

Rationale: Density thresholds in computed tomography (CT) lung scans quantify air trapping (AT) at the whole-lung level but are not informative for AT in specific bronchopulmonary segments. Objectives: To apply a segment-based measure of AT in asthma to investigate the clinical determinants of AT in asthma. Methods: In each of 19 bronchopulmonary segments in CT lung scans from 199 patients with asthma, AT was categorized as present if lung attenuation was less than -856 Hounsfield units at expiration in ⩾15% of the lung area. The resulting AT segment score (0-19) was related to patient outcomes. Measurements and Main Results: AT varied at the lung segment level and tended to persist at the patient and lung segment levels over 3 years. Patients with widespread AT (⩾10 segments) had more severe asthma (P < 0.05). The mean (±SD) AT segment score in patients with a body mass index ⩾30 kg/m2 was lower than in patients with a body mass index <30 kg/m2 (3.5 ± 4.6 vs. 5.5 ± 6.3; P = 0.008), and the frequency of AT in lower lobe segments in obese patients was less than in upper and middle lobe segments (35% vs. 46%; P = 0.001). The AT segment score in patients with sputum eosinophils ⩾2% was higher than in patients without sputum eosinophilia (7.0 ± 6.1 vs. 3.3 ± 4.9; P < 0.0001). Lung segments with AT more frequently had airway mucus plugging than lung segments without AT (48% vs. 18%; P ⩽ 0.0001). Conclusions: In patients with asthma, air trapping is more severe in those with airway eosinophilia and mucus plugging, whereas those who are obese have less severe trapping because their lower lobe segments are spared.

Deep Learning Classification of Usual Interstitial Pneumonia Predicts Outcomes.

Rationale: Computed tomography (CT) enables noninvasive diagnosis of usual interstitial pneumonia (UIP), but enhanced image analyses are needed to overcome the limitations of visual assessment. Objectives: Apply multiple instance learning (MIL) to develop an explainable deep learning algorithm for prediction of UIP from CT and validate its performance in independent cohorts. Methods: We trained an MIL algorithm using a pooled dataset (n = 2,143) and tested it in three independent populations: data from a prior publication (n = 127), a single-institution clinical cohort (n = 239), and a national registry of patients with pulmonary fibrosis (n = 979). We tested UIP classification performance using receiver operating characteristic analysis, with histologic UIP as ground truth. Cox proportional hazards and linear mixed-effects models were used to examine associations between MIL predictions and survival or longitudinal FVC. Measurements and Main Results: In two cohorts with biopsy data, MIL improved accuracy for histologic UIP (area under the curve, 0.77 [n = 127] and 0.79 [n = 239]) compared with visual assessment (area under the curve, 0.65 and 0.71). In cohorts with survival data, MIL-UIP classifications were significant for mortality (n = 239, mortality to April 2021: unadjusted hazard ratio, 3.1; 95% confidence interval [CI], 1.96-4.91; P < 0.001; and n = 979, mortality to July 2022: unadjusted hazard ratio, 3.64; 95% CI, 2.66-4.97; P < 0.001). Individuals classified as UIP positive by the algorithm had a significantly greater annual decline in FVC than those classified as UIP negative (-88 ml/yr vs. -45 ml/yr; n = 979; P < 0.01), adjusting for extent of lung fibrosis. Conclusions: Computerized assessment using MIL identifies clinically significant features of UIP on CT. Such a method could improve confidence in radiologic assessment of patients with interstitial lung disease, potentially enabling earlier and more precise diagnosis.

Structural Predictors of Lung Function Decline in Young Smokers with Normal Spirometry.

Rationale: Chronic obstructive pulmonary disease (COPD) due to tobacco smoking commonly presents when extensive lung damage has occurred. Objectives: We hypothesized that structural change would be detected early in the natural history of COPD and would relate to loss of lung function with time. Methods: We recruited 431 current smokers (median age, 39 yr; 16 pack-years smoked) and recorded symptoms using the COPD Assessment Test (CAT), spirometry, and quantitative thoracic computed tomography (QCT) scans at study entry. These scan results were compared with those from 67 never-smoking control subjects. Three hundred sixty-eight participants were followed every six months with measurement of postbronchodilator spirometry for a median of 32 months. The rate of FEV1 decline, adjusted for current smoking status, age, and sex, was related to the initial QCT appearances and symptoms, measured using the CAT. Measurements and Main Results: There were no material differences in demography or subjective CT appearances between the young smokers and control subjects, but 55.7% of the former had CAT scores greater than 10, and 24.2% reported chronic bronchitis. QCT assessments of disease probability-defined functional small airway disease, ground-glass opacification, bronchovascular prominence, and ratio of small blood vessel volume to total pulmonary vessel volume were increased compared with control subjects and were all associated with a faster FEV1 decline, as was a higher CAT score. Conclusions: Radiological abnormalities on CT are already established in young smokers with normal lung function and are associated with FEV1 loss independently of the impact of symptoms. Structural abnormalities are present early in the natural history of COPD and are markers of disease progression. Clinical trial registered with www.clinicaltrials.gov (NCT03480347).

Atherosclerosis evaluation and cardiovascular risk estimation using coronary computed tomography angiography.

Clinical risk scores based on traditional risk factors of atherosclerosis correlate imprecisely to an individual's complex pathophysiological predisposition to atherosclerosis and provide limited accuracy for predicting major adverse cardiovascular events (MACE). Over the past two decades, computed tomography scanners and techniques for coronary computed tomography angiography (CCTA) analysis have substantially improved, enabling more precise atherosclerotic plaque quantification and characterization. The accuracy of CCTA for quantifying stenosis and atherosclerosis has been validated in numerous multicentre studies and has shown consistent incremental prognostic value for MACE over the clinical risk spectrum in different populations. Serial CCTA studies have advanced our understanding of vascular biology and atherosclerotic disease progression. The direct disease visualization of CCTA has the potential to be used synergistically with indirect markers of risk to significantly improve prevention of MACE, pending large-scale randomized evaluation.

Coronary bypass surgery guided by computed tomography in a low-risk population.

BACKGROUND AND AIMS: In patients with three-vessel disease and/or left main disease, selecting revascularization strategy based on coronary computed tomography angiography (CCTA) has a high level of virtual agreement with treatment decisions based on invasive coronary angiography (ICA). METHODS: In this study, coronary artery bypass grafting (CABG) procedures were planned based on CCTA without knowledge of ICA. The CABG strategy was recommended by a central core laboratory assessing the anatomy and functionality of the coronary circulation. The primary feasibility endpoint was the percentage of operations performed without access to the ICA. The primary safety endpoint was graft patency on 30-day follow-up CCTA. Secondary endpoints included topographical adequacy of grafting, major adverse cardiac and cerebrovascular (MACCE), and major bleeding events at 30 days. The study was considered positive if the lower boundary of confidence intervals (CI) for feasibility was ≥75% (NCT04142021). RESULTS: The study enrolled 114 patients with a mean (standard deviation) anatomical SYNTAX score and Society of Thoracic Surgery score of 43.6 (15.3) and 0.81 (0.63), respectively. Unblinding ICA was required in one case yielding a feasibility of 99.1% (95% CI 95.2%-100%). The concordance and agreement in revascularization planning between the ICA- and CCTA-Heart Teams was 82.9% with a moderate kappa of 0.58 (95% CI 0.50-0.66) and between the CCTA-Heart Team and actual treatment was 83.7% with a substantial kappa of 0.61 (95% CI 0.53-0.68). The 30-day follow-up CCTA in 102 patients (91.9%) showed an anastomosis patency rate of 92.6%, whilst MACCE was 7.2% and major bleeding 2.7%. CONCLUSIONS: CABG guided by CCTA is feasible and has an acceptable safety profile in a selected population of complex coronary artery disease.

Charged Gold Nanoparticles for Target Identification-Alignment and Automatic Segmentation of CT Image-Guided Adaptive Radiotherapy in Small Hepatocellular Carcinoma.

Because of the challenges posed by anatomical uncertainties and the low resolution of plain computed tomography (CT) scans, implementing adaptive radiotherapy (ART) for small hepatocellular carcinoma (sHCC) using artificial intelligence (AI) faces obstacles in tumor identification-alignment and automatic segmentation. The current study aims to improve sHCC imaging for ART using a gold nanoparticle (Au NP)-based CT contrast agent to enhance AI-driven automated image processing. The synthesized charged Au NPs demonstrated notable in vitro aggregation, low cytotoxicity, and minimal organ toxicity. Over time, an in situ sHCC mouse model was established for in vivo CT imaging at multiple time points. The enhanced CT images processed using 3D U-Net and 3D Trans U-Net AI models demonstrated high geometric and dosimetric accuracy. Therefore, charged Au NPs enable accurate and automatic sHCC segmentation in CT images using classical AI models, potentially addressing the technical challenges related to tumor identification, alignment, and automatic segmentation in CT-guided online ART.

Microscopic computed tomography with AI-CNN-powered image analysis: the path to phenotype bleomycin-induced lung injury.

Bleomycin (BLM)-induced lung injury in mice is a valuable model for investigating the molecular mechanisms that drive inflammation and fibrosis and for evaluating potential therapeutic approaches to treat the disease. Given high variability in the BLM model, it is critical to accurately phenotype the animals in the course of an experiment. In the present study, we aimed to demonstrate the utility of microscopic computed tomography (µCT) imaging combined with an artificial intelligence (AI)-convolutional neural network (CNN)-powered lung segmentation for rapid phenotyping of BLM mice. µCT was performed in freely breathing C57BL/6J mice under isoflurane anesthesia on days 7 and 21 after BLM administration. Terminal invasive lung function measurement and histological assessment of the left lung collagen content were conducted as well. µCT image analysis demonstrated gradual and time-dependent development of lung injury as evident by alterations in the lung density, air-to-tissue volume ratio, and lung aeration in mice treated with BLM. The right and left lung were unequally affected. µCT-derived parameters such as lung density, air-to-tissue volume ratio, and nonaerated lung volume correlated well with the invasive lung function measurement and left lung collagen content. Our study demonstrates the utility of AI-CNN-powered µCT image analysis for rapid and accurate phenotyping of BLM mice in the course of disease development and progression.NEW & NOTEWORTHY Microscopic computed tomography (µCT) imaging combined with an artificial intelligence (AI)-convolutional neural network (CNN)-powered lung segmentation is a rapid and powerful tool for noninvasive phenotyping of bleomycin mice over the course of the disease. This, in turn, allows earlier and more reliable identification of therapeutic effects of new drug candidates, ultimately leading to the reduction of unnecessary procedures in animals in pharmacological research.

Diagnostic Performance of Carotid Ring Sign on CT-Angiography in Internal Carotid True Occlusion.

BACKGROUND: To differentiate between pseudo occlusion (PO) and true occlusion (TO) of internal carotid artery (ICA) is important in thrombectomy treatment planning for patients with acute ischemic stroke. Although delayed contrast filling has been differentiated carotid PO from TO, its application has been limited by the implementations of multiphasic computed tomography angiography. In this study, we hypothesized that carotid ring sign, which is readily acquired from single-phasic CTA, can sufficiently differentiate carotid TO from PO. METHODS: One thousand four hundred and twenty patients with anterior circulation stroke receiving endovascular therapy were consecutively recruited through a hospital- and web-based registry. Two hundred patients with nonvisualization of the proximal ICA were included in the analysis after a retrospective screening. Diagnosis of PO or TO of the cervical segment of ICA was made based on digital subtraction angiography. Diagnostic performances of carotid ring sign on arterial-phasic CTA and delayed contrast filling on multiphasic computed tomography angiography were evaluated and compared. RESULTS: One-hundred twelve patients had ICA PO and 88 had TO. Carotid ring sign was more common in patients with TO (70.5% versus 6.3%; P<0.001), whereas delayed contrast filling was more common in PO (94.9% versus 7.7%; P<0.001). The sensitivity and specificity of carotid ring sign in diagnosing carotid TO were 0.70 and 0.94, respectively, whereas sensitivity and specificity of delayed contrast filling was 0.95 and 0.92 in judging carotid PO. CONCLUSIONS: Carotid ring sign is a potent imaging marker in diagnosing ICA TO. Carotid ring sign could be complementary to delayed contrast filling sign in differentiating TO from PO, in particular in centers with only single-phasic CTA.

Treatment Outcome of Severe Respiratory Type B Tularemia Using Fluoroquinolones.

BACKGROUND: Fluoroquinolones lack approval for treatment of tularemia but have been used extensively for milder illness. Here, we evaluated fluoroquinolones for severe illness. METHODS: In an observational study, we identified case-patients with respiratory tularemia from July to November 2010 in Jämtland County, Sweden. We defined severe tularemia by hospitalization for >24 hours and severe bacteremic tularemia by Francisella tularensis subsp. holarctica growth in blood or pleural fluid. Clinical data and drug dosing were retrieved from electronic medical records. Chest images were reexamined. We used Kaplan-Meier curves to evaluate time to defervescence and hospital discharge. RESULTS: Among 67 case-patients (median age, 66 years; 81% males) 30-day mortality was 1.5% (1 of 67). Among 33 hospitalized persons (median age, 71 years; 82% males), 23 had nonbacteremic and 10 had bacteremic severe tularemia. Subpleural round consolidations, mediastinal lymphadenopathy, and unilateral pleural fluid were common on chest computed tomography. Among 29 hospitalized persons with complete outcome data, ciprofloxacin/levofloxacin (n = 12), ciprofloxacin/levofloxacin combinations with doxycycline and/or gentamicin (n = 11), or doxycycline as the single drug (n = 6) was used for treatment. One disease relapse occurred with doxycycline treatment. Treatment responses were rapid, with median fever duration 41.0 hours in nonbacteremic and 115.0 hours in bacteremic tularemia. Increased age-adjusted Charlson comorbidity index predicted severe bacteremic tularemia (odds ratio, 2.7 per score-point; 95% confidence interval, 1.35-5.41). A 78-year-old male with comorbidities and delayed ciprofloxacin/gentamicin treatment died. CONCLUSIONS: Fluoroquinolone treatment is effective for severe tularemia. Subpleural round consolidations and mediastinal lymphadenopathy were typical findings on computed tomography among case-patients in this study.

HERMES-24 Score Derivation and Validation for Simple and Robust Outcome Prediction After Large Vessel Occlusion Treatment.

BACKGROUND: Clinicians need simple and highly predictive prognostic scores to assist practical decision-making. We aimed to develop a simple outcome prediction score applied 24 hours after anterior circulation acute ischemic stroke treatment with endovascular thrombectomy and validate it in patients treated both with and without endovascular thrombectomy. METHODS: Using the HERMES (Highly Effective Reperfusion Evaluated in Multiple Endovascular Stroke Trials) collaboration data set (n=1764), patients in the endovascular thrombectomy arm were divided randomly into a derivation cohort (n=430) and a validation cohort (n=441). From a set of candidate predictors, logistic regression modeling using forward variable selection was used to select a model that was both parsimonious and highly predictive for modified Rankin Scale (mRS) ≤2 at 90 days. The score was validated in validation cohort, control arm (n=893), and external validation cohorts from the ESCAPE-NA1 (Efficacy and Safety of Nerinetide for the Treatment of Acute Ischaemic Stroke; n=1066) and INTERRSeCT (Identifying New Approaches to Optimize Thrombus Characterization for Predicting Early Recanalization and Reperfusion With IV Alteplase and Other Treatments Using Serial CT Angiography; n=614). RESULTS: In the derivation cohort, we selected 2 significant predictors of mRS ≤2 (National Institutes of Health Stroke Scale score at 24 hours and age [ß-coefficient, 0.34 and 0.06]) and derived the HERMES-24 score: age (years)/10+National Institutes of Health Stroke Scale score at 24 hours. The HERMES-24 score was highly predictive for mRS ≤2 (c-statistic 0.907 [95% CI, 0.879-0.935]) in the derivation cohort. In the validation cohort and the control arm, the HERMES-24 score predicts mRS ≤2 (c-statistic, 0.914 [95% CI, 0.886-0.944] and 0.909 [95% CI, 0.887-0.930]). Observed provability of mRS ≤2 ranged between 3.1% and 3.4% when HERMES-24 score ≥25, while it ranged between 90.6% and 93.0% when HERMES-24 score <10 in the derivation cohort, validation cohort, and control arm. The HERMES-24 score also showed c-statistics of 0.894 and 0.889 for mRS ≤2 in the ESCAPE-NA1 and INTERRSeCT populations. CONCLUSIONS: The post-treatment HERMES-24 score is a simple validated score that predicts a 3-month outcome after anterior circulation large vessel occlusion stroke regardless of intervention, which helps prognostic discussion with families on day 2.

ARISE I Consensus Review on the Management of Intracranial Aneurysms.

BACKGROUND: Intracranial aneurysms (IAs) remain a challenging neurological diagnosis associated with significant morbidity and mortality. There is a plethora of microsurgical and endovascular techniques for the treatment of both ruptured and unruptured aneurysms. There is no definitive consensus as to the best treatment option for this cerebrovascular pathology. The Aneurysm, Arteriovenous Malformation, and Chronic Subdural Hematoma Roundtable Discussion With Industry and Stroke Experts discussed best practices and the most promising approaches to improve the management of brain aneurysms. METHODS: A group of experts from academia, industry, and federal regulators convened to discuss updated clinical trials, scientific research on preclinical system models, management options, screening and monitoring, and promising novel device technologies, aiming to improve the outcomes of patients with IA. RESULTS: Aneurysm, Arteriovenous Malformation, and Chronic Subdural Hematoma Roundtable Discussion With Industry and Stroke Experts suggested the incorporation of artificial intelligence to capture sequential aneurysm growth, identify predictors of rupture, and predict the risk of rupture to guide treatment options. The consensus strongly recommended nationwide systemic data collection of unruptured IA radiographic images for the analysis and development of machine learning algorithms for rupture risk. The consensus supported centers of excellence for preclinical multicenter trials in areas such as genetics, cellular composition, and radiogenomics. Optical coherence tomography and magnetic resonance imaging contrast-enhanced 3T vessel wall imaging are promising technologies; however, more data are needed to define their role in IA management. Ruptured aneurysms are best managed at large volume centers, which should include comprehensive patient management with expertise in microsurgery, endovascular surgery, neurology, and neurocritical care. CONCLUSIONS: Clinical and preclinical studies and scientific research on IA should engage high-volume centers and be conducted in multicenter collaborative efforts. The future of IA diagnosis and monitoring could be enhanced by the incorporation of artificial intelligence and national radiographic and biologic registries. A collaborative effort between academic centers, government regulators, and the device industry is paramount for the adequate management of IA and the advancement of the field.

Automated Prediction of Proximal Middle Cerebral Artery Occlusions in Noncontrast Brain Computed Tomography.

BACKGROUND: Early identification of large vessel occlusion (LVO) in patients with ischemic stroke is crucial for timely interventions. We propose a machine learning-based algorithm (JLK-CTL) that uses handcrafted features from noncontrast computed tomography to predict LVO. METHODS: We included patients with ischemic stroke who underwent concurrent noncontrast computed tomography and computed tomography angiography in seven hospitals. Patients from 5 of these hospitals, admitted between May 2011 and March 2015, were randomly divided into training and internal validation (9:1 ratio). Those from the remaining 2 hospitals, admitted between March 2021 and September 2021, were designated for external validation. From each noncontrast computed tomography scan, we extracted differences in volume, tissue density, and Hounsfield unit distribution between bihemispheric regions (striatocapsular, insula, M1-M3, and M4-M6, modified from the Alberta Stroke Program Early Computed Tomography Score). A deep learning algorithm was used to incorporate clot signs as an additional feature. Machine learning models, including ExtraTrees, random forest, extreme gradient boosting, support vector machine, and multilayer perceptron, as well as a deep learning model, were trained and evaluated. Additionally, we assessed the models' performance after incorporating the National Institutes of Health Stroke Scale scores as an additional feature. RESULTS: Among 2919 patients, 83 were excluded. Across the training (n=2463), internal validation (n=275), and external validation (n=95) datasets, the mean ages were 68.5±12.4, 67.6±13.8, and 67.9±13.6 years, respectively. The proportions of men were 57%, 53%, and 59%, with LVO prevalences of 17.0%, 16.4%, and 26.3%, respectively. In the external validation, the ExtraTrees model achieved a robust area under the curve of 0.888 (95% CI, 0.850-0.925), with a sensitivity of 80.1% (95% CI, 72.0-88.1) and a specificity of 88.6% (95% CI, 84.7-92.5). Adding the National Institutes of Health Stroke Scale score to the ExtraTrees model increased sensitivity (from 80.1% to 92.1%) while maintaining specificity. CONCLUSIONS: Our algorithm provides reliable predictions of LVO using noncontrast computed tomography. By enabling early LVO identification, our algorithm has the potential to expedite the stroke workflow.

Mucociliary Clearance is Impaired in Small Airways of Cystic Fibrosis Pigs.

Cystic fibrosis is a genetic disorder characterized by recurrent airway infections, inflammation, impaired mucociliary clearance and progressive decline in lung function. The disease may start in the small airways; however, this is difficult to prove due to limited accessibility of the small airways with the current single photon mucociliary clearance assay. Here, we developed a dynamic positron emission tomography assay with high spatial and temporal resolution. We tested that mucociliary clearance is abnormal in the small airways of newborn cystic fibrosis pigs. Clearance of [68Ga] tagged macro-aggregated albumin from small airways started immediately after delivery and continued for the duration of the study. Initial clearance was fast but slowed down few minutes after delivery. Cystic fibrosis pig small airways cleared significantly less than non-CF pig small airways (non-CF 25.1±3.1% vs. CF 14.6±0.1%). Stimulation of the cystic fibrosis airways with the purinergic secretagogue UTP further impaired clearance (non-CF with UTP 20.9±0.3% vs. CF with UTP 13.0±1.8%). None of the cystic fibrosis pig treated with UTP (N = 6) cleared more than 20% of the delivered dose. These data indicate that mucociliary clearance in the small airways is fast and can easily be missed if the assay is not sensitive enough. The data also indicate that mucociliary clearance is impaired in the small airways of cystic fibrosis pigs. This defect is exacerbated by stimulation of mucus secretions with purinergic agonists.

Novel Patterns in High-Resolution Computed Tomography in Whipple Pneumonia.

With the use of metagenomic next-generation sequencing, patients diagnosed with Whipple pneumonia are being increasingly correctly diagnosed. We report a series of 3 cases in China that showed a novel pattern of movable infiltrates and upper lung micronodules. After treatment, the 3 patients recovered, and lung infiltrates resolved.

"Horses for courses" computed tomography or predicted total lung capacity for size matching in lung transplantation.

Size-matching donors to recipients in lung transplantation continues to be a clinical challenge. Predicted total lung capacity equations, or more simply, donor and recipient heights, while widely used, are imprecise and may not be representative of the pool of donors and recipients. These inherent limitations may result in size discrepancies. The advent of easily accessible software and the widespread availability of computed tomography (CT) imaging in donor assessments have made it possible to directly measure lung volumes in donors and recipients. As a result, there is a growing interest in adopting personalized CT volumetry as an alternative. This article explores both methods and underscores the potential benefits and precision offered by CT.

Quantitative computed tomography analysis of body composition changes in paediatric patients with acute lymphoblastic leukaemia.

Children with acute lymphoblastic leukaemia (ALL) are at risk for obesity and cardiometabolic diseases. To gain insight into body composition changes among children with ALL, we assessed quantitative computed tomography (QCT) data for specific body compartments (subcutaneous adipose tissue [SAT], visceral adipose tissue [VAT], total adipose tissue [TAT], lean tissue [LT], LT/TAT and VAT/SAT at lumbar vertebrae L1 and L2) at diagnosis and at off-therapy for 189 children with ALL and evaluated associations between body mass index (BMI) Z-score and clinical characteristics. BMI Z-score correlated positively with SAT, VAT and TAT and negatively with LT/TAT and VAT/SAT. At off-therapy, BMI Z-score, SAT, VAT and TAT values were higher than at diagnosis, but LT, LT/TAT and VAT/SAT were lower. Patients aged ≥10 years at diagnosis had higher SAT, VAT and TAT and lower LT and LT/TAT than patients aged 2.0-9.9 years. Female patients had lower LT and LT/TAT than male patients. Black patients had less VAT than White patients. QCT analysis showed increases in adipose tissue and decreases in LT during ALL therapy when BMI Z-scores increased. Early dietary and physical therapy interventions should be considered, particularly for patients at risk for obesity.

TomoPyUI: a user-friendly tool for rapid tomography alignment and reconstruction.

The management and processing of synchrotron and neutron computed tomography data can be a complex, labor-intensive and unstructured process. Users devote substantial time to both manually processing their data (i.e. organizing data/metadata, applying image filters etc.) and waiting for the computation of iterative alignment and reconstruction algorithms to finish. In this work, we present a solution to these problems: TomoPyUI, a user interface for the well known tomography data processing package TomoPy. This highly visual Python software package guides the user through the tomography processing pipeline from data import, preprocessing, alignment and finally to 3D volume reconstruction. The TomoPyUI systematic intermediate data and metadata storage system improves organization, and the inspection and manipulation tools (built within the application) help to avoid interrupted workflows. Notably, TomoPyUI operates entirely within a Jupyter environment. Herein, we provide a summary of these key features of TomoPyUI, along with an overview of the tomography processing pipeline, a discussion of the landscape of existing tomography processing software and the purpose of TomoPyUI, and a demonstration of its capabilities for real tomography data collected at SSRL beamline 6-2c.