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Percutaneous coronary intervention (PCI) is an effective treatment for normalizing blood flow in coronary arteries narrowed by stent implantation. Guidewire insertion during PCI requires considerable precision and expertise, and two X-ray videos, a cine loop of angiography and a real-time video of live fluoroscopy, are used to aid guidewire navigation to the target location without entering the wrong blood vessel branches. However, simultaneously watching and interpreting two radiographic videos warrants increased mental effort and intervention time. Additionally, more contrast agents may have to be injected to verify the insertion. Although deep-learning-based dynamic coronary roadmapping (DCR) has been suggested to provide registered images from two different X-ray sources, existing methods may not be suitable for irregular heartbeats or may vary in effectiveness depending on the patient, posing challenges in time-critical situations. To address these challenges, we propose a patient-specific approach for DCR using similarity-based patient data matching in transfer learning with a residual U-Net. The proposed method leverages the anatomical similarities between newly acquired and pre-acquired angiograms by utilizing principal component analysis and cosine similarity to facilitate efficient transfer learning. Moreover, a residual U-Net architecture that incorporates residual blocks and leaky ReLU activation functions to accelerate patient-specific transfer learning is proposed. These advanced techniques resulted in significantly fast transfer learning of less than five minutes from a pre-trained model, as well as high registration image quality with over 30 dB in peak signal-to-noise ratio, while maintaining a registration error of $1.04\pm 0.19$ mm.

Coronary angiography is an X-ray procedure used to examine the arteries of the heart. It provides information about the presence and severity of heart disease and helps doctors assess how well the heart is functioning. This study introduces a new technique for segmenting the coronary arteries in X-ray angiographic images using K-nearest neighbors clustering. The method involves separating thick and thin veins into distinct spaces and then merging them together. To eliminate noise and extract the vessel tree accurately, the algorithm employs morphological techniques like reconstruction, skeletonization, pruning, and dilation, as well as filters such as mean and convolution filters. The resulting segmented vessel tree contains several newly identified thin vessels that have low light intensity in the original image. The algorithm's efficacy is demonstrated by comparing the results with ground truth images. The evaluation criteria, including an accuracy of 0.9747, specificity of 0.9784, and sensitivity of 0.9049, indicate favorable performance compared to other methods. Additionally, the performance of this method is assessed using multiple lesions and instances of vessel blockages. [ABSTRACT FROM AUTHOR]

Purpose: Medical image analysis suffers from a sparsity of annotated data necessary in learning-based models. Cardiorespiratory simulators have been developed to counter the lack of data. However, the resulting data often lack realism. Hence, the proposed method aims to synthesize realistic and fully customizable angiograms of coronary arteries for the training of learning-based biomedical tasks, for cardiologists performing interventions, and for cardiologist trainees. Methods: 3D models of coronary arteries are generated with a fully customizable realistic cardiorespiratory simulator. The transfer of X-ray angiography style to simulator-generated images is performed using a new vessel-specific adaptation of the CycleGAN model. The CycleGAN model is paired with a vesselness-based loss function that is designed as a vessel-specific structural integrity constraint. Results: Validation is performed both on the style and on the preservation of the shape of the arteries of the images. The results show a PSNR of 14.125, an SSIM of 0.898, and an overlapping of 89.5% using the Dice coefficient. Conclusion: We proposed a novel fluoroscopy-based style transfer method for the enhancement of the realism of simulated coronary artery angiograms. The results show that the proposed model is capable of accurately transferring the style of X-ray angiograms to the simulations while keeping the integrity of the structures of interest (i.e., the topology of the coronary arteries). [ABSTRACT FROM AUTHOR]

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.

Coronary artery disease has been the utmost mutual heart disease in the past decades. Various research is going on to prevent this disease. Obstructive CAD occurs when one or more of the coronary arteries which supply blood to myocardium are narrowed owing to plaque build-up on the arteries’ inner walls, causing stenosis. The fundamental task required for the interpretation of coronary angiography is identification and quantification of severity of stenosis within the coronary circulation. Medical experts use X-ray coronary angiography to identify blood vessel/artery stenosis. Due to the artefact, the image has less clarity and it will be challenging for the medical expert to find the stenosis in the coronary artery. The solution to the problem a computational framework is proposed to segment the artery and spot the location of stenosis in the artery. Here the author presented an automatic method to detect stenosis from the X-ray angiogram image. A unified Computational method of Jerman, Level-set, fine-tuning the artery structure, is developed to extract the segmented artery features and detect the artery’s stenosis. The current experimental outcomes illustrate that this computational method achieves average specificity, sensitivity, Accuracy, precision and F-scores of 95%, 97.5%, 98%, 97.5% and 97.5%, respectively.

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. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

Coronary artery disease has been the utmost mutual heart disease in the past decades. Various research is going on to prevent this disease. Obstructive CAD occurs when one or more of the coronary arteries which supply blood to myocardium are narrowed owing to plaque build-up on the arteries' inner walls, causing stenosis. The fundamental task required for the interpretation of coronary angiography is identification and quantification of severity of stenosis within the coronary circulation. Medical experts use X-ray coronary angiography to identify blood vessel/artery stenosis. Due to the artefact, the image has less clarity and it will be challenging for the medical expert to find the stenosis in the coronary artery. The solution to the problem a computational framework is proposed to segment the artery and spot the location of stenosis in the artery. Here the author presented an automatic method to detect stenosis from the X-ray angiogram image. A unified Computational method of Jerman, Level-set, fine-tuning the artery structure, is developed to extract the segmented artery features and detect the artery's stenosis. The current experimental outcomes illustrate that this computational method achieves average specificity, sensitivity, Accuracy, precision and F-scores of 95%, 97.5%, 98%, 97.5% and 97.5%, respectively. [ABSTRACT FROM AUTHOR]

Coronary artery procedures are primarily performed based on X-ray angiography images. However, coronary arteries in X-ray images are often partially broken, complicating diagnoses and procedures owing to lack of visibility. In this paper, we propose a fully automatic method to restore locally broken parts of coronary arteries in X-ray images without using any external information, such as computed tomography images. To this end, we design a new multi-scale generative adversarial network and a vesselness-loss function. The proposed method is optimized for focus on elongated structures and can be utilized in various clinical applications. The proposed method is evaluated and compared with four other existing methods using the performance metrics, PSNR, MSE, and SSIM, and the result shows 34.3, 0.18, and 0.91 averages, respectively for each metric. Based on the performance result, the blocked regions are plausibly reconstructed into such original shapes of blood vessels, which can aid in image-based guiding catheter manipulation during coronary artery procedures. Eventually, the proposed method can be utilized in various clinical applications, e.g., image-based planning and guidance of coronary procedures and prior simulation of results.

Background: Contrast reflux, which is the retrograde movement of contrast against flow direction, is commonly observed during angiography. Despite a vast body of literature on angiography, the hemodynamic factors affecting contrast reflux have not been studied. Numerous methods have been developed to extract flow from angiography, but the reliability of these methods is not yet sufficient to be of routine clinical use. Purpose: To evaluate the effect of baseline blood flow rates and injection conditions on the extent of contrast reflux. To estimate arterial flow rates based on measurement of contrast reflux length. Materials and methods: Iodinated contrast was injected into an idealized tube as well as a physiologically accurate model of the cervico‐cerebral vasculature. A total of 194 high‐speed angiograms were acquired under varying "blood" flow rates and injection conditions (catheter size, injection rate, and injection time). The length of contrast reflux was compared to the input variables and to dimensionless fluid dynamics parameters at the catheter‐tip. Arterial blood flow rates were estimated using contrast reflux length as well as a traditional transit‐time method and compared to measured flow rates. Results: Contrast reflux lengths were significantly affected by contrast injection rate (p < 0.0001), baseline blood flow rate (p = 0.0004), and catheter size (p = 0.04), but not by contrast injection time (p = 0.4). Reflux lengths were found to be correlated to dimensionless fluid dynamics parameters by an exponential function (R2 = 0.6–0.99). When considering the entire dataset in unison, flow estimation errors with the reflux‐length method (39% ± 33%) were significantly higher (p = 0.003) than the transit‐time method (33% ± 36%). However, when subgrouped by catheter, the error with the reflux‐length method was substantially reduced and was significantly lower (14% ± 14%, p < 0.0001) than the transit‐time method. Conclusion: Results show correlations between contrast reflux length and baseline hemodynamic parameters that have not been reported previously. Clinically relevant blood flow rate estimation is feasible by simple measurement of reflux length. In vivo and clinical studies are required to confirm these correlations and to refine the methodology of estimating blood flow by reflux. [ABSTRACT FROM AUTHOR]

Rodents are used extensively as animal models for the preclinical investigation of microvascular-related diseases. However, motion artifacts in currently available imaging methods preclude real-time observation of microvessels in vivo. In this paper, a pixel temporal averaging (PTA) method that enables real-time imaging of microvessels in the mouse brain in vivo is described. Experiments using live mice demonstrated that PTA efficiently eliminated motion artifacts and random noise, resulting in significant improvements in contrast-to-noise ratio. The time needed for image reconstruction using PTA with a normal computer was 250 ms, highlighting the capability of the PTA method for real-time angiography. In addition, experiments with less than one-quarter of photon flux in conventional angiography verified that motion artifacts and random noise were suppressed and microvessels were successfully identified using PTA, whereas conventional temporal subtraction and averaging methods were ineffective. Experiments performed with an X-ray tube verified that the PTA method could also be successfully applied to microvessel imaging of the mouse brain using a laboratory X-ray source. In conclusion, the proposed PTA method may facilitate the real-time investigation of cerebral microvascular-related diseases using small animal models.

Background: Navigation guidance in cardiac interventions is provided by X‐ray angiography. Cumulative radiation exposure is a serious concern for pediatric cardiac interventions. Purpose: A generative learning‐based approach is proposed to predict X‐ray angiography frames to reduce the radiation exposure for pediatric cardiac interventions while preserving the image quality. Methods: Frame predictions are based on a model‐free motion estimation approach using a long short‐term memory architecture and a content predictor using a convolutional neural network structure. The presented model thus estimates contrast‐enhanced vascular structures such as the coronary arteries and their motion in X‐ray sequences in an end‐to‐end system. This work was validated with 56 simulated and 52 patients' X‐ray angiography sequences. Results: Using the predicted images can reduce the number of pulses by up to three new frames without affecting the image quality. The average required acquisition can drop by 30% per second for a 15 fps acquisition. The average structural similarity index measurement was 97% for the simulated dataset and 82% for the patients' dataset. Conclusions: Frame prediction using a learning‐based method is promising for minimizing radiation dose exposure. The required pulse rate is reduced while preserving the frame rate and the image quality. With proper integration in X‐ray angiography systems, this method can pave the way for improved dose management. [ABSTRACT FROM AUTHOR]

In vivo measurement of blood velocity fields and flow descriptors remains challenging due to image artifacts and limited resolution of current imaging methods; however, in vivo imaging data can be used to inform and validate patient-specific computational fluid dynamics (CFD) models. Image-based CFD can be particularly useful for planning surgical interventions in complicated cases such as fusiform aneurysms of the basilar artery, where it is crucial to alter pathological hemodynamics while preserving flow to the distal vasculature. In this study, patient-specific CFD modeling was conducted for two basilar aneurysm patients considered for surgical treatment. In addition to velocity fields, transport of contrast agent was simulated for the preoperative and postoperative conditions using two approaches. The transport of a virtual contrast passively following the flow streamlines was simulated to predict post-surgical flow regions prone to thrombus deposition. In addition, the transport of a mixture of blood with an iodine-based contrast agent was modeled to compare and verify the CFD results with X-ray angiograms. The CFD-predicted patterns of contrast flow were qualitatively compared to in vivo X-ray angiograms acquired before and after the intervention. The results suggest that the mixture modeling approach, accounting for the flow rates and properties of the contrast injection, is in better agreement with the X-ray angiography data. The virtual contrast modeling assessed the residence time based on flow patterns unaffected by the injection procedure, which makes the virtual contrast modeling approach better suited for prediction of thrombus deposition, which is not limited to the peri-procedural state.

Background The 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. Methods In 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. Results The 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. Discussion The 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.

Percutaneous coronary intervention (PCI) is an effective treatment for increasing blood flow in narrowed coronary arteries by implanting stents. When inserting a guidewire during PCI, a cardiologist watches two X-ray videos, a cine loop of angiography and a real-time video of live fluoroscopy to navigate the guidewire to the target location while avoiding entering wrong branches of blood vessels. However, watching the two separate X-ray videos increases a cardiologist’s mental efforts and intervention time. Moreover, more contrast agents may need to be injected to validate the correct insertion. To address this problem, dynamic coronary roadmapping (DCR) has been suggested because it provides registered images of two different X-ray images. In this study, we propose a novel patient-specific deep-learning-based approach for DCR that uses only two types of X-ray images without using an electrocardiogram (ECG). In the proposed approach, a trained deep generative model generates a registered image from the guidewire images in live fluoroscopy and angiographic sequence from diagnostic coronary angiography. Because the proposed approach can implicitly compensate for both cardiac and respiratory motions, it can be applied to arrhythmia patients with irregular ECG signals, unlike conventional image registration through ECG. Qualitative evaluation of 34 PCI cases, including three irregular heartbeat cases, showed low registration errors (26% lower than that of the latest work) and high image quality (more than 30 dB peak signal-to-noise ratio (PSNR)). All registration results were classified as “fit for use” (70.6% “Good” and 29.4% “Acceptable”) by three cardiologists. Furthermore, three irregular beating cases were classified as having “Good” registration quality.

Rodents are used extensively as animal models for the preclinical investigation of microvascular‐related diseases. However, motion artifacts in currently available imaging methods preclude real‐time observation of microvessels in vivo. In this paper, a pixel temporal averaging (PTA) method that enables real‐time imaging of microvessels in the mouse brain in vivo is described. Experiments using live mice demonstrated that PTA efficiently eliminated motion artifacts and random noise, resulting in significant improvements in contrast‐to‐noise ratio. The time needed for image reconstruction using PTA with a normal computer was 250 ms, highlighting the capability of the PTA method for real‐time angiography. In addition, experiments with less than one‐quarter of photon flux in conventional angiography verified that motion artifacts and random noise were suppressed and microvessels were successfully identified using PTA, whereas conventional temporal subtraction and averaging methods were ineffective. Experiments performed with an X‐ray tube verified that the PTA method could also be successfully applied to microvessel imaging of the mouse brain using a laboratory X‐ray source. In conclusion, the proposed PTA method may facilitate the real‐time investigation of cerebral microvascular‐related diseases using small animal models. [ABSTRACT FROM AUTHOR]

Early stages of diseases, including stroke, hypertension, angiogenesis of tumours, spinal cord injuries, etc., are closely associated with the lesions of microvasculature. Rodent models of human vascular diseases are extensively used for the preclinical investigation of the disease evolution and therapy with synchrotron radiation. Therefore, non-invasive and in vivo X-ray imaging with high sensitivity and clarity is desperately needed to visualize the microvessels in live-animal models. Contrast agent is essential for the in vivo X-ray imaging of vessels and angiomatous tissue. Because of the non-rigid motion of adjacent tissues, the short circulation time and the intermittent flow of contrast agents in vessels, it is a great challenge for the traditional X-ray imaging methods to achieve well defined images of microvessels in vivo. In this article, move contrast X-ray imaging (MCXI) based on high-brightness synchrotron radiation is developed to overcome the intrinsic defects in conventional methods. Experiments with live rodents demonstrate the practicability of the MCXI method for sensitive and intact imaging of microvessels in vivo.

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

Purpose: Channelized Hotelling observer (CHO) models have been implemented to assess imaging performance in x‐ray angiography systems. While current methods are appropriate for assessing unprocessed images of moving test objects upon uniform‐exposure backgrounds, they are inadequate for assessing conditions which more appropriately mimic clinical imaging conditions including the combination of moving test objects, complex anthropomorphic backgrounds, and image processing. In support of this broad goal, the purpose of this work was to develop theory and methods to automatically select a subset of task‐specific efficient Gabor channels from a task‐generic Gabor channel base set. Also, previously described theory and methods to manage detectability index (d′) bias due to nonrandom temporal variations in image electronic noise will be revisited herein. Methods: Starting with a base set of 96 Gabor channels, backward elimination of channels was used to automatically identify an "efficient" channel subset which reduced the number of channels retained in the subset while maintaining the magnitude of the d′ estimate. The concept of a pixelwise Hotelling observer (PHO) model was introduced and similarly implemented to assess the performance of the efficient‐channel CHO model. Bias in d′ estimates arising from temporally variable nonstationary noise was modeled as a bivariate probability density function for normal distributions, where one variable corresponds to the signal from the test object and the other variable corresponds to the signal from temporally variable nonstationary noise. Theory and methods were tested on uniform‐exposure unprocessed angiography images with detector target dose (DTD) of 6, 18, and 120 nGy containing static disk‐shaped test objects with diameter in the range of 0.5 to 4 mm. Results: Considering all DTD levels and test object sizes, the proposed method reduced the number of Gabor channels in the final subset by 63–82% compared to the original 96 Gabor channel base set, while maintaining a mean relative performance ((dCHO′/dPHO′)×100%) of 95% ± 4% with respect to the reference PHO model. Experimental results demonstrated that the bivariate approach to account for bias due to temporally variable nonstationary noise resulted in improved correlation between the CHO and PHO models as compared to a previously proposed univariate approach. Conclusions: Computationally efficient backward elimination can be used to select an efficient subset of Gabor channels from an initial channel base set without substantially compromising the magnitude of the d′ estimate. Bias due to temporally variable nonstationary noise can be modeled through a bivariate approach leading to an improved unbiased estimate of d′. [ABSTRACT FROM AUTHOR]

Quantification of coronary artery stenosis on X-ray angiography (XRA) images is of great importance during the intraoperative treatment of coronary artery disease. It serves to quantify the coronary artery stenosis by estimating the clinical morphological indices, which are essential in clinical decision making. However, stenosis quantification is still a challenging task due to the overlapping, diversity and small-size region of the stenosis in the XRA images. While efforts have been devoted to stenosis quantification through low-level features, these methods have difficulty in learning the real mapping from these features to the stenosis indices. These methods are still cumbersome and unreliable for the intraoperative procedures due to their two-phase quantification, which depends on the results of segmentation or reconstruction of the coronary artery. In this work, we are proposing a hierarchical attentive multi-view learning model (HEAL) to achieve a direct quantification of coronary artery stenosis, without the intermediate segmentation or reconstruction. We have designed a multi-view learning model to learn more complementary information of the stenosis from different views. For this purpose, an intra-view hierarchical attentive block is proposed to learn the discriminative information of stenosis. Additionally, a stenosis representation learning module is developed to extract the multi-scale features from the keyframe perspective for considering the clinical workflow. Finally, the morphological indices are directly estimated based on the multi-view feature embedding. Extensive experiment studies on clinical multi-manufacturer dataset consisting of 228 subjects show the superiority of our HEAL against nine comparing methods, including direct quantification methods and multi-view learning methods. The experimental results demonstrate the better clinical agreement between the ground truth and the prediction, which endows our proposed method with a great potential for the efficient intraoperative treatment of coronary artery disease. [ABSTRACT FROM AUTHOR]

Aim: Our aim was to automatically estimate the blood velocity in coronary arteries using cine X-ray angiographic sequence. Estimating the coronary blood velocity is a key approach in investigating patients with angina pectoris and no significant coronary artery disease. Blood velocity estimation is central in assessing coronary flow reserve.Methods and Results: A multi-step automatic method for blood flow velocity estimation based on the information extracted solely from the cine X-ray coronary angiography sequence obtained by invasive selective coronary catheterization was developed. The method includes (1) an iterative process of segmenting coronary arteries modeling and removing the heart motion using a non-rigid registration, (2) measuring the area of the segmented arteries in each frame, (3) fitting the measured sequence of areas with a 7° polynomial to find start and stop time of dye propagation, and (4) estimating the blood flow velocity based on the time of the dye propagation and the length of the artery-tree. To evaluate the method, coronary angiography recordings from 21 patients with no obstructive coronary artery disease were used. In addition, coronary flow velocity was measured in the same patients using a modified transthoracic Doppler assessment of the left anterior descending artery. We found a moderate but statistically significant correlation between flow velocity assessed by trans thoracic Doppler and the proposed method applying both Spearman and Pearson tests.Conclusion: Measures of coronary flow velocity using a novel fully automatic method that utilizes the information from the X-ray coronary angiographic sequence were statistically significantly correlated to measurements obtained with transthoracic Doppler recordings.

Automatic vessel segmentation is a critical area of research in medical image analysis, as it can greatly assist doctors in accurately and efficiently diagnosing vascular diseases. However, accurately extracting the complete vessel structure from images remains a challenge due to issues such as uneven contrast and background noise. Existing methods primarily focus on segmenting individual pixels and often fail to consider vessel features and morphology. As a result, these methods often produce fragmented results and misidentify vessel-like background noise, leading to missing and outlier points in the overall segmentation. To address these issues, this paper proposes a novel approach called the progressive edge information aggregation network for vessel segmentation (PEA-Net). The proposed method consists of several key components. First, a dual-stream receptive field encoder (DRE) is introduced to preserve fine structural features and mitigate false positive predictions caused by background noise. This is achieved by combining vessel morphological features obtained from different receptive field sizes. Second, a progressive complementary fusion (PCF) module is designed to enhance fine vessel detection and improve connectivity. This module complements the decoding path by combining features from previous iterations and the DRE, incorporating nonsalient information. Additionally, segmentation-edge decoupling enhancement (SDE) modules are employed as decoders to integrate upsampling features with nonsalient information provided by the PCF. This integration enhances both edge and segmentation information. The features in the skip connection and decoding path are iteratively updated to progressively aggregate fine structure information, thereby optimizing segmentation results and reducing topological disconnections. Experimental results on multiple datasets demonstrate that the proposed PEA-Net model and strategy achieve optimal performance in both pixel-level and topology-level metrics., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

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Highlights • An automated vessel stenosis quantification method in angiography images is presented. • An improved measure of match method provides an accurate vessel diameter measure. • The method can serve as a generalized framework to handle different image modalities. Abstract Background and Objective X-ray coronary angiography (XCA) remains the gold standard imaging technique for the diagnosis and treatment of cardiovascular disease. Automatic detection and grading of coronary stenoses in XCA are challenging problems due to the complex overlap of different background structures with intensity inhomogeneities. We present a new computerized image based method to accurately identify and quantify the stenosis severity on XCA. Methods A unified framework, consisting of Hessian-based vessel enhancement, level-set skeletonization, improved measure of match measurement, and local extremum identification, is developed to distinctly reveal the vessel structures and accurately determine the stenosis grades. The methodology was validated on 143 consecutive patients who underwent diagnostic XCA through both qualitative and quantitative evaluations. Results The presented algorithm was tested on a set of 267 vessel segments annotated by two expert cardiologists. The experimental results show that the method can effectively localize and quantify the vessel stenoses, achieving average detection accuracy, sensitivity, specificity, and F-score of 93.93%, 91.03%, 93.83%, 89.18%, respectively. Conclusions A fully automatic coronary analysis method is devised for vessel stenosis detection and grading in XCA. The presented approach can potentially serve as a generalized framework to handle different image modalities. [ABSTRACT FROM AUTHOR]

Purpose: Electronic noise associated with passive pixel (PP) x‐ray angiography flat panel detectors is known to compromise fluoroscopic image quality. An active pixel (AP) crystalline silicon x‐ray detector with potential for reduced influence of electronic noise is commercially available. The purpose of this work was to compare the performance of the AP vs PP x‐ray angiography detectors over a detector target dose (DTD) range relevant for invasive cardiology procedures. Methods: A total of 16 passive pixel detector systems representing two models and two active pixel detector systems of a single model were tested. Iodine contrast (160 mg I ml−1) disk‐shaped test objects of diameter 0.5–4.0 mm were embedded in 30 × 30 cm2 25‐cm‐thick PMMA phantom. Detector target dose was 6, 18, and 120 nGy and 1204 test signal present and signal absent images were acquired. A channelized Hotelling observer statistical model (CHO) was used to estimate detectability index (d′) of the detectors for the various test objects. The CHO included correction for bias from finite sampling and that due to temporally variable electronic noise. Results: Detectability index estimates demonstrated similar performance between the two models of PP detectors and relatively improved performance for the AP detectors for all DTD levels and test object diameters. For DTD = 120 nGy and the 4.0 mm test object, d′ of the AP detectors was 13% and 20% greater than that of the PP detectors. For DTD = 6 nGy, d′ of the AP detectors was 42% and 54% greater. Conclusions: The AP x‐ray angiography detector demonstrated superior performance throughout the DTD range tested and especially for DTD consistent with low‐dose fluoroscopy. The improved performance of the AP detectors may facilitate reduced patient dose and/or improved image quality. [ABSTRACT FROM AUTHOR]

Percutaneous transluminal coronary angioplasty (PTCA) is a minimally invasive surgery in the clinical treatment of coronary artery diseases. The successful operation highly depends on the size and accurate placement of the stent. In this study, a virtual stenting (VS) system was designed and implemented to facilitate the planning of PTCA. A three-dimensional (3D) vessel model was reconstructed through fusing intravascular ultrasound (IVUS) sequential images and simultaneously recorded X-ray angiograms during cardiac intervention. The user is allowed to intuitively explore the vessel in an endoscopic manner by creating/updating a fly-through trajectory in the lumen. A virtual stent library including a better variety of commercially available bare metal heart stents was built. The user is allowed to select a proper stent according to the morphology of the vessel and lesion and to move it to the lesion. Also, the user can visually observe the stent expansion/apposition and flexibly adjust its position. The system is used to assist visual diagnosis of the vascular diseases, evaluation of interventional treatment and training of the medical personnel. [ABSTRACT FROM AUTHOR]

Purpose: Early detection of blood vessel pathologies can be made through the evaluation of functional and structural abnormalities in the arteries, including the arterial distensibility measure. We propose a feasibility study on computing arterial distensibility automatically from monoplane 2D X-ray sequences for both small arteries (such as coronary arteries) and larger arteries (such as the aorta). Methods: To compute the distensibility measure, three steps were developed: First, the segment of an artery is extracted using our graph-based segmentation method. Then, the same segment is tracked in the moving sequence using our spatio-temporal segmentation method: the Temporal Vessel Walker. Finally, the diameter of the artery is measured automatically at each frame of the sequence based on the segmentation results. Results: The method was evaluated using one simulated sequence and 4 patients' angiograms depicting the coronary arteries and three depicting the ascending aorta. Results of the simulated sequence achieved a Dice index of 98%, with a mean squared error in diameter measurement of $$0.18\pm 0.31$$ mm. Results obtained from patients' X-ray sequences are consistent with manual assessment of the diameter by experts. Conclusions: The proposed method measures changes in diameter of a specific segment of a blood vessel during the cardiac sequence, automatically based on monoplane 2D X-ray sequence. Such information might become a key to help physicians in the detection of variations of arterial stiffness associated with early stages of various vasculopathies. [ABSTRACT FROM AUTHOR]

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Despite continuous progress in X-ray angiography systems, X-ray coronary angiography is fundamentally limited by its 2D representation of moving coronary arterial trees, which can negatively impact assessment of coronary artery disease and guidance of percutaneous coronary intervention. To provide clinicians with 3D/3D+time information of coronary arteries, methods computing reconstructions of coronary arteries from X-ray angiography are required. Because of several aspects (e.g. cardiac and respiratory motion, type of X-ray system), reconstruction from X-ray coronary angiography has led to vast amount of research and it still remains as a challenging and dynamic research area. In this paper, we review the state-of-the-art approaches on reconstruction of high-contrast coronary arteries from X-ray angiography. We mainly focus on the theoretical features in model-based (modelling) and tomographic reconstruction of coronary arteries, and discuss the evaluation strategies. We also discuss the potential role of reconstructions in clinical decision making and interventional guidance, and highlight areas for future research. [ABSTRACT FROM AUTHOR]

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 F 1 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., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Objective. To develop a novel patient-specific cardio-respiratory motion prediction approach for X-ray angiography time series based on a simple long short-term memory (LSTM) model. Approach. The cardio-respiratory motion behavior in an X-ray image sequence was represented as a sequence of 2D affine transformation matrices, which provide the displacement information of contrasted moving objects (arteries and medical devices) in a sequence. The displacement information includes translation, rotation, shearing, and scaling in 2D. A many-to-many LSTM model was developed to predict 2D transformation parameters in matrix form for future frames based on previously generated images. The method was developed with 64 simulated phantom datasets (pediatric and adult patients) using a realistic cardio-respiratory motion simulator (XCAT) and was validated using 10 different patient X-ray angiography sequences. Main results. Using this method we achieved less than 1 mm prediction error for complex cardio-respiratory motion prediction. The following mean prediction error values were recorded over all the simulated sequences: 0.39 mm (for both motions), 0.33 mm (for only cardiac motion), and 0.47 mm (for only respiratory motion). The mean prediction error for the patient dataset was 0.58 mm. Significance. This study paves the road for a patient-specific cardio-respiratory motion prediction model, which might improve navigation guidance during cardiac interventions., (Creative Commons Attribution license.)

Background: Coronary flow reserve (CFR) has prognostic value in patients with coronary artery disease. However, its measurement is complex, and automatic methods for CFR computation are scarcely available. We developed an automatic method for CFR computation based on coronary angiography and assessed its feasibility., Methods: Coronary angiographies from the Corelab database were annotated by experienced analysts. A convolutional neural network (CNN) model was trained for automatic segmentation of the main coronary arteries during contrast injection. The segmentation performance was evaluated using 5-fold cross-validation. Subsequently, the CNN model was implemented into a prototype software package for automatic computation of the CFR (CFRauto) and applied on a different sample of patients with angiographies performed both at rest and during maximal hyperemia, to assess the feasibility of CFRauto and its agreement with the manual computational method based on frame count (CFRmanual)., Results: Altogether, 137,126 images of 5913 angiographic runs from 2407 patients were used to develop and evaluate the CNN model. Good segmentation performance was observed. CFRauto was successfully computed in 136 out of 149 vessels (91.3%). The average analysis time to derive CFRauto was 18.1 ± 10.3 s per vessel. Moderate correlation (r = 0.51, p < 0.001) was observed between CFRauto and CFRmanual, with a mean difference of 0.12 ± 0.53., Conclusions: Automatic computation of the CFR based on coronary angiography is feasible. This method might facilitate wider adoption of coronary physiology in the catheterization laboratory to assess microcirculatory function.