Vessel Model Research Articles

Hepatic Vein and Arterial Vessel Segmentation in Liver Tumor Patients.

Preoperative observation of liver status in patients with liver tumors by abdominal Computed Tomography (CT) imaging is one of the essential references for formulating surgical plans. Preoperative vessel segmentation in the patient's liver region has become an increasingly important and challenging problem. Almost all existing methods first segment arterial and venous vessels on CT in the arterial and venous phases, respectively. Then, the two are directly registered to complete the reconstruction of the vascular system, ignoring the displacement and deformation of blood vessels caused by changes in body position and respiration in the two phases. We propose an unsupervised domain-adaptive two-stage vessel segmentation framework for simultaneous fine segmentation of arterial and venous vessels on venous phase CT. Specifically, we first achieve domain adaptation for arterial and venous phase CT using a modified cycle-consistent adversarial network. The newly added discriminator can improve the ability to generate and discriminate tiny blood vessels, making the domain-adaptive network more robust. The second-stage supervised training of arterial vessels was then performed on the translated arterial phase CT. In this process, we propose an orthogonal depth projection loss function to enhance the representation ability of the 3D U-shape segmentation network for the geometric information of the vessel model. The segmented venous vessels were also performed on venous phase CT in the second stage. Finally, we invited professional doctors to annotate arterial and venous vessels on the venous phase CT of the test set. The experimental results show that the segmentation accuracy of arterial and venous vessels on venous phase CT is 0.8454 and 0.8087, respectively. Our proposed framework can simultaneously achieve supervised segmentation of venous vessels and unsupervised segmentation of arterial vessels on venous phase CT. Our approach can be extended to other fields of medical segmentation, such as unsupervised domain adaptive segmentation of liver tumors at different CT phases, to facilitate the development of the community.

Research on the Dynamic Characteristics of the CFETR Vacuum Vessel Based on the Modal Synthesis Method

The vacuum vessel is the core component of the Chinese Fusion Engineering Testing Reactor (CFETR); its main function is to remove nuclear heating, provide safety shielding, and maintain a high-quality vacuum environment. Therefore, the safety of the vacuum vessel is of great significance to the CFETR, and examining its dynamic performance is necessary. However, the conventional finite element method takes too long to perform the dynamic analysis of the vacuum vessel, which greatly reduces the efficiency of the design and analysis. Based on the modal synthesis method, this study uses ANSYS software to establish a substructure model of the CFETR vacuum vessel. A modal analysis and harmonic response analysis are conducted, and their results are compared with those of the conventional finite element model. The results show that the substructure model not only has the same accuracy as conventional finite element models, but that it also greatly reduces the time of dynamic calculation.

Fatigue Properties and Residual Stresses of Laser-Welded Heat-Resistant Pressure Vessel Steel, Verification on Vessel Model

Most power plants use the Rankine cycle, where the heat supplied to water and steam is converted into mechanical work; therefore, most components have to be made of heat-resistant steel. Sufficient mechanical properties must be ensured for welded pipes to meet stringent requirements. Therefore, laser-welded 5 mm thick heat-resistant pressure vessel steel plates were subjected to various mechanical tests, including high-cycle fatigue tests. The microstructural notches were determined using X-ray diffraction too to determine critical areas that are susceptible to crack initialization and affect the service life. Finally, a functional model of the pressure vessel subsequently verified the results and assumptions. The presented results ensure the transferability of the results to real-life applications and outline the promising application potential of laser welding for producing vessels and pipes from heat-resistant steel in the industry.

Morphometrics in three dimensional choroidal vessel models constructed from swept-source optical coherence tomography images

We created three types of vessel models: vessel volume, surface, and line models from swept-source optical coherence tomography images and tested experimentally calculated three-dimensional (3D) biomarkers. The choroidal volume (CVolume), surface area (VSurface), and vessel length-associated index (VLI) were measured. The calculated 3D parameters were the mean choroidal thickness, choroidal vascularity index (CVI), vessel length density index (VLDI), vessel length to the stromal (VL–S) ratio, surface-to-volume ratio (S–V ratio), and vessel diameter index (VDI). Cluster analysis showed that the parameters were classified into two clusters: one was represented by the VVolume including the CVolume, VSurface, CVI, S–V ratio, VLI, VDI, and subfoveal choroidal thickness and the other by the VL–S ratio including the VLDI. Regarding the regional distribution, the VVolume, CVolume, VSurface, CVI, VLI, VL–S ratio, and VDI at the foveal center were higher than at the parafovea (P < 0.01). Although the VVolume decreased with age and axial length (AL) elongation, the association of the 3D parameters with age and AL elongation differed. The VLI, VLDI, VL–S ratio, and CVI decreased with age (P < 0.01) but not with AL elongation. The results suggested a structural difference in the choroidal vessel volume reduction between aging and AL elongation. The 3D parameters may provide additional information about the choroidal vasculature.

Reduced Steering Performance in Magnetic Drug Targeting Induced by Shielding Arising from Accumulation of Particles

Abstract Magnetic Drug Targeting is a new approach in cancer therapy, where magnetic nanoparticles are used as carriers for cancer drugs. Commonly, external magnets are employed for steering the particles inside the blood vessels towards a desired direction. However, an unwanted side effect of the steering is the accumulation of the particles underneath the steering magnet. Many researchers address the number of accumulated particles, but, to the best of the authors’ knowledge, the retroaction of the accumulation profile on the generated magnetic field and, therefore, on the magnetic steering force, has not been investigated so far. Thus, in the proposed study, the influence of the accumulation profile on the magnetic force was numerically investigated. Therefore, a 2D model of a blood vessel with particles assumed as an accumulation profile and a nearby magnet was examined. Moreover, the length, thickness, and effective susceptibility of the approximated accumulation profile and the magnet size were varied. The results reveal that the field distribution is significantly affected, especially for high effective susceptibilities. The initially applied profile amplified the magnetic force; however, when the profile accumulated, it reduced the force up to 50 %. Overall, the results reveal that the retroaction of the particle distribution on the magnetic field must be considered in a simulation model.

A Quasi-Static Motion Prediction Model of a Multi-Hull Navigation Vessel in Dynamic Positioning Mode

A Quadmaran vessel is a maritime navigation vessel with four hulls, and is expected to be utilized for high-density water quality observation in aquaculture farms. The performance of the vessel during dynamic positioning (DP) is integral to the accuracy of collected data. However, there are no models available to understand the motion characteristics of the multi-hull vessels in DP mode. Therefore, a computational model was developed to predict the DP motion of the Quadmaran vessel based on quasi-static assumptions. The fluid forces on the hulls, the thrust force of the propellers, and the external wind forces are modelled systematically, assuming the average speed while in DP does not exceed 0.25 m/s. A series of oblique towing tests and static turning tests were conducted on a 1/3 scale model to determine the hydrodynamic coefficients, while the Propeller Open Test was conducted to survey the propeller performance. The wind load was also incorporated into the computational model. Experimental validation tests were conducted in calm water and actual sea areas, and the correlation between experiment and simulation results was good. We concluded that the computational model can predict the DP motion of the Quadmaran vessel with an accuracy of roughly 2 m.

Curvelet Transform-Based Sparsity Promoting Algorithm for Fast Ultrasound Localization Microscopy.

Ultrasound localization microscopy (ULM) based on microbubble (MB) localization was recently introduced to overcome the resolution limit of conventional ultrasound. However, ULM is currently challenged by the requirement for long data acquisition times to accumulate adequate MB events to fully reconstruct vasculature. In this study, we present a curvelet transform-based sparsity promoting (CTSP) algorithm that improves ULM imaging speed by recovering missing MB localization signal from data with very short acquisition times. CTSP was first validated in a simulated microvessel model, followed by the chicken embryo chorioallantoic membrane (CAM), and finally, in the mouse brain. In the simulated microvessel study, CTSP robustly recovered the vessel model to achieve an 86.94% vessel filling percentage from a corrupted image with only 4.78% of the true vessel pixels. In the chicken embryo CAM study, CTSP effectively recovered the missing MB signal within the vasculature, leading to marked improvement in ULM imaging quality with a very short data acquisition. Taking the optical image as reference, the vessel filling percentage increased from 2.7% to 42.2% using 50ms of data acquisition after applying CTSP. CTSP used 80% less time to achieve the same 90% maximum saturation level as compared with conventional MB localization. We also applied CTSP on the microvessel flow speed maps and found that CTSP was able to use only 1.6s of microbubble data to recover flow speed images that have similar qualities as those constructed using 33.6s of data. In the mouse brain study, CTSP was able to reconstruct the majority of the cerebral vasculature using 1–2s of data acquisition. Additionally, CTSP only needed 3.2s of microbubble data to generate flow velocity maps that are comparable to those using 129.6s of data. These results suggest that CTSP can facilitate fast and robust ULM imaging especially under the circumstances of inadequate microbubble localizations.

Redistribution of substitutional alloying elements between α-Fe matrix and cementite after long-term tempering in a low alloying steel

ABSTRACT The partitioning behaviour of non-carbide forming elements (Si, Cu and Ni) and carbide forming elements (Mn and Mo) between α-Fe matrix and cementite (θ) after long-term tempering a quenched reactor pressure vessel (RPV) model steel has been studied by atom probe tomography (APT). It has demonstrated that a non-carbide forming element enriched layer near the interface of α/θ in the α-Fe matrix side is a common feature for tempered steels containing non-carbide forming element, which is widely accepted as the mechanism by which substitutional elements retard the coarsening of cementite. In addition, a carbide forming element enriched layer near the interface of α/θ in the cementite side, Si, Cu and Ni enriched clusters formed in the cementite and Mo2C carbide formed beside cementite were observed, and all this is taken as valuable mechanisms by which addition of substitutional alloying elements can increase tempering resistance of alloying steels.

SynthWakeSAR: A Synthetic SAR Dataset for Deep Learning Classification of Ships at Sea

The classification of vessel types in SAR imagery is of crucial importance for maritime applications. However, the ability to use real SAR imagery for deep learning classification is limited, due to the general lack of such data and/or the labor-intensive nature of labeling them. Simulating SAR images can overcome these limitations, allowing the generation of an infinite number of datasets. In this contribution, we present a synthetic SAR imagery dataset with ship wakes, which comprises 46,080 images for ten different real vessel models. The variety of simulation parameters includes 16 ship heading directions, 6 ship velocities, 8 wind directions, 2 wind velocities, and 3 incidence angles. In addition, we extensively investigate the classification performance for noise-free, noisy, and denoised ship wake scenes. We utilize the standard AlexNet architecture and employ training from scratch. To achieve the best classification performance, we conduct Bayesian optimization to determine hyperparameters. Results demonstrate that the classifications of vessel types based on their SAR signatures are highly efficient, with maximum accuracies of 96.16%, 92.7%, and 93.59%, when training using noise-free, noisy, and denoised datasets, respectively. Thus, we conclude that the best strategy in practical applications should be to train convolutional neural networks on denoised SAR datasets. The results show that the versatility of the SAR simulator can open up new horizons in the application of machine learning to a variety of SAR platforms.

Dynamic responses of submerged components in advanced reactors: experimental and numerical studies

The seismic design of an advanced nuclear reactor must consider the interaction of vessel internal components with the surrounding coolant: fluid–structure interaction (FSI). Available analytical solutions for FSI of submerged components do not accommodate multiple-component, intense seismic inputs and complex reactor and internal geometries. Physical testing of reactor vessels and internals to inform seismic design is impractical and cost-prohibitive, leaving the use of verified and validated, robust numerical models as the only plausible option for analysis and design. Physical data that could be used for validating such numerical models for multi-component shaking are not available. This article describes an experimental program performed on a 6-degree-of-freedom earthquake simulator to generate data that could support validation of seismic FSI numerical models for submerged components in commercial finite element codes. A scaled model of a base-supported reactor vessel with simplified representations of submerged internals was tested to generate submerged-component response histories for a range of seismic inputs. The generated data were used to validate numerical models in the finite element code LS-DYNA. Numerical models were validated for calculation of hydrodynamic pressure responses on internals, in-water frequencies of internals, and acceleration responses of internals. The generated data and the analysis recommendations could aid engineering analysts designing submerged components and systems for seismic effects.

Evaluation of Morphological Changes in Retinal Vessels in Type 1 Diabetes Mellitus Patients with the Use of Adaptive Optics.

Introduction. Diabetes mellitus contributes to the development of microvascular complications in the eye. Moreover, it affects multiple end organs, including brain damage, leading to premature death. The use of adaptive optics technique allows to perform non-invasive in vivo assessment of retinal vessels and to identify changes in arterioles about 100 μm in diameter. The retinal vasculature may be a model of the cerebral vessels both morphologically and functionally. Aim. To evaluate morphological parameters of retinal arterioles in patients with type 1 diabetes mellitus (DM1). Material and methods. The study included 22 DM1 patients (13 females) aged 43.00 ± 9.45 years with a mean diabetes duration of 22.55 ± 10.05 years, and 23 healthy volunteers (10 females) aged 41.09 ± 10.99 years. Blood pressure, BMI, waist circumference, and metabolic control markers of diabetes were measured in both groups. Vascular examinations were performed using an rtx1 adaptive optics retinal camera (Imagine Eyes, Orsay, France); the vessel wall thickness (WT), lumen diameter (LD), wall-to-lumen ratio (WLR), and vascular wall cross-sectional area (WCSA) were assessed. Statistical analysis was performed with the application of IMB SPSS version 23 software. Results. The DM1 group did not differ significantly in age, BMI, waist circumference, blood pressure, or axial length of the eye compared to the control group. Intraocular pressure (IOP) in both groups was normal, but in the DM1 group it was significantly higher. The DM1 group had significantly higher WT, WLR, and WCSA. These parameters correlated significantly with the duration of diabetes, but not with IOP. Conclusions. The presented study demonstrates the presence of significant morphological changes in retinal vessels in DM1 patients without previously diagnosed diabetic retinopathy. Similar changes may occur in the brain and may be early indicators of cardiovascular risk, but further investigation is required to confirm that.

Astragaloside IV alleviates senescence of vascular smooth muscle cells through activating Parkin-mediated mitophagy

Astragaloside IV (AS-IV), as one of the main active components of Astragalus membranaceus, has been reported to have cardiovascular protective effects. However, the role and molecular mechanism of AS-IV in vascular senescence have not been clearly stated. The in vitro aging model was constructed using bleomycin (BLM) in vascular smooth muscle cells (VSMCs). Cell senescence were assessed through Western blotting analysis of aging markers, flow cytometry, and the β-galactosidase (SA-β-Gal) kit. Mitophagy was determined through transmission electron microscopy, TMRM staining, and Western blotting analysis of p62. A model of aging blood vessels was induced by d-gal. The vascular wall thickness of mice was also evaluated by H&E staining. Our data proved that AS-IV plays an anti-senescent role in vitro and in vivo. Results showed that AS-IV effectively improved mitochondrial injury, raised MMP, and mediated mitophagy in BLM-induced senescent VSMCs and d-gal induced aging mice. Parkin expression strengthened AS-IV’s anti-senescent function. In conclusions, AS-IV attenuated BLM-induced VSMC senescence via Parkin to regulate mitophagy. Therefore, AS-IV-mediated Parkin might be a latent therapeutic agent and target for VSMC senescence.

Diagnosing Diabetic Retinopathy in OCTA Images Based on Multilevel Information Fusion Using a Deep Learning Framework.

Objective As an extension of optical coherence tomography (OCT), optical coherence tomographic angiography (OCTA) provides information on the blood flow status at the microlevel and is sensitive to changes in the fundus vessels. However, due to the distinct imaging mechanism of OCTA, existing models, which are primarily used for analyzing fundus images, do not work well on OCTA images. Effectively extracting and analyzing the information in OCTA images remains challenging. To this end, a deep learning framework that fuses multilevel information in OCTA images is proposed in this study. The effectiveness of the proposed model was demonstrated in the task of diabetic retinopathy (DR) classification. Method First, a U-Net-based segmentation model was proposed to label the boundaries of large retinal vessels and the foveal avascular zone (FAZ) in OCTA images. Then, we designed an isolated concatenated block (ICB) structure to extract and fuse information from the original OCTA images and segmentation results at different fusion levels. Results The experiments were conducted on 301 OCTA images. Of these images, 244 were labeled by ophthalmologists as normal images, and 57 were labeled as DR images. An accuracy of 93.1% and a mean intersection over union (mIOU) of 77.1% were achieved using the proposed large vessel and FAZ segmentation model. In the ablation experiment with 6-fold validation, the proposed deep learning framework that combines the proposed isolated and concatenated convolution process significantly improved the DR diagnosis accuracy. Moreover, inputting the merged images of the original OCTA images and segmentation results further improved the model performance. Finally, a DR diagnosis accuracy of 88.1% (95%CI ± 3.6%) and an area under the curve (AUC) of 0.92 were achieved using our proposed classification model, which significantly outperforms the state-of-the-art classification models. As a comparison, an accuracy of 83.7 (95%CI ± 1.5%) and AUC of 0.76 were obtained using EfficientNet. Significance. The visualization results show that the FAZ and the vascular region close to the FAZ provide more information for the model than the farther surrounding area. Furthermore, this study demonstrates that a clinically sophisticated designed deep learning model is not only able to effectively assist in the diagnosis but also help to locate new indicators for certain illnesses.

Application of competency-based education in the Korean anesthesiology residency program and survey analysis.

Although competency-based education (CBE) is becoming a popular form of medical education, it has not been used to train residents. Recently, the Korean Society of Anesthesiologists completed a pilot implementation and evaluation of a CBE program.This study aims to outline the experience. The chief training faculty from each hospital took a one-hour online course about CBE. Emails on the seven core competencies and their evaluation were sent ahead of a pilot core competency evaluation (CCE) to residents and faculty. The pilot CCE took place in late 2021, followed by a survey. A total of 68 out of 84 hospitals participated in the pilot CCE. The survey response rate was 55.9% (38/68) for chief training faculty, 10.2% (91/888) for training faculty, and 30.2% (206/683) for residents. More than half of the training faculty thought that CCE was necessary for the education of residents. Residents' and training faculty's responses about CCE were generally positive, although their understanding of CCE criteria was low. More than 80% of the hospitals had a defibrillator and cardiopulmonary resuscitation manikin while the rarest piece of equipment was an ultrasound vessel model. Only defibrillators were used in more than half of the hospitals. Thoughts about CCE were related to various factors, such as length of employment, location of hospitals, and the number of residents per grade. This study's results may be helpful in improving resident education quality to meet the expectations of both teaching faculty and residents while establishing CBE.

Understanding 3D culture in smooth muscle cell biology and the modelling of atherosclerosis

Background and Aims : Even though the advantages of 3D culture in mimicking cellular environment and drug discovery is widely reported in cancer biology, there are limited studies on 3D culture in vascular biology and atherosclerosis modelling. Our study aims to investigate the significance of 3D spheroid culture on cellular physiology and in atherosclerosis modelling using vascular smooth muscle cells(VSMC). We also explore 3D vessel modelling via co-culturing of endothelial cells(EC) and VSMCs.

BETHSY 9.1b test calculation with TRACE using 3D vessel component

Recently, several advanced multidimensional computational tools for simulating reactor system behavior during real and hypothetical transient scenarios were developed. One of such advanced, best-estimate reactor systems codes is TRAC/RELAP Advanced Computational Engine (TRACE), developed by the U.S. Nuclear Regulatory Commission. The advanced TRACE comes with a graphical user interface called SNAP (Symbolic Nuclear Analysis Package). It is intended for pre- and post-processing, running codes, RELAP5 to TRACE input deck conversion, input deck database generation etc. The TRACE code is still not fully development and it will have all the capabilities of RELAP5. The purpose of the present study was therefore to assess the 3D capability of the TRACE on BETHSY 9.1b test. The TRACE input deck was semi-converted (using SNAP and manual corrections) from the RELAP5 input deck. The 3D fluid dynamics within reactor vessel was modeled and compared to 1D fluid dynamics. The TRACE 3D calculation was compared both to TRACE 1D calculation and RELAP5 calculation. Namely, the geometry used in TRACE is basically the same, what gives very good basis for the comparison of the codes. The only exception is 3D reactor vessel model in case of TRACE 3D calculation. The TRACE V5.0 Patch 1 and RELAP5/MOD3.3 Patch 4 were used for calculations. The BETHSY 9.1b test (International Standard Problem no. 27 or ISP-27) was 5.08 cm equivalent diameter cold leg break without high pressure safety injection and with delayed ultimate procedure. BETHSY facility was a 3-loop replica of a 900 MWe FRAMATOME pressurized water reactor. In general, all presented code calculations were in good agreement with the BETHSY 9.1b test. The TRACE 1D calculation results are comparable to RELAP5 calculated results. For some parameters they are better, this is mostly due to better tuning of the break flow, what influences timing of the transient. When comparing TRACE 1D and TRACE 3D calculation, the latter is slightly better. One reason for comparable results is already good agreement of 1D calculations and there was not much space to further improve the results. The other reason may be that in the facility the phenomena were mostly one dimensional (for example, external downcomer was used for reactor vessel modeling). However, when 3D behavior of the heater rod temperatures was investigated, the advantage of three dimensional treatment was clearly demonstrated.

Numerical Analysis on Effect of Coronary Supply-Demand Equilibrium on Varying Coronary Blockage and Stress Conditions.

In this paper, we present a computational fluid dynamic (CFD) analysis to capture the effect of physical stress and stenosis severity in coronary arteries leading to changes in coronary supply demand oxygen equilibrium. We propose a coupled Od-3d coronary vessel model to predict the variation in flow dynamics of coronary as well as arterial system, modeled using an in-silico model replicating cardiovascular hemodynamics. CFD simulation were solved using subject specific CT scan for coronary and arterial flow and pressure along with metrics related to arterial wall shear stress. Simulations were performed for three heart rates (75, 90 and 120 bpm) and four stenosis states representing different stages of Coronary artery disease (CAD) namely healthy, 50%, 75%, 90% blockage in left anterior descending artery (LAD). Myocardial oxygen supply demand equilibrium were calculated for each cases using hemodynamic surrogate markers naming Diastolic pressure time index for supply and Tension time index for demand. The proposed 0d-3d coupled hemodynamic model of the coronary vessel bed along with supply-demand equilibrium estimated for different stress level and stenosis severity may provide useful insights on the dynamics of CAD manifestation and predict vulnerable regions in coronary bed for early screening and interventions.

LncRNA Meg3 knockdown reduces corneal neovascularization and VEGF-induced vascular endothelial angiogenesis via SDF-1/CXCR4 and Smad2/3 pathway

The crucial effect of vascular endothelial growth factor (VEGF)-induced vascular angiogenesis has been well known in corneal neovascularization (CNV). This research aimed to determine the underlying value and mechanism of Meg3 on CNV in vivo and in vitro. In an alkali-burned mouse model, length and area of new vessels were increased along with thinning of corneal epithelium, accompanied by the overexpression of Meg3. Notably, subconjunctival injection of shMeg3 suppressed the degree of injury in cornea, causing expression of the angiogenesis markers--VEGF-A and CD31 decreased. In VEGF-induced human umbilical vein endothelial cells (HUVECs), knockdown of Meg3 antagonized the enhancement of viability, proliferation, wound healing ability and angiogenesis by VEGF. The proteins expression of VEGF-A, CD31, SDF-1/CXCR4 as well as phosphoraylation-Smad2/3 pathways, which were related to angiogenesis, were reduced with Meg3 deficiency. Overall, knockdown of Meg3 alleviated formation of neovascularization in alkali-burned corneas and reduced VEGF-induced angiogenesis by inhibiting SDF-1/CXCR4 and Smad2/3 signaling in vitro.

Design of multi-layer grid in a giant electrostatic collection vessel of ultra-high sensitivity radon monitor

Developing an ultra-high sensitivity electrostatic collection radon monitor benefits the scientific experiments of China Jinping Underground Laboratory. Here, a one cubic meter electrostatic collection vessel with a multi-layer hemispherical metal grid was designed to increase the collection efficiency of positively charged Po-218 ions. The 3D model of the giant electrostatic collection vessel was constructed using the COMSOL Multiphysics simulation software, and the potential and electric field distributions in the vessel were simulated. Numerical simulation results were obtained according to the different radii and voltages applied to the grid. The electric field between the vessel wall and grid, between two grids, and between the grid and surface of the PIPS detector must be set uniformly to reduce the collection time of the positively charged Po-218 ions. Simulation results showed that setting a charged metal grid in the vessel can optimize the electric field distribution, and setting a two-layer charged metal grid in the giant vessel can further increase the cost performance. The average collection times of the electrostatic collection vessel with the two-layer grid along the vertical and oblique lines approximately 15% and 13% of that without the grid. The rates of positively charged Po-218 ions that could pass through the one and two-layer metal grids were 86.78% and 50%. Optimizing the electric field can greatly increase the sensitivity of radon monitors and reduce the humidity restrictions.

Using temporal and structural data to reconstruct 3D cerebral vasculature from a pair of 2D digital subtraction angiography sequences.

The purpose of this work is to present a new method for reconstructing patient-specific three-dimensional (3D) vasculature of the brain from a pair of digital subtraction angiography (DSA) image sequences from different viewpoints, e.g., from bi-plane angiography. Our long-term goal is to provide high resolution visualization of 3D vasculature with dynamic flow of contrast agent from limited data that is readily available during surgical procedures. The proposed method is the second of a three-stage process composed of 1) augmenting vessel segmentation with vessel radii and timing of the arrival of a bolus of contrast agent, 2) reconstructing a volumetric representation of the augmented vessel data from the augmented 2D segmentations, and 3) generating a 3D model of vessels and flow of contrast agent from the volumetric reconstruction. Unlike previous methods, which are either limited to relatively simple vessel structures or rely on multiple views and/or prior models of the vasculature, our method requires only a single pair of 2D DSA sequences taken from different view directions. We developed a new mathematical algorithm that augments vessel centerlines with vessel radii and bolus arrival times derived directly from the 2D DSA sequences to constrain the 3D reconstruction. We validated this method on digital phantoms derived from clinical data and from fractal models of branching tree structures. In standard reconstruction methods, reconstruction by projection of two views into 3D space results in 'ghosting' artifacts, i.e., false 3D structure that occurs where vessels or vessel segments overlap in the 2D images. For the complex vascular of the brain, this ghosting is severe and is a major hurdle for methods that attempt to generate 3D structure from 2D images. We show that our approach reduces ghosting by up to 99% in digital phantoms derived from clinical data. Our dramatic reduction in ghosting artifacts in 3D reconstructions from a pair of 2D image sequences is an important step towards generating high resolution 3D vasculature with dynamic flow information from a single DSA sequence acquired using bi-plane angiography.