Vessel Structure Research Articles

X-ray coronary angiography background subtraction by adaptive weighted total variation regularized online RPCA

Objective.X-ray coronary angiograms (XCA) are widely used in diagnosing and treating cardiovascular diseases. Various structures with independent motion patterns in the background of XCA images and limitations in the dose of injected contrast agent have resulted in low-contrast XCA images. Background subtraction methods have been developed to enhance the visibility and contrast of coronary vessels in XCA sequences, consequently reducing the requirement for excessive contrast agent injections.Approach.The current study proposes an adaptive weighted total variation regularized online RPCA (WTV-ORPCA) method, which is a low-rank and sparse subspaces decomposition approach to subtract the background of XCA sequences. In the proposed method, the images undergo initial preprocessing using morphological operators to eliminate large-scale background structures and achieve image homogenization. Subsequently, the decomposition algorithm decomposes the preprocessed images into background and foreground subspaces. This step applies an adaptive weighted TV constraint to the foreground subspace to ensure the spatial coherency of the finally extracted coronary vessel images.Main results.To evaluate the effectiveness of the proposed background subtraction method, some qualitative and quantitative experiments are conducted on two clinical and synthetic low-contrast XCA datasets containing videos from 21 patients. The obtained results are compared with six state-of-the-art methods employing three different assessment criteria. By applying the proposed method to the clinical dataset, the mean values of the global contrast-to-noise ratio, local contrast-to-noise ratio, structural similarity index, and reconstruction error (RE) are obtained as5.976,3.173,0.987, and0.026, respectively. These criteria over the low-contrast synthetic dataset were4.851,2.942,0.958, and0.034, respectively.Significance.The findings demonstrate the superiority of the proposed method in improving the contrast and visibility of coronary vessels, preserving the integrity of the vessel structure, and minimizing REs without imposing excessive computational complexity.

Clinical case of an atypical variant of Labbe's vein structure in a patient with pharmacoresistant temporal epilepsy

Among the commonly recognised structural lesions that cause drug-resistant temporal lobe epilepsy, such as mesial temporal sclerosis, focal cortical dysplasia, benign intracerebral tumors, arteriovenous malformations and cavernous angiomas, researchers have recently noted other pathologies, such as, encephalocele. There are a number of publications on the connection between epilepsy and cerebral venous angiomas. However, there are no published cases of the influence of the abnormal structure and location of normal cerebral vessels on the development of epilepsy. This article presents a case of an atypically large and unusually located vein of Labbe that may have been involved in epileptogenesis and also complicated an anteromedial temporal lobectomy. Forced coagulation and excision of the Labbe vein during surgery did not lead to venous infarction and neurological deficit.

Double-crosslinked dECM bioink to print a self-sustaining 3D multi-layered aortic-like construct

Cardiovascular disease is the leading cause of death worldwide, with related mortality increasing from 12.1 million to 18.6 million in the past 30 years.To address the supply limitation of autologous vascular grafts and overcome the limits of current treatment options, 3D bioprinting techniques have been investigated.This study aimed at introducing a self-supporting and multi-layered 3D bioprinted construct as a promising alternative for large-blood vessel replacement. To this end, we developed an alginate-gelatin bioink enriched with decellularized extracellular matrix (dECM) of porcine aorta combined with a two-step crosslinking process. We investigated the feasibility of achieving structural stability and shape fidelity of the bioprinted construct over time through rheological characterization, printability tests, and degradation tests.According to the results of rheology and printability tests, dECM-enriched bioink combined with the double-crosslinking process (internal and external crosslink) showed good printability and high shape fidelity, withstanding more than 35 layers without the need for support. Moreover, the bioprinted construct preserved its structural stability over time, retaining a wall thickness comparable to that of the native aorta. Finally, immortalized mouse fibroblasts embedded in the bioink were well adhered to the bioink and alive over time. The double-crosslinked bioink represents an impactful strategy to produce an alternative conduit with the native hierarchical structure of the large blood vessels.

Online Tree-Structure-Constrained RPCA for Background Subtraction of X-ray Coronary Angiography Images

Background and objectiveBackground subtraction of X-ray coronary angiograms (XCA) can significantly improve the diagnosis and treatment of coronary vessel diseases. The XCA background is complex and dynamic due to structures with different intensities and independent motion patterns, making XCA background subtraction challenging. MethodsThe current work proposes an online tree-structure-constrained robust PCA (OTS-RPCA) method to subtract the XCA background. A morphological closing operation is used as a pre-processing step to remove large-scale structures like the spine, chest and diaphragm. In the following, the XCA sequence is decomposed into three different subspaces: low-rank background, residual dynamic background and vascular foreground. A tree-structured norm is introduced and applied to the vascular submatrix to guarantee the vessel spatial coherency. Moreover, the residual dynamic background is separately extracted to remove noise and motion artifacts from the vascular foreground. The proposed algorithm also employs an adaptive regularization coefficient that tracks the vessel area changes in the XCA frames. ResultsThe proposed method is evaluated on two datasets of real clinical and synthetic low-contrast XCA sequences of 38 patients using the global and local contrast-to-noise ratio (CNR) and structural similarity index (SSIM) criteria. For the real XCA dataset, the average values of global CNR, local CNR and SSIM are 6.27, 3.07 and 0.97, while these values over the synthetic low-contrast dataset are obtained as 5.15, 2.69 and 0.94, respectively. The implemented quantitative and qualitative experiments verify the superiority of the proposed method over seven selected state-of-the-art methods in increasing the coronary vessel contrast and preserving the vessel structure. ConclusionsThe proposed OTS-RPCA background subtraction method accurately subtracts backgrounds from XCA images. Our method might provide the basis for reducing the contrast agent dose and the number of needed injections in coronary interventions.

Diabetic retinopathy data augmentation and vessel segmentation through deep learning based three fully convolution neural networks

ProblemThe eye fundus imaging is used for early diagnosis of most damaging concerns such as diabetic retinopathy, retinal detachments and vascular occlusions. However, the presence of noise, low contrast between background and vasculature during imaging, and vessel morphology lead to uncertain vessel segmentation. AimThis paper proposes a novel retinalblood vessel segmentation method for fundus imaging using a Difference of Gaussian (DoG) filter and an ensemble of three fully convolutional neural network (FCNN) models. MethodsA Gaussian filter with standard deviation σ1 is applied on the preprocessed grayscale fundus image and is subtracted from a similarly applied Gaussian filter with standard deviation σ2 on the same image. The resultant image is then fed into each of the three fully convolutional neural networks as the input. The FCNN models' output is then passed through a voting classifier, and a final segmented vessel structure is obtained.The Difference of Gaussian filter played an essential part in removing the high frequency details (noise) and thus finely extracted the blood vessels from the retinal fundus with underlying artifacts. ResultsThe total dataset consists of 3832 augmented images transformed from 479 fundus images. The result shows that the proposed method has performed extremely well by achieving an accuracy of 96.50%, 97.69%, and 95.78% on DRIVE, CHASE,and real-time clinical datasets respectively. ConclusionThe FCNN ensemble model has demonstrated efficacy in precisely detecting retinal vessels and in the presence of various pathologies and vasculatures.

Robust and smooth couinaud segmentation via anatomical structure-guided point-voxel network

Precise Couinaud segmentation from preoperative liver computed tomography (CT) is crucial for surgical planning and lesion examination. However, this task is challenging as it is defined based on vessel structures, and there is no intensity contrast between adjacent Couinaud segments in CT images. To solve this challenge, we design a multi-scale point-voxel fusion framework, which can more effectively model the spatial relationship of points and the semantic information of the image, producing robust and smooth Couinaud segmentations. Specifically, we first segment the liver and vessels from the CT image and generate 3D liver point clouds and voxel grids embedded with the vessel structure. Then, our method with two input-specific branches extracts complementary feature representations from points and voxels, respectively. The local attention module adaptively fuses features from the two branches at different scales to balance the contribution of different branches in learning more discriminative features. Furthermore, we propose a novel distance loss at the feature level to make the features in the segment more compact, thereby improving the certainty of segmentation between segments. Our experimental results on three public liver datasets demonstrate that our proposed method outperforms several state-of-the-art methods by large margins. Specifically, in out-of-distribution (OOD) testing of LiTS dataset, our method exceeded the voxel-based 3D UNet by approximately 20% in Dice score, and outperformed the point-based PointNet2Plus by approximately 8% in Dice score. Our code and manual annotations of the public datasets presented in this paper are available online: https://github.com/xukun-zhang/Couinaud-Segmentation.

The Association between Food Groups, Nutraceuticals, and Food Supplements Consumption on Vascular Health Outcomes: A Literature Review.

Vascular aging, marked by alterations in the structure and function of blood vessels, including heightened arterial stiffness and impaired endothelial function, is linked to a higher likelihood of developing cardiovascular and age-associated pathological conditions. Oxidative stress and inflammation are key stimulation factors in vascular aging. Engaging in healthy dietary habits could enhance the functioning of blood vessels. The aim of this study was to conduct a literature review of the evidence regarding the relationship between food regimens, nutraceuticals, and dietary supplements and vascular health. A search of electronic databases, including PubMed, Scopus, and Web of Science Core Collection, was performed. Experimental and observational studies evaluating the association between food groups, nutraceuticals, supplements, and endothelial function and/or arterial stiffness were deemed eligible for this narrative review. Based on the current body of the included studies, food groups, nutraceuticals, and dietary supplements may not demonstrate superiority over placebos in enhancing markers of vascular health. To obtain more reliable evidence on the effectiveness of interventions in vascular health, additional RCTs with larger sample sizes, extended follow-up periods, and multi-center participation are necessary. Enhancing the credibility of these RCTs requires better control of dietary variables and more precise measurement of vascular health markers.

Stem‐Cell‐Based Small‐Diameter Blood Vessels with 3D Printing

Cardiovascular disease has emerged as the leading cause of death worldwide. Since coronary arteries, carotid arteries, and other blood vessels are prone to narrowing, small‐diameter artificial blood channels offer a crucial solution for restoring blood flow. Ideal grafts must emulate the structure of natural blood vessels, possess adequate mechanical strength, ensure long‐term patency, and incorporate functional cells with minimal immunogenicity. Enhanced cell sources and engineering methods are vital for the creation of functional small‐diameter blood vessels (SDBVs). Among potential cell sources, stem cells stand out due to their ability to differentiate into multiple cell types, self‐renew, and exhibit low immunogenicity. Additionally, three‐dimensionally (3D) printed vascular stents have attracted widespread attention for their precision and controllable bioink application. The need for tissue‐engineered blood vessels is currently rising, and innovative design concepts integrating stem cells and 3D printing present promising solutions. Herein, the construction requirements of vascular grafts are reviewed, current status of using stem cells as a cell source and 3D printing as an engineering strategy is described, and prospects and challenges for the development of SDBVs in the medical field are discussed.

The sealing performance of PTFE O-rings at low temperature

Abstract Polytetrafluoroethylene (PTFE) has the advantages of high chemical stability, wide operating temperature range, excellent lubrication, etc. It is widely used in aerospace, automotive industry and other engineering fields and plays an important role in sealing applications of low-temperature media. However, in practical engineering applications, the design of PTFE O-rings in low-temperature variable-temperature working conditions lacks a reference basis. Based on the sealing structure of low-temperature pressure vessel, this paper establishes a two-dimensional axisymmetric model using the finite element analysis software Abaqus to study the sealing performance of PTFE O-rings in the static sealing system, and analyzes the effects of temperature, compression rate, cold shrinkage on the deformation of the PTFE O-rings, the Mises stresses, and the contact pressure.

Taohong Siwu decoction enhances the chemotherapeutic efficacy of doxorubicin by promoting tumor vascular normalization

BackgroundInstead of completely suppressing blood vessels inside tumors, vascular normalization therapy is proposed to normalize and prune the abnormal vasculature in tumor microenvironment (TME) to acquire a normal and stable blood flow and perfusion. The theoretical basis for the use of “blood-activating and stasis-resolving” formulas in Traditional Chinese Medicine to treat cancer is highly consistent with the principle of vascular normalization therapy, suggesting the potential application of these traditional formulas in vascular normalization therapy. PurposeTo study the underlying mechanisms of a classical “blood-activating and stasis-resolving” formula, Taohong Siwu decoction (TSD), in enhancing the efficacy of chemotherapy for breast cancer treatment. Study designHUVECs and transgenic zebrafish embryos were used as the major model in vitro. A 4T1 mouse breast cancer model was applied to study tumor vasculature normalization of TSD and the combination effects with DOX. ResultsOur data showed that TSD exhibited anti-angiogenic potential in HUVECs and transgenic zebrafish embryos. After 20 days treatment, TSD significantly normalized the tumor vasculature by remodeling vessel structure, reducing intratumoral hypoxia and vessel leakage, and promoting vessel maturation and blood perfusion in 4T1 breast tumor-bearing mice. Moreover, the anti-tumor efficacy of doxorubicin liposome in 4T1 breast tumors was significantly improved by TSD, including the suppression of tumor cell proliferation, angiogenesis, hypoxia, and the increase of cell apoptosis, which is likely through the vascular normalization induced by TSD. TSD also shifted the macrophage polarization from M2 to M1 phenotype in TME during the combination therapy, as evidenced by the reduced number of CD206+ macrophages and increased number of CD86+ macrophages. Additionally, TSD treatment protected against doxorubicin-induced cardiotoxicity in animals, as evidenced by the reduced cardiomyocytes apoptosis and improved heart function. ConclusionThis study demonstrated for the first time that TSD as a classical Chinese formula can enhance the drug efficacy and reduce the side effects of doxorubicin. These findings can support that TSD could be used as an adjuvant therapy in combination with conventional chemotherapy for the future breast cancer treatment.

CAS‐GAN: A Novel Generative Adversarial Network‐Based Architecture for Coronary Artery Segmentation

ABSTRACTAccurate and automated segmentation of x‐ray coronary angiography (XRCA) is crucial for both diagnosing and treating coronary artery diseases. Despite the outstanding results achieved by deep learning (DL)‐based methods in this area, this task remains challenging due to several factors such as poor image quality, the presence of motion artifacts, and inherent variability in vessel structure sizes. To address this challenge, this paper introduces a novel GAN‐based architecture for coronary artery segmentation using XRCA images. This architecture includes a novel U‐Net variant with two types of self‐attention blocks in the generator segment. An auxiliary path connects the attention block and the prediction block to enhance feature generalization, improving vessel structure delineation, especially thin vessels in low‐contrast regions. In parallel, the discriminator network employs a residual CNN with similar attention blocks for balanced performance and improved predictive capabilities. With a streamlined 6.74 M parameters, the resulting architecture surpasses existing methods in efficiency. We assess its efficacy on three coronary artery datasets: our private “CORONAR,” and the public “DCA1” and “CHUAC” datasets. Empirical results showcase our model's superiority across these datasets, utilizing both original and preprocessed images. Notably, our proposed architecture achieves the highest F1‐score of 0.7972 for the CHUAC dataset, 0.8245 for the DCA1 dataset, and 0.8333 for the CORONAR dataset.

An adaptive spatiotemporal filter for ultrasound localization microscopy based on density canopy clustering

Ultrasound Localization Microscopy (ULM) facilitates structural and hemodynamic imaging of microvessels with a resolution of tens of micrometers. In ULM, the extraction of effective microbubble signals is crucial for image quality. Singular Value Decomposition (SVD) is currently the most prevalent method for microbubble signal extraction in ULM. Most existing ULM studies employ a fixed SVD filter threshold using empirical values which will lead to imaging quality degradation due to the insufficient separation of blood signals. In this study, we propose an adaptive and non-threshold SVD filter based on canopy-density clustering, termed DCC-SVD. This filter automatically classifies the components of the SVD based on the density of their spatiotemporal features, eliminating the need for parameter selection. In in vitro tube phantom, DCC-SVD demonstrated its ability to adaptive separation of blood and bubble signal at varying microbubble concentrations and flow rates. We compared the proposed DCC-SVD method with the Block-match 3D (BM3D) filter and a classical adaptive method called spatial similarity matrix (SSM), using concentration-variable in vivo rat brain data, as well as open-source rat kidney and mouse tumor datasets. The proposed DCC-SVD improved the global spatial resolution by approximately 4 μm from 30.39 μm to 26.02 μm. It also captured vessel structure absent in images obtained by other methods and yielded a smoother vessel intensity profile, making it a promising spatiotemporal filter for ULM imaging.

Design and Optimization of a Double-Layer Composite Pressure Vessel Structure

Design and Optimization of a Double-Layer Composite Pressure Vessel Structure

Skeleton-guided multi-scale dual-coordinate attention aggregation network for retinal blood vessel segmentation

Deep learning plays a pivotal role in retinal blood vessel segmentation for medical diagnosis. Despite their significant efficacy, these techniques face two major challenges. Firstly, they often neglect the severe class imbalance in fundus images, where thin vessels in the foreground are proportionally minimal. Secondly, they are susceptible to poor image quality and blurred vessel edges, resulting in discontinuities or breaks in vascular structures. In response, this paper proposes the Skeleton-guided Multi-scale Dual-coordinate Attention Aggregation (SMDAA) network for retinal vessel segmentation. SMDAA comprises three innovative modules: Dual-coordinate Attention (DCA), Unbalanced Pixel Amplifier (UPA), and Vessel Skeleton Guidance (VSG). DCA, integrating Multi-scale Coordinate Feature Aggregation (MCFA) and Scale Coordinate Attention Decoding (SCAD), meticulously analyzes vessel structures across various scales, adept at capturing intricate details, thereby significantly enhancing segmentation accuracy. To address class imbalance, we introduce UPA, dynamically allocating more attention to misclassified pixels, ensuring precise extraction of thin and small blood vessels. Moreover, to preserve vessel structure continuity, we integrate vessel anatomy and develop the VSG module to connect fragmented vessel segments. Additionally, a Feature-level Contrast (FCL) loss is introduced to capture subtle nuances within the same category, enhancing the fidelity of retinal blood vessel segmentation. Extensive experiments on three public datasets (DRIVE, STARE, and CHASE_DB1) demonstrate superior performance in comparison to current methods. The code is available at https://github.com/wangwxr/SMDAA_NET.

Impact of Jet Fires on Steel Structures: Application of Passive Fire Protection Materials

Jet fires are the second most common fire scenario after spill fires. This type of fire is characteristic of gas and gas–oil fires occurring on oil platforms and gas production and processing plants. The consequences of such fires are characterized by high material damage; this is associated with extensive networks of technological communications, since there is a high density of technological facilities and installations in the territory where these fires occur. At such facilities, there is a large number of steel structures, which under the action of high temperature quickly lose their strength and deform. To protect steel structures in the oil and gas industry, fire protection is used, which consists of different types: boards in the form of flat plates, plasters, and epoxy paints. This paper compares three types of fire protection materials for steel structures under jet fire: board fireproofing, plaster composition, and epoxy coating. When comparing the efficiency in jet fire, cement boards were found to be the best. However, despite the better fire protection efficiency, their low application is expected due to their massiveness and the high cost of such protection and the difficulty of installation. Nevertheless, the development of fire depends on the place of its origin, the size of the initial fire zone, and the stability and massiveness of the metal elements of the vessel structure or the structure of the boards on which the equipment can be placed. Therefore, it is necessary to take these factors into account when selecting fire protection and to apply it depending on the required fire resistance limits of structures, which should be determined depending on the fire development scenarios.

Liver vessel MRI image segmentation based on dual-path diffusion model

Accurate segmentation of liver blood vessels in magnetic resonance imaging (MRI) images is a challenging task due to the complex tree-like structure and anisotropic diffusion properties of blood vessels. To solve this problem, we propose a new Dual-Path Diffusion Model (DPDM) framework. The framework consists of two collaborative diffusion paths: a local feature learning path based on convolution operations and a global context modeling path based on transform blocks. Local path encodes rich shape priors and preserve spatial details, while global path captures long-distance dependencies and enhance representation. In the decoding phase, the boundary features from the boundary path are fused with the features of the ordinary path decoding, which further enhances the shape sensitivity. In addition, we leverage a multi-task learning scheme to jointly optimize vascular segmentation and boundary prediction tasks in an end-to-end manner. Experiments on retrospective clinical dataset demonstrate that the proposed DPDM framework achieves excellent performance on the liver vessel segmentation task. Compared with state-of-the-art methods, our approach achieved a 6.0% and 7.3% performance improvement in Dice coefficient and IoU index, respectively. Our approach offers a promising solution for automated blood vessel segmentation in precision medicine.

Numerical simulation study of the influence of stirred vessel structure on particle suspension characteristics in dilute solutions

The Euler-Euler/RANS approach has been used to simulate the particle suspension in solid–liquid stirred vessels, with experimental validation conducted through the Electrical Sensing Zone Method (ESZ). An innovative evaluation method has been introduced to quantify the critical impeller speed Nc required for particles to achieve a specific uniform suspension state in dilute solutions. The impact of different vessel bottom shapes (flat, round, and W-shaped) on particle suspension characteristics is examined. The results indicate that optimizing the bottom shape to align with flow streamlines markedly enhances particle suspension, a conclusion supported by the Online Micro-sized Particle Analyzer (OMiPA). Further analysis shows baffle configurations transform the tangential and radial velocities into the axial velocity, which is not conducive to providing initial conditions for particle suspension but improves overall suspension uniformity. Overall, effective particle suspension relies on the interaction between main flow and turbulent fluctuations, with tangential and radial flows playing critical roles.

Super-Resolution Ultrasound Imaging Using the Erythrocytes-Part I: Density Images.

A new approach for vascular super-resolution (SR) imaging using the erythrocytes as targets (SUper-Resolution ultrasound imaging of Erythrocytes (SURE) imaging) is described and investigated. SURE imaging does not require fragile contrast agent bubbles, making it possible to use the maximum allowable mechanical index (MI) for ultrasound scanning for an increased penetration depth. A synthetic aperture (SA) ultrasound sequence was employed with 12 virtual sources (VSs) using a 10-MHz GE L8-18i-D linear array hockey stick probe. The axial resolution was [Formula: see text]m) and the lateral resolution was [Formula: see text]m). Field IIpro simulations were conducted on 12.5- μ m radius vessel pairs with varying separations. A vessel pair with a separation of 70 μ m could be resolved, indicating a SURE image resolution below half a wavelength. A Verasonics research scanner was used for the in vivo experiments to scan the kidneys of Sprague-Dawley rats for up to 46 s to visualize their microvasculature by processing from 0.1 up to 45 s of data for SURE imaging and for 46.8 s for SR imaging with a SonoVue contrast agent. Afterward, the renal vasculature was filled with the ex vivo micro-computed tomography (CT) contrast agent Microfil, excised, and scanned in a micro-CT scanner at both a 22.6- μ m voxel size for 11 h and for 20 h in a 5- μ m voxel size for validating the SURE images. Comparing the SURE and micro-CT images revealed that vessels with a diameter of 28 μ m, five times smaller than the ultrasound wavelength, could be detected, and the dense grid of microvessels in the full kidney was shown for scan times between 1 and 10 s. The vessel structure in the cortex was also similar to the SURE and SR images. Fourier ring correlation (FRC) indicated a resolution capability of 29 μ m. SURE images are acquired in seconds rather than minutes without any patient preparation or contrast injection, making the method translatable to clinical use.

Layup design and mechanical properties of an optimal dome profile

AbstractThe development of lightweight and high‐performance composite pressure vessels have become a popular research objective. The purpose of this study is to the layup design and improve the mechanical properties of pressure vessel structure with optimal dome profile under an internal pressure of 70 MPa. First, the prediction equations of fiber thickness for the cylindrical and dome sections are provided. Second, the influence laws of linear parameters on fiber layer thickness distribution are analyzed. Third, the optimal dome profile is used as the pressure vessel structure for the layup design, and the variable polar hole process is applied to solve the fiber thickness accumulation problem near the polar holes. Finally, the composite pressure vessel is modeled using Abaqus finite element software, and the stress and strain distributions of the liner and the composite layer under different internal pressure loads are analyzed under self‐tightening pressure. Results show that the variable polar hole process reduces the maximum thickness value by nearly 50.16% at the maximum fiber thickness of the dome. Under working pressure, the maximum stress of the liner is reduced by 43.41% after applying self‐tightening pressure. In addition, the variable polar hole process can effectively realize the lightweighting of composite pressure vessels while self‐tightening pressure can effectively improve the mechanical properties of the liner. This research provides a valuable reference for the design of composite pressure vessels.Highlights Fiber accumulation occurs in two bandwidth regions near the polar hole. The variable polar hole process can solve the fiber accumulation problem. The optimum dome profile geometric parameters are m = 0.8 and rc = 0.4R. The self‐tightening pressure improves liner loading capacity.

Analysis of damage characteristics of the rectangular steel container under near-earth explosion loading

In this study, the dynamic response of a semi-buried steel vessel under a near-surface explosion shock load was investigated through full-scale model tests. A three-dimensional precise numerical simulation model of the semi-buried steel vessel structure was established, and the dynamic response and damage consequences under different load conditions were analyzed. The influence of the thickness of corrugated steel on the dynamic damage and antiknock properties of the structure was studied. The results show that the weak part of the rectangular steel vessel structure is mainly located at the connection between the blast-facing panel and the beam column under near-surface explosion conditions. The peak stress and displacement of the blasting surface decrease with the increase in the horizontal angle of the incident wave, whilst/while increase with the height of the explosion. Under the same explosive yield, the main failure mode of the structure is the tearing of the plate and beam under plastic deformation. Increasing the thickness of the corrugated steel can significantly restrain the plastic deformation and acceleration of the plate. When the thickness is increased from 2 mm to 6 mm, the antiknock performance is significantly improved. However, when the thickness is increased from 6 mm to 8 mm, the improvement in antiknock performance slows down. Increasing the height of the enclosing soil can also effectively improve the explosion-proof performance of the structure, with full burial providing the best effect. The research results provide a theoretical basis for the application of rectangular steel vessel structures in the field of protection engineering.