Statistical coronary motion models for 2D+t/3D registration of X-ray coronary angiography and CTA

3D/2D Vessel Registration Based on Monte Carlo Tree Search and Manifold Regularization

The augmented intra-operative real-time imaging in vascular interventional surgery, which is generally performed by projecting preoperative computed tomography angiography images onto intraoperative digital subtraction angiography (DSA) images, can compensate for the deficiencies of DSA-based navigation, such as lack of depth information and excessive use of toxic contrast agents. 3D/2D vessel registration is the critical step in image augmentation. A 3D/2D registration method based on vessel graph matching is proposed in this study. For rigid registration, the matching of vessel graphs can be decomposed into continuous states, thus 3D/2D vascular registration is formulated as a search tree problem. The Monte Carlo tree search method is applied to find the optimal vessel matching associated with the highest rigid registration score. For nonrigid registration, we propose a novel vessel deformation model based on manifold regularization. This model incorporates the smoothness constraint of vessel topology into the objective function. Furthermore, we derive simplified gradient formulas that enable fast registration. The proposed technique undergoes evaluation against seven rigid and three nonrigid methods using a variety of data - simulated, algorithmically generated, and manually annotated - across three vascular anatomies: the hepatic artery, coronary artery, and aorta. Our findings show the proposed method's resistance to pose variations, noise, and deformations, outperforming existing methods in terms of registration accuracy and computational efficiency. The proposed method demonstrates average registration errors of 2.14 mm and 0.34 mm for rigid and nonrigid registration, and an average computation time of 0.51 s.

Improved stereo perception in coronary angiography using the X-ray tube as the viewpoint and validation with 3D printed models

Coronary angiography (CAG) provides two-dimensional images, but a clinician who is experienced in percutaneous coronary interventions can use information from these images to interpret spatial depth and infer the three-dimensional (3D) locations of vessels. We hypothesized that CAG results were equivalent to the mirror image of a coronary artery perspective projection, and a stereo perception could be easily established when the viewpoint of the angiogram was the X-ray tube instead of the detector. To eliminate the influence of heartbeat and respiration, a 3D-printed a coronary artery model was constructed for analysis. The effects of gantry movements during digital subtraction angiography (DSA) on the image were used to identify factors that affected DSA image transformation. Then, based on these factors, DSA imaging was simulated using UG NX software with three methods: (i) a perspective projection with the detector as the viewpoint; (ii) a parallel projection; and (iii) a mirror image of the perspective projection with the X-ray tube as the viewpoint. Finally, the resulting 3D images were compared with the DSA image. Our mirror image of the coronary artery perspective projection that used the X-ray tube as the viewpoint fused precisely with the CAG results and provided exact simulations of all the effects of DSA gantry movements on the DSA image. CAG results were equivalent to the mirror image of coronary artery perspective projection, and the stereo perception was easily established using the X-ray tube as the viewpoint.

Multi-modality cardiac image computing: A survey

Multi-modality cardiac imaging plays a key role in the management of patients with cardiovascular diseases. It allows a combination of complementary anatomical, morphological and functional information, increases diagnosis accuracy, and improves the efficacy of cardiovascular interventions and clinical outcomes. Fully-automated processing and quantitative analysis of multi-modality cardiac images could have a direct impact on clinical research and evidence-based patient management. However, these require overcoming significant challenges including inter-modality misalignment and finding optimal methods to integrate information from different modalities. This paper aims to provide a comprehensive review of multi-modality imaging in cardiology, the computing methods, the validation strategies, the related clinical workflows and future perspectives. For the computing methodologies, we have a favored focus on the three tasks, i.e., registration, fusion and segmentation, which generally involve multi-modality imaging data, either combining information from different modalities or transferring information across modalities. The review highlights that multi-modality cardiac imaging data has the potential of wide applicability in the clinic, such as trans-aortic valve implantation guidance, myocardial viability assessment, and catheter ablation therapy and its patient selection. Nevertheless, many challenges remain unsolved, such as missing modality, modality selection, combination of imaging and non-imaging data, and uniform analysis and representation of different modalities. There is also work to do in defining how the well-developed techniques fit in clinical workflows and how much additional and relevant information they introduce. These problems are likely to continue to be an active field of research and the questions to be answered in the future.

Automatic landmark detection and mapping for 2D/3D registration with BoneNet

The 3D musculoskeletal motion of animals is of interest for various biological studies and can be derived from X-ray fluoroscopy acquisitions by means of image matching or manual landmark annotation and mapping. While the image matching method requires a robust similarity measure (intensity-based) or an expensive computation (tomographic reconstruction-based), the manual annotation method depends on the experience of operators. In this paper, we tackle these challenges by a strategic approach that consists of two building blocks: an automated 3D landmark extraction technique and a deep neural network for 2D landmarks detection. For 3D landmark extraction, we propose a technique based on the shortest voxel coordinate variance to extract the 3D landmarks from the 3D tomographic reconstruction of an object. For 2D landmark detection, we propose a customized ResNet18-based neural network, BoneNet, to automatically detect geometrical landmarks on X-ray fluoroscopy images. With a deeper network architecture in comparison to the original ResNet18 model, BoneNet can extract and propagate feature vectors for accurate 2D landmark inference. The 3D poses of the animal are then reconstructed by aligning the extracted 2D landmarks from X-ray radiographs and the corresponding 3D landmarks in a 3D object reference model. Our proposed method is validated on X-ray images, simulated from a real piglet hindlimb 3D computed tomography scan and does not require manual annotation of landmark positions. The simulation results show that BoneNet is able to accurately detect the 2D landmarks in simulated, noisy 2D X-ray images, resulting in promising rigid and articulated parameter estimations.

Optical flow estimation of coronary angiography sequences based on semi-supervised learning

Optical flow is widely used in medical image processing, such as image registration, segmentation, 3D reconstruction, and temporal super-resolution. However, high-precision optical flow training datasets for medical images are challenging to produce. The current optical flow estimation models trained on these non-medical datasets, such as KITTI, Sintel, and FlyingChairs are unsuitable for medical images. In this work, we propose a semi-supervised learning mechanism to estimate the optical flow of coronary angiography. Our proposed method only needs the original medical images, segmentation results of regions of interest, and pre-trained models based on other optical flow datasets to train a new optical flow estimation model suitable for medical images. First, we use the coronary segmentation results to perform image enhancement processing on the coronary vascular region to improve the image contrast between the vascular region and the surrounding tissues. Then, we extract the high-precision optical flow of coronary arteries based on the coronary-enhanced images and the pre-trained optical flow estimation model. After estimating the optical flow, we take it and its corresponding original coronary angiography images as the training dataset to train the optical flow estimation network. Furthermore, we generate a large-scale synthetic Flying-artery dataset based on coronary artery segmentation results and original coronary angiography images, which is used to improve and evaluate the accuracy of optical flow estimation for coronary angiography. The experimental results on the coronary angiography datasets demonstrate that our proposed method can significantly improve the optical flow estimation accuracy of coronary angiography sequences compared with other methods.

Topological recovery for non-rigid 2D/3D registration of coronary artery models

BACKGROUND AND OBJECTIVE

Intra-operative X-ray angiography, the current standard method for visualizing and diagnosing cardiovascular disease, is limited in its ability to provide essential 3D information. These limitations are disadvantages in treating patients. For example, it is a cause of lowering the success rate of interventional procedures. Here, we propose a novel 2D-3D non-rigid registration method to understand vascular geometry during percutaneous coronary intervention.

METHODS

The proposed method uses the local bijection pair distance as a cost function to minimize the effect of inconsistencies from center-line extraction. Moreover, novel cage-based 3D deformation and multi-threaded particle swarm optimization are utilized to implement real-time registration. We evaluated the proposed method for 154 examinations from 10 anonymous patients by coverage percentage, comparing the average distance of the 2D extracted center-line with that of the registered 3D center-line.

RESULTS

The proposed 2D-3D non-rigid registration method achieved an average distance of 1.98 mm with a 0.54 s computation time. Additionally, in aiming to reduce the uncertainty of XA images, we used the proposed method to retrospectively visualize the connections between 2D vascular segments and the distal part of occlusions.

CONCLUSIONS

Ultimately, the proposed 2D/3D non-rigid registration method can successfully register the 3D center-line of coronary arteries with corresponding 2D XA images, and is computationally sufficient for online usage. Therefore, this method can improve the success rate of such procedures as a percutaneous coronary intervention and provide the information necessary to diagnose cardiovascular diseases better.

Iterative closest graph matching for non-rigid 3D/2D coronary arteries registration

Background and objective Fusion of the preoperative computed tomography angiography and intraoperative X-ray angiography images can considerably enhance the visual perception of physicians during percutaneous coronary interventions. This technique can provide 3D information of the arteries and reduce the uncertainty of 2D guidance images. For this purpose, 3D/2D vascular registration with high accuracy and robustness is crucial for performing accurate surgery. Methods In this study, we propose an iterative closest graph matching (ICGM) method that utilizes an alternative iteration framework including correspondence and transformation phases. A coarse-to-fine matching approach based on redundant graph matching is proposed for the correspondence phase. The transformation phase involves rigid and non-rigid transformations, in which rigid transformation is calculated using a closed-form solution, and non-rigid transformation is achieved using a statistical shape model established from a synthetic deformation dataset. Results The proposed method is evaluated and compared with nine state-of-the-art methods on simulated data and clinical datasets. Experiments demonstrate that our method is insensitive to the pose of data and robust to noise and deformation. Moreover, it outperforms other methods in terms of registering real data. Conclusions Given its high capture range, the proposed method can register 3D vessels without prior initialization in clinical practice.

A Patient-Specific 3Dt Coronary Artery Motion Modeling Method Using Hierarchical Deformation with Electrocardiogram. SENSORS (BASEL, SWITZERLAND) 2020;20:E5680. [PMID: 33027998 PMCID: PMC7582594 DOI: 10.3390/s20195680]

Cardiovascular-related diseases are one of the leading causes of death worldwide. An understanding of heart movement based on images plays a vital role in assisting postoperative procedures and processes. In particular, if shape information can be provided in real-time using electrocardiogram (ECG) signal information, the corresponding heart movement information can be used for cardiovascular analysis and imaging guides during surgery. In this paper, we propose a 3D+t cardiac coronary artery model which is rendered in real-time, according to the ECG signal, where hierarchical cage-based deformation modeling is used to generate the mesh deformation used during the procedure. We match the blood vessel's lumen obtained from the ECG-gated 3D+t CT angiography taken at multiple cardiac phases, in order to derive the optimal deformation. Splines for 3D deformation control points are used to continuously represent the obtained deformation in the multi-view, according to the ECG signal. To verify the proposed method, we compare the manually segmented lumen and the results of the proposed method for eight patients. The average distance and dice coefficient between the two models were 0.543 mm and 0.735, respectively. The required time for registration of the 3D coronary artery model was 23.53 s/model. The rendering speed to derive the model, after generating the 3D+t model, was faster than 120 FPS.

Real-time control of respiratory motion: Beyond radiation therapy

Motion management in radiation oncology is an important aspect of modern treatment planning and delivery. Special attention has been paid to control respiratory motion in recent years. However, other medical procedures related to both diagnosis and treatment are likely to benefit from the explicit control of breathing motion. Quantitative imaging - including increasingly important tools in radiology and nuclear medicine - is among the fields where a rapid development of motion control is most likely, due to the need for quantification accuracy. Emerging treatment modalities like focussed-ultrasound tumor ablation are also likely to benefit from a significant evolution of motion control in the near future. In the present article an overview of available respiratory motion systems along with ongoing research in this area is provided. Furthermore, an attempt is made to envision some of the most expected developments in this field in the near future.

Deep feature descriptor based hierarchical dense matching for X-ray angiographic images

Backgroud and Objective: X-ray angiography, a powerful technique for blood vessel visualization, is widely used for interventional diagnosis of coronary artery disease because of its fast imaging speed and perspective inspection ability. Matching feature points in angiographic images is a considerably challenging task due to repetitive weak-textured regions.

METHODS

In this paper, we propose an angiographic image matching method based on the hierarchical dense matching framework, where a novel deep feature descriptor is designed to compute multilevel correlation maps. In particular, the deep feature descriptor is computed by a deep learning model specifically designed and trained for angiographic images, thereby making the correlation maps more distinctive for corresponding feature points in different angiographic images. Moreover, point correspondences are further hierarchically extracted from multilevel correlation maps with the highest similarity response(s), which is relatively robust and accurate. To overcome the problem regarding the lack of training samples, the convolutional neural network (designed for deep feature descriptor) is initially trained on samples from natural images and then fine-tuned on manually annotated angiographic images. Finally, a dense matching completion method, based on the distance between deep feature descriptors, is proposed to generate dense matches between images.

RESULTS

The proposed method has been evaluated on the number and accuracy of extracted matches and the performance of subtraction images. Experiments on a variety of angiographic images show promising matching accuracy, compared with state-of-the-art methods.

CONCLUSIONS

The proposed angiographic image matching method is shown to be accurate and effective for feature matching in angiographic images, and further achieves good performance in image subtraction.

Vesselness-constrained robust PCA for vessel enhancement in x-ray coronary angiograms

Effective vessel enhancement in x-ray coronary angiograms (XCA) is essential for the diagnosis of coronary artery disease, yet challenged by complex background structures of varying intensities as well as motion patterns. As a typical layer-separation method, robust principal component analysis (RPCA) has been proposed to automatically improve vessel visibility via sparse and low-rank decomposition. However, the attenuated motion of vessels in x-ray angiograms leads to the unsatisfactory vessel enhancement performance of the decomposition framework. To address this problem, we propose a vesselness-constrained RPCA method (VC-RPCA), where a vessel-like appearance prior is incorporated into the layer separation framework for accurate vessel enhancement. We first pre-compute the vessel-like appearance prior based on a Frangi filter to highlight the curvilinear structures. After removing large-scale background structures via a morphological closing operation, we then integrate the pre-computed vessel-like appearance prior into a low-rank decomposition framework to separate the fine vessel structures. In addition, we develop an adaptive regularization strategy that imposes structured-sparse constraints to solve the scale issue and capture vessels without salient motion. The proposed method was validated on 13 clinical XCA sequences containing 777 images in total. The contrast-to-noise ratio, Dice coefficient and area under the ROC curve were employed for quantitative evaluation of the vessel enhancement performance. Experiments show that (1) the adaptive regularization strategy helps to obtain a complete coronary tree in the separated vessel layer; (2) our low-rank decomposition framework is robust against false positive/negative responses of the Frangi filter; and (3) the proposed VC-RPCA is computationally fast and outperforms other state-of-the-art RPCA methods for vessel enhancement in the full-contrast and low-contrast scenarios. The results demonstrate that the proposed VC-RPCA can accurately separate coronary arteries and prominently improve vessel visibility in x-ray angiograms.

Registration of angiographic image on real-time fluoroscopic image for image-guided percutaneous coronary intervention

PURPOSE

In percutaneous coronary intervention (PCI), cardiologists must study two different X-ray image sources: a fluoroscopic image and an angiogram. Manipulating a guidewire while alternately monitoring the two separate images on separate screens requires a deep understanding of the anatomy of coronary vessels and substantial training. We propose 2D/2D spatiotemporal image registration of the two images in a single image in order to provide cardiologists with enhanced visual guidance in PCI.

METHODS

The proposed 2D/2D spatiotemporal registration method uses a cross-correlation of two ECG series in each image to temporally synchronize two separate images and register an angiographic image onto the fluoroscopic image. A guidewire centerline is then extracted from the fluoroscopic image in real time, and the alignment of the centerline with vessel outlines of the chosen angiographic image is optimized using the iterative closest point algorithm for spatial registration.

RESULTS

A proof-of-concept evaluation with a phantom coronary vessel model with engineering students showed an error reduction rate greater than 74% on wrong insertion to nontarget branches compared to the non-registration method and more than 47% reduction in the task completion time in performing guidewire manipulation for very difficult tasks. Evaluation with a small number of experienced doctors shows a potentially significant reduction in both task completion time and error rate for difficult tasks. The total registration time with real procedure X-ray (angiographic and fluoroscopic) images takes [Formula: see text] 60 ms, which is within the fluoroscopic image acquisition rate of 15 Hz.

CONCLUSIONS

By providing cardiologists with better visual guidance in PCI, the proposed spatiotemporal image registration method is shown to be useful in advancing the guidewire to the coronary vessel branches, especially those difficult to insert into.

Real-time respiratory phase matching between 2D fluoroscopic images and 3D CT images for precise percutaneous lung biopsy

PURPOSE

A 3D CT image is used along with real-time 2D fluoroscopic images in the state-of-the-art cone-beam CT system to guide percutaneous lung biopsy (PLB). To improve the guiding accuracy by compensating for respiratory motion, we propose an algorithm for real-time matching of 2D fluoroscopic images to multiple 3D CT images of different respiratory phases that is robust to the small movement and deformation due to cardiac motion.

METHODS

Based on the transformations obtained from nonrigid registration between two 3D CT images acquired at expiratory and inspiratory phases, we first generate sequential 3D CT images (or a 4D CT image) and the corresponding 2D digitally reconstructed radiographs (DRRs) of vessels. We then determine 3D CT images corresponding to each real-time 2D fluoroscopic image, by matching the 2D fluoroscopic image to a 2D DRR.

RESULTS

Quantitative evaluations performed with 20 clinical datasets show that registration errors of anatomical features between a 2D fluoroscopic image and its matched 2D DRR are less than 3 mm on average. Registration errors of a target lesion are determined to be roughly 3 mm on average for 10 datasets.

CONCLUSIONS

We propose a real-time matching algorithm to compensate for respiratory motion between a 2D fluoroscopic image and 3D CT images of the lung, regardless of cardiac motion, based on a newly improved matching measure. The proposed algorithm can improve the accuracy of a guiding system for the PLB by providing 3D images precisely registered to 2D fluoroscopic images in real-time, without time-consuming respiratory-gated or cardiac-gated CT images.

Patient-specific respiratory motion estimation using Sparse Motion Field Presentation

Respiratory motion estimation plays a significant role in radiation therapy. Previous motion estimation approaches usually depended on 4DCT, which introduced extra radio dose for patients, and the local motion details were ignored in the statistical model. In this paper, we propose a novel estimation framework, which employs the Sparse Motion Field Presentation (SMFP) method to obtain a coarse motion estimation which preserves patient-specific respiratory motion details and an Adaptive Variable Coefficient (AVC) motion prior registration approach is applied for the accurate estimation. The experimental results show that the proposed framework effectively preserved the local motion details and achieved more accurate motion estimations compared to the Mean Motion Model (MMM) and the Principal Component Analysis (PCA) model. We achieved motion estimations for diaphragmatic breathing type, thoracic breathing type and mixed type, respectively. The accuracy measured in the average symmetric surface distance (standard deviation) were 1.9(0.9) mm, 2.4(1.1) mm and 2.2(1.0) mm, when the sum of squared intensity difference (SSD) were 5.0, 6.1 and 5.6, respectively.

Vessel tree tracking in angiographic sequences

We present a method to track vessels in angiography [contrast filled vessels in two-dimensional (2-D) x-ray fluoroscopy]. Finding correspondence of a vessel tree from consecutive angiogram frames provides significant value in computer-aided clinical applications such as fast vessel tree segmentation, three-dimensional (3-D) vessel topology reconstruction from corresponding centerlines, cardiac motion understanding, etc. However, establishing an accurate vessel tree correspondence (vessel tree tracking) is a nontrivial problem due to nonlinear periodic cardiac and breathing motion in 2-D views, foreshortening, false bifurcations due to 3-D to 2-D projection, occlusion from other anatomies, etc. The vessel tree is represented by BSpline curves. The control points of the BSpline curves are landmarks that are the tracking targets. Our method maximizes the appearance similarity while preserving the vessel structure. A directed acyclic graph (DAG) is employed to represent the appearance and shape structure of the vessel tree: nodes from the DAG encode the appearance of the vessel tree landmarks, and the edges encode the relative locations between landmarks. The vessel tree tracking problem turns into finding the most similar tree from the DAG in the next frame, and it is solved using an efficient dynamic programming algorithm. We performed evaluations on 62 x-ray angiography sequences (above 1000 frames). Experiment results show our algorithm is robust to these challenges and delivers better performance, compared to four existing methods.

A Hidden Markov Model for 3D Catheter Tip Tracking With 2D X-ray Catheterization Sequence and 3D Rotational Angiography

In minimal invasive image guided catheterization procedures, physicians require information of the catheter position with respect to the patient's vasculature. However, in fluoroscopic images, visualization of the vasculature requires toxic contrast agent. Static vasculature roadmapping, which can reduce the usage of iodine contrast, is hampered by the breathing motion in abdominal catheterization. In this paper, we propose a method to track the catheter tip inside the patient's 3D vessel tree using intra-operative single-plane 2D X-ray image sequences and a peri-operative 3D rotational angiography (3DRA). The method is based on a hidden Markov model (HMM) where states of the model are the possible positions of the catheter tip inside the 3D vessel tree. The transitions from state to state model the probabilities for the catheter tip to move from one position to another. The HMM is updated following the observation scores, based on the registration between the 2D catheter centerline extracted from the 2D X-ray image, and the 2D projection of 3D vessel tree centerline extracted from the 3DRA. The method is extensively evaluated on simulated and clinical datasets acquired during liver abdominal catheterization. The evaluations show a median 3D tip tracking error of 2.3 mm with optimal settings in simulated data. The registered vessels close to the tip have a median distance error of 4.7 mm with angiographic data and optimal settings. Such accuracy is sufficient to help the physicians with an up-to-date roadmapping. The method tracks in real-time the catheter tip and enables roadmapping during catheterization procedures.

Reconstruction of coronary arteries from X-ray angiography: A review

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.

Continuous roadmapping in liver TACE procedures using 2D-3D catheter-based registration

Purpose

Fusion of pre/perioperative images and intra-operative images may add relevant information during image-guided procedures. In abdominal procedures, respiratory motion changes the position of organs, and thus accurate image guidance requires a continuous update of the spatial alignment of the (pre/perioperative) information with the organ position during the intervention.

Methods

In this paper, we propose a method to register in real time perioperative 3D rotational angiography images (3DRA) to intra-operative single-plane 2D fluoroscopic images for improved guidance in TACE interventions. The method uses the shape of 3D vessels extracted from the 3DRA and the 2D catheter shape extracted from fluoroscopy. First, the appropriate 3D vessel is selected from the complete vascular tree using a shape similarity metric. Subsequently, the catheter is registered to this vessel, and the 3DRA is visualized based on the registration results. The method is evaluated on simulated data and clinical data.

Results

The first selected vessel, ranked with the shape similarity metric, is used more than 39 % in the final registration and the second more than 21 %. The median of the closest corresponding points distance between 2D angiography vessels and projected 3D vessels is 4.7–5.4 mm when using the brute force optimizer and 5.2–6.6 mm when using the Powell optimizer.

Conclusion

We present a catheter-based registration method to continuously fuse a 3DRA roadmap arterial tree onto 2D fluoroscopic images with an efficient shape similarity.

3D multimodal cardiac data reconstruction using angiography and computerized tomographic angiography registration

BACKGROUND

Computerized tomographic angiography (3D data representing the coronary arteries) and X-ray angiography (2D X-ray image sequences providing information about coronary arteries and their stenosis) are standard and popular assessment tools utilized for medical diagnosis of coronary artery diseases. At present, the results of both modalities are individually analyzed by specialists and it is difficult for them to mentally connect the details of these two techniques. The aim of this work is to assist medical diagnosis by providing specialists with the relationship between computerized tomographic angiography and X-ray angiography.

METHODS

In this study, coronary arteries from two modalities are registered in order to create a 3D reconstruction of the stenosis position. The proposed method starts with coronary artery segmentation and labeling for both modalities. Then, stenosis and relevant labeled artery in X-ray angiography image are marked by a specialist. Proper control points for the marked artery in both modalities are automatically detected and normalized. Then, a geometrical transformation function is computed using these control points. Finally, this function is utilized to register the marked artery from the X-ray angiography image on the computerized tomographic angiography and get the 3D position of the stenosis lesion.

RESULTS

The result is a 3D informative model consisting of stenosis and coronary arteries' information from the X-ray angiography and computerized tomographic angiography modalities. The results of the proposed method for coronary artery segmentation, labeling and 3D reconstruction are evaluated and validated on the dataset containing both modalities.

CONCLUSIONS

The advantage of this method is to aid specialists to determine a visual relationship between the correspondent coronary arteries from two modalities and also set up a connection between stenosis points from an X-ray angiography along with their 3D positions on the coronary arteries from computerized tomographic angiography. Moreover, another benefit of this work is that the medical acquisition standards remain unchanged, which means that no calibration in the acquisition devices is required. It can be applied on most computerized tomographic angiography and angiography devices.

Reconstruction of coronary trees from 3DRA using a 3D+t statistical cardiac prior

A 3D+t description of the coronary tree is important for diagnosis of coronary artery disease and therapy planning. In this paper, we propose a method for finding 3D+t points on coronary artery tree given tracked 2D+t point locations in X-ray rotational angiography images. In order to cope with the ill-posedness of the problem, we use a bilinear model of ventricle as a spatio-temporal constraint on the nonrigid structure of the coronary artery. Based on an energy minimization formulation, we estimate i) bilinear model parameters, ii) global rigid transformation between model and X-ray coordinate systems, and iii) correspondences between 2D coronary artery points on X-ray images and 3D points on bilinear model. We validated the algorithm using a software coronary artery phantom.

Coronary CT angiography (cCTA): automated registration of coronary arterial trees from multiple phases

Coronary computed tomography angiography (cCTA) is a commonly used imaging modality for the evaluation of coronary artery disease. cCTA is generally reconstructed in multiple cardiac phases because different coronary arteries may be better visualized in some phases than in others due to the periodic cardiac motion. We are developing an automated registration method for coronary arterial trees from multiple-phase cCTA that has potential application in building a 'best-quality' tree to facilitate image analysis and detection of stenotic plaques. Given the segmented left or right coronary arterial (LCA or RCA) trees from the multiple phases as input, the adjacent phase pairs, where displacements are relatively small, are registered by a specifically designed method based on a cubic B-spline with fast localized optimization (CBSO). For the phase pairs with large displacements, a global registration using an affine transform with quadratic terms and nonlinear simplex optimization (AQSO) is followed by a local registration using CBSO to refine the AQSO registered volumes. 26 LCA and 26 RCA trees with six cCTA phases from 26 patients were used for registration evaluation. The average distances for the tree pairs between the adjacent phases with small displacements before and after CBSO registration were 0.96  ±  0.79 and 0.76  ±  0.61 mm respectively for LCA, and 0.93  ±  0.97 and 0.64  ±  0.43 mm, respectively for RCA. The average distance differences before and after registration were statistically significant (p < 0.001) for both LCA and RCA trees. The average distances for the distant phases with large displacements before registration, after AQSO registration, and finally after the CBSO registration were 2.85  ±  1.46, 1.62  ±  0.76, and 0.97  ±  0.43 mm, respectively for LCA, and 4.03  ±  2.36, 2.18  ±  1.11, and 0.97  ±  0.44 mm, respectively for RCA. The average distance differences between every two consecutive stages of registration were statistically significant. The corresponding phases of LCA and RCA trees were aligned to an average of less than 1 mm, providing a basis for a best-quality tree construction.

Oriented Gaussian mixture models for nonrigid 2D/3D coronary artery registration

2D/3D registration of patient vasculature from preinterventional computed tomography angiography (CTA) to interventional X-ray angiography is of interest to improve guidance in percutaneous coronary interventions. In this paper we present a novel feature based 2D/3D registration framework, that is based on probabilistic point correspondences, and show its usefulness on aligning 3D coronary artery centerlines derived from CTA images with their 2D projection derived from interventional X-ray angiography. The registration framework is an extension of the Gaussian mixture model (GMM) based point-set registration to the 2D/3D setting, with a modified distance metric. We also propose a way to incorporate orientation in the registration, and show its added value for artery registration on patient datasets as well as in simulation experiments. The oriented GMM registration achieved a median accuracy of 1.06 mm, with a convergence rate of 81% for nonrigid vessel centerline registration on 12 patient datasets, using a statistical shape model. The method thereby outperformed the iterative closest point algorithm, the GMM registration without orientation, and two recently published methods on 2D/3D coronary artery registration.