X-ray Angiography Images Research Articles

Deep Learning for CAD Prediction: X-ray Angiography Insights

This study presents a deep learning-based approach to improve the prediction of coronary artery disease (CAD) using X-ray angiography images. The primary objective is to achieve accurate and automated CAD identification by employing a convolutional neural network (CNN) model. The methodology involves preprocessing the dataset through normalization and augmentation techniques and utilizes a U-Net architecture for precise detection of coronary stenosis. To ensure robustness and generalizability, hyperparameter tuning and dropout regularisation are applied during model training. The proposed model achieves high performance, with an average Dice coefficient of 0.57 and a Jaccard Index of 0.47 on a held-out test set, indicating its effectiveness in segmenting coronary artery stenosis. These findings support the integration of deep learning methods into clinical workflows for enhanced CAD diagnosis and early intervention.

Optimizing neurointerventional procedures: an algorithm for embolization coil detection and automated collimation to enable dose reduction.

Monitoring radiation dose and time parameters during radiological interventions is crucial, especially in neurointerventional procedures, such as aneurysm treatment with embolization coils. The algorithm presented detects the presence of these embolization coils in medical images. It establishes a bounding box as a reference for automated collimation, with the primary objective being to enhance the efficiency and safety of neurointerventional procedures by actively optimizing image quality while minimizing patient dose. Two distinct methodologies are evaluated in our study. The first involves deep learning, employing the Faster R-CNN model with a ResNet-50 FPN as a backbone and a RetinaNet model. The second method utilizes a classical blob detection approach, serving as a benchmark for comparison. We performed a fivefold cross-validation, and our top-performing model achieved mean mAP@75 of 0.84 across all folds on validation data and mean mAP@75 of 0.94 on independent test data. Since we use an upscaled bounding box, achieving 100% overlap between ground truth and prediction is not necessary. To highlight the real-world applications of our algorithm, we conducted a simulation featuring a coil constructed from an alloy wire, effectively showcasing the implementation of automatic collimation. This resulted in a notable reduction in the dose area product, signifying the reduction of stochastic risks for both patients and medical staff by minimizing scatter radiation. Additionally, our algorithm assists in avoiding extreme brightness or darkness in X-ray angiography images during narrow collimation, ultimately streamlining the collimation process for physicians. To our knowledge, this marks the initial attempt at an approach successfully detecting embolization coils, showcasing the extended applications of integrating detection results into the X-ray angiography system. The method we present has the potential for broader application, allowing its extension to detect other medical objects utilized in interventional procedures.

Influence of anatomical structure on antiscatter grid performance in 2D: application to x-ray angiography and a prototype 29:1 ratio grid

Objective. The use of uniform phantoms to assess the influence of x-ray scatter and antiscatter grids on x-ray angiography and fluoroscopy image quality disregards the influence of spatially variable x-ray attenuation of patients. The purpose of this work was to measure scatter to primary ratio (SPR) and antiscatter grid SNR improvement factor (K SNR) using experimental conditions which better mimic patient imaging conditions. Approach. Three adult-sized anthropomorphic phantoms were used. AP and lateral projection images of the thorax and abdomen were acquired with and without an antiscatter grid. Grids with ratio 15:1 and 29:1 (r15, r29) and x-ray fields of view 20, 25 (thorax) and 32, 42 cm (abdomen) were tested. Combined with a-priori measurements of grid scatter and primary transmission fractions, these images were used to calculate 2D SPR and K SNR maps. Main results. Results demonstrated that measurements by uniform phantom do not describe the complex 2D SPR and K SNR distributions associated with anthropomorphic phantoms. The regions of the images with the lowest primary x-ray intensity (greatest attenuation) had the highest SPR and the highest K SNR attributable to the grids. Considering all conditions, the 95th percentile of the SPR maps was in the range 42%–185% greater than the median values and that of the K SNR maps was 4%–20% higher than the median values. The combined influences of SID 120 vs. 107 cm and r29 vs. r15 grid resulted in K SNR in the range 1.05–1.49. Significance. Performance of anti-scatter grids using anatomically complex phantoms highlights the substantial variation of SPR and K SNR within 2D images. Also, this work demonstrates the benefit of the prototype r29 grid for thoracic and abdominal angiography imaging conditions is substantial, especially for large patients and radiodense image regions.

Coronary Vessel Segmentation in X-ray Angiography Images Using Edge-Based Tracking Method

Automated extraction of coronary arteries is an essential process in the diagnosis of treatment for coronary artery disease (CAD) with computer assistance. Accurately outlining the coronary artery is difficult when using X-ray coronary angiography (XCA) because of the low signal-to-noise ratio and the presence of interfering background structures. In this paper, a new approach for segmenting vessels in angiograms is presented, specifically designed to tackle the difficulties arising from non-uniform illumination, artifacts, and noise present in angiographic images. The proposed method employs an edge-based tracking tool to generate an initial probability map for segmentation. A segmentation method based on coronary vessel tracking is presented for finding the border and centerline of the vessel. The proposed method is designed based on two main components: preprocessing and tracking. In the preprocessing stage, a guided filter and edge-sharpening algorithms are used to enhance the features of the original image. In the tracking stage, an initial point is selected, and using the Gaussian property, a semi-circle operator is applied to track the line perpendicular to the vessel. The proposed method demonstrated remarkable performance in terms of sensitivity and specificity, achieving values of 86.93 and 99.61, respectively. Additionally, the method achieved an accuracy rate of 97.81. Notably, the proposed method outperformed existing state-of-the-art segmentation methods, as indicated by its higher dice score. These impressive results signify a significant advancement in the field of vessel segmentation, highlighting the effectiveness and superiority of the proposed approach.

Deep Belief Bayesian Joint Conditional Detection of coronary artery plaque and stenosis in X-ray angiography images

Deep Belief Bayesian Joint Conditional Detection of coronary artery plaque and stenosis in X-ray angiography images

Unsuspected Limitations of 3D Printed Model in Planning of Complex Aortic Aneurysm Endovascular Treatment.

Static 3-dimensional (3D) printing became attractive for operative planning in cases that involve difficult anatomy. An interactive (low cost, fast) 3D print allowing deliberate surgical practice can be used to improve interventional simulation and planning. Endovascular treatment of complex aortic aneurysms is technically challenging, especially in case of narrow aortic lumen or significant aortic angulation (hostile anatomy). The risk of complications such as graft kinking and target vessel occlusion is difficult to assess based solely on traditional software measuring methods and remain highly dependent on surgeon skills and expertise. A patient with juxtarenal AAA with hostile anatomy had a 3-dimensional printed model constructed preoperatively according to computed tomography images. Endovascular graft implantation in the 3D printed aorta with a standard T-Branch Cook (Cook® Medical, Bloomington, IN, USA) was performed preoperatively in the simulation laboratory enabling optimized feasibility, surgical planning and intraoperative decision making. The 3D printed aortic model proved to be radio-opaque and allowed simulation of branched endovascular aortic repair (BREVAR). The assessment of intervention feasibility, as well as optimal branch position and orientation was found to be useful for surgeon confidence and the actual intervention in the patient. There was a remarkable agreement between the 3D printed model and both CT and X-ray angiographic images. Although the technical success was achieved as planned, a previously deployed renal stent caused unexpected difficulty in advancing the renal stent, which was not observed in the 3D model simulation. The 3D printed aortic models can be useful for determining feasibility, optimizing planning and intraoperative decision making in hostile anatomy improving the outcome. Despite already offering satisfying accuracy at present, further advancements could enhance the 3D model capability to replicate minor anatomical deformities and variations in tissue density.

Segmentation of Coronary Arteries from X-ray Angiographic Images Using a Combination of K-Nearest Neighbor Clustering and Morphological Reconstruction Techniques

Segmentation of Coronary Arteries from X-ray Angiographic Images Using a Combination of K-Nearest Neighbor Clustering and Morphological Reconstruction Techniques

CIDN: A context interactive deep network with edge-aware for X-ray angiography images segmentation

Accurate segmentation of X-ray angiography images is imperative for cardiovascular disease diagnosis. Despite significant strides in segmentation through deep learning, challenges persist in precisely delineating vessel edges. This paper introduces a Context Interactive Deep Network (CIDN) tailored for coronary vessel segmentation. CIDN integrates a Bio-inspired Attention Block (BAB) and a Multi-scale Interactive Block (MIB) to optimize the capture of spatial and edge information. The encoder-decoder facilitates optimal interaction between low-level features and high-level semantics. A compound loss function, amalgamating binary cross-entropy and active contour elasticity loss, enhances the recognition of intricate vessel edges. In experiments conducted on both public and private X-ray contrast images, the CIDN exhibits superior performance compared to the state-of-the-art Spatial Multi-scale Attention U-improved Network (SMAU-Net). Specifically, on dataset DCA1, the F1 value reached 0.7675, with an accuracy of 0.9795. Furthermore, on dataset JMA, the CIDN model achieved an F1 value of 0.8732, coupled with an accuracy of 0.9757. Accurate segmentation provides clinicians with precise vessel depictions, aiding in the identification of coronary stenosis and facilitating more informed diagnostic and therapeutic decisions.

A multi-stage neural network approach for coronary 3D reconstruction from uncalibrated X-ray angiography images

We present a multi-stage neural network approach for 3D reconstruction of coronary artery trees from uncalibrated 2D X-ray angiography images. This method uses several binarized images from different angles to reconstruct a 3D coronary tree without any knowledge of image acquisition parameters. The method consists of a single backbone network and separate stages for vessel centerline and radius reconstruction. The output is an analytical matrix representation of the coronary tree suitable for downstream applications such as hemodynamic modeling of local vessel narrowing (i.e., stenosis). The network was trained using a dataset of synthetic coronary trees from a vessel generator informed by both clinical image data and literature values on coronary anatomy. Our multi-stage network achieved sub-pixel accuracy in reconstructing vessel radius (RMSE = 0.16 ± 0.07 mm) and stenosis radius (MAE = 0.27 ± 0.18 mm), the most important feature used to inform diagnostic decisions. The network also led to 52% and 38% reduction in vessel centerline reconstruction errors compared to a single-stage network and projective geometry-based methods, respectively. Our method demonstrated robustness to overcome challenges such as vessel foreshortening or overlap in the input images. This work is an important step towards automated analysis of anatomic and functional disease severity in the coronary arteries.

Adaptation of a channelized Hotelling observer model to accommodate anthropomorphic backgrounds and moving test objects in X-ray angiography.

Prior implementations of the channelized Hotelling observer (CHO) model have succeeded in assessing the performance of X-ray angiography systems under a variety of imaging conditions. However, often times these conditions do not resemble those present in routine clinical imaging scenarios, such as having complex anthropomorphic backgrounds in conjunction with moving test objects. This work builds up on prior established CHO methods and introduces a new approach to switch from the already established "multiple-sample" CHO implementation to a "single-sample" technique. The proposed implementation enables the inclusion of moving test objects upon nonuniform backgrounds by allowing only a single sample to represent the test object present condition that is to be used within the statistical test to estimate the detectability index. To assess the proposed method, two image data sets were acquired with a clinical X-ray angiography system. The first set consisted of a uniform background in combination with static test objects while the second consisted of an anthropomorphic chest phantom in conjunction with moving test objects. The first set was used to validate the proposed approach against the multiple-sample method while the second was used to assess the feasibility of the proposed method under a variety of imaging conditions, including seven object sizes and seven detector target dose (DTD)levels. For the uniform background data set, considering all detectability indices greater or equal than 1, the ratio between the detectability indices of the proposed single-sample approach versus the multiple-sample method was 0.997 ± 0.056 (range 0.884-1.159). The average single-direction width of the 95% confidence intervals (CIs) of the detectability index estimates for the multiple-sample method was 0.38 ± 0.43 (range 0.03-2.20). For the single-sample approach, the average width was 2.52 ± 0.63 (range 1.11-5.44). For the anthropomorphic background image set, the results were consistent with classical quantum-limited signal-to-noise ratio (SNR) theory. The magnitude of the detectability indices varied predictably with changes in both object size and DTD, with the highest value associated with the highest dose and the largest object size. Additionally, the proposed method was able to capture differences in the imaging performance for a given test object across the field of view, which was associated with the attenuation levels provided by different features of the anthropomorphicbackground. A new single-sample variant of the CHO model to assess the performance of X-ray angiography imaging systems is proposed. The new approach is consistent with quantum-limited image quality theory and with a standard implementation of the CHO model. The proposed method enables the assessment of moving test objects in combination with complex, nonuniform image backgrounds, thereby opening the possibility to assess imaging conditions which more closely resemble those used in clinicalcare.

Distance transform learning for structural and functional analysis of coronary artery from dual-view angiography

The structural and functional analysis of coronary arteries in X-ray angiography (XRA) images has great clinical importance for the intraoperative treatment of coronary artery disease. However, due to the complexity of image surroundings, the non-intuition of functional information, and the complex relationships between structure and function, the structural and functional analysis in XRA images is a challenging task. To address these problems, we are proposing an efficient framework, termed AngioSFA. It can effectively associate structural analysis with functional analysis. The AngioSFA mainly consists of a distance transform segmentation network (DTSN) for structural analysis and a dual-view sequential learning network (DSLN) for functional analysis. Specifically, DTSN exploits distance transform in the encoder–decoder architecture to predict the distance map of the artery structure. It considers the geometric property of coronary artery structure to preserve a proper shape prototype. DSLN exploits a dual-view temporal convolutional network to learn the non-intuitive physiological knowledge related to the functional significance from the radius sequences extracted from dual-view XRA images. Furthermore, a commonality sharing module is designed to map the latent embeddings of radius sequences into a shared latent space through orthogonal mapping. Moreover, we introduce a dual-view interaction module to explore the intra-view attentions and the inter-view correlations among the anatomical features of coronary arteries. Extensive experiments on two clinical datasets show that AngioSFA achieves an overall dice of 91.06% for structural analysis and an overall precision of 89.96% for functional analysis. The experimental results and the discussions demonstrate that AngioSFA has a great potential for the efficient intraoperative treatment of coronary artery disease.

Progressive Perception Learning for Main Coronary Segmentation in X-ray Angiography.

Main coronary segmentation from the X-ray angiography images is important for the computer-aided diagnosis and treatment of coronary disease. However, it confronts the challenge at three different image granularities (the semantic, surrounding, and local levels). The challenge includes the semantic confusion between the main and collateral vessels, low contrast between the foreground vessel and background surroundings, and local ambiguity near the vessel boundaries. The traditional hand-crafted feature-based methods may be insufficient because they may lack the semantic relationship information and may not distinguish the main and collateral vessels. The existing deep learning-based methods seem to have issues due to the deficiency in the long-distance semantic relationship capture, the foreground and background interference adaptability, and the boundary detail information preservation. To solve the main coronary segmentation challenge, we propose the progressive perception learning (PPL) framework to inspect these three different image granularities. Specifically, the PPL contains the context, interference, and boundary perception modules. The context perception is designed to focus on the main coronary vessel based on the semantic dependence capture among different coronary segments. The interference perception is designed to purify the feature maps based on the foreground vessel enhancement and background artifact suppression. The boundary perception is designed to highlight the boundary details based on boundary feature extraction through the intersection between the foreground and background predictions. Extensive experiments on 1085 subjects show that the PPL is effective (e.g., the overall Dice is greater than 95%), and superior to thirteen state-of-the-art coronary segmentation methods.

Coronary artery stenosis detection via proposal-shifted spatial-temporal transformer in X-ray angiography

Accurate detection of coronary artery stenosis in X-ray angiography (XRA) images is crucial for the diagnosis and treatment of coronary artery disease. However, stenosis detection remains a challenging task due to complicated vascular structures, poor imaging quality, and fickle lesions. While devoted to accurate stenosis detection, most methods are inefficient in the exploitation of spatio-temporal information of XRA sequences, leading to a limited performance on the task. To overcome the problem, we propose a new stenosis detection framework based on a Transformer-based module to aggregate proposal-level spatio-temporal features. In the module, proposal-shifted spatio-temporal tokenization (PSSTT) scheme is devised to gather spatio-temporal region-of-interest (RoI) features for obtaining visual tokens within a local window. Then, the Transformer-based feature aggregation (TFA) network takes the tokens as the inputs to enhance the RoI features by learning the long-range spatio-temporal context for final stenosis prediction. The effectiveness of our method was validated by conducting qualitative and quantitative experiments on 233 XRA sequences of coronary artery. Our method achieves a high F1 score of 90.88%, outperforming other 15 state-of-the-art detection methods. It demonstrates that our method can perform accurate stenosis detection from XRA images due to the strong ability to aggregate spatio-temporal features.

Centerline-supervision multi-task learning network for coronary angiography segmentation

With convolutional neural networks’ remarkable performance in computer vision, more and more studies are applying deep learning to vessel image segmentation tasks. This work focuses on the task of coronary X-ray angiography segmentation, which is critical in the diagnosis of cardiovascular disorders. For vascular segmentation in coronary X-ray angiography images, we propose a novel deep learning model based on the UNet backbone. We first equip a channel attention module in skip-connections to improve pixel-wise segmentation accuracy by emphasizing the effective channel in low-level features. A centerline auxiliary supervision module is also introduced at the network’s end to provide prior knowledge of vessel connectivity and thick vessels, utilizing the existing binary segmentation annotations efficaciously. Consequently, the network can devote more attention to pixels that ensure vessel tree connectivity and high confidence. Extensive experiments demonstrate the effectiveness of the two modules in improving the performance of the model. We compared our results to the recently proposed networks and revealed that these two modules can be added to other U-shaped networks to enhance performance. In our experiments, our method produced the best results in terms of sensitivity and dice score, with 82.48 and 85.28, respectively.

Automated analysis method to assess pulmonary blood flow distribution using conventional X-ray angiography

Quantitative assessment of the right-to-left ratio of pulmonary blood flow distribution is important for determining the clinical indications for treating pulmonary arterial branch stenosis. A novel theory was recently proposed that can be used to quantitatively assess the right-to-left ratio on conventional X-ray angiography images. In the proposal, further developments were indicated, especially automated calculation. In this study, a new automated algorithm was developed. In the X-ray image, regions of interest were set in right and left lung, and time-signal intensity curves were measured. The new automated algorithm is applied to determine the optimal time window for the analysis of the time-signal intensity curve and to calculate the slope of the curve in the optimized time window. The right-to-left ratios in seven consecutive patients calculated by the new automated algorithm were compared to those calculated by lung perfusion scintigraphy. The ratios were in good agreement with linear regression with a slope of 1.27 and a Pearson correlation coefficient of 0.95. The processing time was less than 10 s, which is one-eighth of the manual processing time. The new automated algorithm is accurate, stable, and fast enough for clinical use in the real world.

A Novel Method for Improving the Accuracy of MR-derived Patient-specific Vascular Models using X-ray Angiography.

MR imaging, a noninvasive radiation-free imaging modality commonly used during clinical follow up, has been widely utilized to reconstruct realistic 3D vascular models for patient-specific analysis. In recent work, we used patient-specific hemodynamic analysis of the circle of Willis to noninvasively assess stroke risk in pediatric Moyamoya disease (MMD)-a progressive steno-occlusive cerebrovascular disorder that leads to recurrent stroke. The objective was to identify vascular regions with critically high wall shear rate (WSR) that signifies elevated stroke risk. However, sources of error such as insufficient resolution of MR images can negatively impact vascular model accuracy, especially in areas of severe pathological narrowing, and thus diminish clinical relevance of simulation results, as local hemodynamics are sensitive to vessel geometry. To improve the accuracy of MR-derived vascular models, we have developed a novel method for adjusting model vessel geometry utilizing 2D X-ray angiography (XA), which is considered the gold standard for clinically assessing vessel caliber. In this workflow, "virtual angiographies" (VAs) of 3D MR-derived vascular models are conducted, producing 2D projections that are compared with corresponding XA images to guide the local adjustment of modeled vessels. This VA-comparison-adjustment loop is iterated until the two agree, as confirmed by an expert neuroradiologist. Using this method, we generated models of the circle of Willis of two patients with a history of unilateral stroke. Blood flow simulations were performed using a Navier-Stokes solver within an isogeometric analysis framework, and WSR distributions were quantified. Results for one patient show as much as 45% underestimation of local WSR in the stenotic left anterior cerebral artery (LACA), and up to a 56% underestimation in the right anterior cerebral artery when using the initial MR-derived model compared to the XA-adjusted model. To evaluate whether XA-based adjustment improves model accuracy, vessel cross-sectional areas of the pre- and post-adjustment models were compared to those seen in 3D CTA images of the same patient. CTA has superior resolution and signal-to-noise ratio compared to MR imaging but is not commonly used in the clinic due to radiation exposure concerns, especially in pediatric patients. While the vessels in the initial model had normalized root mean squared deviations (NRMSDs) ranging from 26% to 182% and 31% to 69% in two patients with respect to CTA, the adjusted vessel NRMSDs were comparatively smaller (32% to 53% and 11% to 42%). In the mildly stenotic LACA of patient 1, the NRMSDs for the pre- and post-adjusted models were 49% and 32%, respectively. These findings suggest that our XA-based adjustment method can considerably improve the accuracy of vascular models, and thus, stroke-risk prediction. An accurate, individualized assessment of stroke risk would be of substantial help in guiding the timing of preventive surgical interventions in pediatric MMD patients.

Multi-constraints based deep learning model for automated segmentation and diagnosis of coronary artery disease in X-ray angiographic images.

BackgroundThe detection of coronary artery disease (CAD) from the X-ray coronary angiography is a crucial process which is hindered by various issues such as presence of noise, insufficient contrast of the input images along with the uncertainties caused by the motion due to respiration and variation of angles of vessels.MethodsIn this article, an Automated Segmentation and Diagnosis of Coronary Artery Disease (ASCARIS) model is proposed in order to overcome the prevailing challenges in detection of CAD from the X-ray images. Initially, the preprocessing of the input images was carried out by using the modified wiener filter for the removal of both internal and external noise pixels from the images. Then, the enhancement of contrast was carried out by utilizing the optimized maximum principal curvature to preserve the edge information thereby contributing to increasing the segmentation accuracy. Further, the binarization of enhanced images was executed by the means of OTSU thresholding. The segmentation of coronary arteries was performed by implementing the Attention-based Nested U-Net, in which the attention estimator was incorporated to overcome the difficulties caused by intersections and overlapped arteries. The increased segmentation accuracy was achieved by performing angle estimation. Finally, the VGG-16 based architecture was implemented to extract threefold features from the segmented image to perform classification of X-ray images into normal and abnormal classes.ResultsThe experimentation of the proposed ASCARIS model was carried out in the MATLAB R2020a simulation tool and the evaluation of the proposed model was compared with several existing approaches in terms of accuracy, sensitivity, specificity, revised contrast to noise ratio, mean square error, dice coefficient, Jaccard similarity, Hausdorff distance, Peak signal-to-noise ratio (PSNR), segmentation accuracy and ROC curve.DiscussionThe results obtained conclude that the proposed model outperforms the existing approaches in all the evaluation metrics thereby achieving optimized classification of CAD. The proposed method removes the large number of background artifacts and obtains a better vascular structure.

Attention Mechanisms Evaluated on Stenosis Detection using X-ray Angiography Images

Coronary stenosis results from unnatural narrowing of the heart arteries due to the accumulation of adipose depots, leading to different heart diseases and yielding top mortality worldwide. Thus far, deep learning-based methods for automatic stenosis over X-ray Coronary Angiography (XCA) have employed state-of-the-art architectures to solve the ImageNet challenge. With the advance of deep learning, contemporary architectures incorporated a variety of attention mechanisms to improve performance. Therefore, this paper presents a study of three attention mechanisms for stenosis detection in XCA images. Extensive experiments and comparisons over different Residual backbone networks are presented to verify the effectiveness of including such attention modules. An improvement of 4%, 10%, and 10% on the accuracy, recall, and F1-score was achieved using the approach, reaching mean values of 0.8787, 0.8610, and 0.8732, respectively.

Simultaneous Biplane High Speed 1000 fps X-ray Angiography (HSAngio).

High-speed 1000-fps x-ray Angiography (HSAngio) images can be used to visualize blood-flow patterns and derive flow velocities during neurointerventional procedures. In this work, we present for the very first-time, orthogonal views of contrast injection in an aneurysm model acquired simultaneously using biplane HSAngio imaging. 3-D printed in-vitro models A and B of two different internal carotid-artery aneurysms were connected to a flow loop (circulation fluid: 60% water, 40% glycerol solution, circulation flow rate: 8 L/s). An automatic programmable injector (KD Scientific Legato 110) injected iodine contrast agent at a rate of 88 mL/min for a duration of 3 sec. With an RQA5 spectrum, 1000 fps HSAngio sequences of the contrast injection were acquired simultaneously on the frontal plane using the Actaeon detector (Direct Conversion, Stockholm) and on the lateral plane using the Aries (Direct Conversion, Stockholm) detector. The start of contrast injection and simultaneous biplane x-ray exposures and detector image acquisitions were manually synchronized to capture the initial inflow of contrast into the aneurysm region. For model A the frontal plane images gave a better visualization of the flow streamlines in the parent artery in the inflow (average velocity 28 cm/s) and outflow (average velocity 24 cm/s) region of the aneurysm. The vortices within the aneurysm region especially within the aneurysm dome were better visualized in the lateral plane images (average velocity 27 cm/s). Biplane HSAngio imaging techniques can give more accurate representations of 3-D blood flow within the complex vascular pathology of the human brain, compared to single-plane imaging.

Recursive Centerline- and Direction-Aware Joint Learning Network with Ensemble Strategy for Vessel Segmentation in X-ray Angiography Images

Background and objectiveAutomatic vessel segmentation from X-ray angiography images is an important research topic for the diagnosis and treatment of cardiovascular disease. The main challenge is how to extract continuous and completed vessel structures from XRA images with poor quality and high complexity. Most existing methods predominantly focus on pixel-wise segmentation and overlook the geometric features, resulting in breaking and absence in segmentation results. To improve the completeness and accuracy of vessel segmentation, we propose a recursive joint learning network embedded with geometric features. MethodsThe network joins the centerline- and direction-aware auxiliary tasks with the primary task of segmentation, which guides the network to explore the geometric features of vessel connectivity. Moreover, the recursive learning strategy is designed by passing the previous segmentation result into the same network iteratively to improve segmentation. To further enhance connectivity, we present a complementary-task ensemble strategy by fusing the outputs of the three tasks for the final segmentation result with majority voting. ResultsTo validate the effectiveness of our method, we conduct qualitative and quantitative experiments on the XRA images of the coronary artery and aorta including aortic arch, thoracic aorta, and abdominal aorta. Our method achieves F1 scores of 85.61±3.48% for the coronary artery, 89.02±2.89% for the aortic arch, 88.22±3.33% for the thoracic aorta, and 83.12±4.61% for the abdominal aorta. ConclusionsCompared with six state-of-the-art methods, our method shows the most complete and accurate vessel segmentation results.