Detection Of Vessels Research Articles

Coating Damage Detection of Vessels Using Underwater Electric Field Based on Machine Learning

Coating Damage Detection of Vessels Using Underwater Electric Field Based on Machine Learning

Smart Blood Vessel Detection System for Laparoscopic Surgery.

Objective: Compared with traditional surgery, laparoscopic surgery offers the advantages of smaller scars and rapid recovery and has gradually become popular. However, laparoscopic surgery has the limitation of low visibility and a lack of touch sense. As such, a physician may unexpectedly damage blood vessels, causing massive bleeding. In clinical settings, Doppler ultrasound is commonly used to detect vascular locations, but this approach is affected by the measuring angle and bone shadow and has poor ability to distinguish arteries from veins. To tackle these problems, a smart blood vessel detection system for laparoscopic surgery is proposed. Methods: Based on the principle of near-infrared spectroscopy, the proposed instrument can access hemoglobin (HbT) parameters at several depths simultaneously and recognize human tissue type by using a neural network. Results: Using the differences in HbT and StO2 between different tissues, vascular and avascular locations can be recognized. Moreover, a mechanically rotatable stick enables the physician to easily operate in body cavities. Phantom and animal experiments were performed to validate the system’s performance. Conclusion: The proposed system has high ability to distinguish vascular from avascular locations at various depths.

Determination of the location of the needle entry point based on an improved pruning algorithm.

Since the emergence of new coronaviruses and their variant virus, a large number of medical resources around the world have been put into treatment. In this case, the purpose of this article is to develop a handback intravenous intelligence injection robot, which reduces the direct contact between medical staff and patients and reduces the risk of infection. The core technology of hand back intravenous intelligent robot is a handlet venous vessel detection and segmentation and the position of the needle point position decision. In this paper, an image processing algorithm based on U-Net improvement mechanism (AT-U-Net) is proposed for core technology. It is investigated using a self-built dorsal hand vein database and the results show that it performs well, with an F1-score of 93.91%. After the detection of a dorsal hand vein, this paper proposes a location decision method for the needle entry point based on an improved pruning algorithm (PT-Pruning). The extraction of the trunk line of the dorsal hand vein is realized through this algorithm. Considering the vascular cross-sectional area and bending of each vein injection point area, the optimal injection point of the dorsal hand vein is obtained via a comprehensive decision-making process. Using the self-built dorsal hand vein injection point database, the accuracy of the detection of the effective injection area reaches 96.73%. The accuracy for the detection of the injection area at the optimal needle entry point is 96.50%, which lays a foundation for subsequent mechanical automatic injection.

A Low-Power, Ambiguous Synthetic Aperture Radar Concept for Continuous Ship Monitoring

This article addresses the design of a low-cost synthetic aperture radar (SAR) for a single dedicated application, namely illegal vessel detection, which can be implemented using a small satellite and is characterized by reduced transmit power and high resolution. Minimum requirements in terms of noise equivalent sigma zero and resolution that ensure acceptable detection performance are derived on the basis of ship statistics extracted from TerraSAR-X data. One peculiarity of the design is that a pulse repetition frequency much smaller than the nominal Doppler bandwidth is selected to increase the swath width beyond the classical SAR limitation without using digital beamforming, as the azimuth ambiguities can be tolerated for this specific application. Several design examples of SAR systems operating in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</i> -band demonstrate the potential of this concept for small ship monitoring over swaths of 50–90 km with antennas smaller than 0.6 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and very low average transmit powers comprised between 20 and 80 W.

Soft Computing Based Discriminator Model for Glaucoma Diagnosis

In this study, a Discriminator Model for Glaucoma Diagnosis (DMGD) using soft computing techniques is presented. As the biomedical images such as fundus images are often acquired in high resolution, the Region of Interest (ROI) for glaucoma diagnosis must be selected at first to reduce the complexity of any system. The DMGD system uses a series of pre-processing; initial cropping by the green channel’s intensity, Spatially Weighted Fuzzy C Means (SWFCM), blood vessel detection and removal by Gaussian Derivative Filters (GDF) and inpainting algorithms. Once the ROI has been selected, the numerical features such as colour, spatial domain features from Local Binary Pattern (LBP) and frequency domain features from LAWS are generated from the corresponding ROI for further classification using kernel based Support Vector Machine (SVM). The DMGD system performances are validated using four fundus image databases; ORIGA, RIM-ONE, DRISHTI-GS1, and HRF with four different kernels; Linear Kernel (LK), Polynomial Kernel (PK), Radial Basis Function (RBFK) kernel, Quadratic Kernel (QK) based SVM classifiers. Results show that the DMGD system classifies the fundus images accurately using the multiple features and kernel based classifies from the properly segmented ROI.

Rider Manta Ray Foraging Optimization-based Generative Adversarial Network and CNN feature for detecting glaucoma

Glaucoma is a leading and rapidly increasing disease in recent years, which affects the eye's optic nerve and causes everlasting vision loss. The early recognition of glaucoma is a significant one for decreasing the risk of permanent loss of sight. Therefore, it is necessary to detect glaucoma disease in the initial stage. Moreover, optic cup segmentation from retinal fundus images is an essential procedure for automatic glaucoma detection. In this paper, an efficient glaucoma detection approach is developed using Rider Manta-Ray Foraging Optimization-based General Adversarial Network ((Rider MRFO-based GAN). Here, the optic disc segmentation process is performed by the Fuzzy Local Information C-Means clustering (FLICM clustering). In addition, the sparking process is also employed in this glaucoma detection method for the blood vessel detection process. Several significant features in this detection model, namely mean, standard deviation, variance, kurtosis, skewness, entropy, and CNN features, are extracted to detect glaucomatous images further. The developed Rider MRFO approach is newly developed by the MRFO technique and Rider Optimization Algorithm (ROA). Additionally, the developed glaucoma detection technique performs better based on various parameters, like specificity, sensitivity, and accuracy. Hence, the developed Rider MRFO-based GAN model showed improved results with the highest accuracy of 0.96, the sensitivity of 0.94, and the specificity of 0.89.

Vascularity and Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging.

Angiogenesis is a key step in the initiation and progression of an invasive breast cancer. High microvessel density by morphological characterization predicts metastasis and poor survival in women with invasive breast cancers. However, morphologic characterization is subject to variability and only can evaluate a limited portion of an invasive breast cancer. Consequently, breast Magnetic Resonance Imaging (MRI) is currently being evaluated to assess vascularity. Recently, through the new field of radiomics, dynamic contrast enhanced (DCE)-MRI is being used to evaluate vascular density, vascular morphology, and detection of aggressive breast cancer biology. While DCE-MRI is a highly sensitive tool, there are specific features that limit computational evaluation of blood vessels. These include (1) DCE-MRI evaluates gadolinium contrast and does not directly evaluate biology, (2) the resolution of DCE-MRI is insufficient for imaging small blood vessels, and (3) DCE-MRI images are very difficult to co-register. Here we review computational approaches for detection and analysis of blood vessels in DCE-MRI images and present some of the strategies we have developed for co-registry of DCE-MRI images and early detection of vascularization.

A small vessel detection using a co-located multi-frequency FMCW MIMO radar

Small vessels detection is a known issue due to its low radar cross section (RCS). An existing shore-based vessel tracking radar is for long-distance commercial vessels detection. Meanwhile, a vessel-mounted radar system known for its reliability has a limitation due to its single radar coverage. The paper presented a co-located frequency modulated continuous waveform (FMCW) maritime radar for small vessel detection utilising a multiple-input multiple-output (MIMO) configuration. The radar behaviour is numerically simulated for detecting a Swerling 1 target which resembles small maritime’s vessels. The simulated MIMO configuration comprised two transmitting and receiving nodes. The proposal is to utilize a multi-frequency FMCW MIMO configuration in a maritime environment by applying the spectrum averaging (SA) to fuse MIMO received signals for range and velocity estimation. The analysis was summarised and displayed in terms of estimation error performance, probability of error and average error. The simulation outcomes an improvement of 2.2 dB for a static target, and 0.1 dB for a moving target, in resulting the 20% probability of range error with the MIMO setup. A moving vessel's effect was observed to degrade the range error estimation performance between 0.6 to 2.7 dB. Meanwhile, the proposed method was proven to improve the 20% probability of velocity error by 1.75 dB. The impact of multi-frequency MIMO was also observed to produce better average error performance.

Long-term Use of Ultrasound for Locating Optimal LVA Sites: A Descriptive Data Analysis.

Preoperative mapping of lymphatic vessels for lymphovenous anastomosis (LVA) surgery is frequently performed by indocyanine green (ICG) lymphography solely; however, other imaging modalities, such as ultrasound (US), might be more efficient, particularly for Caucasian patients. We present our preoperative assessment protocol, experience, and approach of using US for locating optimal LVA sites. Fifty-six (16 males) lymphedema patients who underwent LVA surgery were included in this study, 5 of whom received two LVA operations. In total, 61 LVA procedures with 233 dissected lymphatic vessels were evaluated. Preoperative US was performed by the author S.M. 2 days before intraoperative ICG lymphography. Fluid-predominant lymphedema regions were scanned more profoundly. Skin incisions followed preoperative US and ICG lymphography markings. Detection of lymphatic vessels was compared between ICG lymphography and the US by using the intraoperative verification under the microscope with 20 to 50x magnification as the reference standard. Among the dissected lymphatic vessels, 83.3% could be localized by US, and 70% were detectable exclusively by it. In all, 7.2% of US-detected lymphatic vessels could not be found and verified intraoperatively. Among the lymphatic vessels found by US, only 16% were apparent with ICG before skin incision. In total, 23.2% of the dissected lymphatic vessels could be visualized with ICG lymphography preoperatively. Only 9.9% of the lymphatic vessels could be found by ICG alone. High-frequency US mapping accurately finds functional lymphatic vessels and matching veins. It locates fluid-predominant regions for targeted LVA surgeries. It reveals 3.6 times as many lymphatic vessels as ICG lymphography. In our practice, it has an integral role in planning LVA procedures.

An Augmented Reality Periscope for Submarines with Extended Visual Classification.

Submarines are considered extremely strategic for any naval army due to their stealth capability. Periscopes are crucial sensors for these vessels, and emerging to the surface or periscope depth is required to identify visual contacts through this device. This maneuver has many procedures and usually has to be fast and agile to avoid exposure. This paper presents and implements a novel architecture for real submarine periscopes developed for future Brazilian naval fleet operations. Our system consists of a probe that is connected to the craft and carries a 360 camera. We project and take the images inside the vessel using traditional VR/XR devices. We also propose and implement an efficient computer vision-based MR technique to estimate and display detected vessels effectively and precisely. The vessel detection model is trained using synthetic images. So, we built and made available a dataset composed of 99,000 images. Finally, we also estimate distances of the classified elements, showing all the information in an AR-based interface. Although the probe is wired-connected, it allows for the vessel to stand in deep positions, reducing its exposure and introducing a new way for submarine maneuvers and operations. We validate our proposal through a user experience experiment using 19 experts in periscope operations.

Value of susceptibility weighted imaging (SWI) in assessment of intra-arterial thrombus in patients with acute ischemic stroke

BackgroundIschemic stroke is a major cause of death and disability. Thrombolytic therapy is a standard treatment stroke nowadays for ischemic strokes up to 4.5 h from start of symptoms. Although arterial occlusion can be detected by digital subtraction angiography (DSA), magnetic resonance angiography (MRA), and computed tomography angiography (CTA), the question about thrombus composition and formation times still might not be replied. The use of susceptibility weighted imaging (SWI) for detecting thrombus in acute ischemic stroke is getting to be a strongly investigated field. SWI can show the thrombus as a hypointense susceptibility vessel sign (SVS) in the affected area.ResultsNinety-seven of our patients showed thrombus in MRA study. M1 segment was the most affected MCA segment representing about 57.6%. SWI detected intra-arterial thrombus in 122 patients compared to 97 patients detected by MRA (P = 0.0002). All patients had positive susceptibility sign. 88.8% of patients with positive thrombus in SWI had solitary thrombus, and 11.2% has multiple thrombi; on the other hand, MRA fails to detect any distant thrombi. 81% of patients with abnormally prominent vessel sign (APVS) showed parenchymal changes in these areas. On the other hand, deep structures, namely caudate nucleus, internal capsule and lentiform nucleus, are the least affected areas. All patients with abnormally prominent vessel sign showed arterial occlusion, and only 9 patients with no APVS showed arterial occlusion (P = 0.0001).ConclusionSWI plays an important role in the detection of peripheral thrombi in patients with acute ischemic stroke. Both SWI and MRA might complement each other for visual detection of occluded vessel. We recommend implementation of SWI into routine acute stroke MRI protocols.

Room-Temperature Synthesis of Air-Stable Near-Infrared Emission in FAPbI3 Nanoparticles Embedded in Silica.

Hybrid organic−inorganic and all-inorganic metal halide perovskite nanoparticles (PNPs) have shown their excellent characteristics for optoelectronic applications. We report an atmospheric process to embed formamidinium CH(NH2)2PbI3 (FAPbI3) PNPs in silica protective layer at room temperature (approximately 26 °C) employing (3-aminopropyl) triethoxysilane (APTES). The resulting perovskite nanocomposite (PNCs) achieved a high photoluminescence (PL) quantum yield of 58.0% and good stability under atmospheric moisture conditions. Moreover, the PNCs showed high PL intensity over 1 month of storage (approximately 26 °C) and more than 380 min of PNCs solutions in DI water. The studied near-infrared (NIR) light-emitting diode (LED) combined a NIR-emitting PNCs coating and a blue InGaN-based chip that exhibited a 788 nm electroluminescence spectrum of NIR-LEDs under 2.6 V. This may be a powerful tool to track of muscle and disabled patients in the detection of a blood vessel.

Influence of background preprocessing on the performance of deep learning retinal vessel detection.

Purpose: Segmentation of the vessel tree from retinal fundus images can be used to track changes in the retina and be an important first step in a diagnosis. Manual segmentation is a time-consuming process that is prone to error; effective and reliable automation can alleviate these problems but one of the difficulties is uneven image background, which may affect segmentation performance. Approach: We present a patch-based deep learning framework, based on a modified U-Net architecture, that automatically segments the retinal blood vessels from fundus images. In particular, we evaluate how various pre-processing techniques, images with either no processing, N4 bias field correction, contrast limited adaptive histogram equalization (CLAHE), or a combination of N4 and CLAHE, can compensate for uneven image background and impact final segmentation performance. Results: We achieved competitive results on three publicly available datasets as a benchmark for our comparisons of pre-processing techniques. In addition, we introduce Bayesian statistical testing, which indicates little practical difference ( ) between pre-processing methods apart from the sensitivity metric. In terms of sensitivity and pre-processing, the combination of N4 correction and CLAHE performs better in comparison to unprocessed and N4 pre-processing ( ); but compared to CLAHE alone, the differences are not significant ( to 0.88). Conclusions: We conclude that deep learning is an effective method for retinal vessel segmentation and that CLAHE pre-processing has the greatest positive impact on segmentation performance, with N4 correction helping only in images with extremely inhomogeneous background illumination.

Lymphaticovenous Anastomosis for Age-Related Lymphedema.

Introduction: Primary lymphedema is usually caused by intrinsic disruption or genetic damage to the lymphatics but may also be the result of age-related deterioration of the lymphatics. The aims of this study were to determine the characteristics of age-related lymphedema and to assess the effectiveness of lymphaticovenous anastomosis (LVA) in its treatment. Methods: Eighty-six patients with primary lymphedema affecting 150 lower limbs were divided into three groups according to whether the age of onset was younger than 35 years, 35–64 years, or 65 years or older. Indocyanine green (ICG) lymphography was performed, followed by LVA surgery. ICG lymphography images were visually classified according to whether the pattern was linear, low enhancement (LE), distal dermal backflow (dDB), or extended dermal backflow (eDB). The lower extremity lymphedema (LEL) index score was calculated before and after LVA. Lymphatic vessel diameter and detection rates were also recorded. Results: In the ≥65 group, the lymphedema was bilateral in 54 patients and unilateral in 1 patient. There was statistically significant deterioration in the LEL index score with progression from the linear, LE, dDB through to the eDB pattern in the ≥65 group. The lymphatic vessel diameter was significantly greater in the ≥65 group. The rate of improvement was highest in the ≥65 group. Conclusion: Age-related lymphedema was bilateral and deterioration started distally. The lymphatic vessels in patients with age-related lymphedema tended to be ectatic, which is advantageous for LVA and may increase the improvement rate.

A Novel Endoscopic Ultrasonography Imaging Technique for Depicting Microcirculation in Pancreatobiliary Lesions without the Need for Contrast-Enhancement: A Prospective Exploratory Study.

Detective flow imaging endoscopic ultrasonography (DFI-EUS) provides a new method to image and detect fine vessels and low-velocity blood flow without using ultrasound contrast agents. The aim of this study was to evaluate the utility of DFI-EUS for pancreatobiliary lesions and lymph nodes. Between January 2019 and January 2020, 53 patients who underwent DFI-EUS, e-FLOW EUS, and contrast-enhanced EUS were enrolled. The ability of DFI-EUS and e-FLOW EUS to detect vessels was compared with that of contrast-enhanced EUS. This article describes the DFI technique along with our first experience of its use for vascular assessment of pancreatobiliary lesions. Vessels were imaged in 34 pancreatic solid lesions, eight intraductal papillary mucinous neoplasms (IPMNs), seven gall bladder lesions, and four swollen lymph nodes. DFI-EUS (91%) was significantly superior to e-FLOW EUS (53%) with respect to detection of vessels (p < 0.001) and for discrimination of mural nodules from mucous clots in IPMN and gallbladder lesions from sludge (p = 0.046). Thus, DFI-EUS has the potential to become an essential tool for diagnosis and vascular assessment of various diseases.

Lightweight deep network-enabled real-time low-visibility enhancement for promoting vessel detection in maritime video surveillance

AbstractMaritime video surveillance has become an essential part of the vessel traffic services system, intended to guarantee vessel traffic safety and security in maritime applications. To make maritime surveillance more feasible and practicable, many intelligent vision-empowered technologies have been developed to automatically detect moving vessels from maritime visual sensing data (i.e., maritime surveillance videos). However, when visual data is collected in a low-visibility environment, the essential optical information is often hidden in the dark, potentially resulting in decreased accuracy of vessel detection. To guarantee reliable vessel detection under low-visibility conditions, the paper proposes a low-visibility enhancement network (termed LVENet) based on Retinex theory to enhance imaging quality in maritime video surveillance. LVENet is a lightweight deep neural network incorporating a depthwise separable convolution. The synthetically-degraded image generation and hybrid loss function are further presented to enhance the robustness and generalisation capacities of LVENet. Both full-reference and no-reference evaluation experiments demonstrate that LVENet could yield comparable or even better visual qualities than other state-of-the-art methods. In addition, it takes LVENet just 0⋅0045 s to restore degraded images with size 1920 × 1080 pixels on an NVIDIA 2080Ti GPU, which can adequately meet real-time requirements. Using LVENet, vessel detection performance can be greatly improved with enhanced visibility under low-light imaging conditions.

Hardware and Software Complex for Detection and Visualization of Subsurface Blood Vessels During Brain Tumors Resection

Purpose: The purpose of this work is to prove the possibility of subsurface blood vessels detection during endoscopic resection of brain tumors using the method of endoscopy in red and near infrared light.&#x0D; Material and methods: This work was accomplished with an experimental setup, simulating the geometry of endoscopic resection of brain tumor. The setup realizes the backlight of operational field with light from diagnostic window of electromagnetic spectrum (650 1000 nm) and takes photos of operational field. After that special algorithm increases the contrast of the photos and detect subsurface blood vessels. &#x0D; The pieces of bovine brain have served as brain samples. And thin-walled transparent plastic tube with an internal diameter 1 mm filled with bovine blood has served as a blood vessel. The tube was placed into brain samples on different depths.&#x0D; Results: During the experiments the series of photos of bovine brain with artificial blood vessels located on different depths was received. For every photo contrast was increased and blood vessel was recognized.&#x0D; Conclusion: The series of experiments has showed the possibility to detect the blood vessels with outer diameter 1 mm in the depth of 2 mm and 3 mm in brain tissues using the method of endoscopy in red and near infrared light. The depth of 3 mm is enough for preliminary detection of blood vessel during the endoscopic resection of brain tumor.

Impact of image averaging on vessel detection using optical coherence tomography angiography in eyes with macular oedema and in healthy eyes.

To assess the repeatability of multiple automatic vessel density (VD) measurements and the effect of image averaging on vessel detection by optical coherence tomography angiography (OCTA). An observational study was conducted in a series of healthy volunteers and patients with macular oedema. Five sequential OCTA images were acquired for each eye using the OptoVue HD device. The effect of the averaging of the 5 acquisitions on vessel detection was analysed quantitatively using a pixel-by-pixel automated analysis. In addition, two independent retina experts qualitatively assessed the change in vessel detection in averaged images segmented in 9 boxes and compared to the first non-averaged image. The automatic VD measurement in OCTA images showed a good repeatability with an overall mean intra-class correlation coefficient (ICC) of 0.924. The mean ICC was higher in healthy eyes compared to eyes with macular oedema (0.877 versus 0.960; p < 0.001) and in the superficial vascular plexus versus the deep vascular complex (0.967 versus 0.888; p = 0.001). The quantitative analysis of the effect of the averaging showed that averaged images had a mean gain of 790.4 pixels/box, located around or completing interruptions in the vessel walls, and a mean loss of 727.2 pixels/box. The qualitative analysis of the averaged images showed that 99.6% of boxes in the averaged images had a gain in vessel detection (i.e., vessels detected in the averaged image but not in the non-averaged image). The loss of pixels was due to a reduction in background noise and motion artifacts in all cases and no case of loss of vessel detection was observed. The automatic VD measurement using the OptoVue HD device showed a good repeatability in 5 acquisitions in a row setting. Averaging images increased vessel detection, and in about a third of boxes, decreased the background noise, both in healthy eyes and, in a greater proportion, in eyes with macular oedema.

Vessel Target Detection in Spaceborne–Airborne Collaborative SAR Images via Proposal and Polarization Fusion

This paper focuses on vessel detection through the fusion of synthetic aperture radar (SAR) images acquired from spaceborne–airborne collaborative observations. The vessel target detection task becomes more challenging when it features inshore interferences and structured and shaped targets. We propose a new method, based on target proposal and polarization information exploitation (TPPIE), to fuse the spaceborne–airborne collaborative SAR images for accurate vessel detection. First, a new triple-state proposal matrix (TSPM) is generated by combining the normed gradient-based target proposal and the edge-based morphological candidate map. The TSPM can be used to extract the potential target regions, as well as filtering out the sea clutter and inshore interference regions. Second, we present a new polarization feature, named the absolute polarization ratio (APR), to exploit the intensity information of dual-polarization SAR images. In the APR map, the vessel target regions are further enhanced. Third, the final fused image with enhanced targets and suppressed backgrounds (i.e., improved target-to-clutter ratio; TCR) is attained by taking the Hadamard product of the intersected TSPM from multiple sources and the composite map exploiting the APR feature. Experimental analyses using Gaofen-3 satellite and unmanned aerial vehicle (UAV) SAR imagery indicate that the proposed TPPIE fusion method can yield higher TCRs for fused images and better detection performance for vessel targets, compared to commonly used image fusion approaches.

Impact of Multiphase Computed Tomography Angiography for Endovascular Treatment Decision-Making on Outcomes in Patients with Acute Ischemic Stroke.

Background and PurposeVarious imaging paradigms are used for endovascular treatment (EVT) decision-making and outcome estimation in acute ischemic stroke (AIS). We aim to compare how these imaging paradigms perform for EVT patient selection and outcome estimation. MethodsProspective multi-center cohort study of patients with AIS symptoms with multi-phase computed tomography angiography (mCTA) and computed tomography perfusion (CTP) baseline imaging. mCTA-based EVT-eligibility was defined as presence of large vessel occlusion (LVO) and moderate-to-good collaterals on mCTA. CTP-based eligibility was defined as presence of LVO, ischemic core (defined on relative cerebral blood flow, absolute cerebral blood flow, and cerebral blood volume maps) <70 mL, mismatch-ratio >1.8, absolute mismatch >15 mL. EVT-eligibility and adjusted rates of good outcome (modified Rankin Scale 0–2) based on these imaging paradigms were compared. ResultsOf 289/464 patients with LVO, 263 (91%) were EVT-eligible by mCTA-criteria versus 63 (22%), 19 (7%) and 103 (36%) by rCBF, aCBF, and CBV-CTP-criteria. CTP and mCTA-criteria were discordant in 40% to 53%. Estimated outcomes were best in patients who met both mCTA and CTP eligibility-criteria and were treated with EVT (62% to 87% good outcome). Patients eligible for EVT by mCTA-criteria and not by CTP-criteria receiving EVT achieved good outcome rates of 53% to 57%. Few patients met CTP-criteria and not mCTA-criteria for EVT. ConclusionsSimpler imaging selection criteria that rely on little else than detection of the occluded blood vessel may be more sensitive and less specific, thus resulting in more patients being offered EVT and arguably benefiting from it.