X-ray Coronary Angiography Research Articles

A clinical strategy to improve the diagnostic performance of 3T non-contrast coronary MRA and noninvasively evaluate coronary distensibility: combination of diastole and systole imaging

BackgroundThe clinical application of coronary MR angiography (MRA) combining diastole and systole imaging has never been described comprehensively in coronary artery disease (CAD) patients. We aimed to design an optimal non-contrast coronary MRA scan protocol combining diastolic and systolic imaging and to (1) evaluate its diagnostic performance for detecting significant coronary stenosis; (2) evaluate the feasibility of this protocol to noninvasively measure the coronary distensibility index (CDI). MethodsFrom June 2021 to May 2022, 33 healthy volunteers and 91 suspected CAD patients scheduled for X-ray coronary angiography (CAG) were prospectively enrolled. 3T non-contrast water-fat coronary MRA was carried out twice at diastole and systole. Significant coronary stenosis was defined as a luminal diameter reduction of ≥ 50% using CAG as the reference and was evaluated as follows: (1) by coronary MRA in diastole alone; (2) by coronary MRA in systole alone; (3) by combined coronary MRA in diastole and systole. According to CAG, the patients were divided into significant CAD patients and non-significant CAD patients. The difference in CDI among participants was evaluated. ResultsCombined coronary MRA was completed in 31 volunteers and 76 patients. The per-patient sensitivity, specificity, and accuracy of combined coronary MRA were 97.5%, 83.3%, and 90.8%, respectively. Compared with single diastolic mode, combined coronary MRA showed equally high sensitivity but improved specificity on a per-patient basis (83.3% vs. 63.9%, adjusted P = 0.013). The CDI tested by coronary MRA decreased incrementally from healthy volunteers to non-significant and significant CAD patients. ConclusionCompared with single-phase mode, 3 T non-contrast combined coronary MRA significantly improved specificity and may have potential to be a simple noninvasive method to measure CDI.

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.

Robust PCA Unrolling Network for Super-Resolution Vessel Extraction in X-Ray Coronary Angiography.

Although robust PCA has been increasingly adopted to extract vessels from X-ray coronary angiography (XCA) images, challenging problems such as inefficient vessel-sparsity modelling, noisy and dynamic background artefacts, and high computational cost still remain unsolved. Therefore, we propose a novel robust PCA unrolling network with sparse feature selection for super-resolution XCA vessel imaging. Being embedded within a patch-wise spatiotemporal super-resolution framework that is built upon a pooling layer and a convolutional long short-term memory network, the proposed network can not only gradually prune complex vessel-like artefacts and noisy backgrounds in XCA during network training but also iteratively learn and select the high-level spatiotemporal semantic information of moving contrast agents flowing in the XCA-imaged vessels. The experimental results show that the proposed method significantly outperforms state-of-the-art methods, especially in the imaging of the vessel network and its distal vessels, by restoring the intensity and geometry profiles of heterogeneous vessels against complex and dynamic backgrounds. The source code is available at https://github.com/Binjie-Qin/RPCA-UNet.

LRSE-Net: Lightweight Residual Squeeze-and-Excitation Network for Stenosis Detection in X-ray Coronary Angiography

Coronary heart disease is the primary cause of death worldwide. Among these, ischemic heart disease and stroke are the most common diseases induced by coronary stenosis. This study presents a Lightweight Residual Squeeze-and-Excitation Network (LRSE-Net) for stenosis classification in X-ray Coronary Angiography images. The proposed model employs redundant kernel deletion and tensor decomposition by Depthwise Separable Convolutions to reduce the model parameters up to 48.6 x concerning a Vanilla Residual Squeeze-and-Excitation Network. Furthermore, the reduction ratios of each Squeeze-and-Excitation module are optimized individually to improve the feature recalibration. Experimental results for Stenosis Detection on the publicly available Deep Stenosis Detection Dataset and Angiographic Dataset demonstrate that the proposed LRSE-Net achieves the best Accuracy—0.9549/0.9543, Sensitivity—0.6320/0.8792, Precision—0.5991/0.8944, and F1-score—0.6103/0.8944, as well as competitive Specificity of 0.9620/0.9733.

Theoretical comparison of energy-resolved and digital-subtraction angiography.

X-ray coronary angiography is a sub-optimal vascular imaging technique because it cannot suppress un-enhanced anatomy that may obscure the visualization of coronary arterydisease. The purpose of this paper is to evaluate the theoretical image quality of energy-resolved x-ray angiography (ERA) implemented with spectroscopic x-ray detectors (SXDs), which may overcome limitations of x-ray coronaryangiography. We modeled the large-area signal-difference-to-noise (SDNR) of contrast-enhanced vasculature in ERA images and compared with that of digital-subtraction angiography (DSA), which served as a gold standard vascular imaging technique. To this end, we used calibrated numerical models of the response of cadmium telluride SXDs including the effects of charge sharing, electronic noise, and energy thresholding. Our models assumed zero scatter, no pulse pile up and small signals such that image contrast is approximately linear in the area density of contrast agents. For DSA, we similarly modeled x-ray detection by cesium iodide energy-integrating detectors using validated numerical models. For ERA, we investigated iodine and gadolinium (Gd) contrast agents, two-material and three-material decompositions, analog charge summing for charge sharing correction, and optimized image quality with respect to the tube voltage and energy thresholds assuming cadmium telluride SXDs with three energybins. Our analysis reveals that a three-material decomposition using iodine contrast agents will require x-ray exposures that are approximately 400 times those of DSA to achieve the same SDNR as DSA in coronary applications, and is therefore not feasible in a clinical setting. However, three-material decompositions with Gd contrast agents have the potential to provide SDNR that is ∼45% of that of DSA for matched patient air kerma. For two-material decompositions that suppress soft-tissue, ERA has the potential to produce images with SDNR that is 50%-75% of that of DSA for matched patient air kermas but lower levels of tube loading. Achieving these SDNR levels will require the use of analog charge summing for charge sharing correction, which increased SDNR by up to a factor of 1.7 depending on the contrast agent and whether or not a two-material or three-material decomposition wasassumed. We conclude that three-material ERA implemented with Gd contrast agents and two-material ERA implemented with either iodine or Gd contrast agents, should be investigated as alternatives to DSA in situations where motion artifacts preclude the use of DSA, such as in coronaryimaging.

CAR-Net: A Deep Learning-Based Deformation Model for 3D/2D Coronary Artery Registration.

Percutaneous coronary intervention is widely applied for the treatment of coronary artery disease under the guidance of X-ray coronary angiography (XCA) image. However, the projective nature of XCA causes the loss of 3D structural information, which hinders the intervention. This issue can be addressed by the deformable 3D/2D coronary artery registration technique, which fuses the pre-operative computed tomography angiography volume with the intra-operative XCA image. In this study, we propose a deep learning-based neural network for this task. The registration is conducted in a segment-by-segment manner. For each vessel segment pair, the centerlines that preserve topological information are decomposed into an origin tensor and a spherical coordinate shape tensor as network input through independent branches. Features of different modalities are fused and processed for predicting angular deflections, which is a special type of deformation field implying motion and length preservation constraints for vessel segments. The proposed method achieves an average error of 1.13 mm on the clinical dataset, which shows the potential to be applied in clinical practice.

X-ray coronary centerline extraction based on C-UNet and a multifactor reconnection algorithm

Background and objective: Accurate extraction of the coronary artery centerline is crucial in the processes of coronary artery reconstruction, coronary artery stenosis or lesion detection, and surgical navigation. Furthermore, in clinical medicine, the complex background of angiography, low signal-to-noise ratio, and complex vascular structure make coronary artery centerline extraction challenging. In this study, a direct centerline extraction method is proposed that automatically and accurately extracts vascular centerlines from X-ray coronary angiography images based on deep learning and conventional methods.Methods: In this study, a coronary artery centerline extraction method is proposed that comprises two parts: the preliminary centerline extraction network based on U-Net with a residual network, called C-UNet, and the multifactor centerline reconnection algorithm based on the geometric characteristics of blood vessels.Results: The qualitative and quantitative results demonstrate the effectiveness of the presented method. In this study, three widely used evaluation indices were adopted to evaluate the performance of the method: precision, recall, and F1_Score. The experimental results show that this method can accurately extract coronary artery centerlines.Conclusions: The proposed centerline extraction method accurately extracts centerlines from X-ray coronary angiography images and improves both the accuracy and continuity of centerline extraction.

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.

Development of deep learning segmentation models for coronary X-ray angiography: Quality assessment by a new global segmentation score and comparison with human performance

Introduction and objectivesAlthough 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. MethodsPatients 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. Results1664 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<0.001 for both paired and global differences). The GDS for the BAI and EAI was 0.9234±0.0361 and 0.9348±0.0284, respectively. The DSC for the coronary tree was 0.8904±0.0464 and 0.9134±0.0410 for the BAI and EAI, respectively. The EAI outperformed the BAI in all coronary segmentation tasks, but performed less well in some catheter segmentation tasks. ConclusionsWe successfully developed AI models capable of CAG segmentation, with good performance as assessed by all scores.

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.

Improving convolutional neural network learning based on a hierarchical bezier generative model for stenosis detection in X-ray images

Background and Objective:Automatic detection of stenosis on X-ray Coronary Angiography (XCA) images may help diagnose early coronary artery disease. Stenosis is manifested by a buildup of plaque in the arteries, decreasing the blood flow to the heart, increasing the risk of a heart attack. Convolutional Neural Networks (CNNs) have been successfully applied to identify pathological, regular, and featured tissues on rich and diverse medical image datasets. Nevertheless, CNNs find operative and performing limitations while working with small and poorly diversified databases. Transfer learning from large natural image datasets (such as ImageNet) has become a de-facto method to improve neural networks performance in the medical image domain.Methods:This paper proposes a novel Hierarchical Bezier-based Generative Model (HBGM) to improve the CNNs training process to detect stenosis. Herein, artificial image patches are generated to enlarge the original database, speeding up network convergence. The artificial dataset consists of 10,000 images containing 50% stenosis and 50% non-stenosis cases. Besides, a reliable Fréchet Inception Distance (FID) is used to evaluate the generated data quantitatively. Therefore, by using the proposed framework, the network is pre-trained with the artificial datasets and subsequently fine-tuned using the real XCA training dataset. The real dataset consists of 250 XCA image patches, selecting 125 images for stenosis and the remainder for non-stenosis cases. Furthermore, a Convolutional Block Attention Module (CBAM) was included in the network architecture as a self-attention mechanism to improve the efficiency of the network.Results:The results showed that the pre-trained networks using the proposed generative model outperformed the results concerning training from scratch. Particularly, an accuracy, precision, sensitivity, and F1-score of 0.8934, 0.9031, 0.8746, 0.8880, 0.9111, respectively, were achieved. The generated artificial dataset obtains a mean FID of 84.0886, with more realistic visual XCA images.Conclusions:Different ResNet architectures for stenosis detection have been evaluated, including attention modules into the network. Numerical results demonstrated that by using the HBGM is obtained a higher performance than training from scratch, even outperforming the ImageNet pre-trained models.

Vessel segmentation for X-ray coronary angiography using ensemble methods with deep learning and filter-based features

BackgroundAutomated segmentation of coronary arteries is a crucial step for computer-aided coronary artery disease (CAD) diagnosis and treatment planning. Correct delineation of the coronary artery is challenging in X-ray coronary angiography (XCA) due to the low signal-to-noise ratio and confounding background structures.MethodsA novel ensemble framework for coronary artery segmentation in XCA images is proposed, which utilizes deep learning and filter-based features to construct models using the gradient boosting decision tree (GBDT) and deep forest classifiers. The proposed method was trained and tested on 130 XCA images. For each pixel of interest in the XCA images, a 37-dimensional feature vector was constructed based on (1) the statistics of multi-scale filtering responses in the morphological, spatial, and frequency domains; and (2) the feature maps obtained from trained deep neural networks. The performance of these models was compared with those of common deep neural networks on metrics including precision, sensitivity, specificity, F1 score, AUROC (the area under the receiver operating characteristic curve), and IoU (intersection over union).ResultsWith hybrid under-sampling methods, the best performing GBDT model achieved a mean F1 score of 0.874, AUROC of 0.947, sensitivity of 0.902, and specificity of 0.992; while the best performing deep forest model obtained a mean F1 score of 0.867, AUROC of 0.95, sensitivity of 0.867, and specificity of 0.993. Compared with the evaluated deep neural networks, both models had better or comparable performance for all evaluated metrics with lower standard deviations over the test images.ConclusionsThe proposed feature-based ensemble method outperformed common deep convolutional neural networks in most performance metrics while yielding more consistent results. Such a method can be used to facilitate the assessment of stenosis and improve the quality of care in patients with CAD.

Cost-Minimization Analysis for Cardiac Revascularization in 12 Health Care Systems Based on the EuroCMR/SPINS Registries

ObjectivesThe aim of this study was to compare the costs of a noninvasive cardiac magnetic resonance (CMR)–guided strategy versus 2 invasive strategies with and without fractional flow reserve (FFR). BackgroundCoronary artery disease (CAD) is a major contributor to the public health burden. Stress perfusion CMR has excellent accuracy to detect CAD. International guidelines recommend as a first step noninvasive testing of patients in stable condition with known or suspected CAD. However, nonadherence in routine clinical practice is high. MethodsIn the EuroCMR (European Cardiovascular Magnetic Resonance) registry (n = 3,647, 59 centers, 18 countries) and the U.S.-based SPINS (Stress-CMR Perfusion Imaging in the United States) registry (n = 2,349, 13 centers, 11 states), costs were calculated for 12 health care systems (8 in Europe, the United States, 2 in Latin America, and 1 in Asia). Costs included diagnostic examinations (CMR and x-ray coronary angiography [CXA] with and without FFR), revascularizations, and complications during 1-year follow-up. Seven subgroup analyses covered low- to high-risk cohorts. Patients with ischemia-positive CMR underwent CXA and revascularization at the treating physician’s discretion (CMR+CXA strategy). In the hypothetical invasive CXA+FFR strategy, costs were calculated for initial CXA and FFR in vessels with ≥50% stenoses, assuming the same proportion of revascularizations and complications as with the CMR+CXA strategy and FFR-positive rates as given in the published research. In the CXA-only strategy, costs included CXA and revascularizations of ≥50% stenoses. ResultsConsistent cost savings were observed for the CMR+CXA strategy compared with the CXA+FFR strategy in all 12 health care systems, ranging from 42% ± 20% and 52% ± 15% in low-risk EuroCMR and SPINS patients with atypical chest pain, respectively, to 31% ± 16% in high-risk SPINS patients with known CAD (P < 0.0001 vs 0 in all groups). Cost savings were even higher compared with CXA only, at 63% ± 11%, 73% ± 6%, and 52% ± 9%, respectively (P < 0.0001 vs 0 in all groups). ConclusionsIn 12 health care systems, a CMR+CXA strategy yielded consistent moderate to high cost savings compared with a hypothetical CXA+FFR strategy over the entire spectrum of risk. Cost savings were consistently high compared with CXA only for all risk groups.

Coronary microvascular disease: The "Meeting Point" of Cardiology, Rheumatology and Endocrinology.

Exertional chest pain/dyspnea or chest pain at rest are the main symptoms of coronary artery disease (CAD), which are traditionally attributed to insufficiency of the epicardial coronary arteries. However, 2/3 of women and 1/3 of men with angina and 10% of patients with acute myocardial infarction have no evidence of epicardial coronary artery stenosis in X-ray coronary angiography. In these cases, coronary microvascular disease (CMD) is the main causative factor. To present the pathophysiology of CMD in Cardiology, Rheumatology and Endocrinology. The pathophysiology of CMD in Cardiology, Rheumatology and Endocrinology was evaluated. It includes impaired microvascular vasodilatation, which leads to inability of the organism to deal with myocardial oxygen needs and, hence, development of ischemic pain. CMD, observed in inflammatory autoimmune rheumatic and endocrine/metabolic disorders, brings together Cardiology, Rheumatology and Endocrinology. Causative factors include persistent systemic inflammation and endocrine/metabolic abnormalities influencing directly the coronary microvasculature. In the past, the evaluation of microcirculation was feasible only with the use of invasive techniques, such as coronary flow reserve assessment. Currently, the application of advanced imaging modalities, such as cardiovascular magnetic resonance (CMR), can evaluate CMD non-invasively and without ionizing radiation. CMD may present with a variety of symptoms with 1/3 to 2/3 of them expressed as typical chest pain in effort, more commonly found in women during menopause than in men. Atypical presentation includes chest pain at rest or exertional dyspnea,but post exercise symptoms are not uncommon. The treatment with nitrates is less effective in CMD, because their vasodilator action in coronary micro-circulation is less pronounced than in the epicardial coronary arteries. Although both classic and new medications have been used in the treatment of CMD, there are still many questions regarding both the pathophysiology and the treatment of this disorder. The potential effects of anti-rheumatic and endocrine medications on the evolution of CMD need further evaluation. CMD is a multifactorial disease leading to myocardial ischemia/fibrosis alone or in combination with epicardial coronary artery disease. Endothelial dysfunction/vasospasm, systemic inflammation, and/or neuroendocrine activation may act as causative factors and bring Cardiology, Rheumatology and Endocrinology together. Currently, the application of advanced imaging modalities, and specifically CMR, allows reliable assessment of the extent and severity of CMD. These measurements should not be limited to "pure cardiac patients", as it is known that CMD affects the majority of patients with autoimmune rheumatic and endocrine/metabolic disorders.

Hybrid classical–quantum Convolutional Neural Network for stenosis detection in X-ray coronary angiography

Despite advances in Deep Learning, the Convolutional Neural Networks methods still manifest limitations in medical applications because datasets are usually restricted in the number of samples or include poorly contrasted images. Such a case is found in stenosis detection using X-rays coronary angiography. In this study, the emerging field of quantum computing is applied in the context of hybrid neural networks. So, a hybrid transfer-learning paradigm is used for stenosis detection, where a quantum network drives and improves the performance of a pre-trained classical network. An intermediate layer between the classical and quantum network post-processes the classical features by mapping them into a hypersphere of fixed radius through a hyperbolic tangent function. Next, these normalized features are processed in the quantum network, and through a SoftMax function, the class probabilities are obtained: stenosis and non-stenosis. Furthermore, a distributed variational quantum circuit is implemented to split the data into multiple quantum circuits within the quantum network, improving the training time without compromising the stenosis detection performance. The proposed method is evaluated on a small X-ray coronary angiography dataset containing 250 image patches (50%–50% of positive and negative stenosis cases). The hybrid classical–quantum network significantly outperformed the classical network. Evaluation results showed a boost concerning the classical transfer learning paradigm in the accuracy of 9%, recall of 20%, and F1-score of 11%, reaching 91.8033%, 94.9153%, and 91.8033%, respectively.

A Simple Method for Automatic 3D Reconstruction of Coronary Arteries From X-Ray Angiography.

Automatic three-dimensional (3-D) reconstruction of the coronary arteries (CA) from medical imaging modalities is still a challenging task. In this study, we present a deep learning-based method of automatic identification of the two ends of the vessel from X-ray coronary angiography (XCA). We also present a method of using template models of CA in matching the two-dimensional segmented vessels from two different angles of XCA. For the deep learning network, we used a U-net consisting of an encoder (Resnet) and a decoder. The two ends of the vessel were manually labeled to generate training images. The network was trained with 2,342, 1,907, and 1,523 labeled images for the left anterior descending (LAD), left circumflex (LCX), and right coronary artery (RCA), respectively. For template models of CA, ten reconstructed 3-D models were averaged for each artery. The accuracy of correspondence using template models was compared with that of manual matching. The deep learning network pointed the proximal region (20% of the total length) in 97.7, 97.5, and 96.4% of 315, 201, and 167 test images for LAD, LCX, and RCA, respectively. The success rates in pointing the distal region were 94.9, 89.8, and 94.6%, respectively. The average distances between the projected points from the reconstructed 3-D model to the detector and the points on the segmented vessels were not statistically different between the template and manual matchings. The computed FFR was not significantly different between the two matchings either. Deep learning methodology is feasible in identifying the two ends of the vessel in XCA, and the accuracy of using template models is comparable to that of manual correspondence in matching the segmented vessels from two angles.

Coronary Magnetic Resonance Angiography in Chronic Coronary Syndromes.

Cardiovascular disease is the leading cause of mortality worldwide, with atherosclerotic coronary artery disease (CAD) accounting for the majority of cases. X-ray coronary angiography and computed tomography coronary angiography (CCTA) are the imaging modalities of choice for the assessment of CAD. However, the use of ionising radiation and iodinated contrast agents remain drawbacks. There is therefore a clinical need for an alternative modality for the early identification and longitudinal monitoring of CAD without these associated drawbacks. Coronary magnetic resonance angiography (CMRA) could be a potential alternative for the detection and monitoring of coronary arterial stenosis, without exposing patients to ionising radiation or iodinated contrast agents. Further advantages include its versatility, excellent soft tissue characterisation and suitability for repeat imaging. Despite the early promise of CMRA, widespread clinical utilisation remains limited due to long and unpredictable scan times, onerous scan planning, lower spatial resolution, as well as motion related image quality degradation. The past decade has brought about a resurgence in CMRA technology, with significant leaps in image acceleration, respiratory and cardiac motion estimation and advanced motion corrected or motion-resolved image reconstruction. With the advent of artificial intelligence, great advances are also seen in deep learning-based motion estimation, undersampled and super-resolution reconstruction promising further improvements of CMRA. This has enabled high spatial resolution (1 mm isotropic), 3D whole heart CMRA in a clinically feasible and reliable acquisition time of under 10 min. Furthermore, latest super-resolution image reconstruction approaches which are currently under evaluation promise acquisitions as short as 1 min. In this review, we will explore the recent technological advances that are designed to bring CMRA closer to clinical reality.

Clinical Application of Non-Contrast-Enhanced Dixon Water-Fat Separation Compressed SENSE Whole-Heart Coronary MR Angiography at 3.0T With and Without Nitroglycerin.

3.0 T non-contrast-enhanced nitroglycerin (NTG)-assisted whole-heart coronary magnetic resonance angiography (MRA) employing Dixon water-fat separation and compressed SENSE (CS-SENSE) acceleration is a promising method for diagnosing coronary artery disease (CAD). To evaluate the diagnostic performance of this technique for detecting clinically-relevant (≥50% diameter reducing) CAD and to evaluate the difference in NTG-induced coronary vasodilation between patients with and without clinically-relevant CAD. Prospective. Sixty-six patients with suspected CAD. 3.0T; CSSENSE, Dixon water-fat separation, three-dimensional segmented turbo field gradient-echo sequence for whole-heart coronary MRA. Overall image quality of coronary MRA was calculated on the basis of all visible coronary segments. The diagnostic performance of coronary MRA for detecting a ≥50% reduction in coronary artery diameter with and without NTG was compared using X-ray coronary angiography (CAG) as the reference. According to CAG, patients were divided into a non-clinically-relevant CAD group and clinically-relevant CAD group, and the difference in NTG-induced vasodilation between the groups was evaluated. Unpaired/paired Student's t-test, Mann-Whitney U test, paired Wilcoxon signed-rank test, χ2 test, McNemar test. A two-tailed P value <0.05 was considered significant. Overall image quality was increased significantly in the coronary MRA images after NTG. The diagnostic performance of the non-NTG vs. NTG-assisted coronary MRA was as follows on a per-patient basis: sensitivity 94.3% vs. 94.3%, specificity 64.5% vs. 83.9%, positive predictive value 75.0% vs. 86.8%, negative predictive value 90.9% vs. 92.9%, and accuracy 80.3% vs. 89.4%, respectively. NTG-induced vasodilation was significantly lower in the clinically-relevant CAD group than in the non-clinically-relevant CAD group (13.7 ± 8.1% vs. 24.1 ± 16.3%). Non-contrast Dixon water-fat separation CS-SENSE coronary MRA at 3.0T can noninvasively detect clinically-relevant CAD and sublingual NTG improved performance. Combining pre- and post-NTG coronary MRA may provide a simple noninvasive and nonionizing test to evaluate coronary vasodilation function. 1 TECHNICAL EFFICACY STAGE: 2.

Assessment of Non-contrast-enhanced Dixon Water-fat Separation Compressed Sensing Whole-heart Coronary MR Angiography at 3.0 T: A Single-center Experience

The clinical utility of Dixon water-fat separation coronary MR angiography (CMRA) with compressed sensing (CS) reconstruction has not been determined in a patient population. This study was designed to evaluate the performance of 3.0 T non-contrast-enhanced Dixon water-fat separation CS whole-heart CMRA sequence in vitro and in vivo. In vitro phantom MRI, we compared key parameters of the SENSE and CS images. And in this prospective in vivo study, from November 2019 to October 2020, 94 participants were recruited for 3.0 T non-contrast-enhanced Dixon water-fat separation CS whole-heart CMRA. The accuracy of CMRA for detecting a ≥ 50% reduction in diameter was determined using X-ray coronary angiography (CA) as the reference method. Compared with SENSE, CS with an appropriate acceleration factor offers both higher SNR/CNR (p < 0.05) and a shortened acquisition. Fifty-eight patients successfully completed the CMRA and CA. The sensitivity, specificity, positive predictive values, negative predictive values, and accuracy of 3.0 T non-contrast-enhanced Dixon water-fat separation CS whole-heart CMRA according to a patient-based analysis were 96.4%, 66.7%, 73.0%, 95.2% and 81.0%, respectively. The area under the receiver-operator characteristic (ROC) curve (AUC) of 3.0 T non-contrast-enhanced Dixon water-fat separation CS whole-heart CMRA for detecting significant coronary artery stenosis is 0.908, 0.895, and 0.904 in patient-, vessel-, and segment-based analyses respectively. 3.0 T non-contrast-enhanced Dixon water-fat separation whole-heart CMRA using appropriate CS is a promising noninvasive and radiation-free technique to detect clinically significant coronary stenosis on patients with suspected CAD.

Stenosis-DetNet: Sequence consistency-based stenosis detection for X-ray coronary angiography.

The automatic detection of coronary artery stenosis on X-ray images is important in coronary heart disease diagnosis. Conventional methods cannot accurately detect all stenosis areas because of heartbeat, respiratory movements and weak vascular features in single-frame contrast images. This paper proposes the use of Stenosis-DetNet, which is a method based on object detection networks. A sequence feature fusion module and a sequence consistency alignment module are designed to maximize temporal information to achieve accurate detection results. The sequence feature fusion module fuses all candidate box features and uses the temporal information to enhance these features. The sequence consistency alignment module optimizes the initial results by using the coronary artery displacement information and image features of the adjacent images and leads to the final detection of coronary artery stenosis. In the experiment, 166 X-ray image sequences were used for training and testing. Compared with the three existing stenosis detection methods, the precision and sensitivity of Stensis-DetNet were 94.87 % and 82.22 %, respectively, which were better than those of the other three methods. Our proposed method effectively suppressed the false positive and false negative results of stenosis detection in sequence angiography images. It was superior to the state-of-art methods.