3D-2D registration of cerebral angiograms: a method and evaluation on clinical images

Real-Time 3D Instrument Tip Tracking Using 2D X-Ray Fluoroscopy With Vessel Deformation Correction Under Free Breathing

OBJECTIVE

Accurate localization of the instrument tip within the hepatic vein is crucial for the success of transjugular intrahepatic portosystemic shunt (TIPS) procedures. Real-time tracking of the instrument tip in X-ray images is greatly influenced by vessel deformation due to patient's pose variation, respiratory motion, and puncture manipulation, frequently resulting in failed punctures.

METHOD

We propose a novel framework called deformable instrument tip tracking (DITT) to obtain the real-time tip positioning within the 3D deformable vasculature. First, we introduce a pose alignment module to improve the rigid matching between the preoperative vessel centerline and the intraoperative instrument centerline, in which the accurate matching of 3D/2D centerline features is implemented with an adaptive point sampling strategy. Second, a respiration compensation module using monoplane X-ray image sequences is constructed and provides the motion prior to predict intraoperative liver movement. Third, a deformation correction module is proposed to rectify the vessel deformation during procedures, in which a manifold regularization and the maximum likelihood-based acceleration are introduced to obtain the accurate and fast deformation learning.

RESULTS

Experimental results on simulated and clinical datasets show an average tracking error of 1.59 0.57 mm and 1.67 0.54 mm, respectively.

CONCLUSION

Our framework can track the tip in 3D vessel and dynamically overlap the branch roadmapping onto X-ray images to provide real-time guidance.

SIGNIFICANCE

Accurate and fast (43ms per frame) tip tracking with the proposed framework possesses a good potential for improving the outcomes of TIPS treatment and minimizes the usage of contrast agent.

Comparison of global and local optimization methods for intensity-based 2D-3D registration

Intensity-based 2D-3D registration methods are commonly used in musculoskeletal research and image-guided therapy to align 2D X-ray images with 3D CT scans. However, their success rate (SR) is limited by local optimization methods, which often cause the optimization of the underlying cost function to get stuck at a local minimum, resulting in false alignments. Global optimization methods aim to mitigate this problem, but despite their increasing popularity, the existing literature lacks consensus on which one is the most appropriate. In this work, we compare 11 global and 4 local optimization methods on thousands of typical registration examples of single- and dual-plane fluoroscopy, including three datasets of varying complexity. In addition, we evaluate the differences between global and local methods, determine the best overall method, and validate its suitability for real clinical data. The results demonstrate that global methods that require a large number of function evaluations (NFEV) are generally the most robust. Furthermore, hyperparameter tuning can significantly improve their performance and is generalizable across datasets. Evolutionary strategy (ES) is the best-performing optimization method in our study, achieving a mean SR of ∼95% for all test models, ∼77% for the knee bones, and ∼95-100% for cerebral angiograms when using dual-plane registration setup. Nevertheless, in cases where good initialization is available, local methods are still preferable due to their reduced NFEV. The use of global optimization improves the overall robustness and ease-of-use of 2D-3D registration, potentially accelerating its adaptation in routine medical practice and biomedical research.

FMB: Dual-view fusion and registration of 2D DSA images and 3D MRA images for neurointerventional-based procedures

OBJECTIVE

Alignment between preoperative images (high-resolution magnetic resonance imaging, magnetic resonance angiography) and intraoperative medical images (digital subtraction angiography) is currently required in neurointerventional surgery. Treating a lesion is usually guided by a 2D DSA silhouette image. DSA silhouette images increase procedure time and radiation exposure time due to the lack of anatomical information, but information from MRA images can be utilized to compensate for this in order to improve procedure efficiency. In this paper, we abstract this into the problem of relative pose and correspondence between a 3D point and its 2D projection. Multimodal images have a large amount of noise and anomalies that are difficult to resolve using conventional methods. According to our research, there are fewer multimodal fusion methods to perform the full procedure.

APPROACH

Therefore, the paper introduces a registration pipeline for multimodal images with fused dual views is presented. Deep learning methods are introduced to accomplish feature extraction of multimodal images to automate the process. Besides, the paper proposes a registration method based on the Factor of Maximum Bounds (FMB). The key insights are to relax the constraints on the lower bound, enhance the constraints on the upper bounds, and mine more local consensus information in the point set using a second perspective to generate accurate pose estimation.

MAIN RESULTS

Compared to existing 2D/3D point set registration methods, this method utilizes a different problem formulation, searches the rotation and translation space more efficiently, and improves registration speed.

SIGNIFICANCE

Experiments with synthesized and real data show that the proposed method was achieved in accuracy, robustness, and time efficiency.

Image-to-Graph Convolutional Network for 2D/3D Deformable Model Registration of Low-Contrast Organs

Organ shape reconstruction based on a single-projection image during treatment has wide clinical scope, e.g., in image-guided radiotherapy and surgical guidance. We propose an image-to-graph convolutional network that achieves deformable registration of a three-dimensional (3D) organ mesh for a low-contrast two-dimensional (2D) projection image. This framework enables simultaneous training of two types of transformation: from the 2D projection image to a displacement map, and from the sampled per-vertex feature to a 3D displacement that satisfies the geometrical constraint of the mesh structure. Assuming application to radiation therapy, the 2D/3D deformable registration performance is verified for multiple abdominal organs that have not been targeted to date, i.e., the liver, stomach, duodenum, and kidney, and for pancreatic cancer. The experimental results show shape prediction considering relationships among multiple organs can be used to predict respiratory motion and deformation from digitally reconstructed radiographs with clinically acceptable accuracy.

Analysis of potential time saving in brain arteriovenous malformation stereotactic radiosurgery planning using a new software platform

To evaluate the utility of integrating a 3D vessel tree co-registration software platform into the stereotactic radiosurgery (SRS) workflow and its time saving for brain arteriovenous malformation (bAVM) treatment in adults compared to the conventional stereotactic head frame workflow. Eight consecutive adult bAVM cases were selected and retrospectively reviewed. Total number of angiograms and SRS procedures were 8. The electronic medical records were analyzed by time stamps to determine the length of time for each component of the set-up, transport, and frame removal. Times were averaged and the start of sedation by anesthesia used as a surrogate for the start of the frame application process. Reductions in workflow times were then modeled assuming cerebral angiography as a separate procedure. There were 8 adult bAVM cases included. Six were female. All patients had a single treatment session. Average age was 51.5 years (Range: 36-71). All patients were treated under monitored anesthesia care. In 6 patients, the AVM was deeply located (basal ganglia, midbrain, brainstem); in 2 cases, the lesion was frontal. Spetzler-Martin grades were 4 (50%) Grade 2 and 4 (50%) Grade 3. The average prescription isodose volume (PIV) and 12 Gy volumes (V_12Gy) were 0.85 cc and 1.74 cc, respectively. The mean time from frame application to arrival in the angiography room was 111.5 minutes (range 40 to 171 min; median 107 min; SD 35.3 min); transport from angiography room to SRS was 47.5 minutes (range 15 to 107 min; median 36 min; SD 31.1 min), and frame removal after SRS was 20.5 minutes (range 10 to 47 min; median 16 min; SD 11.6 min). The average total additional time for the entire process of frame application, patient transportation, and frame removal was 132 minutes (range 87 to 181 min; median 127.5 min; SD 28.4 min). Therefore, assuming a non-frame based workflow and with angiography performed ahead of the actual radiosurgical treatment, the total time savings on the day of treatment was estimated at 132 minutes (range 87 to 181 min; median 127.5 min; SD 28.4 min). The ability to perform angiography, image fusion, and treatment planning for the actual day-of-delivery using 3-dimensional vessel tree co-registration could result in significant time savings over traditional workflow practices. Further experience with this system will evaluate its accuracy, reproducibility, and potential broader use in SRS workflow paradigms for the treatment of vascular pathologies. For bAVMs, the benefits of this time savings might allow for streamlined workflows on the day of SRS.

A new 2D-3D registration gold-standard dataset for the hip joint based on uncertainty modeling

Purpose

Estimation of the accuracy of 2D‐3D registration is paramount for a correct evaluation of its outcome in both research and clinical studies. Publicly available datasets with standardized evaluation methodology are necessary for validation and comparison of 2D‐3D registration techniques. Given the large use of 2D‐3D registration in biomechanics, we introduced the first gold standard validation dataset for computed tomography (CT)‐to‐x‐ray registration of the hip joint, based on fluoroscopic images with large rotation angles. As the ground truth computed with fiducial markers is affected by localization errors in the image datasets, we proposed a new methodology based on uncertainty propagation to estimate the accuracy of a gold standard dataset.

Methods

The gold standard dataset included a 3D CT scan of a female hip phantom and 19 2D fluoroscopic images acquired at different views and voltages. The ground truth transformations were estimated based on the corresponding pairs of extracted 2D and 3D fiducial locations. These were assumed to be corrupted by Gaussian noise, without any restrictions of isotropy. We devised the multiple projective points criterion (MPPC) that jointly optimizes the transformations and the noisy 3D fiducial locations for all views. The accuracy of the transformations obtained with the MPPC was assessed in both synthetic and real experiments using different formulations of the target registration error (TRE), including a novel formulation of the TRE (uTRE) derived from the uncertainty analysis of the MPPC.

Results

The proposed MPPC method was statistically more accurate compared to the validation methods for 2D‐3D registration that did not optimize the 3D fiducial positions or wrongly assumed the isotropy of the noise. The reported results were comparable to previous published works of gold standard datasets. However, a formulation of the TRE commonly found in these gold standard datasets was found to significantly miscalculate the true TRE computed in synthetic experiments with known ground truths. In contrast, the uncertainty‐based uTRE was statistically closer to the true TRE.

Conclusions

We proposed a new gold standard dataset for the validation of CT‐to‐X‐ray registration of the hip joint. The gold standard transformations were derived from a novel method modeling the uncertainty in extracted 2D and 3D fiducials. Results showed that considering possible noise anisotropy and including corrupted 3D fiducials in the optimization resulted in improved accuracy of the gold standard. A new uncertainty‐based formulation of the TRE also appeared as a good alternative to the unknown true TRE that has been replaced in previous works by an alternative TRE not fully reflecting the gold standard accuracy.

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.

Learning an Attention Model for Robust 2-D/3-D Registration Using Point-To-Plane Correspondences

Minimally invasive procedures rely on image guidance for navigation at the operation site to avoid large surgical incisions. X-ray images are often used for guidance, but important structures may be not well visible. These structures can be overlaid from pre-operative 3-D images and accurate alignment can be established using 2-D/3-D registration. Registration based on the point-to-plane correspondence model was recently proposed and shown to achieve state-of-the-art performance. However, registration may still fail in challenging cases due to a large portion of outliers. In this paper, we describe a learning-based correspondence weighting scheme to improve the registration performance. By learning an attention model, inlier correspondences get higher attention in the motion estimation while the outlier correspondences are suppressed. Instead of using per-correspondence labels, our objective function allows to train the model directly by minimizing the registration error. We demonstrate a highly increased robustness, e.g. increasing the success rate from 84.9% to 97.0% for spine registration. In contrast to previously proposed learning-based methods, we also achieve a high accuracy of around 0.5mm mean re-projection distance. In addition, our method requires a relatively small amount of training data, is able to learn from simulated data, and generalizes to images with additional structures which are not present during training. Furthermore, a single model can be trained for both, different views and different anatomical structures.

Multilevel 2D-3D Intensity-Based Image Registration

2D-3D image registration is an important task for computer-aided minimally invasive vascular therapies. A crucial component for practical image registration is the use of multilevel strategies to avoid local optima and to speed-up runtime. However, due to the different dimensionalities of the 2D fixed and 3D moving image, the setup of multilevel strategies is not straightforward.

In this work, we propose an intensity-driven 2D-3D multiresolution registration approach using the normalized gradient fields (NGF) distance measure. We discuss and empirically analyze the impact on the choice of 2D and 3D image resolutions. Furthermore, we show that our approach produces results that are comparable or superior to other state-of-the-art methods.

Robust Multi-View 2-D/3-D Registration Using Point-To-Plane Correspondence Model

In minimally invasive procedures, the clinician relies on image guidance to observe and navigate the operation site. In order to show structures which are not visible in the live X-ray images, such as vessels or planning annotations, X-ray images can be augmented with pre-operatively acquired images. Accurate image alignment is needed and can be provided by 2-D/3-D registration. In this paper, a multi-view registration method based on the point-to-plane correspondence model is proposed. The correspondence model is extended to be independent of the used camera coordinates and different multi-view registration schemes are introduced and compared. Evaluation is performed for a wide range of clinically relevant registration scenarios. We show for different applications that registration using correspondences from both views simultaneously provides accurate and robust registration, while the performance of the other schemes varies considerably. Our method also outperforms the state-of-the-art method for cerebral angiography registration, achieving a capture range of 18 mm and an accuracy of 0.22±0.07 mm. Furthermore, investigations on the minimum angle between the views are performed in order to provide accurate and robust registration, while minimizing the obstruction to the clinical workflow. We show that small angles around 30° are sufficient to provide reliable registration results.

2D-3D Point Set Registration Based on Global Rotation Search

Simultaneously determining the relative pose and correspondence between a set of 3D points and its 2D projection is a fundamental problem in computer vision, and the problem becomes more difficult when the point sets are contaminated by noise and outliers. Traditionally, this problem is solved by local optimization methods, which usually start from an initial guess of the pose and alternately optimize the pose and the correspondence. In this paper, we formulate the problem as optimizing the pose of the 3D points in the SE(3) space to make its 2D projection best align with the 2D point set, which is measured by the cardinality of the inlier set on the 2D projection plane. We propose four geometric bounds for the position of the projection of a 3D point on the 2D projection plane and solve the 2D-3D point set registration problem by combining a global optimal rotation search and a grid search of translation. Compared with existing global optimization approaches, the proposed method utilizes a different problem formulation and more efficiently searches the translation space, which improves the registration speed. Experiments with synthetic and real data showed that the proposed approach significantly outperformed state-of-the-art local and global methods.

Framework for 2D-3D image fusion of infrared thermography with preoperative MRI

Multimodal medical image fusion combines information of one or more images in order to improve the diagnostic value. While previous applications mainly focus on merging images from computed tomography, magnetic resonance imaging (MRI), ultrasonic and single-photon emission computed tomography, we propose a novel approach for the registration and fusion of preoperative 3D MRI with intraoperative 2D infrared thermography. Image-guided neurosurgeries are based on neuronavigation systems, which further allow us track the position and orientation of arbitrary cameras. Hereby, we are able to relate the 2D coordinate system of the infrared camera with the 3D MRI coordinate system. The registered image data are now combined by calibration-based image fusion in order to map our intraoperative 2D thermographic images onto the respective brain surface recovered from preoperative MRI. In extensive accuracy measurements, we found that the proposed framework achieves a mean accuracy of 2.46 mm.

3D-2D registration in endovascular image-guided surgery: evaluation of state-of-the-art methods on cerebral angiograms

PURPOSE

Image guidance for minimally invasive surgery is based on spatial co-registration and fusion of 3D pre-interventional images and treatment plans with the 2D live intra-interventional images. The spatial co-registration or 3D-2D registration is the key enabling technology; however, the performance of state-of-the-art automated methods is rather unclear as they have not been assessed under the same test conditions. Herein we perform a quantitative and comparative evaluation of ten state-of-the-art methods for 3D-2D registration on a public dataset of clinical angiograms.

METHODS

Image database consisted of 3D and 2D angiograms of 25 patients undergoing treatment for cerebral aneurysms or arteriovenous malformations. On each of the datasets, highly accurate "gold-standard" registrations of 3D and 2D images were established based on patient-attached fiducial markers. The database was used to rigorously evaluate ten state-of-the-art 3D-2D registration methods, namely two intensity-, two gradient-, three feature-based and three hybrid methods, both for registration of 3D pre-interventional image to monoplane or biplane 2D images.

RESULTS

Intensity-based methods were most accurate in all tests (0.3 mm). One of the hybrid methods was most robust with 98.75% of successful registrations (SR) and capture range of 18 mm for registrations of 3D to biplane 2D angiograms. In general, registration accuracy was similar whether registration of 3D image was performed onto mono- or biplanar 2D images; however, the SR was substantially lower in case of 3D to monoplane 2D registration. Two feature-based and two hybrid methods had clinically feasible execution times in the order of a second.

CONCLUSIONS

Performance of methods seems to fall below expectations in terms of robustness in case of registration of 3D to monoplane 2D images, while translation into clinical image guidance systems seems readily feasible for methods that perform registration of the 3D pre-interventional image onto biplanar intra-interventional 2D images.

Dynamic 2-D/3-D Rigid Registration Framework Using Point-To-Plane Correspondence Model

In image-guided interventional procedures, live 2-D X-ray images can be augmented with preoperative 3-D computed tomography or MRI images to provide planning landmarks and enhanced spatial perception. An accurate alignment between the 3-D and 2-D images is a prerequisite for fusion applications. This paper presents a dynamic rigid 2-D/3-D registration framework, which measures the local 3-D-to-2-D misalignment and efficiently constrains the update of both planar and non-planar 3-D rigid transformations using a novel point-to-plane correspondence model. In the simulation evaluation, the proposed method achieved a mean 3-D accuracy of 0.07 mm for the head phantom and 0.05 mm for the thorax phantom using single-view X-ray images. In the evaluation on dynamic motion compensation, our method significantly increases the accuracy comparing with the baseline method. The proposed method is also evaluated on a publicly-available clinical angiogram data set with "gold-standard" registrations. The proposed method achieved a mean 3-D accuracy below 0.8 mm and a mean 2-D accuracy below 0.3 mm using single-view X-ray images. It outperformed the state-of-the-art methods in both accuracy and robustness in single-view registration. The proposed method is intuitive, generic, and suitable for both initial and dynamic registration scenarios.

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.

Simultaneous 3D-2D image registration and C-arm calibration: Application to endovascular image-guided interventions

PURPOSE

Three-dimensional to two-dimensional (3D-2D) image registration is a key to fusion and simultaneous visualization of valuable information contained in 3D pre-interventional and 2D intra-interventional images with the final goal of image guidance of a procedure. In this paper, the authors focus on 3D-2D image registration within the context of intracranial endovascular image-guided interventions (EIGIs), where the 3D and 2D images are generally acquired with the same C-arm system. The accuracy and robustness of any 3D-2D registration method, to be used in a clinical setting, is influenced by (1) the method itself, (2) uncertainty of initial pose of the 3D image from which registration starts, (3) uncertainty of C-arm's geometry and pose, and (4) the number of 2D intra-interventional images used for registration, which is generally one and at most two. The study of these influences requires rigorous and objective validation of any 3D-2D registration method against a highly accurate reference or "gold standard" registration, performed on clinical image datasets acquired in the context of the intervention.

METHODS

The registration process is split into two sequential, i.e., initial and final, registration stages. The initial stage is either machine-based or template matching. The latter aims to reduce possibly large in-plane translation errors by matching a projection of the 3D vessel model and 2D image. In the final registration stage, four state-of-the-art intrinsic image-based 3D-2D registration methods, which involve simultaneous refinement of rigid-body and C-arm parameters, are evaluated. For objective validation, the authors acquired an image database of 15 patients undergoing cerebral EIGI, for which accurate gold standard registrations were established by fiducial marker coregistration.

RESULTS

Based on target registration error, the obtained success rates of 3D to a single 2D image registration after initial machine-based and template matching and final registration involving C-arm calibration were 36%, 73%, and 93%, respectively, while registration accuracy of 0.59 mm was the best after final registration. By compensating in-plane translation errors by initial template matching, the success rates achieved after the final stage improved consistently for all methods, especially if C-arm calibration was performed simultaneously with the 3D-2D image registration.

CONCLUSIONS

Because the tested methods perform simultaneous C-arm calibration and 3D-2D registration based solely on anatomical information, they have a high potential for automation and thus for an immediate integration into current interventional workflow. One of the authors' main contributions is also comprehensive and representative validation performed under realistic conditions as encountered during cerebral EIGI.

Vessel-based registration of an optical shape sensing catheter for MR navigation

PURPOSE

Magnetic resonance navigation (MRN), achieved with an upgraded MRI scanner, aims to guide therapeutic nanoparticles from their release in the hepatic vascular network to embolize highly vascularized liver tumors. Visualizing the catheter in real-time within the arterial network is important for selective embolization within the MR gantry. To achieve this, a new MR-compatible catheter tracking technology based on optical shape sensing is used.

METHODS

This paper proposes a vessel-based registration pipeline to co-align this novel catheter tracking technology to the patient's diagnostic MR angiography (MRA) with 3D roadmapping. The method first extracts the 3D hepatic arteries from a diagnostic MRA based on concurrent deformable models, creating a detailed representation of the patient's internal anatomy. Once the optical shape sensing fibers, inserted in a double-lumen catheter, is guided into the hepatic arteries, the 3D centerline of the catheter is inferred and updated in real-time using strain measurements derived from fiber Bragg gratings sensors. Using both centerlines, a diffeomorphic registration based on a spectral representation of the high-level geometrical primitives is applied.

RESULTS

Results show promise in registration accuracy in five phantom models created from stereolithography of patient-specific vascular anatomies, with maximum target registration errors below 2 mm. Furthermore, registration accuracy with the shape sensing tracking technology remains insensitive to the magnetic field of the MR magnet.

CONCLUSIONS

This study demonstrates that an accurate registration procedure of a shape sensing catheter with diagnostic imaging is feasible.

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.

Robust 3D-2D image registration: application to spine interventions and vertebral labeling in the presence of anatomical deformation

We present a framework for robustly estimating registration between a 3D volume image and a 2D projection image and evaluate its precision and robustness in spine interventions for vertebral localization in the presence of anatomical deformation. The framework employs a normalized gradient information similarity metric and multi-start covariance matrix adaptation evolution strategy optimization with local-restarts, which provided improved robustness against deformation and content mismatch. The parallelized implementation allowed orders-of-magnitude acceleration in computation time and improved the robustness of registration via multi-start global optimization. Experiments involved a cadaver specimen and two CT datasets (supine and prone) and 36 C-arm fluoroscopy images acquired with the specimen in four positions (supine, prone, supine with lordosis, prone with kyphosis), three regions (thoracic, abdominal, and lumbar), and three levels of geometric magnification (1.7, 2.0, 2.4). Registration accuracy was evaluated in terms of projection distance error (PDE) between the estimated and true target points in the projection image, including 14 400 random trials (200 trials on the 72 registration scenarios) with initialization error up to ±200 mm and ±10°. The resulting median PDE was better than 0.1 mm in all cases, depending somewhat on the resolution of input CT and fluoroscopy images. The cadaver experiments illustrated the tradeoff between robustness and computation time, yielding a success rate of 99.993% in vertebral labeling (with 'success' defined as PDE <5 mm) using 1,718 664 ± 96 582 function evaluations computed in 54.0 ± 3.5 s on a mid-range GPU (nVidia, GeForce GTX690). Parameters yielding a faster search (e.g., fewer multi-starts) reduced robustness under conditions of large deformation and poor initialization (99.535% success for the same data registered in 13.1 s), but given good initialization (e.g., ±5 mm, assuming a robust initial run) the same registration could be solved with 99.993% success in 6.3 s. The ability to register CT to fluoroscopy in a manner robust to patient deformation could be valuable in applications such as radiation therapy, interventional radiology, and an assistant to target localization (e.g., vertebral labeling) in image-guided spine surgery.