Projection Distance Error Research Articles

Deformable registration of preoperative MR and intraoperative long-length tomosynthesis images for guidance of spine surgery via image synthesis

PurposeImproved integration and use of preoperative imaging during surgery hold significant potential for enhancing treatment planning and instrument guidance through surgical navigation. Despite its prevalent use in diagnostic settings, MR imaging is rarely used for navigation in spine surgery. This study aims to leverage MR imaging for intraoperative visualization of spine anatomy, particularly in cases where CT imaging is unavailable or when minimizing radiation exposure is essential, such as in pediatric surgery. MethodsThis work presents a method for deformable 3D-2D registration of preoperative MR images with a novel intraoperative long-length tomosynthesis imaging modality (viz., Long-Film [LF]). A conditional generative adversarial network is used to translate MR images to an intermediate bone image suitable for registration, followed by a model-based 3D-2D registration algorithm to deformably map the synthesized images to LF images. The algorithm’s performance was evaluated on cadaveric specimens with implanted markers and controlled deformation, and in clinical images of patients undergoing spine surgery as part of a large-scale clinical study on LF imaging. ResultsThe proposed method yielded a median 2D projection distance error of 2.0 mm (interquartile range [IQR]: 1.1–3.3 mm) and a 3D target registration error of 1.5 mm (IQR: 0.8–2.1 mm) in cadaver studies. Notably, the multi-scale approach exhibited significantly higher accuracy compared to rigid solutions and effectively managed the challenges posed by piecewise rigid spine deformation. The robustness and consistency of the method were evaluated on clinical images, yielding no outliers on vertebrae without surgical instrumentation and 3% outliers on vertebrae with instrumentation. ConclusionsThis work constitutes the first reported approach for deformable MR to LF registration based on deep image synthesis. The proposed framework provides access to the preoperative annotations and planning information during surgery and enables surgical navigation within the context of MR images and/or dual-plane LF images.

3D–2D image registration in the presence of soft-tissue deformation in image-guided transbronchial interventions

Purpose. Target localization in pulmonary interventions (e.g. transbronchial biopsy of a lung nodule) is challenged by deformable motion and may benefit from fluoroscopic overlay of the target to provide accurate guidance. We present and evaluate a 3D–2D image registration method for fluoroscopic overlay in the presence of tissue deformation using a multi-resolution/multi-scale (MRMS) framework with an objective function that drives registration primarily by soft-tissue image gradients. Methods. The MRMS method registers 3D cone-beam CT to 2D fluoroscopy without gating of respiratory phase by coarse-to-fine resampling and global-to-local rescaling about target regions-of-interest. A variation of the gradient orientation () similarity metric (denoted ) was developed to downweight bone gradients and drive registration via soft-tissue gradients. Performance was evaluated in terms of projection distance error at isocenter (PDEiso). Phantom studies determined nominal algorithm parameters and capture range. Preclinical studies used a freshly deceased, ventilated porcine specimen to evaluate performance in the presence of real tissue deformation and a broad range of 3D–2D image mismatch. Results. Nominal algorithm parameters were identified that provided robust performance over a broad range of motion (0–20 mm), including an adaptive parameter selection technique to accommodate unknown mismatch in respiratory phase. The metric yielded median PDEiso = 1.2 mm, compared to 6.2 mm for conventional Preclinical studies with real lung deformation demonstrated median PDEiso = 1.3 mm with MRMS + registration, compared to 2.2 mm with a conventional transform. Runtime was 26 s and can be reduced to 2.5 s given a prior registration within ∼5 mm as initialization. Conclusions. MRMS registration via soft-tissue gradients achieved accurate fluoroscopic overlay in the presence of deformable lung motion. By driving registration via soft-tissue image gradients, the method avoided false local minima presented by bones and was robust to a wide range of motion magnitude.

Realistic C-arm to pCT registration for vertebral localization in spine surgery

Spine surgeries are vulnerable to wrong-level surgeries and postoperative complications because of their complex structure. Unavailability of the 3D intraoperative imaging device, low-contrast intraoperative X-ray images, variable clinical and patient conditions, manual analyses, lack of skilled technicians, and human errors increase the chances of wrong-site or wrong-level surgeries. State of the art work refers 3D-2D image registration systems and other medical image processing techniques to address the complications associated with spine surgeries. Intensity-based 3D-2D image registration systems had been widely practiced across various clinical applications. However, these frameworks are limited to specific clinical conditions such as anatomy, dimension of image correspondence, and imaging modalities. Moreover, there are certain prerequisites for these frameworks to function in clinical application, such as dataset requirement, speed of computation, requirement of high-end system configuration, limited capture range, and multiple local maxima. A simple and effective registration framework was designed with a study objective of vertebral level identification and its pose estimation from intraoperative fluoroscopic images by combining intensity-based and iterative control point (ICP)–based 3D-2D registration. A hierarchical multi-stage registration framework was designed that comprises coarse and finer registration. The coarse registration was performed in two stages, i.e., intensity similarity-based spatial localization and source-to-detector localization based on the intervertebral distance correspondence between vertebral centroids in projected and intraoperative X-ray images. Finally, to speed up target localization in the intraoperative application, based on 3D-2D vertebral centroid correspondence, a rigid ICP-based finer registration was performed. The mean projection distance error (mPDE) measurement and visual similarity between projection image at finer registration point and intraoperative X-ray image and surgeons’ feedback were held accountable for the quality assurance of the designed registration framework. The average mPDE after peak signal to noise ratio (PSNR)–based coarse registration was 20.41mm. After the coarse registration in spatial region and source to detector direction, the average mPDE reduced to 12.18mm. On finer ICP-based registration, the mean mPDE was finally reduced to 0.36 mm. The approximate mean time required for the coarse registration, finer registration, and DRR image generation at the final registration point were 10 s, 15 s, and 1.5 min, respectively. The designed registration framework can act as a supporting tool for vertebral level localization and its pose estimation in an intraoperative environment. The framework was designed with the future perspective of intraoperative target localization and its pose estimation irrespective of the target anatomy.Graphical abstract

Multi-body 3D–2D registration for image-guided reduction of pelvic dislocation in orthopaedic trauma surgery

Surgical reduction of pelvic dislocation is a challenging procedure with poor long-term prognosis if reduction does not accurately restore natural morphology. The procedure often requires long fluoroscopic exposure times and trial-and-error to achieve accurate reduction. We report a method to automatically compute the target pose of dislocated bones in preoperative CT and provide 3D guidance of reduction using routine 2D fluoroscopy. A pelvic statistical shape model (SSM) and a statistical pose model (SPM) were formed from an atlas of 40 pelvic CT images. Multi-body bone segmentation was achieved by mapping the SSM to a preoperative CT via an active shape model. The target reduction pose for the dislocated bone is estimated by fitting the poses of undislocated bones to the SPM. Intraoperatively, multiple bones are registered to fluoroscopy images via 3D–2D registration to obtain 3D pose estimates from 2D images. The method was examined in three studies: (1) a simulation study of 40 CT images simulating a range of dislocation patterns; (2) a pelvic phantom study with controlled dislocation of the left innominate bone; (3) a clinical case study investigating feasibility in images acquired during pelvic reduction surgery. Experiments investigated the accuracy of registration as a function of initialization error (capture range), image quality (radiation dose and image noise), and field of view (FOV) size. The simulation study achieved target pose estimation with translational error of median 2.3 mm (1.4 mm interquartile range, IQR) and rotational error of 2.1° (1.3° IQR). 3D–2D registration yielded 0.3 mm (0.2 mm IQR) in-plane and 0.3 mm (0.2 mm IQR) out-of-plane translational error, with in-plane capture range of ±50 mm and out-of-plane capture range of ±120 mm. The phantom study demonstrated 3D–2D target registration error of 2.5 mm (1.5 mm IQR), and the method was robust over a large dose range, down to 5 Gy/frame (an order of magnitude lower than the nominal fluoroscopic dose). The clinical feasibility study demonstrated accurate registration with both preoperative and intraoperative radiographs, yielding 3.1 mm (1.0 mm IQR) projection distance error with robust performance for FOV ranging from 340 × 340 mm2 to 170 × 170 mm2 (at the image plane). The method demonstrated accurate estimation of the target reduction pose in simulation, phantom, and a clinical feasibility study for a broad range of dislocation patterns, initialization error, dose levels, and FOV size. The system provides a novel means of guidance and assessment of pelvic reduction from routinely acquired preoperative CT and intraoperative fluoroscopy. The method has the potential to reduce radiation dose by minimizing trial-and-error and to improve outcomes by guiding more accurate reduction of joint dislocations.

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.

C-arm Positioning Using Virtual Fluoroscopy for Image-Guided Surgery.

Fluoroscopically guided procedures often involve repeated acquisitions for C-arm positioning at the cost of radiation exposure and time in the operating room. A virtual fluoroscopy system is reported with the potential of reducing dose and time spent in C-arm positioning, utilizing three key advances: robust 3D-2D registration to a preoperative CT; real-time forward projection on GPU; and a motorized mobile C-arm with encoder feedback on C-arm orientation. Geometric calibration of the C-arm was performed offline in two rotational directions (orbit α, orbit β). Patient registration was performed using image-based 3D-2D registration with an initially acquired radiograph of the patient. This approach for patient registration eliminated the requirement for external tracking devices inside the operating room, allowing virtual fluoroscopy using commonly available systems in fluoroscopically guided procedures within standard surgical workflow. Geometric accuracy was evaluated in terms of projection distance error (PDE) in anatomical fiducials. A pilot study was conducted to evaluate the utility of virtual fluoroscopy to aid C-arm positioning in image guided surgery, assessing potential improvements in time, dose, and agreement between the virtual and desired view. The overall geometric accuracy of DRRs in comparison to the actual radiographs at various C-arm positions was PDE (mean ± std) = 1.6 ± 1.1 mm. The conventional approach required on average 8.0 ± 4.5 radiographs spent "fluoro hunting" to obtain the desired view. Positioning accuracy improved from 2.6° ± 2.3° (in α) and 4.1° ± 5.1° (in β) in the conventional approach to 1.5° ± 1.3° and 1.8° ± 1.7°, respectively, with the virtual fluoroscopy approach. Virtual fluoroscopy could improve accuracy of C-arm positioning and save time and radiation dose in the operating room. Such a system could be valuable to training of fluoroscopy technicians as well as intraoperative use in fluoroscopically guided procedures.

Registration of MRI to intraoperative radiographs for target localization in spinal interventions

Decision support to assist in target vertebra localization could provide a useful aid to safe and effective spine surgery. Previous solutions have shown 3D–2D registration of preoperative CT to intraoperative radiographs to reliably annotate vertebral labels for assistance during level localization. We present an algorithm (referred to as MR-LevelCheck) to perform 3D–2D registration based on a preoperative MRI to accommodate the increasingly common clinical scenario in which MRI is used instead of CT for preoperative planning.Straightforward adaptation of gradient/intensity-based methods appropriate to CT-to-radiograph registration is confounded by large mismatch and noncorrespondence in image intensity between MRI and radiographs. The proposed method overcomes such challenges with a simple vertebrae segmentation step using vertebra centroids as seed points (automatically defined within existing workflow). Forwards projections are computed using segmented MRI and registered to radiographs via gradient orientation (GO) similarity and the CMA-ES (covariance-matrix-adaptation evolutionary-strategy) optimizer. The method was tested in an IRB-approved study involving 10 patients undergoing cervical, thoracic, or lumbar spine surgery following preoperative MRI.The method successfully registered each preoperative MRI to intraoperative radiographs and maintained desirable properties of robustness against image content mismatch and large capture range. Robust registration performance was achieved with projection distance error (PDE) (median ± IQR) = 4.3 ± 2.6 mm (median ± IQR) and 0% failure rate. Segmentation accuracy for the continuous max-flow method yielded dice coefficient = 88.1 ± 5.2, accuracy = 90.6 ± 5.7, RMSE = 1.8 ± 0.6 mm, and contour affinity ratio (CAR) = 0.82 ± 0.08. Registration performance was found to be robust for segmentation methods exhibiting RMSE <3 mm and CAR >0.50.The MR-LevelCheck method provides a potentially valuable extension to a previously developed decision support tool for spine surgery target localization by extending its utility to preoperative MRI while maintaining characteristics of accuracy and robustness.

WE‐AB‐BRA‐09: Registration of Preoperative MRI to Intraoperative Radiographs for Automatic Vertebral Target Localization

Purpose:Accurate localization of target vertebrae is essential to safe, effective spine surgery, but wrong‐level surgery occurs with surprisingly high frequency. Recent research yielded the “LevelCheck” method for 3D‐2D registration of preoperative CT to intraoperative radiographs, providing decision support for level localization. We report a new method (MR‐LevelCheck) to perform 3D‐2D registration based on preoperative MRI, presenting a solution for the increasingly common scenario in which MRI (not CT) is used for preoperative planning.Methods:Direct extension of LevelCheck is confounded by large mismatch in image intensity between MRI and radiographs. The proposed method overcomes such challenges with a simple vertebrae segmentation. Using seed points at centroids, vertebrae are segmented using continuous max‐flow method and dilated by 1.8 mm to include surrounding cortical bone (inconspicuous in T2w‐MRI). MRI projections are computed (analogous to DRR) using segmentation and registered to intraoperative radiographs. The method was tested in a retrospective IRB‐approved study involving 11 patients undergoing cervical, thoracic, or lumbar spine surgery following preoperative MRI. Registration accuracy was evaluated in terms of projection‐distance‐error (PDE) between the true and estimated location of vertebrae in each radiograph.Results:The method successfully registered each preoperative MRI to intraoperative radiographs and maintained desirable properties of robustness against image content mismatch, and large capture range. Segmentation achieved Dice coefficient = 89.2 ± 2.3 and mean‐absolute‐distance (MAD) = 1.5 ± 0.3 mm. Registration demonstrated robust performance under realistic patient variations, with PDE = 4.0 ± 1.9 mm (median ± iqr) and converged with run‐time = 23.3 ± 1.7 s.Conclusion:The MR‐LevelCheck algorithm provides an important extension to a previously validated decision support tool in spine surgery by extending its utility to preoperative MRI. With initial studies demonstrating PDE &lt;5 mm and 0% failure rate, the method is now in translation to larger scale prospective clinical studies.S. Vogt and G. Kleinszig are employees of Siemens Healthcare

3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch

In image-guided spine surgery, robust three-dimensional to two-dimensional (3D–2D) registration of preoperative computed tomography (CT) and intraoperative radiographs can be challenged by the image content mismatch associated with the presence of surgical instrumentation and implants as well as soft-tissue resection or deformation. This work investigates image similarity metrics in 3D–2D registration offering improved robustness against mismatch, thereby improving performance and reducing or eliminating the need for manual masking.The performance of four gradient-based image similarity metrics (gradient information (GI), gradient correlation (GC), gradient information with linear scaling (GS), and gradient orientation (GO)) with a multi-start optimization strategy was evaluated in an institutional review board-approved retrospective clinical study using 51 preoperative CT images and 115 intraoperative mobile radiographs. Registrations were tested with and without polygonal masks as a function of the number of multistarts employed during optimization. Registration accuracy was evaluated in terms of the projection distance error (PDE) and assessment of failure modes (PDE > 30 mm) that could impede reliable vertebral level localization.With manual polygonal masking and 200 multistarts, the GC and GO metrics exhibited robust performance with 0% gross failures and median PDE < 6.4 mm (±4.4 mm interquartile range (IQR)) and a median runtime of 84 s (plus upwards of 1–2 min for manual masking). Excluding manual polygonal masks and decreasing the number of multistarts to 50 caused the GC-based registration to fail at a rate of >14%; however, GO maintained robustness with a 0% gross failure rate. Overall, the GI, GC, and GS metrics were susceptible to registration errors associated with content mismatch, but GO provided robust registration (median PDE = 5.5 mm, 2.6 mm IQR) without manual masking and with an improved runtime (29.3 s).The GO metric improved the registration accuracy and robustness in the presence of strong image content mismatch. This capability could offer valuable assistance and decision support in spine level localization in a manner consistent with clinical workflow.

Automatic Masking for Robust 3D-2D Image Registration in Image-Guided Spine Surgery.

During spinal neurosurgery, patient-specific information, planning, and annotation such as vertebral labels can be mapped from preoperative 3D CT to intraoperative 2D radiographs via image-based 3D-2D registration. Such registration has been shown to provide a potentially valuable means of decision support in target localization as well as quality assurance of the surgical product. However, robust registration can be challenged by mismatch in image content between the preoperative CT and intraoperative radiographs, arising, for example, from anatomical deformation or the presence of surgical tools within the radiograph. In this work, we develop and evaluate methods for automatically mitigating the effect of content mismatch by leveraging the surgical planning data to assign greater weight to anatomical regions known to be reliable for registration and vital to the surgical task while removing problematic regions that are highly deformable or often occluded by surgical tools. We investigated two approaches to assigning variable weight (i.e., "masking") to image content and/or the similarity metric: (1) masking the preoperative 3D CT ("volumetric masking"); and (2) masking within the 2D similarity metric calculation ("projection masking"). The accuracy of registration was evaluated in terms of projection distance error (PDE) in 61 cases selected from an IRB-approved clinical study. The best performing of the masking techniques was found to reduce the rate of gross failure (PDE > 20 mm) from 11.48% to 5.57% in this challenging retrospective data set. These approaches provided robustness to content mismatch and eliminated distinct failure modes of registration. Such improvement was gained without additional workflow and has motivated incorporation of the masking methods within a system under development for prospective clinical studies.

WE-AB-BRA-01: 3D-2D Image Registration for Target Localization in Spine Surgery: Comparison of Similarity Metrics Against Robustness to Content Mismatch

Purpose: In image-guided spine surgery, mapping 3D preoperative images to 2D intraoperative images via 3D-2D registration can provide valuable assistance in target localization. However, the presence of surgical instrumentation, hardware implants, and soft-tissue resection/displacement causes mismatches in image content, confounding existing registration methods. Manual/semi-automatic methods to mask such extraneous content is time consuming, user-dependent, error prone, and disruptive to clinical workflow. We developed and evaluated 2 novel similarity metrics within a robust registration framework to overcome such challenges in target localization. Methods: An IRB-approved retrospective study in 19 spine surgery patients included 19 preoperative 3D CT images and 50 intraoperative mobile radiographs in cervical, thoracic, and lumbar spine regions. A neuroradiologist provided truth definition of vertebral positions in CT and radiography. 3D-2D registration was performed using the CMA-ES optimizer with 4 gradient-based image similarity metrics: (1) gradient information (GI); (2) gradient correlation (GC); (3) a novel variant referred to as gradient orientation (GO); and (4) a second variant referred to as truncated gradient correlation (TGC). Registration accuracy was evaluated in terms of the projection distance error (PDE) of the vertebral levels. Results: Conventional similarity metrics were susceptible to gross registration error and failure modes associated with the presence of surgical instrumentation: for GI, the median PDE and interquartile range was 33.0±43.6 mm; similarly for GC, PDE = 23.0±92.6 mm respectively. The robust metrics GO and TGC, on the other hand, demonstrated major improvement in PDE (7.6 ±9.4 mm and 8.1± 18.1 mm, respectively) and elimination of gross failure modes. Conclusion: The proposed GO and TGC similarity measures improve registration accuracy and robustness to gross failure in the presence of strong image content mismatch. Such registration capability could offer valuable assistance in target localization without disruption of clinical workflow. G. Kleinszig and S. Vogt are employees of Siemens Healthcare.

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.

3D–2D registration for surgical guidance: effect of projection view angles on registration accuracy

An algorithm for intensity-based 3D–2D registration of CT and x-ray projections is evaluated, specifically using single- or dual-projection views to provide 3D localization. The registration framework employs the gradient information similarity metric and covariance matrix adaptation evolution strategy to solve for the patient pose in six degrees of freedom. Registration performance was evaluated in an anthropomorphic phantom and cadaver, using C-arm projection views acquired at angular separation, Δθ, ranging from ∼0°–180° at variable C-arm magnification. Registration accuracy was assessed in terms of 2D projection distance error and 3D target registration error (TRE) and compared to that of an electromagnetic (EM) tracker. The results indicate that angular separation as small as Δθ ∼10°–20° achieved TRE <2 mm with 95% confidence, comparable or superior to that of the EM tracker. The method allows direct registration of preoperative CT and planning data to intraoperative fluoroscopy, providing 3D localization free from conventional limitations associated with external fiducial markers, stereotactic frames, trackers and manual registration.

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.

Integrated surface model optimization for freehand three-dimensional echocardiography

The major obstacle of three-dimensional (3-D) echocardiography is that the ultrasound image quality is too low to reliably detect features locally. Almost all available surface-finding algorithms depend on decent quality boundaries to get satisfactory surface models. We formulate the surface model optimization problem in a Bayesian framework, such that the inference made about a surface model is based on the integration of both the low-level image evidence and the high-level prior shape knowledge through a pixel class prediction mechanism. We model the probability of pixel classes instead of making explicit decisions about them. Therefore, we avoid the unreliable edge detection or image segmentation problem and the pixel correspondence problem. An optimal surface model best explains the observed images such that the posterior probability of the surface model for the observed images is maximized. The pixel feature vector as the image evidence includes several parameters such as the smoothed grayscale value and the minimal second directional derivative. Statistically, we describe the feature vector by the pixel appearance probability model obtained by a nonparametric optimal quantization technique. Qualitatively, we display the imaging plane intersections of the optimized surface models together with those of the ground-truth surfaces reconstructed from manual delineations. Quantitatively, we measure the projection distance error between the optimized and the ground-truth surfaces. In our experiment, we use 20 studies to obtain the probability models offline. The prior shape knowledge is represented by a catalog of 86 left ventricle surface models. In another set of 25 test studies, the average epicardial and endocardial surface projection distance errors are 3.2 +/- 0.85 mm and 2.6 +/- 0.78 mm, respectively.