Vascular Segments Research Articles

Synthetic OCT-A blood vessel maps using fundus images and generative adversarial networks

Vessel segmentation in fundus images permits understanding retinal diseases and computing image-based biomarkers. However, manual vessel segmentation is a time-consuming process. Optical coherence tomography angiography (OCT-A) allows direct, non-invasive estimation of retinal vessels. Unfortunately, compared to fundus images, OCT-A cameras are more expensive, less portable, and have a reduced field of view. We present an automated strategy relying on generative adversarial networks to create vascular maps from fundus images without training using manual vessel segmentation maps. Further post-processing used for standard en face OCT-A allows obtaining a vessel segmentation map. We compare our approach to state-of-the-art vessel segmentation algorithms trained on manual vessel segmentation maps and vessel segmentations derived from OCT-A. We evaluate them from an automatic vascular segmentation perspective and as vessel density estimators, i.e., the most common imaging biomarker for OCT-A used in studies. Using OCT-A as a training target over manual vessel delineations yields improved vascular maps for the optic disc area and compares to the best-performing vessel segmentation algorithm in the macular region. This technique could reduce the cost and effort incurred when training vessel segmentation algorithms. To incentivize research in this field, we will make the dataset publicly available to the scientific community.

A Straightforward Bifurcation Pattern-Based Fundus Image Registration Method.

Fundus image registration is crucial in eye disease examination, as it enables the alignment of overlapping fundus images, facilitating a comprehensive assessment of conditions like diabetic retinopathy, where a single image's limited field of view might be insufficient. By combining multiple images, the field of view for retinal analysis is extended, and resolution is enhanced through super-resolution imaging. Moreover, this method facilitates patient follow-up through longitudinal studies. This paper proposes a straightforward method for fundus image registration based on bifurcations, which serve as prominent landmarks. The approach aims to establish a baseline for fundus image registration using these landmarks as feature points, addressing the current challenge of validation in this field. The proposed approach involves the use of a robust vascular tree segmentation method to detect feature points within a specified range. The method involves coarse vessel segmentation to analyze patterns in the skeleton of the segmentation foreground, followed by feature description based on the generation of a histogram of oriented gradients and determination of image relation through a transformation matrix. Image blending produces a seamless registered image. Evaluation on the FIRE dataset using registration error as the key parameter for accuracy demonstrates the method's effectiveness. The results show the superior performance of the proposed method compared to other techniques using vessel-based feature extraction or partially based on SURF, achieving an area under the curve of 0.526 for the entire FIRE dataset.

Extreme coronary radiation doses from intravascular brachytherapy

PurposeTo evaluate coronary artery integrity after very high radiation doses from intravascular brachytherapy (IVBT) in the setting of source asymmetry. MethodsTen patients treated for right coronary artery (RCA) in-stent restenosis (ISR) between 2017 and 2021 and for whom follow-up angiograms were available were identified from departmental records. Procedural angiograms, taken to document source position, were used to estimate vascular wall doses. The 2.5 mm proximal source marker was used to estimate the distance from source center to the media and adventitia. Distances were converted to dose (Gy) using the manufacturers' dose fall-off table, measured in water. Follow-up films were scrutinized for any sign of late vascular damage. ResultsThe average minimal distance from catheter center to the adjacent media and the adventitia was 0.9 mm (±0.2) mm and 1.4 mm (±0.2), respectively. The average maximum media and adventitial doses adjacent to the source were 75 Gy (±26) and 39 Gy (±14), respectively. Follow-up angiograms were available from 0.6 years to 3.9 years following IVBT (median: 1.6 years). No IVBT-treated vascular segment showed signs of degeneration, dissection or aneurysm. ConclusionIVBT vascular wall doses are frequently far higher than prescribed. The lack of complications in this unselected group of patients gives a modicum of reassurance that raising the prescription dose is unlikely to lead to a sudden appearance of complications.

Early results of endovascular treatment using percutaneous vacuum-assisted thromboaspiration in acute lower limb ischemia

Introduction. Open balloon thrombectomy and embolectomy remain the preferred initial option in the management of acute lower limb ischemia (ALI), but various endovascular techniques have become accessible and are growing in popularity. The aim of the study was to assess our early experience with percutaneous vacuum-assisted thromboaspiration using the Penumbra/Indigo® system for non-traumatic ALI. Material and methods. The study group comprised 13 patients with ALI who received treatment between September 2022 and June 2023; with 7 (53.8%) being males. The median age was 71 years (25%-75%IQR 62.5-77.5). ALI cases were classified according to the Rutherford scale: grade I – 2 (15.3%), grade IIA – 7 (53.8%), and grade IIB – 4 (30.7%). In 10 (76.9%) cases, ischemia was classified as “acute-on-chronic.” The occluded native vascular segment, determined through preoperative computer tomography angiography (n=4; 30.7%), duplex scanning (n=5; 38.4%), or both examinations (n=4; 30.7%), were as follows: superficial femoral artery (n=7) and popliteal artery (n=2). In two patients, thrombosis of the below-knee femoropopliteal bypass with autogenous vein was identified, while two others presented with femoral artery stent thrombosis. An embolic etiology of ALI was observed in 4 (30.7%) cases, and thrombotic etiology in 9 (69.2%) cases. Endovascular access was established via the ipsilateral common femoral artery (n=10), crossover (n=2), or brachial artery (n=1). Thromboaspiration was carried out using dedicated CAT6™ and CAT8™ catheters. Results. The technical success rate of vacuum-assisted thromboaspiration was 92.3%. Subsequent angiography revealed accompanying occlusive-stenotic lesions in all instances, necessitating transluminal angioplasty, and in 8 (61.5%) patients, additional stenting was required. Catheter-directed thrombolysis was utilized as an adjunct in one patient. There were 2 (15.3%) instances of distal embolization, both addressed within the same surgical session. Survival and limb salvage rates at the 30th-day follow-up stood at 100%. Conclusions. Utilizing the Penumbra/Indigo® system, percutaneous vacuum-assisted thromboaspiration appears to be a safe and effective minimally invasive technique for treating ALI. This method allows for the concurrent correction of coexisting chronic peripheral arterial lesions.

CCS-UNet: a cross-channel spatial attention model for accurate retinal vessel segmentation.

Precise segmentation of retinal vessels plays an important role in computer-assisted diagnosis. Deep learning models have been applied to retinal vessel segmentation, but the efficacy is limited by the significant scale variation of vascular structures and the intricate background of retinal images. This paper supposes a cross-channel spatial attention U-Net (CCS-UNet) for accurate retinal vessel segmentation. In comparison to other models based on U-Net, our model employes a ResNeSt block for the encoder-decoder architecture. The block has a multi-branch structure that enables the model to extract more diverse vascular features. It facilitates weight distribution across channels through the incorporation of soft attention, which effectively aggregates contextual information in vascular images. Furthermore, we suppose an attention mechanism within the skip connection. This mechanism serves to enhance feature integration across various layers, thereby mitigating the degradation of effective information. It helps acquire cross-channel information and enhance the localization of regions of interest, ultimately leading to improved recognition of vascular structures. In addition, the feature fusion module (FFM) module is used to provide semantic information for a more refined vascular segmentation map. We evaluated CCS-UNet based on five benchmark retinal image datasets, DRIVE, CHASEDB1, STARE, IOSTAR and HRF. Our proposed method exhibits superior segmentation efficacy compared to other state-of-the-art techniques with a global accuracy of 0.9617/0.9806/0.9766/0.9786/0.9834 and AUC of 0.9863/0.9894/0.9938/0.9902/0.9855 on DRIVE, CHASEDB1, STARE, IOSTAR and HRF respectively. Ablation studies are also performed to evaluate the the relative contributions of different architectural components. Our proposed model is potential for diagnostic aid of retinal diseases.

Standardized Radiomics Analysis of Clinical Myocardial Perfusion Stress SPECT Images to Identify Coronary Artery Calcification.

Myocardial perfusion (MP) stress single-photon emission computed tomography (SPECT) is an established diagnostic test for patients suspected of coronary artery disease (CAD). Meanwhile, coronary artery calcification (CAC) scoring obtained from diagnostic CT is a highly sensitive test, offering incremental diagnostic information in identifying patients with significant CAD yet normal MP stress SPECT (MPSS) scans. However, after decades of wide utilization of MPSS, CAC is not commonly reimbursed (e.g. by the CMS), nor widely deployed in community settings. We studied the potential of complementary information deduced from the radiomics analysis of normal MPSS scans in predicting the CAC score. We collected data from 428 patients with normal (non-ischemic) MPSS (99mTc-sestamibi; consensus reading). A nuclear medicine physician verified iteratively reconstructed images (attenuation-corrected) to be free from fixed perfusion defects and artifactual attenuation. Three-dimensional images were automatically segmented into four regions of interest (ROIs), including myocardium and three vascular segments (left anterior descending [LAD]-left circumference [LCX]-right coronary artery [RCA]). We used our software package, standardized environment for radiomics analysis (SERA), to extract 487 radiomic features in compliance with the image biomarker standardization initiative (IBSI). Isotropic cubic voxels were discretized using fixed bin-number discretization (eight schemes). We first performed blind-to-outcome feature selection focusing on a priori usefulness, dynamic range, and redundancy of features. Subsequently, we performed univariate and multivariate machine learning analyses to predict CAC scores from i) selected radiomic features, ii) 10 clinical features, and iii) combined radiomics + clinical features. Univariate analysis invoked Spearman correlation with Benjamini-Hotchberg false-discovery correction. The multivariate analysis incorporated stepwise linear regression, where we randomly selected a 15% test set and divided the other 85% of data into 70% training and 30% validation sets. Training started from a constant (intercept) model, iteratively adding/removing features (stepwise regression), invoking the Akaike information criterion (AIC) to discourage overfitting. Validation was run similarly, except that the training output model was used as the initial model. We randomized training/validation sets 20 times, selecting the best model using log-likelihood for evaluation in the test set. Assessment in the test set was performed thoroughly by running the entire operation 50 times, subsequently employing Fisher's method to verify the significance of independent tests. Unsupervised feature selection significantly reduced 8×487 features to 56. In univariate analysis, no feature survived the false-discovery rate (FDR) to directly correlate with CAC scores. Applying Fisher's method to the multivariate regression results demonstrated combining radiomics with the clinical features to enhance the significance of the prediction model across all cardiac segments. Conclusions: Our standardized and statistically robust multivariate analysis demonstrated significant prediction of the CAC score for all cardiac segments when combining MPSS radiomic features with clinical features, suggesting radiomics analysis can add diagnostic or prognostic value to standard MPSS for wide clinical usage.

Association of retinal microvascular density and complexity with incident coronary heart disease

Background and aimsThe high mortality rate and huge disease burden of coronary heart disease (CHD) highlight the importance of its early detection and timely intervention. Given the non-invasive nature of fundus photography and recent development in the quantification of retinal microvascular parameters with deep learning techniques, our study aims to investigate the association between incident CHD and retinal microvascular parameters. MethodsUK Biobanks participants with gradable fundus images and without a history of diagnosed CHD at recruitment were included for analysis. A fully automated artificial intelligence system was used to extract quantitative measurements that represent the density and complexity of the retinal microvasculature, including fractal dimension (Df), number of vascular segments (NS), vascular skeleton density (VSD) and vascular area density (VAD). ResultsA total of 57,947 participants (mean age 55.6 ± 8.1 years; 56% female) without a history of diagnosed CHD were included. During a median follow-up of 11.0 (interquartile range, 10.88 to 11.19) years, 3211 incident CHD events occurred. In multivariable Cox proportional hazards models, we found decreasing Df (adjusted HR = 0.80, 95% CI, 0.65–0.98, p = 0.033), lower NS of arteries (adjusted HR = 0.69, 95% CI, 0.54–0.88, p = 0.002) and venules (adjusted HR = 0.77, 95% CI, 0.61–0.97, p = 0.024), and reduced arterial VSD (adjusted HR = 0.72, 95% CI, 0.57–0.91, p = 0.007) and venous VSD (adjusted HR = 0.78, 95% CI, 0.62–0.98, p = 0.034) were related to an increased risk of incident CHD. ConclusionsOur study revealed a significant association between retinal microvascular parameters and incident CHD. As the lower complexity and density of the retinal vascular network may indicate an increased risk of incident CHD, this may empower its prediction with the quantitative measurements of retinal structure.

Cerebral AVM segmentation from 3D rotational angiography images by convolutional neural networks

Background and objective3D rotational angiography (3DRA) provides high quality images of the cerebral arteriovenous malformation (AVM) nidus that can be reconstructed in 3D. However, these reconstructions are limited to only 3D visualization without possible interactive exploration of geometric characteristics of cerebral structures. Refined understanding of the AVM angioarchitecture prior to treatment is mandatory and vascular segmentation is an important preliminary step that allow physicians analyze the complex vascular networks and can help guide microcatheters navigation and embolization of AVM. MethodsA deep learning method was developed for the segmentation of 3DRA images of AVM patients. The method uses a fully convolutional neural network with a U-Net-like architecture and a DenseNet backbone. A compound loss function, combining Cross Entropy and Focal Tversky, is employed for robust segmentation. Binary masks automatically generated from region-growing segmentation have been used to train and validate our model. ResultsThe developed network was able to achieve the segmentation of the vessels and the malformation and significantly outperformed the region-growing algorithm. Our experiments were performed on 9 AVM patients. The trained network achieved a Dice Similarity Coefficient (DSC) of 80.43%, surpassing other U-Net like architectures and the region-growing algorithm on the manually approved test set by physicians. ConclusionsThis work demonstrates the potential of a learning-based segmentation method for characterizing very complex and tiny vascular structures even when the training phase is performed with the results of an automatic or a semi-automatic method. The proposed method can contribute to the planning and guidance of endovascular procedures.

Diagnostic Ability of Perivascular Fat Attenuation Index in Predicting Atherosclerotic Plaque Formation Proximal to Myocardial Bridging of the Left Anterior Descending Artery within 3 Years

This study was designed to investigate the association between the perivascular fat attenuation index (FAI) and atherosclerotic plaque formation proximal to myocardial bridging (MB) of the left anterior descending artery (LAD) within 3 years. LAD-MB patients who underwent coronary computed tomography angiographyat least twice between January 2016 and December 2022 were retrospectively included in this study. In total, 99 LAD-MB patients were included in the study. Based on the formation of atherosclerotic plaques proximal to LAD-MB during follow-up, the patients were classified into two groups: LAD-MB with plaque formation and LAD-MB without plaque formation within 3 years. The anatomical features, clinical factors, and proximal perivascular FAI of LAD-MB were measured and recorded. The association of the previously mentioned factors with the presence of atherosclerotic plaque proximal to LAD-MB was determined. The results showed that MB length, MB stenosis, and the perivascular FAI were significant predictors of the formation of atherosclerotic plaques proximal to LAD-MB. The area under the curveof the combined predictive model incorporating MB length, MB stenosis, and the perivascular FAI was 0.901(95% confidence interval: 0.824-0.952), with higher diagnostic performance than any other single parameter (all P<0.05). Moreover, dynamic changes in the perivascular FAI of the vascular segments proximal to LAD-MB were higher in high-risk plaques than in non-high-risk plaques (P=0.003). The combined use of the perivascular FAI, MB length, and MB stenosis may enable prediction of the probability of atherosclerotic plaque formation proximal to LAD-MB within 3 years. Dynamic changes in perivascular FAI were associated with the vulnerability of plaques proximal to LAD-MB.

Robust vessel segmentation in laser speckle contrast images based on semi-weakly supervised learning

Objective. The goal of this study is to develop a robust semi-weakly supervised learning strategy for vessel segmentation in laser speckle contrast imaging (LSCI), addressing the challenges associated with the low signal-to-noise ratio, small vessel size, and irregular vascular aberration in diseased regions, while improving the performance and robustness of the segmentation method. Approach. For the training dataset, the healthy vascular images denoted as normal-vessel samples were manually labeled, while the diseased LSCI images involving tumor or embolism were denoted as abnormal-vessel samples and annotated as pseudo labels by the traditional semantic segmentation methods. In the training phase, the pseudo labels were constantly updated to improve the segmentation accuracy based on DeepLabv3+. Objective evaluation was conducted on the normal-vessel test set, while subjective evaluation was performed on the abnormal-vessel test set. Main results. The proposed method achieved an IOU of 0.8671, a Dice of 0.9288, and a mean relative percentage difference (mRPD) with supervised learning of 0.5% in the objective evaluation. In the subjective evaluation, our method significantly outperformed other methods in main vessel segmentation, tiny vessel segmentation, and blood vessel connection. Additionally, our method exhibited robustness when abnormal-vessel style noise was added to normal-vessel samples using a style translation network. Significance. The proposed semi-weakly supervised learning strategy demonstrates high efficiency and excellent robustness for vascular segmentation in LSCI, providing a potential tool for assessing the morphological and structural features of vessels in clinical applications.

Hemodynamic Investigation of the Flow Diverter Treatment of Intracranial Aneurysm

Flow diverter stents (FDS) are increasingly used for the treatment of complex intracranial aneurysms such as fusiform, giant, or wide-neck aneurysms. The primary goal of these devices is to reconstruct the diseased vascular segment by diverting blood flow from the aneurysm. The resulting intra-aneurysmal flow reduction promotes progressive aneurysm thrombosis and healing of the disease. In the present study, a numerical investigation was performed for modeling blood flow inside a patient-specific intracranial aneurysm virtually treated with FDS. The aim of the study is to investigate the effects of FDS placement prior to the actual endovascular treatment and to compare the effectiveness of devices differing in porosity. Numerical simulations were performed under pulsatile flow conditions, taking into account the non-Newtonian behavior of blood. Two possible post-operative conditions with virtual stent deployment were simulated. Hemodynamic parameters were calculated and compared between the pre-operative (no stent placement) and post-operative (virtual stent placement) aneurysm models. FDS placement significantly reduced intra-aneurysmal flow velocity and increased the Relative Residence Time (RRT) on the aneurysm, thus promoting thrombus formation within the dilatation and aneurysm occlusion. The results highlighted an increase in the effectiveness of FDS as its porosity increased. The proposed analysis provides pre-operative knowledge on the impact of FDS on intracranial hemodynamics, allowing the selection of the most effective treatment for the specific patient.

Relationships between quantitative retinal microvascular characteristics and cognitive function based on automated artificial intelligence measurements.

Introduction: The purpose of this study is to assess the relationship between retinal vascular characteristics and cognitive function using artificial intelligence techniques to obtain fully automated quantitative measurements of retinal vascular morphological parameters. Methods: A deep learning-based semantic segmentation network ResNet101-UNet was used to construct a vascular segmentation model for fully automated quantitative measurement of retinal vascular parameters on fundus photographs. Retinal photographs centered on the optic disc of 3107 participants (aged 50-93years) from the Beijing Eye Study 2011, a population-based cross-sectional study, were analyzed. The main parameters included the retinal vascular branching angle, vascular fractal dimension, vascular diameter, vascular tortuosity, and vascular density. Cognitive function was assessed using the Mini-Mental State Examination (MMSE). Results: The results showed that the mean MMSE score was 26.34 ± 3.64 (median: 27; range: 2-30). Among the participants, 414 (13.3%) were classified as having cognitive impairment (MMSE score < 24), 296 (9.5%) were classified as mild cognitive impairment (MMSE: 19-23), 98 (3.2%) were classified as moderate cognitive impairment (MMSE: 10-18), and 20 (0.6%) were classified as severe cognitive impairment (MMSE < 10). Compared with the normal cognitive function group, the retinal venular average diameter was significantly larger (p = 0.013), and the retinal vascular fractal dimension and vascular density were significantly smaller (both p < 0.001) in the mild cognitive impairment group. The retinal arteriole-to-venular ratio (p = 0.003) and vascular fractal dimension (p = 0.033) were significantly decreased in the severe cognitive impairment group compared to the mild cognitive impairment group. In the multivariate analysis, better cognition (i.e., higher MMSE score) was significantly associated with higher retinal vascular fractal dimension (b = 0.134, p = 0.043) and higher retinal vascular density (b = 0.152, p = 0.023) after adjustment for age, best corrected visual acuity (BCVA) (logMAR) and education level. Discussion: In conclusion, our findings derived from an artificial intelligence-based fully automated retinal vascular parameter measurement method showed that several retinal vascular morphological parameters were correlated with cognitive impairment. The decrease in retinal vascular fractal dimension and decreased vascular density may serve as candidate biomarkers for early identification of cognitive impairment. The observed reduction in the retinal arteriole-to-venular ratio occurs in the late stages of cognitive impairment.

Retinal Vascular Image Segmentation Using Improved UNet Based on Residual Module.

In recent years, deep learning technology for clinical diagnosis has progressed considerably, and the value of medical imaging continues to increase. In the past, clinicians evaluated medical images according to their individual expertise. In contrast, the application of artificial intelligence technology for automatic analysis and diagnostic assistance to support clinicians in evaluating medical information more efficiently has become an important trend. In this study, we propose a machine learning architecture designed to segment images of retinal blood vessels based on an improved U-Net neural network model. The proposed model incorporates a residual module to extract features more effectively, and includes a full-scale skip connection to combine low level details with high-level features at different scales. The results of an experimental evaluation show that the model was able to segment images of retinal vessels accurately. The proposed method also outperformed several existing models on the benchmark datasets DRIVE and ROSE, including U-Net, ResUNet, U-Net3+, ResUNet++, and CaraNet.

End-to-End 3D Liver CT Image Synthesis from Vasculature Using a Multi-Task Conditional Generative Adversarial Network

Acquiring relevant, high-quality, and heterogeneous medical images is essential in various types of automated analysis, used for a variety of downstream data augmentation tasks. However, a large number of real image samples are expensive to obtain, especially for 3D medical images. Therefore, there is an urgent need to synthesize realistic 3D medical images. However, the existing generator models have poor stability and lack the guidance of prior medical knowledge. To this end, we propose a multi-task (i.e., segmentation task and generation task) 3D generative adversarial network (GAN) for the synthesis of 3D liver CT images (3DMT-GAN). To the best of our knowledge, this is the first application for a 3D liver CT image synthesis task. Specifically, we utilize a mask of vascular segmentation as the input because it contains structural information about a variety of rich anatomical structures. We use the semantic mask of the liver as prior medical knowledge to guide the 3D CT image generation, reducing the calculation of a large number of backgrounds, thus making the model more focused on the generation of the region of the liver. In addition, we introduce a stable multiple gradient descent algorithm (MGDA) reconstruction method into our model to balance the weights of the multi-task framework. Experiments were conducted on a real dataset, and the experimental results show that the segmentation task achieves a Dice similarity coefficient (DSC) of 0.87, while the synthesis task outperforms existing state-of-the-art methods. This study demonstrates the feasibility of using vascular images to synthesize images of the liver.

Klotho inversely relates with carotid intima- media thickness in atherosclerotic patients with normal renal function (eGFR ≥60 mL/min/1.73m2): a proof-of-concept study.

Klotho protein is predominantly expressed in the kidneys and has also been detected in vascular tissue and peripheral blood circulating cells to a lesser extent. Carotid artery intima-media thickness (CIMT) burden, a marker of subclinical atherosclerosis, has been associated with reductions in circulating Klotho levels in chronic kidney disease patients, who show reduced levels of this protein at all stages of the disease. However, the contribution of serum Klotho and its expression levels in peripheral blood circulating cells and in the carotid artery wall on the CIMT in the absence of kidney impairment has not yet been evaluated. We conducted a single-center study in 35 atherosclerotic patients with preserved kidney function (eGFR≥60 mL/min/1.73m2) subjected to elective carotid surgery. Serum levels of Klotho and cytokines TNFa, IL6 and IL10 were determined by ELISA and transcripts encoding for Klotho (KL), TNF, IL6 and IL10 from vascular segments were measured by qRT-PCR. Klotho protein expression in the intima-media and adventitia areas was analyzed using immunohistochemistry. APatients with higher values of CIMT showed reduced Klotho levels in serum (430.8 [357.7-592.9] vs. 667.8 [632.5-712.9] pg/mL; p<0.001), mRNA expression in blood circulating cells and carotid artery wall (2.92 [2.06-4.8] vs. 3.69 [2.42-7.13] log.a.u., p=0.015; 0.41 [0.16-0.59] vs. 0.79 [0.37-1.4] log.a.u., p=0.013, respectively) and immunoreactivity in the intimal-medial area of the carotids (4.23 [4.15-4.27] vs. 4.49 [4.28-4.63] log µm2 p=0.008). CIMT was inversely related with Klotho levels in serum (r= -0.717, p<0.001), blood mRNA expression (r=-0.426, p=0.011), and with carotid artery mRNA and immunoreactivity levels (r= -0.45, p=0.07; r= -0.455, p= 0.006, respectively). Multivariate analysis showed that serum Klotho, together with the gene expression levels of tumor necrosis factor TNFa in blood circulating cells, were independent determinants of CIMT values (adjusted R2 = 0.593, p<0.001). The results of this study in subjects with eGFR≥60mL/min/1.73m2 show that patients with carotid artery atherosclerosis and higher values of CIMT present reduced soluble Klotho levels, as well as decreased KL mRNA expression in peripheral blood circulating cells and Klotho protein levels in the intima-media of the carotid artery wall.

5-HT7 receptors mediate dilation of rat cremaster muscle arterioles in vivo.

Serotonin (5-HT) infusion in vivo causes hypotension and a fall in total peripheral resistance. However, the vascular segment and the receptors that mediate this response remain in question. We hypothesized that 5-HT7 receptors mediate arteriolar dilation to 5-HT in skeletal muscle microcirculation. Cremaster muscles of isoflurane-anesthetized male Sprague-Dawley rats were prepared for in vivo microscopy of third- and fourth-order arterioles and superfused with physiological salt solution at 34°C. Quantitative real-time PCR (RT-PCR) was applied to pooled samples of first- to third-order cremaster arterioles (2-4 rats/sample) to evaluate 5-HT7 receptor expression. Topical 5-HT (1-10 nmols) or the 5-HT1/7 receptor agonist, 5-carboxamidotryptamine (10-30 nM), dilated third- and fourth-order arterioles, responses that were abolished by 1 μM SB269970, a selective 5-HT7 receptor antagonist. In contrast, dilation induced by the muscarinic agonist, methacholine (100 nmols) was not inhibited by SB269970. Serotonin (10 nmols) failed to dilate cremaster arterioles in 5-HT7 receptor knockout rats whereas arterioles in wild-type litter mates dilated to 1 nmol 5-HT, a response blocked by 1 μM SB269970. Quantitative RT-PCR revealed that cremaster arterioles expressed mRNA for 5-HT7 receptors. 5-HT7 receptors mediate dilation of small arterioles in skeletal muscle and likely contribute to 5-HT-induced hypotension, in vivo.

Is spontaneous coronary artery dissection (SCAD) related to local anatomy and hemodynamics? An exploratory study

AimsSpontaneous coronary artery dissection (SCAD) is an increasingly diagnosed cause of myocardial infarction with unclear pathophysiology. The aim of the study was to test if vascular segments site of SCAD present distinctive local anatomy and hemodynamic profiles. MethodsCoronary arteries with spontaneously healed SCAD (confirmed by follow-up angiography) underwent three-dimensional reconstruction, morphometric analysis with definition of vessel local curvature and torsion, and computational fluid dynamics (CFD) simulations with derivation of time-averaged wall shear stress (TAWSS) and topological shear variation index (TSVI). The (reconstructed) healed proximal SCAD segment was visually inspected for co-localization with curvature, torsion, and CFD-derived quantities hot spots. ResultsThirteen vessels with healed SCAD underwent the morpho-functional analysis. Median time between baseline and follow-up coronary angiograms was 57 (interquartile range [IQR] 45–95) days. In seven cases (53.8%), SCAD was classified as type 2b and occurred in the left anterior descending artery or near a bifurcation. In all cases (100%), at least one hot spot co-localized within the healed proximal SCAD segment, in 9 cases (69.2%) ≥ 3 hot spots were identified. Healed SCAD in proximity of a coronary bifurcation presented lower TAWSS peak values (6.65 [IQR 6.20–13.20] vs. 3.81 [2.53–5.17] Pa, p = 0.008) and hosted less frequently TSVI hot spots (100% vs. 57.1%, p = 0.034). ConclusionVascular segments of healed SCAD were characterized by high curvature/torsion and WSS profiles reflecting increased local flow disturbances. Hence, a pathophysiological role of the interaction between vessel anatomy and shear forces in SCAD is hypothesized.

Cerebrovascular segmentation from mesoscopic optical images using Swin Transformer

Vascular segmentation is a crucial task in biomedical image processing, which is significant for analyzing and modeling vascular networks under physiological and pathological states. With advances in fluorescent labeling and mesoscopic optical techniques, it has become possible to map the whole-mouse-brain vascular networks at capillary resolution. However, segmenting vessels from mesoscopic optical images is a challenging task. The problems, such as vascular signal discontinuities, vessel lumens, and background fluorescence signals in mesoscopic optical images, belong to global semantic information during vascular segmentation. Traditional vascular segmentation methods based on convolutional neural networks (CNNs) have been limited by their insufficient receptive fields, making it challenging to capture global semantic information of vessels and resulting in inaccurate segmentation results. Here, we propose SegVesseler, a vascular segmentation method based on Swin Transformer. SegVesseler adopts 3D Swin Transformer blocks to extract global contextual information in 3D images. This approach is able to maintain the connectivity and topology of blood vessels during segmentation. We evaluated the performance of our method on mouse cerebrovascular datasets generated from three different labeling and imaging modalities. The experimental results demonstrate that the segmentation effect of our method is significantly better than traditional CNNs and achieves state-of-the-art performance.

Oxygen-saturation-related functional parameter as a biomarker for diabetes mellitus-extraction method and clinical validation.

Purpose: To develop a computational method for oxygen-saturation-related functional parameter analysis of retinal vessels based on traditional color fundus photography, and to explore their characteristic alterations in type 2 diabetes mellitus (DM). Methods: 50 type 2 DM patients with no-clinically detectable retinopathy (NDR) and 50 healthy subjects were enrolled in the study. An optical density ratio (ODR) extraction algorithm based on the separation of oxygen-sensitive and oxygen-insensitive channels in color fundus photography was proposed. With precise vascular network segmentation and arteriovenous labeling, ODRs were acquired from different vascular subgroups, and the global ODR variability (ODRv) was calculated. Student's t-test was used to analyze the differences of the functional parameters between groups, and regression analysis and receiver operating characteristic (ROC) curves were used to explore the discrimination efficiency of DM patients from healthy subjects based on these functional parameters. Results: There was no significant difference in the baseline characteristics between the NDR and healthy normal groups. The ODRs of all vascular subgroups except the micro venule were significantly higher (, respectively) while ODRv was significantly lower () in NDR group than that in healthy normal group. In the regression analysis, the increased ODRs except micro venule and decreased ODRv were significantly correlated with the incidence of DM, and the C-statistic for discrimination DM with all ODR is 0.777 (95% CI 0.687-0.867, ). Conclusion: A computational method to extract the retinal vascular oxygen-saturation-related optical density ratios (ODRs) with single color fundus photography was developed, and increased ODRs and decreased ODRv of retinal vessels could be new potential image biomarkers of DM.

In vitro comparison of middle meningeal artery embolization with Squid liquid embolic agent and Contour polyvinyl alcohol particles

BackgroundLiquid embolic agents and polyvinyl alcohol (PVA) particles have been used for the embolization of the middle meningeal artery (MMA) for the treatment of chronic subdural hematomas. However, the vascular...