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BACKGROUND: Coronary artery segmentation is a prerequisite in computer-aided diagnosis of Coronary Artery Disease (CAD). However, segmentation of coronary arteries in Coronary Computed Tomography Angiography (CCTA) images faces several challenges. The current segmentation approaches are unable to effectively address these challenges and existing problems such as the need for manual interaction or low segmentation accuracy. OBJECTIVE: A Multi-scale Feature Learning and Rectification (MFLR) network is proposed to tackle the challenges and achieve automatic and accurate segmentation of coronary arteries. METHODS: The MFLR network introduces a multi-scale feature extraction module in the encoder to effectively capture contextual information under different receptive fields. In the decoder, a feature correction and fusion module is proposed, which employs high-level features containing multi-scale information to correct and guide low-level features, achieving fusion between the two-level features to further improve segmentation performance. RESULTS: The MFLR network achieved the best performance on the dice similarity coefficient, Jaccard index, Recall, F1-score, and 95% Hausdorff distance, for both in-house and public datasets. CONCLUSION: Experimental results demonstrate the superiority and good generalization ability of the MFLR approach. This study contributes to the accurate diagnosis and treatment of CAD, and it also informs other segmentation applications in medicine. [ABSTRACT FROM AUTHOR]

Background: Papillary muscle (PM) infarction (PMI) detected by cardiac magnetic resonance imaging (CMR) is associated with poor outcomes. Whether PM parameters provide more value for mitral regurgitation (MR) management currently remains unclear. Therefore, we examined the prognostic value of PMI using CMR in patients with MR. Methods: Between March 2018 and July 2023, we retrospectively enrolled 397 patients with MR undergoing CMR. CMR was used to detect PMI qualitatively and quantitively. We also collected baseline clinical, echocardiography, and follow‐up data. Results: Of the 397 patients with MR (52.4 ± 13.9 years), 117 (29.5%) were assigned to the PMI group, with 280 (70.5%) in the non‐PMI group. PMI was demonstrated more in the posteromedial PM (PM‐PM, 98/117) than in the anterolateral PM (AL‐PM, 45/117). Compared with patients without PMI, patients with PMI had a decreased AL‐PM (41.5 ± 5.4 vs. 45.6 ± 5.3)/PM‐PM diastolic length (35.0 ± 5.2 vs. 37.9 ± 4.0), PM‐longitudinal strain (LS, 20.4 ± 6.1 vs. 24.9 ± 4.6), AL‐PM‐LS (19.7 ± 6.8 vs. 24.7 ± 5.6)/PM‐PM‐LS (21.2 ± 7.9 vs. 25.2 ± 6.0), and increased inter‐PM distance (25.7 ± 8.0 vs. 22.7 ± 6.2, all p < 0.001). Multiple logistic regression analyses identified male sex (odds ratio [OR] = 3.65, 95% confidence interval = 1.881−7.081, p < 0.001) diabetes mellitus (OR/95% CI/p = 2.534/1.13–5.68/0.024), AL‐PM diastolic length (OR/95% CI/p = 0.841/0.77–0.92/< 0.001), PM‐PM diastolic length (OR/95% CI/p = 0.873/0.79–0.964/0.007), inter‐PM distance (OR/95% CI/p = 1.087/1.028–1.15/0.003), AL‐PM‐LS (OR/95% CI/p = 0.892/0.843–0.94/< 0.001), and PM‐PM‐LS (OR/95% CI/p = 0.95/0.9–0.992/0.021) as independently associated with PMI. Over a 769 ± 367‐day follow‐up, 100 (25.2%) patients had arrhythmia. Cox regression analyses indicated that PMI (hazard ratio [HR]/95% CI/p = 1.644/1.062–2.547/0.026), AL‐PM‐LS (HR/95% CI/p = 0.937/0.903–0.973/0.001), and PM‐PM‐LS (HR/95% CI/p = 0.933/0.902–0.965/< 0.001) remained independently associated with MR. Conclusions: The CMR‐derived PMI and LS parameters improve the evaluation of PM dysfunction, indicating a high risk for arrhythmia, and provide additive risk stratification for patients with MR. [ABSTRACT FROM AUTHOR]

Background: The curved planar reformation (CPR) technique is one of the most commonly used methods in clinical practice to locate coronary arteries in medical images. Purpose: The artery centerline is the cornerstone for the generation of the CPR image. Here, we describe the development of a new fully automatic artery centerline tracker with the aim of increasing the efficiency and accuracy of the process. Methods: We propose a COronary artery Centerline Tracker (COACT) framework which consists of an ostium point finder (OPFinder) model, an intersection point detector (IPDetector) model and a set of centerline tracking strategies. The output of OPFinder is the ostium points. The function of the IPDetector is to predict the intersections of a sample sphere and the centerlines. The centerline tracking process starts from two ostium points detected by the OPFinder, and combines the results of the IPDetector with a series of strategies to gradually reconstruct the coronary artery centerline tree. Results: Two coronary CT angiography (CCTA) datasets were used to validate the models. Dataset1 contains 160 cases (32 for test and 128 for training) and dataset2 contains 70 cases (20 for test and 50 for training). The results show that the average distance between the ostium points predicted by the OPFinder and the manually annotated ostium points was 0.88 mm, which is similar to the differences between the results obtained by two observers (0.85 mm). For the IPDetector, the average overlap of the predicted and ground truth intersection points was 97.82% and this is also close to the inter‐observer agreement of 98.50%. For the entire coronary centerline tree, the overlap between the results obtained by COACT and the gold standard was 94.33%, which is slightly lower than the inter‐observer agreement, 98.39%. Conclusions: We have developed a fully automatic centerline tracking method for CCTA scans and achieved a satisfactory result. The proposed algorithms are also incorporated in the medical image analysis platform TIMESlice (https://slice‐doc.netlify.app) for further studies. [ABSTRACT FROM AUTHOR]

Background and Aims: Mitral annular calcification (MAC) by computed tomography (CT) is reported as an independent predictor of poor outcomes. However, it currently remains unclear if quantitative MAC parameters provide more value for mitral valve disease (MVD) management, therefore, we examined the prognostic value of MAC scores using noncontrast cardiac‐CT in MVD patients. Methods: Between January 2020 and December 2021, we prospectively enrolled 300 consecutive patients with MVD (MAC‐present = 80 and MAC‐absent = 220) undergoing preoperative cardiac‐CT and mitral valve (MV) surgery. Noncontrast cardiac‐CT images were used to qualitatively detect MAC (present or absent) and evaluate MAC scores. For analyses, we also collected baseline clinical data, intraoperative conversion (from MV repair to MV replacement), and follow‐up arrhythmia data. Results: Compared with the MAC‐absent group, MAC‐present patients were older (62 ± 7 vs. 58 ± 9 years, p <.001), mostly women (55% vs. 39.5%, p =.017), and also had aortic valve calcification (57.5% vs. 23.2%, p <.001), mitral stenosis (82.5% vs. 61.8%, p <.001), atrial fibrillation (30% vs. 11.8%, p <.001), and larger left atrial end‐diastolic dimension (LADD, 49 [44–56] versus 46 [41–50], p =.001]. Furthermore, MAC‐present patients underwent more MV replacements (61.8% vs. 82.5%, p =.001) and experienced a higher intraoperative conversion prevalence (11.8% vs. 61.3%, p <.001). Multiple logistic regression analyses showed that the female gender (odds ratio [OR]/95% confidence interval [CI]/p = 2.001/1.042–3.841/0.037) and MAC scores (OR/95% CI/p = 10.153/4.434–23.253/p <.001) were independent predictors of intraoperative conversion. During a follow‐up of 263 ± 134 days, MAC‐present patients had more arrhythmias (42.5% vs. 9.5%, p <.001). Also, MAC‐scores (hazard ratio [HR]/95% CI/p = 6.841/3.322–14.089/p <.001) and LADD (HR/95% CI/p = 1.039/1.018–1.060/p <.001) were independently associated with arrhythmias by Cox regression analyses. Conclusions: Noncontrast cardiac CT‐derived MAC‐scores showed a high risk for intraoperative conversion and follow‐up arrhythmias in MVD‐patients. [ABSTRACT FROM AUTHOR]

Introduction: The purpose of this study was to analyze the difference in clinical and aortic morphological features between the bovine aortic arch and normal aortic arch in patients with acute type B aortic dissection (aTBAD). Methods: A total of 133 patients diagnosed with aTBAD were retrospectively collected. Based on aortic arch morphology, they were divided into the bovine aortic arch group (n = 20) and the normal aortic arch group (n = 113). Aortic morphological features were assessed on computed tomographic angiography. Clinical and aortic morphological features were then compared between the bovine aortic arch and normal aortic arch groups. Results: Patients in the bovine aortic arch group were significantly younger and with higher weight and BMI than the normal aortic arch group (p < 0.001, p = 0.045, and p = 0.016, respectively). The total aortic length in the bovine aortic arch group was significantly shorter than that in the normal aortic arch group (p = 0.039). The tortuosity of descending thoracic aorta, the tortuosity of descending aorta, and the angulation of aortic arch were significantly lower in the bovine aortic arch group (p = 0.004, p = 0.015, and p = 0.023, respectively). The width of descending aorta, the height of aorta arch, and the angle of ascending aorta were significantly smaller in the bovine aortic arch group (p = 0.045, p = 0.044, and p = 0.042, respectively). Conclusion: When the aTBAD occurred, patients with bovine aortic arch were prone to be younger and with higher BMI than those with normal aortic arch. The aortic curvature and the total aortic length were lower in patients with bovine aortic arch. [ABSTRACT FROM AUTHOR]

Background: Pulmonary embolism is a kind of cardiovascular disease that threatens human life and health. Since pulmonary embolism exists in the pulmonary artery, improving the segmentation accuracy of pulmonary artery is the key to the diagnosis of pulmonary embolism. Traditional medical image segmentation methods have limited effectiveness in pulmonary artery segmentation. In recent years, deep learning methods have been gradually adopted to solve complex problems in the field of medical image segmentation. Purpose: Due to the irregular shape of the pulmonary artery and the adjacent‐complex tissues, the accuracy of the existing pulmonary artery segmentation methods based on deep learning needs to be improved. Therefore, the purpose of this paper is to develop a segmentation network, which can obtain higher segmentation accuracy and further improve the diagnosis effect. Methods: In this study, the pulmonary artery segmentation performance from the network model and loss function is improved, proposing a pulmonary artery segmentation network (PA‐Net) to segment the pulmonary artery region from 2D CT images. Reverse Attention and edge attention are used to enhance the expression ability of the boundary. In addition, to better use feature information, the channel attention module is introduced in the decoder to highlight the important channel features and suppress the unimportant channels. Due to blurred boundaries, pixels near the boundaries of the pulmonary artery may be difficult to segment. Therefore, a new contour loss function based on the active contour model is proposed in this study to segment the target region by assigning dynamic weights to false positive and false negative regions and accurately predict the boundary structure. Results: The experimental results show that the segmentation accuracy of this proposed method is significantly improved in comparison with state‐of‐the‐art segmentation methods, and the Dice coefficient is 0.938 ± 0.035, which is also confirmed from the 3D reconstruction results. Conclusions: Our proposed method can accurately segment pulmonary artery structure. This new development will provide the possibility for further rapid diagnosis of pulmonary artery diseases such as pulmonary embolism. Code is available at https://github.com/Yuanyan19/PA‐Net. [ABSTRACT FROM AUTHOR]

Background: Many cardiovascular diseases are closely related to the composition of epicardial adipose tissue (EAT). Accurate segmentation of EAT can provide a reliable reference for doctors to diagnose the disease. The distribution and composition of EAT often have significant individual differences, and the traditional segmentation methods are not effective. In recent years, deep learning method has been gradually introduced into EAT segmentation task. Purpose: The existing EAT segmentation methods based on deep learning have a large amount of computation and the segmentation accuracy needs to be improved. Therefore, the purpose of this paper is to develop a lightweight EAT segmentation network, which can obtain higher segmentation accuracy with less computation and further alleviate the problem of false‐positive segmentation. Methods: First, the obtained computed tomography was preprocessed. That is, the threshold range of EAT was determined to be −190, −30 HU according to prior knowledge, and the non‐adipose pixels were excluded by threshold segmentation to reduce the difficulty of training. Second, the image obtained after thresholding was input into the lightweight RDU‐Net network to perform the training, validating, and testing process. RDU‐Net uses a residual multi‐scale dilated convolution block in order to extract a wider range of information without changing the current resolution. At the same time, the form of residual connection is adopted to avoid the problem of gradient expansion or gradient explosion caused by too deep network, which also makes the learning easier. In order to optimize the training process, this paper proposes PNDiceLoss, which takes both positive and negative pixels as learning targets, fully considers the class imbalance problem, and appropriately highlights the status of positive pixels. Results: In this paper, 50 CCTA images were randomly selected from the hospital, and the commonly used Dice similarity coefficient (DSC), Jaccard similarity, accuracy (ACC), specificity (SP), precision (PC), and Pearson correlation coefficient are used as evaluation metrics. Bland–Altman analysis results show that the extracted EAT volume is consistent with the actual volume. Compared with the existing methods, the segmentation results show that the proposed method achieves better performance on these metrics, achieving the DSC of 0.9262. The number of false‐positive pixels has been reduced by more than half. Pearson correlation coefficient reached 0.992, and linear regression coefficient reached 0.977 when measuring the volume of EAT obtained. In order to verify the effectiveness of the proposed method, experiments are carried out in the cardiac fat database of VisualLab. On this database, the proposed method also achieved good results, and the DSC value reached 0.927 in the case of only 878 slices. Conclusions: A new method to segment and quantify EAT is proposed. Comprehensive experiments show that compared with some classical segmentation algorithms, the proposed method has the advantages of shorter time‐consuming, less memory required for operations, and higher segmentation accuracy. The code is available at https://github.com/lvanlee/EAT_Seg/tree/main/EAT_seg. [ABSTRACT FROM AUTHOR]

Background: Malignant middle cerebral artery infarction (MMI) is a life-threatening cerebral vascular event. Early decompressive craniectomy (DC) has proven to be an effective treatment strategy. However, the ideal candidate for DC continues to be debated. Purpose: To investigate whether a hyperdense middle cerebral artery sign (HMCAS) provides prognostic value after DC in patients with MMI. Material and Methods: We reviewed clinical information and radiological parameters on computed tomography of 42 patients with MMI who underwent DC. Functional outcome was assessed according to the modified Rankin scale (mRS) at three months as follows: favorable outcome (mRS ≤ 4) versus unfavorable outcome (mRS > 4). Logistic regression analysis was used to identify predictors of functional outcome after DC in patients with MMI. Results: Age (odds ratio [OR] = 0.87; 95% confidence interval [CI] = 0.78–0.97; P = 0.014) and HMCAS (OR = 7.40; 95% CI = 1.35–40.48; P = 0.021) were associated with functional outcome. The area under the receiver operating characteristic curve for predicting favorable outcome using the combination of age and HMCAS was 0.882, and the sensitivity and specificity were 0.947 and 0.696, respectively. Conclusion: Patients with MMI with HMCAS, as well as younger patients, often showed a favorable outcome after DC in this study. [ABSTRACT FROM AUTHOR]

The development of electromagnetic interference (EMI) shielding materials with excellent EMI shielding effectiveness (SE), and superior mechanical performance is of great importance in daily life and in the military, where they are used for intelligent transportation systems and ammunition. Generally, shielding performances can be realized via reflection loss and absorption loss. However, it is still a challenge to enhance these two kinds of electromagnetic wave loss in the same material at the same time to achieve ideal shielding performance. Herein, a flexible Ag NDs/CNT@PC/Ni‐9 film composed of five conductive layers and four porous layers is prepared by alternating Ag NDs/CNT layers and PC/Ni layers through self‐assembly. The obtained film with a thickness of 890 μm exhibits a remarkable SE performance of 138 dB in the X‐band. Additionally, with a series of continuous volcanic mechanical impacts (>10 000 g), the maximum instantaneous acceleration is decreased by 30.02% due to the buffering of the film, which constitutes a much‐improved energy absorption cushion relative to that of traditional buffering materials, such as foam metal and rubber. A material integrated with strong EMI shielding and high mechanical shock resistance is designed, which provides an effective strategy for the development of functional materials for extreme environments. [ABSTRACT FROM AUTHOR]

Background: Atrial fibrillation (AF) is common arrhythmia in valvular heart disease (VHD) and is associated with adverse outcomes. Hypothesis: To evaluate the left atrial (LA) function in patients with AF‐VHD by cardiovascular magnetic resonance imaging feature tracking (CMR‐FT) using LA strain (εs/εe/εa) and their corresponding strain rate (SRs/SRe/SRa). Methods: This was a retrospective cross‐sectional inter‐reader and intra‐reader reproducibility conducted from July 1, 2020, to January 31, 2021. A total of 39 patients with AF‐VHD (rheumatic heart valvular disease [RHVD] [n = 22], degenerative heart valvular disease [DHVD] [n = 17]) underwent MRI scans performed with drug‐controlled heart rate before correcting the rhythm and valves through maze procedure. Fifteen participants with normal cardiac MRI were included as healthy control. εs/SRs, εe/SRe, and εa/SRa, corresponding to LA reservoir, conduit, and booster‐pump function, were assessed using Feature Tracking software (CVI42 v5.12.1). Results: Compared with healthy controls, LA global strain parameters (εs/εe/εa/SRs/SRe/SRa) were significantly decreased (all p < 0.001), while LA size and volume were increased in AF‐VHD group (all p < 0.001). In the subgroup, RHVD group showed lower LA total ejection fraction (LATEF) and strain data than DHVD group (12.6% ± 3.3% vs. 19.4 ± 8.6, p = 0.001). Decreased LATEF was significantly related to altered LA strain and strain rate, especially in εs, εe, and SRs (Pearson/Spearman r/ρ = 0.856/0.837/0.562, respectively; all p < 0.001). Interstudy and intrastudy reproducibility were consistent for LA volumetry and strain parameters (intraclass correlation coefficient: 0.88–0.99). Conclusions: CMR‐FT can be used to assess the LA strain parameters, and identify LA dysfunction and deformation noninvasively, which could be a helpful functional imaging biomarker in the clinical treatment of AF‐VHD. [ABSTRACT FROM AUTHOR]

Objective: To quantitatively evaluate the impaired perfusion status of patients with symptomatic internal carotid artery (ICA) steno-occlusive disease and to explore the risk factors of impaired perfusion with computed tomography perfusion (CTP). Methods: The clinical and imaging data of 187 patients with ICA steno-occlusive disease were retrospectively analyzed. The ICA stenosis rate was divided into Grades I–IV (70–79%; 80–89%; 90–99%; 100%), and the circle of Willis was classified as four types (types I–IV). According to the literature, the value of cerebral blood flow/cerebral blood volume (CBF/CBV) of 7.55/min was used as cut-off to predict symptomatic patients. All patients were categorized into two groups: those with impaired perfusion [ n = 99 (52.9%)] and those without impaired perfusion [ n = 88 (47.1%)]. Symmetrical bilateral internal watershed areas were selected as the regions of interest (ROIs). Statistical analysis was made on the status of impaired perfusion and the risk factors of impaired perfusion. Results: Univariate analysis revealed that systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP), types of the circle of Willis, and clinical features at admission differed between the two groups (patients with or without impaired perfusion) (p < 0.05). Multiple logistic stepwise regression analysis showed that MAP [odds ratio (OR) = 0.946, 95% confidential interval (CI) = 0.917–0.974, p < 0.001] and type IV (type I vs. IV: OR = 4.987, 95% CI = 1.955–12.723, p = 0.001) at admission were independently associated with impaired perfusion in the internal watershed areas. Conclusion: MAP and the type of circle of Willis at admission are independent risk factors associated with the impaired perfusion in patients with ICA steno-occlusive disease. [ABSTRACT FROM AUTHOR]

For continuous monitoring of cardiovascular diseases, this article presents a novel framework for heart sound acquisition. The proposed approach uses compressed sensing for signal sampling, and a two-stage reconstruction is developed for reconstruction. The first stage aims to give a tentative recovered signal, on which a peak detection technique is developed to identify whether there is a peak in current segment and, if so, its location. With such information, an adaptive dictionary is selected for the second round reconstruction. Because the selected dictionary is adaptive to the morphology of current frame, the signal reconstruction performance is consequently promoted. Experiment results indicate that a satisfactory performance can be obtained when the frame length is 256 and the signal morphology is divided into 16 categories. Furthermore, the proposed algorithm is compared with a series of counterparts, and the results well demonstrate the advantages of our proposal, especially at high compression ratios. [ABSTRACT FROM AUTHOR]

Objective: To explore the biomarkers of cerebral small vessel disease (CSVD) associated with cognitive impairment. Methods: A total of 69 patients with CSVD were enrolled in the study, and baseline clinical and imaging data were reviewed retrospectively. The following neuroimaging biomarkers of CSVD were identified: high-grade white matter hyperintensity (HWMH), cerebral microbleeds (CMB), enlarged perivascular space (PVS), and lacunar infarct (LI). A total score for CSVD was calculated. The deep medullary veins (DMVs) were divided into six segments according to the regional anatomy. The total DMV score (0–18) was derived from the sum of the scores of the six individual segments, the scores of which ranged from 0 to 3, for a semiquantitative assessment of the DMV that was based on segmental continuity and visibility. Results: The DMV score, patient age, and total CSVD score were independently associated with the presence or absence of cognitive impairment in patients with CSVD (P < 0.05). By integrating patient age and the total CSVD and DMV scores, the area under the curve of the receiver operating characteristic curve (AUROC) for predicting CSVD associated with cognitive impairment was 0.885, and the sensitivity and specificity were 64.71 and 94.23%, respectively. Conclusions: The DMV score may be a novel imaging biomarker for CSVD associated with cognitive impairment. The integration of the DMV score with age and total CSVD score should increase the predictive value of the DMV score for CSVD associated with cognitive impairment. [ABSTRACT FROM AUTHOR]

Purpose: Epicardial fat is the adipose tissue between the serosal pericardial wall layer and the visceral layer. It is distributed mainly around the atrioventricular groove, atrial septum, ventricular septum and coronary arteries. Studies have shown that the density, thickness, volume and other characteristics of epicardial adipose tissue (EAT) are independently correlated with a variety of cardiovascular diseases. Given this association, the accurate determination of EAT volume is an essential aim of future research. Therefore, the purpose of this study was to establish a framework for fully automatic EAT segmentation and quantification in coronary computed tomography angiography (CCTA) scans. Methods: A set of 103 scans are randomly selected from our medical center. An automatic pipeline has been developed to segment and quantify the volume of EAT. First, a multi‐slice deep neural network is used to simultaneously segment the pericardium in multiple adjacent slices. Then a deformable model is employed to reduce false positive and negative regions in the segmented binary pericardial images. Finally, the pericardium mask is used to define the region of interest (ROI) and the threshold method is utilized to extract the pixels ranging from −175 Hounsfield units (HU) to −15 HU for the segmentation of EAT. Results: The Dice indices of the pericardial segmentation using the proposed method with respect to the manual delineation results of two radiology experts were 97.1% ± 0.7% and 96.9% ± 0.6%, respectively. The inter‐observer variability was also assessed, resulting in a Dice index of 97.0% ± 0.7%. For the EAT segmentation results, the Dice indices between the proposed method and the two radiology experts were 93.4% ± 1.5% and 93.3% ± 1.3%, respectively, and the same measurement between the experts themselves was 93.6% ± 1.9%. The Pearson's correlation coefficients between the EAT volumes computed from the results of the proposed method and the manual delineation by the two experts were 1.00 and 0.99 and the same coefficients between the experts was 0.99. Conclusions: This work describes the development of a fully automatic EAT segmentation and quantification method from CCTA scans and the results compare favorably with the assessments of two independent experts. The proposed method is also packaged with a graphical user interface which can be found at https://github.com/MountainAndMorning/EATSeg. [ABSTRACT FROM AUTHOR]

Objective: To explore the relation between diffusion-weighted and susceptibility weighted imaging (DWI-SWI) mismatch and collateral circulation or prognosis in patients with occluded M1 segments of middle cerebral artery (MCA). Methods: We enrolled 59 patients with MCA M1-segment occlusion for a retrospective review of baseline clinical and imaging data. As markers of circulatory collaterals, prominent laterality of posterior (PLPCA) and anterior (PLACA) cerebral arteries on magnetic resonance angiography (MRA) studies and a hyperintense vessel sign (HVS) on T2 fluid-attenuated inversion recovery (FLAIR) images were collectively scored. The extent of acute cerebral infarction was then quantified on DWI, using the Alberta Stroke Program Early CT Score (DWI-ASPECTS). Hypointensity vessel sign prominence (PVS) was also evaluated by SWI and similarly scored (SWI-ASPECT) to calculate DWI-SWI mismatch [(DWI-ASPECTS) – (SWI-ASPECTS)], ranging from −10 to 10 points. Results: DWI-SWI mismatch showed significant associations with PLPCA, PLACA, HVS prominence, and collective collateral scores (all, p < 0.05). National Institutes of Health Stroke Scale (NIHSS), DWI-SWI mismatch, and DWI-ASPECTS also differed significantly according to patient prognosis (good vs. poor) after MCA M1-segment occlusion (p < 0.05). In binary logistic regression analyses, NIHSS and DWI-SWI mismatch emerged as independent prognostic factors (p < 0.05). Conclusions: Collateral circulation may be an important aspect of DWI-SWI mismatch, which in this study correlated with prognostic outcomes of MCA M1-segment occlusion. [ABSTRACT FROM AUTHOR]

Background: This study was conducted to explore the risk factors of anterior circulation intracranial aneurysm rupture based on extracranial carotid artery (ECA) tortuosity. Methods: This retrospective study, conducted from January 1, 2017, to March 1, 2021, collected and reviewed the clinical and imaging data of 308 patients with anterior circulation intracranial aneurysm [133 (43.2%) patients in the ruptured aneurysm group; 175 (56.8%) patients in the unruptured aneurysm group]. Computed tomography angiography (CTA) of the head and neck was used to determine the ECA tortuosity (normal, simple tortuosity, kink, coil) and the morphologic parameters of the aneurysms. The relationship of aneurysm rupture to ECA tortuosity and the morphologic parameters were analyzed. Results: After univariate analysis, kink, angle of flow inflow (FA), aspect ratio (AR), aneurysm length (L), the distance from the tortuosity to the aneurysm (distance), and size ratio (SR) were significantly correlated with anterior circulation intracranial aneurysm rupture (p < 0.05). Spearman correlation analysis showed that ECA tortuosity was correlated with FA and SR (p < 0.05). Multiple logistic analyses showed that FA [odds ratio (OR), 1.013; 95% CI, 1.002–1.025], SR (OR, 1.521; 95% CI, 1.054–2.195), and kink (OR, 1.823; 95% CI, 1.074–3.096) were independently associated with aneurysm rupture. Conclusion: Study results suggest that FA, SR, and ECA kink were independent risk factors associated with anterior circulation intracranial aneurysm rupture. [ABSTRACT FROM AUTHOR]

Malignant cerebral edema (MCE) after an ischemic stroke results in a poor outcome or death. Early prediction of MCE helps to identify subjects that could benefit from a surgical decompressive craniectomy. Net water uptake (NWU) in an ischemic lesion is a predictor of MCE; however, CT perfusion and lesion segmentation are required. This paper proposes a new Image Patch-based Net Water Uptake (IP-NWU) procedure that only uses non-enhanced admission CT and does not need lesion segmentation. IP-NWU is calculated by comparing the density of ischemic and contralateral normal patches selected from the middle cerebral artery (MCA) area using standard reference images. We also compared IP-NWU with the Segmented Region-based NWU (SR-NWU) procedure in which segmented ischemic regions from follow-up CT images are overlaid onto admission images. Furthermore, IP-NWU and its combination with imaging features are used to construct predictive models of MCE with a radiomics approach. In total, 116 patients with an MCA infarction (39 with MCE and 77 without MCE) were included in the study. IP-NWU was significantly higher for patients with MCE than those without MCE (p < 0.05). IP-NWU can predict MCE with an AUC of 0.86. There was no significant difference between IP-NWU and SR-NWU, nor between their predictive efficacy for MCE. The inter-reader and interoperation agreement of IP-NWU was exceptional according to the Intraclass Correlation Coefficient (ICC) analysis (inter-reader: ICC = 0.92; interoperation: ICC = 0.95). By combining IP-NWU with imaging features through a random forest classifier, the radiomics model achieved the highest AUC (0.96). In summary, IP-NWU and radiomics models that combine IP-NWU with imaging features can precisely predict MCE using only admission non-enhanced CT images scanned within 24 h from onset. [ABSTRACT FROM AUTHOR]

In recent years, penetrating weapons have been used more and more to attack increasingly hard targets; therefore, the impact of such a penetrating process has increased to an extremely high level. As an important component of a fuze, the reliability of the ceramic capacitor in high-impact environments is key for the normal working of the fuze. In this paper, we found that a high-impact causes parameter drift of the multilayer ceramic capacitor (MLCC), which further causes the fuze to misfire. This paper mainly studies the internal mechanism of the MLCC's parameter drift during high impact. Firstly, transient physical phenomena, such as capacitance fluctuation and the leakage current increase of the ceramic capacitor under a high acceleration impact, were studied experimentally by a Machete hammer, revealing the relationship between the capacitance change, leakage current change, and acceleration under different working conditions. Secondly, a mechanical model of the ceramic capacitor is established to simulate the change in capacitance value, which shows that the main factor of the capacitance change is the deformation-derived change in the facing area between the electrodes. Lastly, an equivalent circuit model is established to simulate the change in the leakage current, which shows that the main factor of the leakage current change is the piezoelectric resistance of the ceramic dielectric. [ABSTRACT FROM AUTHOR]

Objective: To assess the suitability of deep medullary vein visibility in susceptibility weighted imaging—magnetic resonance imaging studies as a method for the diagnosis and evaluation of cerebral small vessel disease progression. Methods: A total of 92 patients with CSVD were enrolled and baseline clinical and imaging data were reviewed retrospectively. Neuroimaging biomarkers of CSVD including high-grade white matter hyperintensity (HWMH), cerebral microbleed (CMB), enlarged perivascular space (PVS), and lacunar infarct (LI) were identified and CSVD burden was calculated. Cases were grouped accordingly as mild, moderate, or severe. The DMV was divided into six segments according to the regional anatomy. The total DMV score (0–18) was calculated as the sum of the six individual segmental scores, which ranged from 0 to 3, for a semi-quantitative assessment of the DMV based on segmental continuity and visibility. Results: The DMV score was independently associated with the presence of HWMH, PVS, and LI (P < 0.05), but not with presence and absence of CMB (P > 0.05). Correlation between the DMV score and the CSVD burden was significant (P < 0.05) [OR 95% C.I., 1.227 (1.096–1.388)]. Conclusion: The DMV score was associated with the presence and severity of CSVD. [ABSTRACT FROM AUTHOR]

Energy absorbing materials are crucial for the protection of electronic devices in various applications. In particular, the protection of materials from multiple extreme mechanical impacts imposes stringent requirements on the characteristics of energy absorption and recoverability. In this paper, a novel design of composite material, elastic polymer-modified porous carbon, is proposed to meet such urgent requirements. At the micro level, the polymer fibers form an elastic skeleton in which porous carbon particles are enveloped. Due to such microstructure, the composite material exhibits excellent performance of energy absorption and recoverability simultaneously, which are validated via various experiments. Furthermore, the microphysical mechanism of its superior energy absorption characteristics is demonstrated theoretically. Additionally, the optimized mass proportions of the two composite phases are discussed. In general, this novel design of energy absorbing material improves the reliability of electronic devices and systems exposed to multiple extreme mechanical impacts. [ABSTRACT FROM AUTHOR]

Objective: To explore the relationships of asymmetric deep medullary veins (ADMV) to asymmetric cortical veins (ACV), leptomeningeal collaterals and prognosis in patients with occlusion of a large cerebral artery. Methods: Clinical and imaging data of 56 patients with occlusion of a large cerebral artery were collected and reviewed. We assessed the time delayed between stroke onset and MR imaging (within 24 h of stroke onset), extension of cerebral infarction using the Alberta stroke program early CT score based on diffusion-weighted imaging (ASPECTs). ADMV and ACV were assessed using susceptibility-weighted imaging. The presence of ADMV (ACV) was defined as deep medullary veins (cortical veins) of the affected hemisphere that were greater in number and diameter than in the contralateral hemisphere. To evaluate leptomeningeal collaterals, the hyperintense vessel sign (HVS) was detected using T2 weighted fluid attenuated inversion recovery images. At 90 days, a modified Rankin scale score (mRS) was assessed to evaluate the clinical outcome. Results: Of 56 patients, 27 presented with ADMV. Those patients who presented with and without ADMV differed significantly in HVS and ACV (P < 0.05) but not in time delayed between stroke onset and MR imaging, age, gender, stroke risk factors, baseline NIHSS score, or modified Rankin scale score at 3 months (P > 0.05). Logistic regression analysis found that the presence of ADMV was independently related to HVS and ACV (ACV: OR 95% C.I., 1.287–4.368; HVS: OR 95% C.I., 1.132–4.887). Conclusions: The presence of ADMV on SWI was associated with prominent ACV and good leptomeningeal collateral flow but was not related to prognosis in patients with occlusion of a large cerebral artery. [ABSTRACT FROM AUTHOR]

As a critical step in the transition to a low-carbon economy, smart meters are being rolled out in large numbers throughout the world. To date, however, no systematic review of the development and implementation of smart meters have been published, so for maximizing the benefit of the benefit of smart meter, this study has been carried out to identify the main concepts and issues of promoting smart metering and related service system. Including key physical components and communication networks of smart meters compared to conventional meters, various policies to support smart meter roll-out and potential benefits to the participators. Challenges and issues involved in the deployment of smart meter infrastructure, such as technical, commercial and ethical debates, are also outlined. Results suggest that existing studies predominately focus on energy efficiency and demand management, while end-user engagement with smart metering systems, especially consumers' social, economic and behavioural aspects, are rarely considered. Areas of future research should include consideration of privacy and security concerns of end-users, and the necessary learning/educating processes and behavioural change of end-users to maximize the benefit of smart metering systems. [ABSTRACT FROM AUTHOR]

Analysis of the morphology and function of the right ventricle (RV) can be used for the prediction and diagnosis of cardiovascular disease. Accurate description of the structure and function of heart can be provided by analyzing cardiac magnetic resonance imaging (MRI) images. Noise interference and intensity inhomogeneity of MRI images can be addressed by using a local intensity clustering (LIC) model. However, the segmentation of the RV in MRI images still remains a challenge mainly due to its ill-defined borders. To address such a challenge, an algorithm for segmenting the RV based on a local motion intensity clustering (LMIC) model is proposed in this paper. The LMIC model combines the LIC model with the motion intensity information, due to cardiac motion and blood flow. The motion intensity is calculated by using the Lucas Kanade optical flow method and utilized in the LMIC model as an energy parameter. Because the motion intensity of the RV region is stronger than other areas, the RV can be accurately segmented by this approach. Experimental results demonstrate that the LMIC model is able to address the challenge of the ill-defined RV borders in cardiac MRI images and improved RV segmentation accuracy over existing methods. [ABSTRACT FROM AUTHOR]

Background Deep medullary veins (DMVs) are a biomarker of severity and prognosis in patients with acute cerebral infarction. However, their clinical significance remains unclear in patients with transient ischemic attack (TIA). Purpose To determine whether prominent deep medullary veins (PDMVs) are a predictive biomarker for stroke risk after TIA. Material and Methods Clinical and imaging data of 49 patients with TIA and 49 sex- and age-matched controls were studied. PDMVs were defined as DMVs with a score of 3 (TDMVs) or asymmetric DMVs (ADMVs), and the relationship between PDMVs and clinical features was analyzed. The DMV score based on susceptibility weighted imaging (SWI) ranged from 0 (not visible) to 3 (very prominent) and was calculated for both hemispheres separately. A different score in each hemisphere was defined as ADMVs and an equal score was defined as symmetric DMVs. The asymmetry and score of DMVs were compared between the two groups and with respect to the time from TIA onset to imaging analysis. Results Agreement between neuroradiologists for the DMV asymmetry/score on SWI was excellent. The frequency of ADMVs and TDMVs was significantly higher in patients with TIA than controls ( P < 0.05). The patients showed no correlation between the time from TIA onset to imaging and the DMV asymmetry/score ( P > 0.05); PDMVs were not correlated with age, blood pressure, or diabetes. However, PDMVs were associated with the ABCD2 score (≥4), clinical symptoms, and duration of TIA (≥10 min). Conclusion Prominent deep medullary veins is a predictive biomarker for the risk of stroke in many patients having suffered from TIA. [ABSTRACT FROM AUTHOR]

Pathological image enhancement is a significant topic in the field of pathological image processing. This paper proposes a high dynamic range (HDR) pathological image enhancement method based on improved bias field correction and guided image filter (GIF). Firstly, a preprocessing including stain normalization and wavelet denoising is performed for Haematoxylin and Eosin (H and E) stained pathological image. Then, an improved bias field correction model is developed to enhance the influence of light for high-frequency part in image and correct the intensity inhomogeneity and detail discontinuity of image. Next, HDR pathological image is generated based on least square method using low dynamic range (LDR) image, H and E channel images. Finally, the fine enhanced image is acquired after the detail enhancement process. Experiments with 140 pathological images demonstrate the performance advantages of our proposed method as compared with related work. [ABSTRACT FROM AUTHOR]

Projection and back-projection are the most computationally intensive parts in Computed Tomography (CT) reconstruction, and are essential to acceleration of CT reconstruction algorithms. Compared to back-projection, parallelization efficiency in projection is highly limited by racing condition and thread unsynchronization. In this paper, a strategy of Fixed Sampling Number Projection (FSNP) is proposed to ensure the operation synchronization in the ray-driven projection with Graphical Processing Unit (GPU). Texture fetching is also used utilized to further accelerate the interpolations in both projection and back-projection. We validate the performance of this FSNP approach using both simulated and real cone-beam CT data. Experimental results show that compare to the conventional approach, the proposed FSNP method together with texture fetching is 10~16 times faster than the conventional approach based on global memory, and thus leads to more efficient iterative algorithm in CT reconstruction. [ABSTRACT FROM AUTHOR]

Low dose CT (LDCT) images are often significantly degraded by severely increased mottled noise/artifacts, which can lead to lowered diagnostic accuracy in clinic. The nonlocal means (NLM) filtering can effectively remove mottled noise/artifacts by utilizing large-scale patch similarity information in LDCT images. But the NLM filtering application in LDCT imaging also requires high computation cost because intensive patch similarity calculation within a large searching window is often required to be used to include enough structure-similarity information for noise/artifact suppression. To improve its clinical feasibility, in this study we further optimize the parallelization of NLM filtering by avoiding the repeated computation with the row-wise intensity calculation and the symmetry weight calculation. The shared memory with fast I/O speed is also used in row-wise intensity calculation for the proposed method. Quantitative experiment demonstrates that significant acceleration can be achieved with respect to the traditional straight pixel-wise parallelization. [ABSTRACT FROM AUTHOR]

Median type filters coupled with the Laplacian distribution assumption have shown a high efficiency in suppressing impulse noise. We however demonstrate in this paper that the Gaussian distribution assumption is more preferable than Laplacian distribution assumption in suppressing impulse noise, especially for high noise densities. This conclusion is supported by numerical experiments with different noise densities and filter models. [ABSTRACT FROM AUTHOR]

This paper proposes a set of criteria for evaluation of serious games (SGs) which are intended as effective methods of engaging energy users and lowering consumption. We discuss opportunities for using SGs in energy research which go beyond existing feedback mechanisms, including use of immersive virtual worlds for learning and testing behaviours, and sparking conversations within households. From a review of existing SG evaluation criteria, we define a tailored set of criteria for energy SG development and evaluation. The criteria emphasise the need for the game to increase energy literacy through applicability to real-life energy use/management; clear, actionable goals and feedback; ways of comparing usage socially and personal relevance. Three existing energy games are evaluated according to this framework. The paper concludes by outlining directions for future development of SGs as an effective tool in social science research, including games which inspire reflection on trade-offs and usage at different scales. [ABSTRACT FROM PUBLISHER]