2D-3D Registration Method Research Articles

A Rigorous 2D-3D Registration Method for a High-Speed Bi-Planar Videoradiography Imaging System.

High-speed biplanar videoradiography can derive the dynamic bony translations and rotations required for joint cartilage contact mechanics to provide insights into the mechanical processes and mechanisms of joint degeneration or pathology. A key challenge is the accurate registration of 3D bone models (from MRI or CT scans) with 2D X-ray image pairs. Marker-based or model-based 2D-3D registration can be performed. The former has higher registration accuracy owing to corresponding marker pairs. The latter avoids bead implantation and uses radiograph intensity or features. A rigorous new method based on projection strategy and least-squares estimation that can be used for both methods is proposed and validated by a 3D-printed bone with implanted beads. The results show that it can achieve greater marker-based registration accuracy than the state-of-the-art RSA method. Model-based registration achieved a 3D reconstruction accuracy of 0.79 mm. Systematic offsets between detected edges in the radiographs and their actual position were observed and modeled to improve the reconstruction accuracy to 0.56 mm (tibia) and 0.64 mm (femur). This method is demonstrated on in vivo data, achieving a registration precision of 0.68 mm (tibia) and 0.60 mm (femur). The proposed method allows the determination of accurate 3D kinematic parameters that can be used to calculate joint cartilage contact mechanics.

T‐ESVO: Improved Event‐Based Stereo Visual Odometry via Adaptive Time‐Surface and Truncated Signed Distance Function

The emerging event cameras have the potential to be an excellent complement for standard cameras within various visual tasks, especially in illumination‐changing environments or situations requiring high‐temporal resolution. Herein, an event‐based stereo visual odometry (VO) system via adaptive time‐surface (TS) and truncated signed distance function (TSDF), namely, T‐ESVO, is proposed . The system consists of three carefully designed components, including the event processing unit, the mapping unit, and the tracking unit. Specifically, the event processing unit adopts a novel spatial–temporal adaptive TS that can deal with different camera motions in various environments. The mapping unit introduces the TSDF to describe the 3D representation of environments and achieves depth estimation based on the global historical depth information contained in the environmental TSDF description. The tracking unit achieves the 6‐DoF pose estimation through an 3D–2D registration method based on the left/right TS selection mechanism and the depth point selection mechanism. The effectiveness and robustness of the proposed system are evaluated on various datasets, and the experimental results show that T‐ESVO achieves good performance in both accuracy and robustness when compared with other state‐of‐the‐art event‐based stereo VO systems.

Geometrical Self-Calibration of CBCT Systems

In this paper, we intend to provide a method for calibration of geometric CBCT systems without the need for phantom and prior knowledge related to the object which is normally used in 2D-3D registration methods for geometrical calibration. To this end, we use an iterative algorithm based on comparison between measured data and DRRs1 which are obtained from reconstructed images with different geometries to find geometrical parameters of the system. Our proposed method shows strong agreement with phantom based methods.

Enhancing the depth perception of DSA images with 2D-3D registration.

Today, cerebrovascular disease has become an important health hazard. Therefore, it is necessary to perform a more accurate and less time-consuming registration of preoperative three-dimensional (3D) images and intraoperative two-dimensional (2D) projection images which is very important for conducting cerebrovascular disease interventions. The 2D-3D registration method proposed in this study is designed to solve the problems of long registration time and large registration errors in 3D computed tomography angiography (CTA) images and 2D digital subtraction angiography (DSA) images. To make a more comprehensive and active diagnosis, treatment and surgery plan for patients with cerebrovascular diseases, we propose a weighted similarity measure function, the normalized mutual information-gradient difference (NMG), which can evaluate the 2D-3D registration results. Then, using a multi-resolution fusion optimization strategy, the multi-resolution fused regular step gradient descent optimization (MR-RSGD) method is presented to attain the optimal value of the registration results in the process of the optimization algorithm. In this study, we adopt two datasets of the brain vessels to validate and obtain similarity metric values which are 0.0037 and 0.0003, respectively. Using the registration method proposed in this study, the time taken for the experiment was calculated to be 56.55s and 50.8070s, respectively, for the two sets of data. The results show that the registration methods proposed in this study are both better than the Normalized Mutual (NM) and Normalized Mutual Information (NMI). The experimental results in this study show that in the 2D-3D registration process, to evaluate the registration results more accurately, we can use the similarity metric function containing the image gray information and spatial information. To improve the efficiency of the registration process, we can choose the algorithm with gradient optimization strategy. Our method has great potential to be applied in practical interventional treatment for intuitive 3D navigation.

Method for 2D-3D Registration under Inverse Depth and Structural Semantic Constraints for Digital Twin City

A digital twin city maps a virtual three-dimensional (3D) city model to the geographic information system, constructs a virtual world, and integrates real sensor data to achieve the purpose of virtual–real fusion. Focusing on the accuracy problem of vision sensor registration in the virtual digital twin city scene, this study proposes a 2D-3D registration method under inverse depth and structural semantic constraints. First, perspective and inverse depth images of the virtual scene were obtained by using perspective view and inverse-depth nascent technology, and then the structural semantic features were extracted by the two-line minimal solution set method. A simultaneous matching and pose estimation method under inverse depth and structural semantic constraints was proposed to achieve the 2D-3D registration of real images and virtual scenes. The experimental results show that the proposed method can effectively optimize the initial vision sensor pose and achieve high-precision registration in the digital twin scene, and the Z-coordinate error is reduced by 45%. An application experiment of monocular image multi-object spatial positioning was designed, which proved the practicability of this method, and the influence of model data error on registration accuracy was analyzed.

Data-Driven Deformable 3D-2D Registration for Guiding Neuroelectrode Placement in Deep Brain Stimulation.

Deep brain stimulation is a neurosurgical procedure used in treatment of a growing spectrum of movement disorders. Inaccuracies in electrode placement, however, can result in poor symptom control or adverse effects and confound variability in clinical outcomes. A deformable 3D-2D registration method is presented for high-precision 3D guidance of neuroelectrodes. The approach employs a model-based, deformable algorithm for 3D-2D image registration. Variations in lead design are captured in a parametric 3D model based on a B-spline curve. The registration is solved through iterative optimization of 16 degrees-of-freedom that maximize image similarity between the 2 acquired radiographs and simulated forward projections of the neuroelectrode model. The approach was evaluated in phantom models with respect to pertinent imaging parameters, including view selection and imaging dose. The results demonstrate an accuracy of (0.2 ± 0.2) mm in 3D localization of individual electrodes. The solution was observed to be robust to changes in pertinent imaging parameters, which demonstrate accurate localization with ≥20° view separation and at 1/10th the dose of a standard fluoroscopy frame. The presented approach provides the means for guiding neuroelectrode placement from 2 low-dose radiographic images in a manner that accommodates potential deformations at the target anatomical site. Future work will focus on improving runtime though learning-based initialization, application in reducing reconstruction metal artifacts for 3D verification of placement, and extensive evaluation in clinical data from an IRB study underway.

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.

QUANTITATIVE EVALUATION METHOD FOR TIBIOFIBULAR JOINT ALIGNMENT IN ANKLE OSTEOARTHRITIS BASED ON 3D BONE SIZE

Purpose: A remedy for ankle osteoarthritis is artificial joint replacement surgery. By estimating the relative positional difference (alignment) between the bone before and after deformation, precise artificial joint replacement surgery can be performed. By using the estimated alignment, an artificial ankle joint can ensure high satisfaction with less pain and can perform necessary functions. Although bone alignment is currently estimated from X-rays and computed tomography (CT) images, it is difficult to measure three-dimensional (3D) data from two-dimensional (2D) images. Although 3D data can be estimated using a 2D–3D registration method, it requires capturing the bone images multiple times and imposing considerable burden on the patient. In this paper, we propose a method to estimate the 3D bone alignment based on the size of the bone using principal component analysis (PCA), which requires scanning only one data. Method: In the proposed method, PCA is used to create a 3D bone model based on the bone thickness, and the bone alignment is estimated by the Go-ICP algorithm. In this study, the images of the foot were captured using a CT device, and the 3D bone model was created by stacking CT images. For improving the accuracy of the alignment, a reference model based on the bone thickness was created using PCA. Subsequently, the bone was overlapped and the alignment was estimated. Result: For the evaluation of the accuracy of alignment in the proposed method, three types of methods were used to create a bone model. The proposed method was found to be the most accurate with respect to the true value in five of the six evaluation criteria. In addition, the alignment of the tibiofibular joint was evaluated using the proposed method. Regarding the displacement in the [Formula: see text]-axis direction, there were significant differences in both Stage 3B and Stage 4 compared with the healthy subjects. In addition, the magnitude of rotation in the [Formula: see text]-axis direction showed a significant difference in stage 3B as compared with a healthy subject. Conclusion: Using the proposed method, we have shown that bone alignment can be estimated three-dimensionally by scanning the bone data once only. In addition, by comparing with the reference model using PCA based on the thickness of the bone, the accuracy of alignment is improved as compared with the reference model, which is not based on the bone thickness.

AN AUTOMATIC ICP-BASED 2D-3D REGISTRATION METHOD FOR A HIGH-SPEED BIPLANAR VIDEORADIOGRAPHY IMAGING SYSTEM

Abstract. High-Speed Biplanar Videoradiography (HSBV) is an X-ray based non-invasive imaging system that can be used to derive dynamic bony translations and rotations. The 2D-3D registration process matches a 3D bone model acquired from magnetic resonance imaging (MRI) or computed tomography (CT) scans with the 2D X-ray image pairs. This study focuses on the registration of MRI data as it can acquire detailed soft tissue contrast that cannot be easily discerned in CT scans. A novel 2D-3D registration method is reported in this paper that is suitable for the MRI-based bone models with high precision and high efficiency. In addition, an automatic initialization procedure with 64 starting poses is established to avoid user intervention in the registration. The method has been tested using the HSBV image sequence of a knee joint during walking. Thirty-five consecutive poses from the sequence were tested for the registration, and 50 non-consecutive poses randomly selected from the sequence were tested for the automatic initialization. The registration precision for each axis was 0.49 to 0.54 mm. For the initialization validation test, 48 over 50 frames were successfully initialized and two failed due to portions of the joint falling outside of the field-of-view of the system. The average time for each initialization is only about 6 min. The improved 2D-3D registration will allow determination of precise 3D kinematic parameters with high efficiency. These kinematic parameters can be used to calculate joint cartilage contact mechanics that provide insight into the mechanical processes and mechanisms of joint degeneration or pathology.

Validation of a New Method for 2D Fusion Imaging Registration in a System Prepared Only for 3D

Purpose: To validate a new 2D-3D registration method of fusion imaging during aortic repair in a system prepared only for 3D-3D registration and to compare radiation doses and accuracy. Materials and Methods: The study involved 189 patients, including 94 patients (median age 70 years; 85 men) who underwent abdominal endovascular aneurysm repair (EVAR) with 2D-3D fusion on an Artis zee imaging system and 95 EVAR patients (median age 70 years; 81 men) from a prior study who had 3D-3D registration done using cone beam computed tomography (CBCT). For the 2D-3D registration, an offline CBCT of the empty operating table was imported into the intraoperative dataset and superimposed on the preoperative computed tomography angiogram (CTA). Then 2 intraoperative single-frame 2D images of the skeleton were aligned with the patient’s skeleton on the preoperative CTA to complete the registration process. A digital subtraction angiogram was done to correct any misalignment of the aortic CTA volume. Values are given as the median [interquartile range (IQR) Q1, Q3]. Results: The 2D-3D registration had an accuracy of 4.0 mm (IQR 3.0, 5.0) after bone matching compared with the final correction with DSA (78% within 5 mm). By applying the 2D-3D protocol the radiation exposure (dose area product) from the registration of the fusion image was significantly reduced compared with the 3D-3D registration [1.12 Gy∙cm2 (IQR 0.41, 2.14) vs 43.4 Gy∙cm2 (IQR 37.1, 49.0), respectively; p<0.001). Conclusion: The new 2D-3D registration protocol based on 2 single-frame images avoids an intraoperative CBCT and can be used for fusion imaging registration in a system originally designed for 3D-3D only. This 2D-3D registration protocol is accurate and leads to a significant reduction in radiation exposure.

Validation of a New Method of 2D Single Frame Fusion Registration in a System Only Prepared for 3D Registration

Introduction - Fusion imaging has been shown to reduce the amount of radiation exposure and contrast use during endovascular aortic repairs (EVAR). The dynamic overlay of fusion imaging relies on the accurate registration of the preoperative CTA with the intraoperative position of the patient on the operation table. Originally this has required an intraoperative cone-beam CT (CBCT, 3D-3D registration). More recently 2D-3D registration methods have been developed which allow to be performed with only two single frame 2D intraoperative images instead of the CBCT.

Intensity-based 2D-3D registration for an ACL reconstruction navigation system.

To improve the positioning accuracy of tunnels for anterior cruciate ligament (ACL) reconstruction, we proposed an intensity-based 2D-3D registration method for an ACL reconstruction navigation system. Methods for digitally reconstructed radiograph (DRR) generation, similarity measurement, and optimization are crucial to 2D-3D registration. We evaluated the accuracy, success rate, and processing time of different methods: (a) ray-casting and splating were compared for DRR generation; (b) normalized mutual information (NMI), Mattes mutual information (MMI), and Spearman's rank correlation coefficient (SRC) were assessed for similarity between registrations; and (c) gradient descent (GD) and downhill simplex (DS) were compared for optimization. The combination of splating, SRC, and GD provided the best composite performance and was applied in an augmented reality (AR) ACL reconstruction navigation system. The accuracy of the navigation system could fulfill the clinical needs of ACL reconstruction, with an end pose error of 2.50mm and an angle error of 2.74°.

The effect of functional bracing on the arthrokinematics of anterior cruciate ligament injured knees during lunge exercise

BackgroundFunctional knee braces are extensively used for partially and completely torn anterior cruciate ligament (ACL) patients and those who have undergone ACL graft reconstruction, in order to support the healing ACL, improve the joint’s functional stability, and restore the normal joint kinematics. Research questionDoes wearing braces alter the arthrokinematics of the ACL deficient knees during lung exercise? MethodsFor ten male unilateral ACL deficient subjects, 3D knee models were reconstructed from CT images, acquired in rest position. Sagittal plane fluoroscopy was then performed throughout a complete cycle of lunge in braced and non-braced conditions. The 3D kinematics of the knees were obtained using a 2D-3D registration method in which six anatomical bony landmarks on the fluoroscopic images were matched with those on the 3D models. ResultsNo significant difference was found between the tibial anterior-posterior translations and abduction-adduction motions of the braced and non-braced knees. A significant decrease, however, was observed after bracing in the tibial internal rotation at 45° flexion during eccentric (non-braced: 5.9° (±6.7°) vs. braced: 2.4° (±7.0°); p = 0.045), and at 30° flexion during concentric (non-braced: 2.3° (±6.9°) vs. braced: −1.6° (±8.1°); p = 0.001) phases of the lunge cycle. SignificanceThe immediate effect of knee bracing is limited to controlling the tibial rotation of the ACL deficient individuals during the lunge exercise. Hence, care should be taken in prescribing the lunge exercise for rehabilitation of ACL injured patients with high anterior-posterior knee instability, even when wearing knee braces.

The 2D-3D Registration Method in Image Fusion Is Accurate and Helps to Reduce the Used Contrast Medium, Radiation, and Procedural Time in Standard EVAR Procedures

This study aimed to evaluate the accuracy and the effectiveness of 2D-3D registrationmethod of image fusion (IF) technology in endovascular aneurysm repair (EVAR). We performed a review of our institutional endovascular aortic database of patients who had undergone EVAR between 2011 and 2015 before and after the installation of a 3D IF computed tomography system in our hybrid operating room. The accuracy was assessed in 14 endovascular procedures and showed a median registration error of 1.8mm at the origin of the right renal artery and 1.0mm at the origin of the left renal artery and a complete visual accuracy in 42% of the cases. EVAR was performed using the intraoperative IF technique with a 2D-3D registration method in 105 patients (group IF), whereas 47 patients done without served as controls. The IF group had a significantly reduced amount of used contrast compared with controls with a median of 58mL and P<0.0001. The intraoperative exposition to radiation was similar between the 2 groups with a median dose area product of 2,343.7 cGy cm2 in the IF group and 3,219cGycm2 among the controls(P=0.457). The radiation dose in the sub group IF (including patients operated by the 2 most experienced surgeons) was lower than that in sub controls (median, 1,087cGcm2 vs. 2,705.3cGcm2, P=0.012). The procedure time and the time of intraoperative radiation did not differ between the study groups (P=0.117 and 0.106, respectively), as did not fluoroscopy time in the sub group IF (median, 6.3min, vs. 9.5min, P=0.067), but for the 2 most experienced surgeons, the procedural time was shortened when using IF (P=0.002). The 2D-3D registration method of IF guidance is accurate to delineate the vessels of interest and could help the execution of the EVAR procedures with a significantly reduced amount of contrast medium and also with reduced radiation and shorter procedural duration when surgeons are more familiar with EVAR and IF.

2D–3D synchronous/asynchronous camera fusion for visual odometry

We propose a robust and direct 2D–3D registration method for camera synchronization. Once the cameras are synchronized—or for synchronous setups—we also propose a visual odometry framework that benefits from both 2D and 3D acquisitions. Our method does not require a precise set of 2D-to-3D correspondences, handles occlusions and works when the scene is only partially known. It is carried out through a 2D–3D based initial motion estimation followed by a constrained nonlinear optimization for motion refinement. The problems of occlusion and that of missing scene parts are handled by comparing the image-based reconstruction and 3D sensor measurements. The results of our experiments demonstrate that the proposed framework allows to obtain a good initial motion estimate and a significant improvement through refinement.

Correction of patient motion in cone-beam CT using 3D–2D registration

Cone-beam CT (CBCT) is increasingly common in guidance of interventional procedures, but can be subject to artifacts arising from patient motion during fairly long (~5–60 s) scan times. We present a fiducial-free method to mitigate motion artifacts using 3D–2D image registration that simultaneously corrects residual errors in the intrinsic and extrinsic parameters of geometric calibration. The 3D–2D registration process registers each projection to a prior 3D image by maximizing gradient orientation using the covariance matrix adaptation-evolution strategy optimizer. The resulting rigid transforms are applied to the system projection matrices, and a 3D image is reconstructed via model-based iterative reconstruction. Phantom experiments were conducted using a Zeego robotic C-arm to image a head phantom undergoing 5–15 cm translations and 5–15° rotations. To further test the algorithm, clinical images were acquired with a CBCT head scanner in which long scan times were susceptible to significant patient motion. CBCT images were reconstructed using a penalized likelihood objective function. For phantom studies the structural similarity (SSIM) between motion-free and motion-corrected images was >0.995, with significant improvement (p < 0.001) compared to the SSIM values of uncorrected images. Additionally, motion-corrected images exhibited a point-spread function with full-width at half maximum comparable to that of the motion-free reference image. Qualitative comparison of the motion-corrupted and motion-corrected clinical images demonstrated a significant improvement in image quality after motion correction. This indicates that the 3D–2D registration method could provide a useful approach to motion artifact correction under assumptions of local rigidity, as in the head, pelvis, and extremities. The method is highly parallelizable, and the automatic correction of residual geometric calibration errors provides added benefit that could be valuable in routine use.

Multi-stage 3D–2D registration for correction of anatomical deformation in image-guided spine surgery

A multi-stage image-based 3D–2D registration method is presented that maps annotations in a 3D image (e.g. point labels annotating individual vertebrae in preoperative CT) to an intraoperative radiograph in which the patient has undergone non-rigid anatomical deformation due to changes in patient positioning or due to the intervention itself.The proposed method (termed msLevelCheck) extends a previous rigid registration solution (LevelCheck) to provide an accurate mapping of vertebral labels in the presence of spinal deformation. The method employs a multi-stage series of rigid 3D–2D registrations performed on sets of automatically determined and increasingly localized sub-images, with the final stage achieving a rigid mapping for each label to yield a locally rigid yet globally deformable solution. The method was evaluated first in a phantom study in which a CT image of the spine was acquired followed by a series of 7 mobile radiographs with increasing degree of deformation applied. Second, the method was validated using a clinical data set of patients exhibiting strong spinal deformation during thoracolumbar spine surgery. Registration accuracy was assessed using projection distance error (PDE) and failure rate (PDE > 20 mm—i.e. label registered outside vertebra).The msLevelCheck method was able to register all vertebrae accurately for all cases of deformation in the phantom study, improving the maximum PDE of the rigid method from 22.4 mm to 3.9 mm. The clinical study demonstrated the feasibility of the approach in real patient data by accurately registering all vertebral labels in each case, eliminating all instances of failure encountered in the conventional rigid method.The multi-stage approach demonstrated accurate mapping of vertebral labels in the presence of strong spinal deformation. The msLevelCheck method maintains other advantageous aspects of the original LevelCheck method (e.g. compatibility with standard clinical workflow, large capture range, and robustness against mismatch in image content) and extends capability to cases exhibiting strong changes in spinal curvature.

3D-2D ultrasound feature-based registration for navigated prostate biopsy: a feasibility study.

The aim of this paper is to describe a 3D-2D ultrasound feature-based registration method for navigated prostate biopsy and its first results obtained on patient data. A system combining a low-cost tracking system and a 3D-2D registration algorithm was designed. The proposed 3D-2D registration method combines geometric and image-based distances. After extracting features from ultrasound images, 3D and 2D features within a defined distance are matched using an intensity-based function. The results are encouraging and show acceptable errors with simulated transforms applied on ultrasound volumes from real patients.

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

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. 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. 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. 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.

3D–2D registration in mobile radiographs: algorithm development and preliminary clinical evaluation

An image-based 3D–2D registration method is presented using radiographs acquired in the uncalibrated, unconstrained geometry of mobile radiography. The approach extends a previous method for six degree-of-freedom (DOF) registration in C-arm fluoroscopy (namely ‘LevelCheck’) to solve the 9-DOF estimate of geometry in which the position of the source and detector are unconstrained. The method was implemented using a gradient correlation similarity metric and stochastic derivative-free optimization on a GPU. Development and evaluation were conducted in three steps. First, simulation studies were performed that involved a CT scan of an anthropomorphic body phantom and 1000 randomly generated digitally reconstructed radiographs in posterior–anterior and lateral views. A median projection distance error (PDE) of 0.007 mm was achieved with 9-DOF registration compared to 0.767 mm for 6-DOF. Second, cadaver studies were conducted using mobile radiographs acquired in three anatomical regions (thorax, abdomen and pelvis) and three levels of source-detector distance (~800, ~1000 and ~1200 mm). The 9-DOF method achieved a median PDE of 0.49 mm (compared to 2.53 mm for the 6-DOF method) and demonstrated robustness in the unconstrained imaging geometry. Finally, a retrospective clinical study was conducted with intraoperative radiographs of the spine exhibiting real anatomical deformation and image content mismatch (e.g. interventional devices in the radiograph that were not in the CT), demonstrating a PDE = 1.1 mm for the 9-DOF approach. Average computation time was 48.5 s, involving 687 701 function evaluations on average, compared to 18.2 s for the 6-DOF method. Despite the greater computational load, the 9-DOF method may offer a valuable tool for target localization (e.g. decision support in level counting) as well as safety and quality assurance checks at the conclusion of a procedure (e.g. overlay of planning data on the radiograph for verification of the surgical product) in a manner consistent with natural surgical workflow.