Search

Carotid artery stenting (CAS) is an established treatment for local stenosis. The most common complication is new ipsilateral ischemic lesions (NIILs). This study aimed to develop models considering lesion morphological and compositional features, and radiomics to predict NIILs.One hundred and forty-six patients who underwent brain MRI and high-resolution vessel wall MR imaging (hrVWI) before and after CAS were retrospectively recruited. Lumen and outer wall boundaries were segmented on hrVWI as well as atherosclerotic components. A traditional model was constructed with patient clinical information, and lesion morphological and compositional features. Least absolute shrinkage and selection operator algorithm was performed to determine key radiomics features for reconstructing a radiomics model. The model in predicting NIILs was trained and its performance was tested.Sixty-one patients were NIIL-positive and eighty-five negative. Volume percentage of intraplaque hemorrhage (IPH) and patients' clinical presentation (symptomatic/asymptomatic) were risk factors of NIILs. The traditional model considering these two features achieved an area under the curve (AUC) of 0.778 and 0.777 in the training and test cohorts, respectively. Twenty-two key radiomics features were identified and the model based on these features achieved an AUC of 0.885 and 0.801 in the two cohorts. The AUCs of the combined model considering IPH volume percentage, clinical presentation, and radiomics features were 0.893 and 0.842 in the training and test cohort respectively.Compared with traditional features (clinical and compositional features), the combination of traditional and radiomics features improved the power in predicting NIILs after CAS.• Volume percentage of IPH and symptomatic events were independent risk factors of new ipsilateral ischemic lesions (NIILs). • Radiomics features derived from carotid artery high-resolution vessel wall imaging had great potential in predicting NIILs after CAS. • The combination model with radiomics and traditional features further improved the diagnostic performance than traditional features alone.

This study investigated the association between the macroscopic mechanical response of aortic dissection (AoD) flap, its fibre features, and patient physiological features and clinical presentations.Uniaxial test was performed with tissue strips in both circumferential and longitudinal directions from 35 patients with (AoD:CC) and without (AoD:w/oCC) cerebral/coronary complications, and 19 patients with rheumatic or valve-related heart diseases (RH). A Bayesian inference framework was used to estimate the expectation of material constants (CThe elastin area percentage was negatively associated with age (p = 0.008), while collagen increased about 6% from age 40 to 70 (p = 0.03). Elastin fibre was less dispersed and wavier in old patients and no significant association was found between patient age and collagen fibre dispersion or waviness. Features of fibrous microstructures, either elastin or collagen, were comparable between AoD:CC and AoD:w/oCC group. Elastin and collagen area percentages were positively correlated with CFibre dispersion and waviness in the aortic dissection flap changed with patient age and clinical presentations, and these can be captured by the material constants in the strain energy density function.Aortic dissection (AoD) is a severe cardiovascular disease. Understanding the mechanical property of intimal flap is essential for its risk evaluation. In this study, mechanical testing and histology examination were combined to quantify the relationship between mechanical presentations and microstructure features. A Bayesian inference framework was employed to estimate the expectation of the material constants in the modified Mooney-Rivlin constitutive equation. It was found that fibre dispersion and waviness in the AoD flap changed with patient age and clinical presentations, and these could be captured by the material constants. This study firstly demonstrated that the expectation of material constants can be used to characterise tissue microstructures and differentiate patients with different clinical presentations.

To assess the complementary value of high-resolution multi-contrast MRI (hrMRI) in identifying symptomatic patients with intracranial atherosclerosis (ICAS) who are likely to experience recurrent ischemic cerebrovascular events.In this retrospective cohort study, eighty patients with acute ischemic events attributed to ICAS who underwent hrMRI examination between January 2015 and January 2019 were included. Median follow-up for all patients was 30 months (range: 1 to 52 months) and recurrent ischemic cerebrovascular events were recorded. Cox regression analysis and time-dependent ROC were performed to quantify the association between the plaque characteristics and recurrent events.During the follow-up, 14 patients experienced recurrent ischemic cerebrovascular events. Young males and those with diabetes and poor medication persistence were more likely to experience recurrent events. ICAS in patients with recurrence had significantly higher enhancement ratio and steepness which is defined as the ratio between the plaque height and length than those without (p0.001 and p = 0.015, respectively). After adjustment of clinical factors, enhancement ratio (HR, 13.13 [95% CI, 3.58-48.20], p0.001) and plaque steepness (HR, 110.27 [95% CI, 4.75-2560.91], p = 0.003) were independent imaging biomarkers associated with recurrent events. Time-dependent ROC indicated that integrated high enhancement ratio and steepness into clinical risk factors improved discrimination power with the ROC increased from 0.79 to 0.94 (p = 0.008).The enhancement ratio and plaque steepness improved the accuracy over traditional clinical risk factors in predicting recurrent ischemic cerebrovascular events for patients with ICAS.• High-resolution magnetic resonance imaging helps clinicians to evaluate high-risk Intracranial plaque. • The higher enhancement ratio and plaque steepness (= height/length) were the primary biomarkers associated with future ischemic cerebrovascular events. • High-resolution magnetic resonance imaging combined with clinical characteristics showed a higher accuracy for the prediction of recurrent events in patients with intracranial atherosclerosis.

Background Since December 2019, coronavirus disease 2019 (COVID‐19) has emerged as an international pandemic. COVID‐19 patients with myocardial injury might need special attention. However, an understanding on this aspect remains unclear. This study aimed to illustrate clinical characteristics and the prognostic value of myocardial injury to COVID‐19 patients. Methods This retrospective, single‐center study finally included 304 hospitalized COVID‐19 cases confirmed by real‐time reverse‐transcriptase polymerase chain reaction from January 11 to March 25, 2020. Myocardial injury was determined by serum high‐sensitivity troponin I (Hs‐TnI). The primary endpoint was COVID‐19‐associated mortality. Results Of 304 COVID‐19 patients (median age, 65 years; 52.6% males), 88 patients (27.3%) died (61 patients with myocardial injury, 27 patients without myocardial injury on admission). COVID‐19 patients with myocardial injury had more comorbidities (hypertension, chronic obstructive pulmonary disease, cardiovascular disease, and cerebrovascular disease); lower lymphocyte counts, higher C‐reactive protein (CRP; median, 84.9 vs. 28.5 mg/L; p, COVID patients with myocardial injury on admission is associated with more severe clinical presentation and biomarkers. Myocardial injury (hazard ratio = 4.55; p

BACKGROUND: Assessment of subclinical myocardial dysfunction by using feature tracking has shown promise in prognosis evaluation of heart failure with preserved ejection fraction (HFpEF). Global early diastolic longitudinal strain rate (eGLSR) can identify earlier diastolic dysfunction; however, limited data are available on its prognostic value in HFpEF. PURPOSE: To evaluate the association between left ventricular (LV) eGLSR and primary composite outcomes (all-cause death or heart failure hospitalization) in patients with HFpEF. MATERIALS AND METHODS: In this retrospective study, consecutive patients with HFpEF (included from January 2010 to March 2013) underwent cardiovascular MRI. The correlation between eGLSR and variables was assessed by using linear regression. The association between eGLSR (obtained with use of feature tracking) and outcomes was analyzed by using Cox proportional regression. RESULTS: A total of 186 patients with HFpEF (mean age ± standard deviation, 59 years ± 12; 77 women) were included. The eGLSR was weakly correlated with LV end-diastole volume index (Pearson correlation coefficient [r] = –0.35; P < .001), heart rate (r = 0.35; P < .001), and LV ejection fraction (r = 0.30; P < .001) and moderately correlated with LV end-systole volume index (r = –0.41; P < .001). At a median follow-up of 9.2 years (interquartile range, 8.7–10.0 years), 72 patients experienced primary composite outcomes. Impaired eGLSR, defined as an eGLSR of less than 0.57 per second, was associated with a greater rate of heart failure hospitalization or all-cause death (hazard ratio, 2.0 [95% CI: 1.1, 3.7]; P = .02) after adjusting for multiple clinical and imaging-based variables. CONCLUSION: Left ventricular global early diastolic longitudinal strain rate obtained from cardiovascular MRI feature tracking was independently associated with adverse outcomes in patients with heart failure with preserved ejection fraction. © RSNA, 2021 Online supplemental material is available for this article. An earlier incorrect version appeared online. This article was corrected on October 22, 2021.

BACKGROUND: Intracranial atherosclerotic plaque causing mild luminal stenosis might lead to acute ischemic events. However, the difference between culprit and nonculprit lesions is unclear, as are the factors associated with favorable treatment outcomes. PURPOSE: To quantify characteristics of intracranial atherosclerosis with mild luminal stenosis and to identify factors associated with lesion type (culprit or nonculprit) and with clinical outcomes. STUDY TYPE: Prospective POPULATION: 293 patients who had acute stroke with mild luminal stenosis (

Background and Purpose: Intracranial atherosclerosis is one of the main causes of stroke, and high-resolution magnetic resonance imaging provides useful imaging biomarkers related to the risk of ischemic events. This study aims to evaluate differences in histogram features between culprit and nonculprit intracranial atherosclerosis using high-resolution magnetic resonance imaging. Methods: Two hundred forty-seven patients with intracranial atherosclerosis who underwent high-resolution magnetic resonance imaging sequentially between January 2015 and December 2016 were recruited. Quantitative features, including stenosis, plaque burden, minimum luminal area, intraplaque hemorrhage, enhancement ratio, and dispersion of signal intensity (coefficient of variation), were analyzed based on T2-, T1-, and contrast-enhanced T1-weighted images. Step-wise regression analysis was used to identify key determinates differentiating culprit and nonculprit plaques and to calculate the odds ratios (ORs) with 95% CIs. Results: In total, 190 plaques were identified, of which 88 plaques (37 culprit and 51 nonculprit) were located in the middle cerebral artery and 102 (57 culprit and 45 nonculprit) in the basilar artery. Nearly 90% of culprit lesions had a degree of luminal stenosis of P =0.047), minimum luminal area (OR, 1.468 [95% CI, 1.032–2.087]; P =0.033), and coefficient of variation (OR, 13.425 [95% CI, 3.987–45.204]; P P =0.029), intraplaque hemorrhage (OR, 2.847 [95% CI, 0.971–10.203]; P =0.046), and coefficient of variation (OR, 10.068 [95% CI, 2.820–21.343]; P Conclusions: Features characterized by high-resolution magnetic resonance imaging provided complementary values over luminal stenosis in defined lesion type for intracranial atherosclerosis; the dispersion of signal intensity in histogram analysis was a particularly effective predictive parameter.

Objectives This study sought to determine if plaque structural stress (PSS) and other plaque stress parameters are increased in plaques that cause future major adverse cardiovascular event...

Almost the entire world, not only China, is currently experiencing the outbreak of a novel coronavirus that causes respiratory disease, severe pneumonia, and even death. The outbreak began in Wuhan, China, in December of 2019 and is currently still ongoing. This novel coronavirus is highly contagious and has resulted in a continuously increasing number of infections and deaths that have already surpassed the SARS-CoV outbreak that occurred in China between 2002 and 2003. It is now officially a pandemic, announced by WHO on the 11th of March. Currently, the 2019 novel coronavirus (SARS-CoV-2) can be identified by virus isolation or viral nucleic acid detection; however, false negatives associated with the nucleic acid detection provide a clinical challenge and thus make the imaging examination crucial. Imaging exams have been a main clinical diagnostic criteria for the 2019 novel coronavirus disease (COVID-19) in China. Imaging features of multiple patchy areas of ground glass opacity and consolidation predominately in the periphery of the lungs are characteristic manifestations on chest CT and extremely helpful in the early detection and diagnosis of this disease, which aids prompt diagnosis and the eventual control of this emerging global health emergency. Key Points • In December 2019, China, an outbreak of pneumonia caused by a novel, highly contagious coronavirus raised grave concerns and posed a huge threat to global public health. • Among the infected patients, characteristic findings on CT imaging include multiple, patchy, ground-glass opacity, crazy-paving pattern, and consolidation shadows, mainly distributed in the peripheral and subpleural areas of both lungs, which are very helpful for the frontline clinicians. • Imaging examination has become the indispensable means not only in the early detection and diagnosis but also in monitoring the clinical course, evaluating the disease severity, and may be presented as an important warning signal preceding the negative RT-PCR test results.

Cardiovascular disease (CVD) is a common disease with high mortality rate, and carotid atherosclerosis (CAS) is one of the leading causes of cardiovascular disease. Multisequence carotid MRI can not only identify carotid atherosclerotic plaque constituents with high sensitivity and specificity, but also obtain different morphological features, which can effectively help doctors improve the accuracy of diagnosis. However, it is difficult to evaluate the accurate evolution of local changes in carotid atherosclerosis in multi-sequence MRI due to the inconsistent parameters of different sequence images and the geometric space mismatch caused by the motion deviation of tissues and organs. To solve these problems, we propose a cross-scale multi-modal image registration method based on the Siamese U-Net. The network uses sub-networks with image inputs of different sizes to extract various features, and a special padding module is designed to make the network available for training on cross-scale features. In addition, to improve the registration performance, a multi-scale loss function under Gaussian smoothing is applied for optimization. For the experiments, we have collected a multi-sequence MRI image dataset from 11 patients with carotid atherosclerosis for a retrospective study. We evaluate our overall architectures by cross-validation on our carotid dataset. The experimental results show that our method can generate precise and reliable results with cross-scale multi-sequence inputs and the registration accuracy can be greatly improved by using the Gaussian smoothing loss function. The DSC of our Siamese structure can reach 84.1% on the carotid data set with cross-size input. With the use of GDSC loss, the average DSC can be improved by 5.23%, while the average distance between fixed landmarks and moving landmarks can be decreased by 6.46%.Our code is made publicly available at: https://github.com/MingHan98/Cross-scale-Siamese-Unet.

The comprehensive management of chronic liver disease and subsequent portal hypertension (PH) remains a challenge in clinical treatment. The consequences of PH are life-threatening, including varic...

Objective: MARK4 (microtubule affinity-regulating kinase 4) regulates NLRP3 (nucleotide-binding oligomerization domain, leucine-rich repeat, and pyrin domain containing 3) inflammasome activation. The aim of the study is to examine the role of MARK4 in hematopoietic cells during atherosclerosis. Methods and Results: We show increased MARK4 expression in human atherosclerotic lesions compared with adjacent areas. MARK4 is coexpressed with NLRP3, and they colocalize in areas enriched in CD68-positive but α-SMA (α-smooth muscle actin)–negative cells. Expression of MARK4 and NLRP3 in the atherosclerotic lesions is associated with the production of active IL (interleukin)-1β and IL-18. To directly assess the role of hematopoietic MARK4 in NLRP3 inflammasome activation and atherosclerotic plaque formation, Ldlr (low-density lipoprotein receptor)-deficient mice were lethally irradiated and reconstituted with either wild-type or Mark4 -deficient bone marrow cells, and were subsequently fed a high-fat diet and cholesterol diet for 9 weeks. Mark4 deficiency in bone marrow cells led to a significant reduction of lesion size, together with decreased circulating levels of IL-18 and IFN-γ (interferon-γ). Furthermore, Mark4 deficiency in primary murine bone marrow–derived macrophages prevented cholesterol crystal–induced NLRP3 inflammasome activation, as revealed by reduced caspase-1 activity together with reduced production of IL-1β and IL-18. Conclusions: MARK4-dependent NLRP3 inflammasome activation in the hematopoietic cells regulates the development of atherosclerosis.

Segmentation of lumen (LB) and outer wall boundaries (OB) of carotid artery in magnetic resonance (MR) images is essential for carotid atherosclerotic disease diagnosis. However, the limited image signal-to-noise ratio, flow artefact, and varied lumen and outer wall become significant obstacles for automatic segmentation. A fully automatic framework is proposed for LB and OB segmentation in MR images. First, the lumen is identified by the support vector machine using a special strategy and LB is segmented by the geodesic star-shape-constrained graph cut. Then a novel global optimisation is developed to segment OB based on the graph cut, which consists of shape priors and appearance priors. The shape priors are learned from labelled shapes on LB and OB, while the appearance priors are modelled by Gaussian mixture models. A novel shape constraint is also designed as the constraint term. To evaluate author's method, extensive experiments are carried out from 160 MR images belonging to 16 patients. Experimental results demonstrate that the proposed method can yield high accuracy with fully automatic segmentation. Moreover, the advantages of the proposed method have been shown in terms of high flexibility and accuracy without user interactions in comparison with other methods.

Background Intracranial artery dissection (IAD) often causes headache and cerebral vascular ischemic events. The imaging characteristics of IAD remain unclear. This study aims to characterize the appearance of culprit and non-culprit IAD using high-resolution cardiovascular magnetic resonance imaging (hrCMR) and quantify the incremental value of hrCMR in identifying higher risk lesions. Methods Imaging data from patients who underwent intervention examination or treatment using digital subtraction angiography (DSA) and hrCMR using a 3 T CMR system within 30 days after the onset of neurological symptoms were collected. The CMR protocol included diffusion-weighted imaging (DWI), black blood T1-, T2- and contrast-enhanced T1-weighted sequences. Lesions were classified as culprit and non-culprit according to imaging findings and patient clinical presentations. Univariate and multivariate analyses were performed to assess the difference between culprit and non-culprit lesions and complementary value of hrCMR in identifying higher risk lesions. Results In total, 75 patients were included in this study. According to the morphology, lesions could be classified into five types: Type I, classical dissection (n = 50); Type II, fusiform aneurysm (n = 1); Type III, long dissected aneurysm (n = 3); Type IV, dolichoectatic dissecting aneurysm (n = 9) and Type V, saccular aneurysm (n = 12). Regression analyses showed that age and hypertension were both associated with culprit lesions (age: OR, 0.83; 95% CI 0.75–0.92; p Conclusion hrCMR is helpful in visualizing and characterizing IAD. It provides a significant complementary value over DSA for the diagnosis of IAD.

Advances in medical imaging have enabled patient-specific biomechanical modelling of arterial lesions such as atherosclerosis and aneurysm. Geometry acquired from in-vivo imaging is already pressurized and a zero-pressure computational start shape needs to be identified. The backward displacement algorithm was proposed to solve this inverse problem, utilizing fixed-point iterations to gradually approach the start shape. However, classical fixed-point implementations were reported with suboptimal convergence properties under large deformations. In this paper, a dynamic learning rate guided by the deformation gradient tensor was introduced to control the geometry update. The effectiveness of this new algorithm was demonstrated for both idealized and patient-specific models. The proposed algorithm led to faster convergence by accelerating the initial steps and helped to avoid the non-convergence in large-deformation problems.