Solution-Processable Large-Area Black Phosphorus/Reduced Graphene Oxide Schottky Junction for High-Temperature Broadband Photodetectors.

2D materials-based broadband photodetectors have extensive applications in security monitoring and remote sensing fields, especially in supersonic aircraft that require reliable performance under extreme high-temperature conditions. However, the integration of large-area heterostructures with 2D materials often involves high-temperature deposition methods, and also limited options and size of substrates. Herein, a liquid-phase spin-coating method is presented based on the interface engineering to prepare larger-area Van der Waals heterojunctions of black phosphorus (BP)/reduced graphene oxide (RGO) films at room temperature on arbitrary substrates of any required size. Importantly, this method avoids the common requirement of high-temperature, and prevents the curling or stacking in 2D materials during the liquid-phase film formation. The BP/RGO films-based devices exhibit a wide spectral photo-response, ranging from the visible of 532 nm to infrared range of 2200 nm. Additionally, due to Van der Waals interface of Schottky junction, the array devices provide infrared detection at temperatures up to 400 K, with an outstanding photoresponsivity (R) of 12 A W-1 and a specific detectivity (D*) of ≈2.4 × 109 Jones. This work offers an efficient approach to fabricate large-area 2D Schottky junction films by solution-coating for high-temperature infrared photodetectors.

Evaluation of the quality and the productivity of neuroradiological reading of multiple sclerosis follow-up MRI scans using an intelligent automation software.

PURPOSE: The assessment of multiple sclerosis (MS) lesions on follow-up magnetic resonance imaging (MRI) is tedious, time-consuming, and error-prone. Automation of low-level tasks could enhance the radiologist in this work. We evaluate the intelligent automation software Jazz in a blinded three centers study, for the assessment of new, slowly expanding, and contrast-enhancing MS lesions. METHODS: In three separate centers, 117 MS follow-up MRIs were blindly analyzed on fluid attenuated inversion recovery (FLAIR), pre- and post-gadolinium T1-weighted images using Jazz by 2 neuroradiologists in each center. The reading time was recorded. The ground truth was defined in a second reading by side-by-side comparison of both reports from Jazz and the standard clinical report. The number of described new, slowly expanding, and contrast-enhancing lesions described with Jazz was compared to the lesions described in the standard clinical report. RESULTS: A total of 96 new lesions from 41 patients and 162 slowly expanding lesions (SELs) from 61 patients were described in the ground truth reading. A significantly larger number of new lesions were described using Jazz compared to the standard clinical report (63 versus 24). No SELs were reported in the standard clinical report, while 95 SELs were reported on average using Jazz. A total of 4 new contrast-enhancing lesions were found in all reports. The reading with Jazz was very time efficient, taking on average 2min33s ± 1min0s per case. Overall inter-reader agreement for new lesions between the readers using Jazz was moderate for new lesions (Cohen kappa = 0.5) and slight for SELs (0.08). CONCLUSION: The quality and the productivity of neuroradiological reading of MS follow-up MRI scans can be significantly improved using the dedicated software Jazz.

Large-Area Black Phosphorus/PtSe2 Schottky Junction for High Operating Temperature Broadband Photodetectors.

High operating temperature (HOT) broadband photodetectors are urgently necessary for extreme condition applications in infrared-guided missiles, infrared night vision, fire safety imaging, and space exploration sensing. However, conventional photodetectors show dramatic carrier mobility decreases and carrier losses with low photoresponsivity at HOT due to the increased carrier scattering in channels at high temperatures. Herein, the HOT broadband photodetectors from room temperature to 470 K are developed for the first time by large-area black phosphorus (BP)/PtSe2 films device arrays via a depletion-enhanced photocarrier dynamics strategy. Attributed to the 2D Schottky junction at BP/PtSe2 interface and resulting in full depleted working channels, the BP/PtSe2 photodetector arrays exhibit high tolerance to carrier mobility decrease during the increasing operating temperature in a wide wavelength range from 532 to 2200 nm. Thus, the photodetector shows a state-of-the-art operating temperature at 470 K with the photo-responsivity (R) and specific detectivity (D*) of 25 A W-1 and 6.4 × 1011 Jones under 1850 nm illumination, respectively. Moreover, BP/PtSe2 photodetector arrays show high-uniformity photo-response in a large area. This work provides new strategies for high-performance broadband photodetector arrays with HOT by Schottky junction of large-area BP/PtSe2 films.

Targeting CD24 as a novel immunotherapy for solid cancers.

Cluster of differentiation 24 (CD24), a mucin-like highly glycosylated molecule has been extensively studied as a cancer stem cell marker in a variety of solid cancers. The functional role of CD24 is either fulfilled by combining with ligands or participating in signal transduction, which mediate the initiation and progression of neoplasms. Recently, CD24 was also described as an innate immune checkpoint with apparent significance in several types of solid cancers. Herein, we review the current understanding of the molecular fundamentals of CD24, the role of CD24 in tumorigenesis and cancer progression, the possibility as a promising therapeutic target and summarized different therapeutic agents or strategies targeting CD24 in solid cancers. Video Abstract.

Diffusion-weighted imaging and arterial spin labeling radiomics features may improve differentiation between radiation-induced brain injury and glioma recurrence.

OBJECTIVES: To determine whether radiomics features derived from diffusion-weighted imaging (DWI) and arterial spin labeling (ASL) can improve the differentiation between radiation-induced brain injury (RIBI) and tumor recurrence (TR) in glioma patients. METHODS: A total of 4199 radiomics features were extracted from conventional MRI, apparent diffusion coefficient (ADC), and cerebral blood flow (CBF) maps, obtained from 96 pathologically confirmed WHO grade 2~4 gliomas with enhancement after standard treatment. The intraclass correlation coefficient (ICC) was used to test segmentation stability between two doctors. Radiomics features were selected using the Mann-Whitney U test, LASSO regression, and RFE algorithms. Four machine learning classifiers were adopted to establish radiomics models. The diagnostic performance of multiparameter, conventional, and single-parameter MRI radiomics models was compared using the area under the curve (AUC). The models were evaluated in the subsequent independent validation set (n = 30). RESULTS: Eight important radiomics features (3 from conventional MRI, 1 from ADC, and 4 from CBF) were selected. Support vector machine (SVM) was chosen as the optimal classifier. The diagnostic performance of the multiparameter MRI radiomics model (AUC 0.96) was higher than that of the conventional MRI (AUC 0.88), ADC (AUC 0.91), and CBF (AUC 0.95) radiomics models. For subgroup analysis, the multiparameter MRI radiomics model showed similar performance, with AUCs of 0.98 in WHO grade 2~3 and 0.96 in WHO grade 4. CONCLUSION: The incorporation of noninvasive DWI and ASL into the MRI radiomics model improved the diagnostic performance in differentiating RIBI from TR; ASL, especially, played a significant role. KEY POINTS: • The multiparameter MRI radiomics model was superior to the conventional MRI radiomics model in differentiating glioma recurrence from radiation-induced brain injury. • Diffusion and perfusion MRI could improve the ability of the radiomics model in predicting the progression in patients with glioma. • Arterial spin labeling played an important role in predicting glioma progression using radiomics models.

Machine learning detects symptomatic patients with carotid plaques based on 6-type calcium configuration classification on CT angiography.

OBJECTIVES: While the link between carotid plaque composition and cerebrovascular vascular (CVE) events is recognized, the role of calcium configuration remains unclear. This study aimed to develop and validate a CT angiography (CTA)-based machine learning (ML) model that uses carotid plaques 6-type calcium grading, and clinical parameters to identify CVE patients with bilateral plaques. MATERIAL AND METHODS: We conducted a multicenter, retrospective diagnostic study (March 2013-May 2020) approved by the institutional review board. We included adults (18 +) with bilateral carotid artery plaques, symptomatic patients having recently experienced a carotid territory ischemic event, and asymptomatic patients either after 3 months from symptom onset or with no such event. Four ML models (clinical factors, calcium configurations, and both with and without plaque grading [ML-All-G and ML-All-NG]) and logistic regression on all variables identified symptomatic patients. Internal validation assessed discrimination and calibration. External validation was also performed, and identified important variables and causes of misclassifications. RESULTS: We included 790 patients (median age 72, IQR [61-80], 42% male, 64% symptomatic) for training and internal validation, and 159 patients (age 68 [63-76], 36% male, 39% symptomatic) for external testing. The ML-All-G model achieved an area-under-ROC curve of 0.71 (95% CI 0.58-0.78; p < .001) and sensitivity 80% (79-81). Performance was comparable on external testing. Calcified plaque, especially the positive rim sign on the right artery in older and hyperlipidemic patients, had a major impact on identifying symptomatic patients. CONCLUSION: The developed model can identify symptomatic patients using plaques calcium configuration data and clinical information with reasonable diagnostic accuracy. CLINICAL RELEVANCE: The analysis of the type of calcium configuration in carotid plaques into 6 classes, combined with clinical variables, allows for an effective identification of symptomatic patients. KEY POINTS: • While the association between carotid plaques composition and cerebrovascular events is recognized, the role of calcium configuration remains unclear. • Machine learning of 6-type plaque grading can identify symptomatic patients. Calcified plaques on the right artery, advanced age, and hyperlipidemia were the most important predictors. • Fast acquisition of CTA enables rapid grading of plaques upon the patient's arrival at the hospital, which streamlines the diagnosis of symptoms using ML.

Rheology of graphene oxide stabilized Pickering emulsions.

Pickering emulgels stabilized by graphene oxide (GO) with didodecyldimethylammonium bromide (DDAB) as an auxiliary surfactant and liquid paraffin as the oil phase have proved to be an excellent 3D printable ink. This paper elucidates the structure of such emulgels by a combination of microscopy before and after intensive shear as well as broadband dielectric spectroscopy and rheology in the linear and nonlinear regime. An increase of the DDAB surfactant and GO-contents leads to a systematic increase of modulus and viscosity, a reduction of the limits of the nonlinear regime and a more complicated variation of the normal forces, with negative normal forces at high shear rate  for low GO-contents and positive normal forces at high GO-contents. The interfacial jamming behavior studied by morphology, rheology and dielectric spectroscopy is explained based on droplet deformation, jamming and recovery phenomena.

Preparation of CF/PEEK Composites with High Mechanical Performance Using PEEK Derivatives as the Sizing Agent.

Owing to their excellent physical and chemical properties, the carbon fibre reinforced poly(ether-ether-ketone) composites (CF/PEEK) are widely used in aerospace applications such as rockets, missiles, and high-speed vehicles. However, both carbon fibre (CF) and poly(ether-ether-ketone) (PEEK) have inert molecular chain structures, which seriously affect the interfacial properties of CF/PEEK composites. In this study, to improve the properties of CF/PEEK composites, carboxylated PEEK (PEEK-COOH) with different carboxylation degrees is synthesized as the sizing agent by a "two-step" method. Then, the activated CF surface is coated by PEEK-COOH sizing layers with different functionalization degrees to prepare the CF/PEEK composites. The results show that the interfacial properties of CF/PEEK composites are improved after applying the sizing agent. When the carboxylation degree of PEEK-COOH is 19.61%, the flexural strength, flexural modulus, and interlaminar shear strength (ILSS) of CF/PEEK composites reach 489.34 MPa, 25.387 GPa, and 81.3 MPa, respectively. In addition, the use of PEEK-COOH sizing agents can form an excellent transition layer between CF and PEEK, creating an efficient stress transfer system and facilitating an even stress distribution between CF and PEEK. Furthermore, the main mechanism of material fracture changes from CF debonding to CF and resin fracture.

Validation of a deep learning model for traumatic brain injury detection and NIRIS grading on non-contrast CT: a multi-reader study with promising results and opportunities for improvement.

PURPOSE: This study aimed to assess and externally validate the performance of a deep learning (DL) model for the interpretation of non-contrast computed tomography (NCCT) scans of patients with suspicion of traumatic brain injury (TBI). METHODS: This retrospective and multi-reader study included patients with TBI suspicion who were transported to the emergency department and underwent NCCT scans. Eight reviewers, with varying levels of training and experience (two neuroradiology attendings, two neuroradiology fellows, two neuroradiology residents, one neurosurgery attending, and one neurosurgery resident), independently evaluated NCCT head scans. The same scans were evaluated using the version 5.0 of the DL model icobrain tbi. The establishment of the ground truth involved a thorough assessment of all accessible clinical and laboratory data, as well as follow-up imaging studies, including NCCT and magnetic resonance imaging, as a consensus amongst the study reviewers. The outcomes of interest included neuroimaging radiological interpretation system (NIRIS) scores, the presence of midline shift, mass effect, hemorrhagic lesions, hydrocephalus, and severe hydrocephalus, as well as measurements of midline shift and volumes of hemorrhagic lesions. Comparisons using weighted Cohen's kappa coefficient were made. The McNemar test was used to compare the diagnostic performance. Bland-Altman plots were used to compare measurements. RESULTS: One hundred patients were included, with the DL model successfully categorizing 77 scans. The median age for the total group was 48, with the omitted group having a median age of 44.5 and the included group having a median age of 48. The DL model demonstrated moderate agreement with the ground truth, trainees, and attendings. With the DL model's assistance, trainees' agreement with the ground truth improved. The DL model showed high specificity (0.88) and positive predictive value (0.96) in classifying NIRIS scores as 0-2 or 3-4. Trainees and attendings had the highest accuracy (0.95). The DL model's performance in classifying various TBI CT imaging common data elements was comparable to that of trainees and attendings. The average difference for the DL model in quantifying the volume of hemorrhagic lesions was 6.0 mL with a wide 95% confidence interval (CI) of - 68.32 to 80.22, and for midline shift, the average difference was 1.4 mm with a 95% CI of - 3.4 to 6.2. CONCLUSION: While the DL model outperformed trainees in some aspects, attendings' assessments remained superior in most instances. Using the DL model as an assistive tool benefited trainees, improving their NIRIS score agreement with the ground truth. Although the DL model showed high potential in classifying some TBI CT imaging common data elements, further refinement and optimization are necessary to enhance its clinical utility.

Imaging Predictors of Neurologic Outcome After Pediatric Arterial Ischemic Stroke.

BACKGROUND AND PURPOSE: To assess whether initial imaging characteristics independently predict 1-year neurological outcomes in childhood arterial ischemic stroke patients. METHODS: We used prospectively collected demographic and clinical data, imaging data, and 1-year outcomes from the VIPS study (Vascular Effects of Infection in Pediatric Stroke). In 288 patients with first-time stroke, we measured infarct volume and location on the acute magnetic resonance imaging studies and hemorrhagic transformation on brain imaging studies during the acute presentation. Neurological outcome was assessed with the Pediatric Stroke Outcome Measure. We used univariate and multivariable ordinal logistic regression models to test the association between imaging characteristics and outcome. RESULTS: Univariate analysis demonstrated that infarcts involving uncinate fasciculus, angular gyrus, insular cortex, or that extended from cortex to the subcortical nuclei were significantly associated with poorer outcomes with odds ratios ranging from 1.95 to 3.95. All locations except the insular cortex remained significant predictors of poor outcome on multivariable analysis. When infarct volume was added to the model, the locations did not remain significant. Larger infarct volumes and younger age at stroke onset were significantly associated with poorer outcome, but the strength of the relationships was weak. Hemorrhagic transformation did not predict outcome. CONCLUSIONS: In the largest pediatric arterial ischemic stroke cohort collected to date, we showed that larger infarct volume and younger age at stroke were associated with poorer outcomes. We made the novel observation that the strength of these associations was modest and limits the ability to use these characteristics to predict outcome in children. Infarcts affecting specific locations were significantly associated with poorer outcomes in univariate and multivariable analyses but lost significance when adjusted for infarct volume. Our findings suggest that infarcts that disrupt critical networks have a disproportionate impact upon outcome after childhood arterial ischemic stroke.

Virtual monochromatic dual-energy CT reconstructions improve detection of cerebral infarct in patients with suspicion of stroke.

PURPOSE: Early infarcts are hard to diagnose on non-contrast head CT. Dual-energy CT (DECT) may potentially increase infarct differentiation. The optimal DECT settings for differentiation were identified and evaluated. METHODS: One hundred and twenty-five consecutive patients who presented with suspected acute ischemic stroke (AIS) and underwent non-contrast DECT and subsequent DWI were retrospectively identified. The DWI was used as reference standard. First, virtual monochromatic images (VMI) of 25 patients were reconstructed from 40 to 140 keV and scored by two readers for acute infarct. Sensitivity, specificity, positive, and negative predictive values for infarct detection were compared and a subset of VMI energies were selected. Next, for a separate larger cohort of 100 suspected AIS patients, conventional non-contrast CT (NCT) and selected VMI were scored by two readers for the presence and location of infarct. The same statistics for infarct detection were calculated. Infarct location match was compared per vascular territory. Subgroup analyses were dichotomized by time from last-seen-well to CT imaging. RESULTS: A total of 80-90 keV VMI were marginally more sensitive (36.3-37.3%) than NCT (32.4%; p > 0.680), with marginally higher specificity (92.2-94.4 vs 91.1%; p > 0.509) for infarct detection. Location match was superior for VMI compared with NCT (28.7-27.4 vs 19.5%; p < 0.010). Within 4.5 h from last-seen-well, 80 keV VMI more accurately detected infarct (58.0 vs 54.0%) and localized infarcts (27.1 vs 11.9%; p = 0.004) than NCT, whereas after 4.5 h, 90 keV VMI was more accurate (69.3 vs 66.3%). CONCLUSION: Non-contrast 80-90 keV VMI best differentiates normal from infarcted brain parenchyma.

Demographics and clinical characteristics of acute traumatic brain injury patients in the different Neuroimaging Radiological Interpretation System (NIRIS) categories.

PURPOSE: To characterize the demographics, clinical and imaging findings, and outcomes of traumatic brain injury (TBI) patients in each of NeuroImaging Radiological Interpretation System (NIRIS) categories. MATERIAL AND METHODS: We considered all consecutive patients transported to Stanford Hospital's emergency department by ambulance or helicopter between November 2015 and April 2017. We retained adult patients (> 18 years old) for whom a trauma alert was triggered and who underwent a non-contrast head computer tomography (CT) because of suspected TBI. We reviewed the non-contrast CT scans in these patients for the NIH TBI common data elements (CDEs). We recorded, then assessed differences in terms of demographics, clinical characteristics, imaging CDEs, and outcomes in patients from the different NIRIS categories. RESULTS: In all, 1152 patients were included in this study. Patients with NIRIS 0 imaging findings were significantly younger than patients in other NIRIS categories (P<0.001). Motor vehicle accidents and falls from height were the most common mechanisms of injury across NIRIS categories. GCS scores decreased with increasing NIRIS category imaging findings and were significantly lower in patients with NIRIS 4 imaging findings (P<0.001). Significant differences in NIRIS categories were observed for all imaging CDEs (P<0.001), in agreement with the definition of the different NIRIS categories. Mortality increased progressively with increasing NIRIS severity. CONCLUSIONS: TBI patients in different NIRIS categories have different clinical characteristics, hospital courses and outcomes. This natural history assessment of patients from different NIRIS categories could thus serve as a reference standard for future TBI clinical trials.

JOURNAL CLUB: Use of Gradient Boosting Machine Learning to Predict Patient Outcome in Acute Ischemic Stroke on the Basis of Imaging, Demographic, and Clinical Information.

OBJECTIVE: When treatment decisions are being made for patients with acute ischemic stroke, timely and accurate outcome prediction plays an important role. The optimal rehabilitation strategy also relies on long-term outcome predictions. The decision-making process involves numerous biomarkers including imaging features and demographic information. The objective of this study was to integrate common stroke biomarkers using machine learning methods and predict patient recovery outcome at 90 days. MATERIALS AND METHODS: A total of 512 patients were enrolled in this retrospective study. Extreme gradient boosting (XGB) and gradient boosting machine (GBM) models were used to predict modified Rankin scale (mRS) scores at 90 days using biomarkers available at admission and 24 hours. Feature selections were performed using a greedy algorithm. Fivefold cross validation was applied to estimate model performance. RESULTS: For binary prediction of an mRS score of greater than 2 using biomarkers available at admission, XGB and GBM had an AUC of 0.746 and 0.748, respectively. Adding the National Institutes of Health Stroke Score at 24 hours and performing feature selection improved the AUC of XGB to 0.884 and the AUC of GBM to 0.877. With the addition of the recanalization outcome, XGB's AUC improved to 0.807 for nonrecanalized patients and dropped to 0.670 for recanalized patients. GBM's AUC improved to 0.781 for nonrecanalized patients and dropped to 0.655 for recanalized patients. CONCLUSION: Decision tree-based GBMs can predict the recovery outcome of stroke patients at admission with a high AUC. Breaking down the patient groups on the basis of recanalization and nonrecanalization can potentially help with the treatment decision process.

Carotid Artery Imaging Is More Strongly Associated With the 10-Year Atherosclerotic Cardiovascular Disease Score Than Coronary Artery Imaging.

PURPOSE: The aim of this study was to compare coronary and carotid artery imaging and determine which one shows the strongest association with atherosclerotic cardiovascular disease (ASCVD) score. PATIENTS AND METHODS: Two separate series patients who underwent either coronary computed tomography angiography (CTA) or carotid CTA were included. We recorded the ASCVD scores and assessed the CTA imaging. Two thirds were used to build predictive models, and the remaining one third generated predicted ASCVD scores. The Bland-Altman analysis analyzed the concordance. RESULTS: A total of 110 patients were included in each group. There was no significant difference between clinical characteristics. Three imaging variables were included in the carotid model. Two coronary models (presence of calcium or Agatston score) were created. The bias between true and predicted ASCVD scores was 0.37 ± 5.72% on the carotid model, and 2.07 ± 7.18% and 2.47 ± 7.82% on coronary artery models, respectively. CONCLUSIONS: Both carotid and coronary artery imaging features can predict ASCVD score. The carotid artery was more associated to the ASCVD score than the coronary artery.

Validation of the NeuroImaging Radiological Interpretation System for Acute Traumatic Brain Injury.

PURPOSE: The aim of the study was to refine and validate the NeuroImaging Radiological Interpretation System (NIRIS), which was developed to predict management and clinical outcome based on noncontrast head computerized tomography findings in patients suspected of acute traumatic brain injury (TBI). METHODS: We assessed the performance of the NIRIS score in a prospective, single-center cohort of patients suspected of TBI (n = 648) and compared the performance of NIRIS with that of the Marshall and Rotterdam scoring systems. We also revised components of the NIRIS scoring system using decision tree methodologies implemented on pooled data from the retrospective and prospective studies (N = 1190). RESULTS: The NIRIS performed similarly to the Marshall and Rotterdam scoring systems in predicting mortality and markedly better in terms of predicting more granular elements of disposition and management of TBI patients, such as admission, follow-up imaging, intensive care unit stay, and neurosurgical procedures. The revised NIRIS classification correctly predicted disposition and outcome in 91.2% (331/363) after excluding patients with other major extracranial traumatic injuries or intracranial nontraumatic injuries. CONCLUSIONS: The present study further demonstrates the predictive value of NIRIS in guiding standardized clinical management and decision-making regarding treatment options for TBI patients.

Semiautomated Characterization of Carotid Artery Plaque Features From Computed Tomography Angiography to Predict Atherosclerotic Cardiovascular Disease Risk Score.

PURPOSE: To investigate whether selected carotid computed tomography angiography (CTA) quantitative features can predict 10-year atherosclerotic cardiovascular disease (ASCVD) risk scores. METHODS: One hundred seventeen patients with calculated ASCVD risk scores were considered. A semiautomated imaging analysis software was used to segment and quantify plaque features. Eighty patients were randomly selected to build models using 14 imaging variables and the calculated ASCVD risk score as the end point (continuous and binarized). The remaining 37 patients were used as the test set to generate predicted ASCVD scores. The predicted and observed ASCVD risk scores were compared to assess properties of the predictive model. RESULTS: Nine of 14 CTA imaging variables were included in a model that considered the plaque features in a continuous fashion (model 1) and 6 in a model that considered the plaque features dichotomized (model 2). The predicted ASCVD risk scores were 18.87% ± 13.26% and 18.39% ± 11.6%, respectively. There were strong correlations between the observed ASCVD and the predicted ASCVDs, with r = 0.736 for model 1 and r = 0.657 for model 2. The mean biases between observed ASCVD and predicted ASCVDs were -1.954% ± 10.88% and -1.466% ± 12.04%, respectively. CONCLUSIONS: Selected quantitative imaging carotid features extracted from the semiautomated carotid artery analysis can predict the ASCVD risk scores.

Reduced Intravoxel Incoherent Motion Microvascular Perfusion Predicts Delayed Cerebral Ischemia and Vasospasm After Aneurysm Rupture.

BACKGROUND AND PURPOSE: Proximal artery vasospasm and delayed cerebral ischemia (DCI) after cerebral aneurysm rupture result in reduced cerebral perfusion and microperfusion and significant morbidity and mortality. Intravoxel incoherent motion (IVIM) magnetic resonance imaging extracts microvascular perfusion information from a multi-b value diffusion-weighted sequence. We determined whether decreased IVIM perfusion may identify patients with proximal artery vasospasm and DCI. METHODS: We performed a pilot retrospective cohort study of patients with ruptured cerebral aneurysms. Consecutive patients who underwent a brain magnetic resonance imaging with IVIM after ruptured aneurysm treatment were included. Patient demographic, treatment, imaging, and outcome data were determined by electronic medical record review. Primary outcome was DCI development with proximal artery vasospasm that required endovascular treatment. Secondary outcomes included mortality and clinical outcomes at 6 months. RESULTS: Sixteen patients (11 females, 69%; P=0.9) were included. There were no differences in age, neurological status, or comorbidities between patients who subsequently underwent endovascular treatment of DCI (10 patients; DCI+ group) and those who did not (6 patients; DCI- group). Compared with DCI- patients, DCI+ patients had decreased IVIM perfusion fraction f (0.09±0.03 versus 0.13±0.01; P=0.03), reduced diffusion coefficient D (0.82±0.05 versus 0.92±0.07×10-3 mm2/s; P=0.003), and reduced blood flow-related parameter fD* (1.18±0.40 versus 1.83±0.40×10-3 mm2/s; P=0.009). IVIM pseudodiffusion coefficient D* did not differ between DCI- (0.011±0.002) and DCI+ (0.013±0.005 mm2/s; P=0.4) patients. No differences in mortality or clinical outcome were identified. CONCLUSIONS: Decreased IVIM perfusion fraction f and blood flow-related parameter fD* correlate with DCI and proximal artery vasospasm development after cerebral aneurysm rupture.

Assessing the Relationship Between American Heart Association Atherosclerotic Cardiovascular Disease Risk Score and Coronary Artery Imaging Findings.

OBJECTIVE: The aim of this study was to characterize the relationship between computed tomography angiography imaging characteristics of coronary artery and atherosclerotic cardiovascular disease (ASCVD) score. METHODS: We retrospectively identified all patients who underwent a coronary computed tomography angiography at our institution from December 2013 to July 2016, then we calculated the 10-year ASCVD score. We characterized the relationship between coronary artery imaging findings and ASCVD risk score. RESULTS: One hundred fifty-one patients met our inclusion criteria. Patients with a 10-year ASCVD score of 7.5% or greater had significantly more arterial segments showing stenosis (46.4%, P = 0.008) and significantly higher maximal plaque thickness (1.25 vs 0.53, P = 0.001). However, among 56 patients with a 10-year ASCVD score of 7.5% or greater, 30 (53.6%) had no arterial stenosis. Furthermore, among the patients with a 10-year ASCVD score of less than 7.5%, 24 (25.3%) had some arterial stenosis. CONCLUSIONS: There is some concordance but not a perfect overlap between 10-year ASCVD risk scores and coronary artery imaging findings.