3D Vessel Research Articles

Detection of Dolichoectasia and Atherosclerosis by Automated MRA Tortuosity Metrics in a Population-Based Study.

Intracranial vessel tortuosity is a key component of dolichoectasia and has been associated with atherosclerosis and adverse neurologic outcomes. However, the evaluation of tortuosity is mainly a descriptive assessment. To compare the performance of three automated tortuosity metrics (angle metric [AM], distance metric [DM], and distance-to-axis metric [DTA]) for detection of dolichoectasia and presence of segment-specific plaques. Observational, cross-sectional metric assessment. 1899 adults from the general population; mean age = 76 years, female = 59%, and black = 29%. 3-T, three-dimensional (3D) time-of-flight MRA and 3D vessel wall MRI. Tortuosity metrics and mean luminal area were quantified for designated segments of the internal carotid artery, middle cerebral artery, anterior cerebral artery, posterior cerebral artery, vertebral artery, and entire length of basilar artery (BA). Qualitative interpretations ofBA dolichoectasia were assessed based on Smoker's visual criteria. Descriptive statistics (2-sample t-tests, Pearson chi-square tests) for group comparisons. Receiver operating characteristics area under the curve (AUC) for detection of BA dolichoectasia or segment-specific plaque. Model inputs included 1) tortuosity metrics, 2) mean luminal area, and 3) demographics (age, race, and sex). Qualitative dolichoectasia was identified in 336 (18%) participants, and atherosclerotic plaques were detected in 192 (10%) participants. AM-, DM-, and DTA-calculated tortuosity were good individual discriminators of basilar dolichoectasia (AUCs: 0.76, 0.74, and 0.75, respectively), with model performance improving with the mean lumen area: (AUCs: 0.88, 0.87, and 0.87, respectively). Combined characteristics (tortuosity and mean luminal area) identified plaques with better performance in the anterior (AUCs ranging from 0.66 to 0.78) than posterior (AUCs ranging from 0.54 to 0.65) circulation, with all models improving by the addition of demographics (AUCs ranging from 0.62 to 0.84). Quantitative vessel tortuosity metrics yield good diagnostic accuracy for the detection of dolichoectasia. 1 TECHNICAL EFFICACY STAGE: 2.

Observer-Based Control of a Microrobot Navigating within a 3D Blood Vessel along a Trajectory Delivered by a Joystick Device

In this paper, an observer-based state feedback control strategy for trajectory tracking of a magnetic microrobot navigating within a 3D blood vessel is proposed. The desired trajectory to be followed by the microrobot is generated by an operator using a joystick device. To deal with the significant effect of both external disturbances and parametric uncertainties, often encountered in biological environments, a state feedback stabilization, that enforces the output tracking despite any environmental disturbances, is developed. Then, for the purpose of implementation, a state observer is developed to recover the whole state from the measured position of the microrobot. The state feedback and observer gains are determined separately by solving a set of linear matrix inequalities derived in the framework of Lyapunov stability theory. Simulation runs are performed to demonstrate the performance of the proposed control strategy.

Comparison of free-running whole-heart 5D and 4D flow imaging to standard 2D phase contrast in patients with right-sided congenital heart disease

Abstract Funding Acknowledgements Type of funding sources: None. Background Flow measurements by cardiac magnetic resonance (CMR) are part of a routine assessment in patients with congenital heart disease (CHD). Standard 2D phase contrast (2DPC) sequences, however, require precise prescription of image planes leading to long scan times which could be overcome by 3D-acquisitons covering the entire cardiac anatomy in a single acquisition. Thus, recently introduced accelerated free-breathing 3D whole heart flow sequences promise to simplify CMR exams allowing an easier acquisition and retrospective flow measurements in virtually all vessels. Purpose Comparison of standard 2DPC to an accelerated 4D flow sequence with prospective respiratory navigation (1), and a free-running radial fully self-gated respiratory and cardiac motion-resolved 5D flow sequence (5D flow) (2,3). Methods Patients with right-sided CHD and no intra- or extracardiac shunt underwent a CMR on a 1.5T scanner. 2DPC flow sequences and a 4D flow data set were obtained (1). In addition, a whole-heart free-running 3D radial PC CMR sequence was acquired (2). Using a compressed-sensing-based image reconstruction framework (2,3), 5D flow images were obtained, and the end-expiratory phase was used for analysis. 3D vessel segmentation was based on retrospectively computed phase-contrast angiography images for each 4D and 5D flow datasets. Flow measured in the ascending, descending aorta (AA, DA), main, right and left pulmonary artery (MPA, RPA, LPA) and superior vena cava (SVC) as well as the internal consistency for determination of systemic and pulmonary output were compared between all three sequences by Bland-Altman analyses. Results Twenty CHD patients (age 31±11y, 7 women, tetralogy of Fallot N=7, Ross operation N=10; pulmonary regurgitation, atrial switch and Ebstein’s disease each N=1) were included. Mean peak velocity of MPA/ right ventricle to pulmonary artery conduit was 1.9 (range 0.9; 3.5) m/s. Compared to 2DPC, both 4D and 5D flow moderately underestimated absolute flow with significant lower values for the RPA for 4D flow (Table 1). The bias (i.e. the mean absolute difference) of MPA flow between 2DPC and 4D flow was related to the MPA peak velocity (r2=0.3, p = 0.03) with a similar trend for the comparison of 2DPC vs. 5D flow (r2=0.142, p = 0.141). The comparison of the internal consistency for determination of systemic and pulmonary output showed similar results for 2DPC and 5D flow and a slightly lower agreement for the 4D flow sequence (Table 2). Conclusions In this preliminary study, 4D and 5D flow provide results comparable to 2DPC, however, both sequences showed a trend towards lower absolute flow values. Complex flow patterns due to the right-sided pathologies appear to have an impact on the difference observed between sequences as shown for the MPA. The internal consistency of flow quantification was similar for the 2DPC and 5D flow sequences. The presented observations are currently being evaluated in a larger study.

Local Attention Transformer-Based Full-View Finger-Vein Identification

Multi-view finger-vein recognition technology has attracted increasing attentions in recent years. Despite recent advances in the multi-view finger-vein identification, existing solutions employ multiple monocular cameras from different views to record two-dimensional (2D) projections of 3D vein vessels, which causes the following problems: 1) 2D images collected from limited views (two or three views) are insufficient for robust 3D vein vessel feature representation. Furthermore, image sequences of the same finger acquired from different views usually show significant differences. As a result, the existing works are still sensitive to positional variations of the fingers, specifically those caused by finger roll movements. 2) Using multiple cameras can lead to increased costs. Moreover, it is impossible to employ several cameras to acquire full-view images because of the limited space on capturing devices. To address the above issues, we present FV-LT, a Full-View Finger-Vein identification system based on a Local attention Transformer, by implementing an image acquisition device with a single camera. First, we design and implement a finger-vein acquisition prototype device that utilizes a single camera and a LED group to rotate along a finger for full-view image collection. This allows capturing all vein patterns concealed beneath human skin to form a complete representation of finger features. Second, given the full-view vein images, we propose a local attention transformer-based approach to extract dependency features of a token (a patch or an image) on its neighborhood’s tokens among image patches and among full-view images, respectively. These dependency features are shown to be robust to positional variations induced by finger rolls. Based on the public database of full-view finger-vein images captured by our designed device, we verify the performance of the proposed FV-LT. The experimental results show that FV-LT significantly outperforms existing 2D/multi-view based approaches with respect to improving the tolerance against finger roll and achieving the state-of-the-art identification accuracy.

Realization of NIR-II 3D whole-body contour and tumor blood vessels imaging in small animals using rotational stereo vision technique.

Optical imaging in the second near-infrared (NIR-II, 1000 to 1700nm) region is capable of deep tumor vascular imaging due to low light scattering and low autofluorescence. Non-invasive real-time NIR-II fluorescence imaging is instrumental in monitoring tumor status. Our aim is to develop an NIR-II fluorescence rotational stereo imaging system for 360-deg three-dimensional (3D) imaging of whole-body blood vessels, tumor vessels, and 3D contour of mice. Our study combined an NIR-II camera with a 360-deg rotational stereovision technique for tumor vascular imaging and 3D surface contour for mice. Moreover, self-made NIR-II fluorescent polymer dots were applied in high-contrast NIR-II vascular imaging, along with a 3D blood vessel enhancement algorithm for acquiring high-resolution 3D blood vessel images. The system was validated with a custom-made 3D printing phantom and in vivo experiments of 4T1 tumor-bearing mice. The results showed that the NIR-II 3D 360-deg tumor blood vessels and mice contour could be reconstructed with 0.15mm spatial resolution, 0.3mm depth resolution, and 5mm imaging depth in an ex vivo experiment. The pioneering development of an NIR-II 3D 360-deg rotational stereo imaging system was first applied in small animal tumor blood vessel imaging and 3D surface contour imaging, demonstrating its capability of reconstructing tumor blood vessels and mice contour. Therefore, the 3D imaging system can be instrumental in monitoring tumor therapy effects.

Deep-learning-based 3D blood flow reconstruction in transmissive laser speckle imaging.

Transmissive laser speckle imaging (LSI) is useful for monitoring large field-of-view (FOV) blood flow in thick tissues. However, after longer transmissions, the contrast of the transmitted speckle images is more likely to be blurred by multiple scattering, resulting in decreased accuracy and spatial resolution of deep vessels. This study proposes a deep-learning-based strategy for high spatiotemporal resolution three-dimensional (3D) reconstruction from a single transilluminated laser speckle contrast image, providing more structural and functional details without multifocus two-dimensional (2D) imaging or 3D optical imaging with point/line scanning. Based on the correlation transfer equation, a large training dataset is generated by convolving vessel masks with depth-dependent point spread functions (PSF). The UNet and ResNet are used for deblurring and depth estimation. The blood flow in the reconstructed 3D vessels is estimated by a depth-dependent contrast model. The proposed method is evaluated with simulated data and phantom experiments, achieving high-fidelity structural reconstruction with a depth-independent estimation of blood flow. This fast 3D blood flow imaging technique is suitable for real-time monitoring of thick tissue and the diagnosis of vascular diseases.

3D vessel extraction using a scale-adaptive hybrid parametric tracker.

3D vessel extraction has great significance in the diagnosis of vascular diseases. However, accurate extraction of vessels from computed tomography angiography (CTA) data is challenging. For one thing, vessels in different body parts have a wide range of scales and large curvatures; for another, the intensity distributions of vessels in different CTA data vary considerably. Besides, surrounding interfering tissue, like bones or veins with similar intensity, also seriously affects vessel extraction. Considering all the above imaging and structural features of vessels, we propose a new scale-adaptive hybrid parametric tracker (SAHPT) to extract arbitrary vessels of different body parts. First, a geometry-intensity parametric model is constructed to calculate the geometry-intensity response. While geometry parameters are calculated to adapt to the variation in scale, intensity parameters can also be estimated to meet non-uniform intensity distributions. Then, a gradient parametric model is proposed to calculate the gradient response based on a multiscale symmetric normalized gradient filter which can effectively separate the target vessel from surrounding interfering tissue. Last, a hybrid parametric model that combines the geometry-intensity and gradient parametric models is constructed to evaluate how well it fits a local image patch. In the extraction process, a multipath spherical sampling strategy is used to solve the problem of anatomical complexity. We have conducted many quantitative experiments using the synthetic and clinical CTA data, asserting its superior performance compared to traditional or deep learning-based baselines.

Numerical investigation of the injection angle of carrier nanoparticles under the effect of different magnetic fields

Cancer is considered as one of the most leading causes of human mortality. There are different ways for treatment of cancer each has their own pros and cons. Magnetic drug targeting (MDT), among others, is one of the most promising methods for cancer treatment with minimum side effects. In the present study the effect of injection angle and injection rate of nanoparticles on the capture efficiency (CE) of nanoparticles in a 3D blood vessel. Three different magnetic sources have been considered, including permanent magnet, current carrying wire, and coil wire. The parameters considered in this study, include injection angle (30°<α<90°), and injection rate (0mm3.s-1≤Q̇inj≤320mm3.s-1). Regarding the injection site, two scenarios have been considered, (1) injecting from the same side of the magnet location; and (2) injecting from the opposite side of the magnet location. Finite element (FE) method was used to solve the governing equations. Results showed that the maximum values of CE are related to the current carrying wire, permanent magnet, and coil wire with the value of 94%, 80%, 68%, respectively. It was found that the maximum CE of nanoparticles (91.7%) in scenario 2 is obtained at injection rate of Q̇inj=31.4mm3.s-1 when the current carrying wire was used as the magnetic source. Also, it was revealed that no particle is captured at 0≤Q̇inj≤62.8mm3.s-1 in scenario 1. Finally, it was revealed that for all injection angles and rate, the CE of nanoparticles in scenario 2 is higher than that of scenario 1.

Three-dimensional aneurysm wall enhancement in fusiform intracranial aneurysms is associated with aneurysmal symptoms.

Aneurysm wall enhancement (AWE) in high-resolution magnetic resonance imaging (HR-MRI) is a potential biomarker for evaluating unstable aneurysms. Fusiform intracranial aneurysms (FIAs) frequently have a complex and curved structure. We aimed to develop a new three-dimensional (3D) aneurysmal wall enhancement (AWE) characterization method to enable comprehensive FIA evaluation and to investigate the ability of 3D-AWE to predict symptomatic FIA. We prospectively recruited patients with unruptured FIAs and received 3 T HR-MRI imaging from September 2017 to January 2019. 3D models of aneurysms and parent arteries were generated. Boundaries of the FIA were determined using 3D vessel diameter measurements. Dmax was the greatest diameter in the cross-section, while Lmax was the length of the centerline of the aneurysm. Signal intensity of the FIA was normalized to the pituitary stalk and then mapped onto the 3D model, then the average enhancement (3D-AWEavg), maximum enhancement (3D-AWEmax), enhancement area (AWEarea), and enhancement ratio (AWEratio) were calculated as AWE indicators, and the surface area of the entire aneurysm (Aarea) was also calculated. Areas with high AWE were defined as those with a value >0.9 times the signal intensity of the pituitary stalk. Multivariable logistic regression analyses were performed to determine independent predictors of aneurysm-related symptoms. FIA subtypes were defined as fusiform, dolichoectasia, and transitional. Differences between the three FIA subtypes were also examined. Forty-seven patients with 47 FIAs were included. Mean patient age was 55 ± 12.62 years and 74.5% were male. Twenty-nine patients (38.3%) were symptomatic. After adjusting for baseline differences in age, hypertension, Lmax, and FIA subtype, the multivariate logistics regression models showed that 3D-AWEavg (odds ratio [OR], 4.029; p = 0.019), 3D-AWEmax (OR, 3.437; p = 0.022), AWEarea (OR, 1.019; p = 0.008), and AWEratio (OR, 2.490; p = 0.045) were independent predictors of aneurysm-related symptoms. Dmax and Aarea were larger and 3D-AWEavg, 3D-AWEmax, AWEarea, and AWEratio were higher with the transitional subtype than the other two subtypes. The new 3D AWE method, which enables the use of numerous new metrics, can predict symptomatic FIAs. Different 3D-AWE between the three FIA subtypes may be helpful in understanding the pathophysiology of FIAs.

SproutAngio: an open-source bioimage informatics tool for quantitative analysis of sprouting angiogenesis and lumen space

Three-dimensional image analyses are required to improve the understanding of the regulation of blood vessel formation and heterogeneity. Currently, quantitation of 3D endothelial structures or vessel branches is often based on 2D projections of the images losing their volumetric information. Here, we developed SproutAngio, a Python-based open-source tool, for fully automated 3D segmentation and analysis of endothelial lumen space and sprout morphology. To test the SproutAngio, we produced a publicly available in vitro fibrin bead assay dataset with a gradually increasing VEGF-A concentration (https://doi.org/10.5281/zenodo.7240927). We demonstrate that our automated segmentation and sprout morphology analysis, including sprout number, length, and nuclei number, outperform the widely used ImageJ plugin. We also show that SproutAngio allows a more detailed and automated analysis of the mouse retinal vasculature in comparison to the commonly used radial expansion measurement. In addition, we provide two novel methods for automated analysis of endothelial lumen space: (1) width measurement from tip, stalk and root segments of the sprouts and (2) paired nuclei distance analysis. We show that these automated methods provided important additional information on the endothelial cell organization in the sprouts. The pipelines and source code of SproutAngio are publicly available (https://doi.org/10.5281/zenodo.7381732).

Efficiency increasing reconstruction of 3D‐Vessel scan data using a NURBS surface

AbstractIn this paper, a new reconstruction method using a mathematical description of the surface with non‐uniform rational B‐splines (so‐called NURBS) of 3D vessel scans is presented. With the advantage of a file size of less than 1% of the original STL file, the developed reconstruction method can be used in numerical simulations such as the placement or extension of a flow diverter. Along a generated centerline, various geometric functions can also be calculated for diagnostic purposes, such as the cross‐sectional area of the vessel, which can be used to locate a vessel stenosis. As for isogeometric analysis, this method can generally be used to reconstruct any tubular geometry.

LIVE-Net: Comprehensive 3D vessel extraction framework in CT angiography

The extraction of vessels from computed tomography angiography (CTA) is significant in diagnosing and evaluating vascular diseases. However, due to the anatomical complexity, wide intensity distribution, and small volume proportion of vessels, vessel extraction is laborious and time-consuming, and it is easy to lead to error-prone diagnostic results in clinical practice. This study proposes a novel comprehensive vessel extraction framework, called the Local Iterative-based Vessel Extraction Network (LIVE-Net), to achieve 3D vessel segmentation while tracking vessel centerlines. LIVE-Net contains dual dataflow pathways that work alternately: an iterative tracking network and a local segmentation network. The former can generate the fine-grain direction and radius prediction of a vascular patch by using the attention-embedded atrous pyramid network (aAPN), and the latter can achieve 3D vascular lumen segmentation by constructing the multi-order self-attention U-shape network (MOSA-UNet). LIVE-Net is trained and evaluated on two datasets: the MICCAI 2008 Coronary Artery Tracking Challenge (CAT08) dataset and head and neck CTA dataset from the clinic. Experimental results of both tracking and segmentation show that our proposed LIVE-Net exhibits superior performance compared with other state-of-the-art (SOTA) networks. In the CAT08 dataset, the tracked centerlines have an average overlap of 95.2%, overlap until first error of 91.2%, overlap with the clinically relevant vessels of 98.3%, and error distance inside of 0.21 mm. The corresponding tracking overlap metrics in the head and neck CTA dataset are 96.7%, 91.0%, and 99.8%, respectively. In addition, the results of the consistent experiment also show strong clinical correspondence. For the segmentation of bilateral carotid and vertebral arteries, our method can not only achieve better accuracy with an average dice similarity coefficient (DSC) of 90.03%, Intersection over Union (IoU) of 81.97%, and 95% Hausdorff distance (95%HD) of 3.42 mm , but higher efficiency with an average time of 67.25 s , even three times faster compared to some methods applied in full field view. Both the tracking and segmentation results prove the potential clinical utility of our network.

Mapping port wine stain in vivo by optical coherence tomography angiography and multi-metric characterization.

Port wine stain (PWS) is a congenital cutaneous capillary malformation composed of ecstatic vessels, while the microstructure of these vessels remains largely unknown. Optical coherence tomography angiography (OCTA) serves as a non-invasive, label-free and high-resolution tool to visualize the 3D tissue microvasculature. However, even as the 3D vessel images of PWS become readily accessible, quantitative analysis algorithms for their organization have mainly remained limited to analysis of 2D images. Especially, 3D orientations of vasculature in PWS have not yet been resolved at a voxel-wise basis. In this study, we employed the inverse signal-to-noise ratio (iSNR)-decorrelation (D) OCTA (ID-OCTA) to acquire 3D blood vessel images in vivo from PWS patients, and used the mean-subtraction method for de-shadowing to correct the tail artifacts. We developed algorithms which mapped blood vessels in spatial-angular hyperspace in a 3D context, and obtained orientation-derived metrics including directional variance and waviness for the characterization of vessel alignment and crimping level, respectively. Combining with thickness and local density measures, our method served as a multi-parametric analysis platform which covered a variety of morphological and organizational characteristics at a voxel-wise basis. We found that blood vessels were thicker, denser and less aligned in lesion skin in contrast to normal skin (symmetrical parts of skin lesions on the cheek), and complementary insights from these metrics led to a classification accuracy of ∼90% in identifying PWS. An improvement in sensitivity of 3D analysis was validated over 2D analysis. Our imaging and analysis system provides a clear picture of the microstructure of blood vessels within PWS tissues, which leads to a better understanding of this capillary malformation disease and facilitates improvements in diagnosis and treatment of PWS.

Quantitative coronary three‐dimensional geometry and its association with atherosclerotic disease burden and composition

Isolate features of the coronary anatomy have been associated with the pathophysiology of atherosclerotic disease. Computational methods have been described to allow precise quantification of the complex three-dimensional (3D) coronary geometry. The present study tested whether quantitative parameters that describe the spatial 3D coronary geometry is associated with the extension and composition of the underlying coronary artery disease (CAD). Patients with CAD scheduled for percutaneous intervention were investigated with coronary computed tomography angiography (CCTA), and invasive coronary angiography, and virtual histology intravascular ultrasound (IVUS-VH). For all target vessels, 3D centerlines were extracted from CCTA images and processed to quantify 23 geometric indexes, grouped into 3 main categories as follows: (i) length-based; (ii) curvature-based, torsion-based, and curvature/torsion-combined; (iii) vessel path-based. The geometric variables were compared with IVUS-VH parameters assessing the extent and composition of coronary atherosclerosis. A total of 36 coronary patients (99 vessels) comprised the study population. From the 23 geometric indexes, 18 parameters were significantly (p < 0.05) associated with at least 1 IVUS-VH parameter at a univariate analysis. All three main geometric categories provided parameters significantly related with atherosclerosis variables. The 3D geometric indexes were associated with the degree of atherosclerotic extension, as well as with plaque composition. Geometric features remained significantly associated with all IVUS-VH parameters even after multivariate adjustment for clinical characteristics. Quantitative 3D vessel morphology emerges as a relevant factor associated with atherosclerosis in patients with established CAD.

The lymphatic endothelium-derived follistatin: activin A axis regulates neutrophil motility in response to Pseudomonas aeruginosa.

The lymphatic system plays an active role during infection, however the role of lymphatic-neutrophil interactions in host-defense responses is not well understood. During infection with pathogens such as Pseudomonas aeruginosa, Staphylococcus aureus and Yersinia pestis, neutrophils traffic from sites of infection through the lymphatic vasculature, to draining lymph nodes to interact with resident lymphocytes. This process is poorly understood, in part, due to the lack of in vitro models of the lymphatic system. Here we use a 3D microscale lymphatic vessel model to examine neutrophil-lymphatic cell interactions during host defense responses to pathogens. In previous work, we have shown that follistatin is secreted at high concentrations by lymphatic endothelial cells during inflammation. Follistatin inhibits activin A, a member of the TGF-β superfamily, and, together, these molecules form a signaling pathway that plays a role in regulating both innate and adaptive immune responses. Although follistatin and activin A are constitutively produced in the pituitary, gonads and skin, their major source in the serum and their effects on neutrophils are poorly understood. Here we report a microfluidic model that includes both blood and lymphatic endothelial vessels, and neutrophils to investigate neutrophil-lymphatic trafficking during infection with P. aeruginosa. We found that lymphatic endothelial cells produce secreted factors that increase neutrophil migration toward P. aeruginosa, and are a significant source of both follistatin and activin A during Pseudomonas infection. We determined that follistatin produced by lymphatic endothelial cells inhibits activin A, resulting in increased neutrophil migration. These data suggest that the follistatin:activin A ratio influences neutrophil trafficking during infection with higher ratios increasing neutrophil migration.

Lymphatic uptake of biotherapeutics through a 3D hybrid discrete-continuum vessel network in the skin tissue.

Subcutaneous administration is a common approach for the delivery of biotherapeutics, which is achieved mainly through the absorption across lymphatic vessels. In this paper, the drug transport and lymphatic uptake through a three-dimensional hybrid discrete-continuum vessel network in the skin tissue are investigated through high-fidelity numerical simulations. We find that the local lymphatic uptake through the explicit vessels significantly affects macroscopic drug absorption. The diffusion of drug solute through the explicit vessel network affects the lymphatic uptake after the injection. This effect, however, cannot be captured using previously developed continuum models. The lymphatic uptake is dominated by the convection due to lymphatic drainage driven by the pressure difference, which is rarely studied in experiments and simulations. Furthermore, the effects of injection volume and depth on the lymphatic uptake are investigated in a multi-layered domain. We find that the injection volume significantly affects the rate of lymphatic uptake through the heterogeneous vessel network, while the injection depth has little influence, which is consistent with the experimental results. At last, the binding and metabolism of drug molecules are studied to bridge the simulation to the experimentally measured drug clearance. We provide a new approach to study the diffusion and convection of drug molecules into the lymphatic system through the hybrid vessel network.

Generation of Human Blood Vessel Organoids from Pluripotent Stem Cells.

An organoid is defined as an engineered multicellular in vitro tissue that mimics its corresponding in vivo organ such that it can be used to study defined aspects of that organ in a tissue culture dish. The breadth and application of human pluripotent stem cell (hPSC)-derived organoid research have advanced significantly to include the brain, retina, tear duct, heart, lung, intestine, pancreas, kidney, and blood vessels, among several other tissues. The development of methods for the generation of human microvessels, specifically, has opened the way for modeling human blood vessel development and disease in vitro and for the testing and analysis of new drugs or tissue tropism in virus infections, including SARS-CoV-2. Complex and lengthy protocols lacking visual guidance hamper the reproducibility of many stem cell-derived organoids. Additionally, the inherent stochasticity of organoid formation processes and self-organization necessitates the generation of optical protocols to advance the understanding of cell fate acquisition and programming. Here, a visually guided protocol is presented for the generation of 3D human blood vessel organoids (BVOs) engineered from hPSCs. Presenting a continuous basement membrane, vascular endothelial cells, and organized articulation with mural cells, BVOs exhibit the functional, morphological, and molecular features of human microvasculature. BVO formation is initiated through aggregate formation, followed by mesoderm and vascular induction. Vascular maturation and network formation are initiated and supported by embedding aggregates in a 3D collagen and solubilized basement membrane matrix. Human vessel networks form within 2-3 weeks and can be further grown in scalable culture systems. Importantly, BVOs transplanted into immunocompromised mice anastomose with the endogenous mouse circulation and specify into functional arteries, veins, and arterioles. The present visually guided protocol will advance human organoid research, particularly in relation to blood vessels in normal development, tissue vascularization, and disease.

Large-scale perfused tissues via synthetic 3D soft microfluidics

The vascularization of engineered tissues and organoids has remained a major unresolved challenge in regenerative medicine. While multiple approaches have been developed to vascularize in vitro tissues, it has thus far not been possible to generate sufficiently dense networks of small-scale vessels to perfuse large de novo tissues. Here, we achieve the perfusion of multi-mm3 tissue constructs by generating networks of synthetic capillary-scale 3D vessels. Our 3D soft microfluidic strategy is uniquely enabled by a 3D-printable 2-photon-polymerizable hydrogel formulation, which allows for precise microvessel printing at scales below the diffusion limit of living tissues. We demonstrate that these large-scale engineered tissues are viable, proliferative and exhibit complex morphogenesis during long-term in-vitro culture, while avoiding hypoxia and necrosis. We show by scRNAseq and immunohistochemistry that neural differentiation is significantly accelerated in perfused neural constructs. Additionally, we illustrate the versatility of this platform by demonstrating long-term perfusion of developing neural and liver tissue. This fully synthetic vascularization platform opens the door to the generation of human tissue models at unprecedented scale and complexity.

Graph Convolution Based Cross-Network Multiscale Feature Fusion for Deep Vessel Segmentation.

Vessel segmentation is widely used to help with vascular disease diagnosis. Vessels reconstructed using existing methods are often not sufficiently accurate to meet clinical use standards. This is because 3D vessel structures are highly complicated and exhibit unique characteristics, including sparsity and anisotropy. In this paper, we propose a novel hybrid deep neural network for vessel segmentation. Our network consists of two cascaded subnetworks performing initial and refined segmentation respectively. The second subnetwork further has two tightly coupled components, a traditional CNN-based U-Net and a graph U-Net. Cross-network multi-scale feature fusion is performed between these two U-shaped networks to effectively support high-quality vessel segmentation. The entire cascaded network can be trained from end to end. The graph in the second subnetwork is constructed according to a vessel probability map as well as appearance and semantic similarities in the original CT volume. To tackle the challenges caused by the sparsity and anisotropy of vessels, a higher percentage of graph nodes are distributed in areas that potentially contain vessels while a higher percentage of edges follow the orientation of potential nearby vessels. Extensive experiments demonstrate our deep network achieves state-of-the-art 3D vessel segmentation performance on multiple public and in-house datasets.

Shape-Aware 3D Small Vessel Segmentation with Local Contrast Guided Attention.

The automated segmentation and analysis of small vessels from in vivo imaging data is an important task for many clinical applications. While current filtering and learning methods have achieved good performance on the segmentation of large vessels, they are sub-optimal for small vessel detection due to their apparent geometric irregularity and weak contrast given the relatively limited resolution of existing imaging techniques. In addition, for supervised learning approaches, the acquisition of accurate pixel-wise annotations in these small vascular regions heavily relies on skilled experts. In this work, we propose a novel self-supervised network to tackle these challenges and improve the detection of small vessels from 3D imaging data. First, our network maximizes a novel shape-aware flux-based measure to enhance the estimation of small vasculature with non-circular and irregular appearances. Then, we develop novel local contrast guided attention(LCA) and enhancement(LCE) modules to boost the vesselness responses of vascular regions of low contrast. In our experiments, we compare with four filtering-based methods and a state-of-the-art self-supervised deep learning method in multiple 3D datasets to demonstrate that our method achieves significant improvement in all datasets. Further analysis and ablation studies have also been performed to assess the contributions of various modules to the improved performance in 3D small vessel segmentation. Our code is available at https://github.com/dengchihwei/LCNetVesselSeg.