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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.

Abstract Background Myocardial bridges are congenital anatomical abnormalities in which myocardium covers a segment of coronary arteries, leading to stenocardia, myocardial ischemia, and sudden cardiac death in severe cases. However, automatic diagnosis of myocardial bridge presents significant challenges. Method A novel framework of myocardial bridge detection with x-ray angiography sequence is proposed, which can realize automatic detection of vessel stenosis and myocardial bridge. Firstly, we employ a novel neural network model for coronary vessel segmentation, which consists of both CNNs and transformer structures to effectively extract both local and global information of the vessels. Secondly, we describe the vessel segment information, establish the vessel tree in the image, and fuse the vessel tree information between sequences. Finally, based on vessel stenosis detection, we realize automatic detection of the myocardial bridge by querying the blood vessels between the image sequence information. Results In experiment, we evaluate the segmentation results using two metrics, Dice and ASD, and achieve scores of 0.917 and 1.39, respectively. In the stenosis detection, we achieve an average accuracy rate of 92.7% in stenosis detection among 262 stenoses. In multi-frame image processing, vessels in different frames can be well-matched, and the accuracy of myocardial bridge detection achieves 75%. Conclusions Our experimental results demonstrate that the algorithm can automatically detect stenosis and myocardial bridge, providing a new idea for subsequent automatic diagnosis of coronary vessels.

Abstract X-ray coronary angiography is the most common tool for the diagnosis and treatment of coronary artery disease. It involves the injection of contrast agents into coronary vessels using a catheter to highlight the coronary vessel structure. Typically, multiple 2D X-ray projections are recorded from different angles to improve visualization. Recent advances in the development of deep-learning-based tools promise significant improvement in diagnosing and treating coronary artery disease. However, the limited public availability of annotated X-ray coronary angiography image datasets presents a challenge for objective assessment and comparison of existing tools and the development of novel methods. To address this challenge, we introduce a novel ARCADE dataset with 2 objectives: coronary vessel classification and stenosis detection. Each objective contains 1500 expert-labeled X-ray coronary angiography images representing: i) coronary artery segments; and ii) the locations of stenotic plaques. These datasets will serve as a benchmark for developing new methods and assessing existing approaches for the automated diagnosis and risk assessment of coronary artery disease.

Abstract 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.

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 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 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 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 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 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.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., (© 2024 Institute of Physics and Engineering in Medicine.)

Detecting coronary stenosis accurately in X-ray angiography (XRA) is important for diagnosing and treating coronary artery disease (CAD). However, challenges arise from factors like breathing and heart motion, poor imaging quality, and the complex vascular structures, making it difficult to identify stenosis fast and precisely. In this study, we proposed a Quantum Diffusion Model with Spatio-Temporal Feature Sharing to Real-time detect Stenosis (STQD-Det). Our framework consists of two modules: Sequential Quantum Noise Boxes module and spatio-temporal feature module. To evaluate the effectiveness of the method, we conducted a 4-fold cross-validation using a dataset consisting of 233 XRA sequences. Our approach achieved the F1 score of 92.39% with a real-time processing speed of 25.08 frames per second. These results outperform 17 state-of-the-art methods. The experimental results show that the proposed method can accomplish the stenosis detection quickly and accurately.

Background For patients with acute ischemic stroke undergoing endovascular mechanical thrombectomy with x-ray angiography, the use of adjuncts to maintain vessel patency, such as stents or antiplatelet medications, can increase risk of periprocedural complications. Criteria for using these adjuncts are not well defined. Purpose To evaluate use of MRI to guide critical decision making by using a combined biplane x-ray neuroangiography 3.0-T MRI suite during acute ischemic stroke intervention. Materials and Methods This retrospective observational study evaluated consecutive patients undergoing endovascular intervention for acute ischemic stroke between July 2019 and May 2020 who underwent either angiography with MRI or angiography alone. Cerebral tissue viability was assessed by using MRI as the reference standard. For statistical analysis, Fisher exact test and Student t test were used to compare groups. Results Of 47 patients undergoing acute stroke intervention, 12 patients (median age, 69 years; interquartile range, 60-77 years; nine men) underwent x-ray angiography with MRI whereas the remaining 35 patients (median age, 80 years; interquartile range, 68-86 years; 22 men) underwent angiography alone. MRI results influenced clinical decision making in one of three ways: whether or not to perform initial or additional mechanical thrombectomy, whether or not to place an intracranial stent, and administration of antithrombotic or blood pressure medications. In this initial experience, decision making during endovascular acute stroke intervention in the combined angiography-MRI suite was better informed at MRI, such that therapy was guided in real time by the viability of the at-risk cerebral tissue. Conclusion Integrating intraprocedural 3.0-T MRI into acute ischemic stroke treatment was feasible and guided decisions of whether or not to continue thrombectomy, to place stents, or to administer antithrombotic medication or provide blood pressure medications. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Lev and Leslie-Mazwi in this issue.

Introduction and objectives: Although automatic artificial intelligence (AI) coronary angiography (CAG) segmentation is arguably the first step toward future clinical application, it is underexplored. We aimed to (1) develop AI models for CAG segmentation and (2) assess the results using similarity scores and a set of criteria defined by expert physicians. Methods: Patients undergoing CAG were randomly selected in a retrospective study at a single center. Per incidence, an ideal frame was segmented, forming a baseline human dataset (BH), used for training a baseline AI model (BAI). Enhanced human segmentation (EH) was created by combining the best of both. An enhanced AI model (EAI) was trained using the EH. Results were assessed by experts using 11 weighted criteria, combined into a Global Segmentation Score (GSS: 0–100 points). Generalized Dice Score (GDS) and Dice Similarity Coefficient (DSC) were also used for AI models assessment. Results: 1664 processed images were generated. GSS for BH, EH, BAI and EAI were 96.9+/-5.7; 98.9+/-3.1; 86.1+/-10.1 and 90+/-7.6, respectively (95% confidence interval, p

Objectives: Coronary angiography (CAG)-derived physiology methods have been developed in an attempt to simplify and increase the usage of coronary physiology, based mostly on dynamic fluid computational algorithms. We aimed to develop a different approach based on artificial intelligence methods, which has seldom been explored., Methods: Consecutive patients undergoing invasive instantaneous free-wave ratio (iFR) measurements were included. We developed artificial intelligence (AI) models capable of classifying target lesions as positive (iFR ≤ 0.89) or negative (iFR > 0.89). The predictions were then compared to the true measurements., Results: Two hundred-fifty measurements were included, and 3 models were developed. Model 3 had the best overall performance: accuracy, negative predictive value (NPV), positive predictive value (PPV), sensitivity, and specificity were 69%, 88%, 44%, 74%, and 67%, respectively. Performance differed per target vessel. For the left anterior descending artery (LAD), model 3 had the highest accuracy (66%), while model 2 the highest NPV (86%) and sensitivity (91%). PPV was always low/modest. Model 1 had the highest specificity (68%). For the right coronary artery, model 1's accuracy was 86%, NPV was 97%, and specificity was 87%, but all models had low PPV (maximum 25%) and low/modest sensitivity (maximum 60%). For the circumflex, model 1 performed best: accuracy, NPV, PPV, sensitivity, and specificity were 69%, 96%, 24%, 80%, and 68%, respectively., Conclusions: We developed 3 AI models capable of binary iFR estimation from CAG images. Despite modest accuracy, the consistently high NPV is of potential clinical significance, as it would enable avoiding further invasive maneuvers after CAG. This pivotal study offers proof of concept for further development.

Abstract 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.

Background: 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., Purpose: 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., Methods: 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., Results: 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 anthropomorphic background., Conclusions: 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 clinical care., (© 2023 American Association of Physicists in Medicine.)

Abstract Coronary Artery Disease (CAD) is commonly diagnosed using X-ray angiography, in which images are taken as radio-opaque dye is flushed through the coronary vessels to visualize the severity of vessel narrowing, or stenosis. Cardiologists typically use visual estimation to approximate the percent diameter reduction of the stenosis, and this directs therapies like stent placement. A fully automatic method to segment the vessels would eliminate potential subjectivity and provide a quantitative and systematic measurement of diameter reduction. Here, we have designed a convolutional neural network, AngioNet, for vessel segmentation in X-ray angiography images. The main innovation in this network is the introduction of an Angiographic Processing Network (APN) which significantly improves segmentation performance on multiple network backbones, with the best performance using Deeplabv3+ (Dice score 0.864, pixel accuracy 0.983, sensitivity 0.918, specificity 0.987). The purpose of the APN is to create an end-to-end pipeline for image pre-processing and segmentation, learning the best possible pre-processing filters to improve segmentation. We have also demonstrated the interchangeability of our network in measuring vessel diameter with Quantitative Coronary Angiography. Our results indicate that AngioNet is a powerful tool for automatic angiographic vessel segmentation that could facilitate systematic anatomical assessment of coronary stenosis in the clinical workflow.

Background: Dual-energy (DE) x-ray angiography with photon-counting detectors (PCDs) may enable single-exposure DE imaging of coronary vasculature., Purpose: To compare the iodine signal-difference-to-noise ratio (SDNR) of single-exposure DE angiography with digital subtraction angiography (DSA) and kV-switching DE angiography for matched patient x-ray exposure., Methods: In a phantom study, we determined the technique parameters that maximized the iodine SDNR per root entrance air kerma for DSA, kV-switching DE angiography and single-exposure DE angiography. We measured SDNR from images of a phantom consisting of an iodine step-wedge immersed in a water tank of either 20  or 30 cm in thickness. We also imaged a phantom with simulated vessels embedded in background clutter and measured vessel SDNR. For this second phantom, we also applied anti-correlated noise reduction (ACNR) and calculated the resulting iodine SDNR. All images were acquired using a cadmium telluride PCD with two energy bins and analog charge summing for charge sharing suppression. The energy-discrimination capabilities were only used for the single-exposure DE approach. Optimized techniques were compared in terms of SDNR per root air kerma for two levels of x-ray scatter., Results: For the same patient x-ray exposure, the SDNR of single-exposure DE imaging without ACNR was 75% to 85% of that of kV-switching DE imaging (also without ACNR) and DSA, the latter two of which had nearly equal SDNR. The single-exposure DE approach required ∼50% of the tube load of the kV-switching approach to achieve the same SDNR. For matched patient air kermas, the single exposure approach required only ∼25% of the tube load of the kV-switching approach. ACNR increased SDNR by 2.4 and 3.0 for kV-switching and single-exposure DE imaging, respectively., Conclusions: Photon-counting, single-exposure DE angiography can suppress soft tissues and provide iodine SDNR levels comparable to DSA and kV-switching DE angiography for matched patient radiation exposures. When ACNR is used to reduce DE image noise, the SDNR of single-exposure DE imaging and kV-switching DE imaging exceed that of DSA by more than a factor of two. Compared to kV-switching DE imaging, single-exposure DE imaging requires substantially lower tube loading to achieve the same SDNR., (© 2023 American Association of Physicists in Medicine.)

Abstract Our aim was to investigate whether the previously observed higher contrast-to-noise ratio (CNR) and better image quality of Digital Variance Angiography (DVA) - compared to Digital Subtraction Angiography (DSA) - can be used to reduce radiation exposure in lower limb X-ray angiography. This prospective study enrolled 30 peripheral artery disease patients (mean ± SD age 70 ± 8 years) undergoing diagnostic angiography. In all patients, both normal (1.2 µGy/frame; 100%) and low-dose (0.36 µGy/frame; 30%) protocols were used for the acquisition of images in three anatomical regions (abdominal, femoral, crural). The CNR of DSA and DVA images were calculated, and the visual quality was evaluated by seven specialists using a 5-grade Likert scale. For investigating non-inferiority, the difference of low-dose DVA and normal dose DSA scores (DVA30-DSA100) was analyzed. DVA produced two- to three-fold CNR and significantly higher visual score than DSA. DVA30 proved to be superior to DSA100 in the crural region (difference 0.25 ± 0.07, p