Average Registration Time Research Articles

Fast and Accurate Data Extraction for Near Real-Time Registration of 3-D Ultrasound and Computed Tomography in Orthopedic Surgery

Automatic, accurate and real-time registration is an important step in providing effective guidance and successful anatomic restoration in ultrasound (US)-based computer assisted orthopedic surgery. We propose a method in which local phase-based bone surfaces, extracted from intra-operative US data, are registered to pre-operatively segmented computed tomography data. Extracted bone surfaces are downsampled and reinforced with high curvature features. A novel hierarchical simplification algorithm is used to further optimize the point clouds. The final point clouds are represented as Gaussian mixture models and iteratively matched by minimizing the dissimilarity between them using an L2 metric. For 44 clinical data sets from 25 pelvic fracture patients and 49 phantom data sets, we report mean surface registration accuracies of 0.31 and 0.77 mm, respectively, with an average registration time of 1.41 s. Our results suggest the viability and potential of the chosen method for real-time intra-operative registration in orthopedic surgery.

Rendering-Based Video-CT Registration with Physical Constraints for Image-Guided Endoscopic Sinus Surgery.

We present a system for registering the coordinate frame of an endoscope to pre- or intra- operatively acquired CT data based on optimizing the similarity metric between an endoscopic image and an image predicted via rendering of CT. Our method is robust and semi-automatic because it takes account of physical constraints, specifically, collisions between the endoscope and the anatomy, to initialize and constrain the search. The proposed optimization method is based on a stochastic optimization algorithm that evaluates a large number of similarity metric functions in parallel on a graphics processing unit. Images from a cadaver and a patient were used for evaluation. The registration error was 0.83 mm and 1.97 mm for cadaver and patient images respectively. The average registration time for 60 trials was 4.4 seconds. The patient study demonstrated robustness of the proposed algorithm against a moderate anatomical deformation.

Registration of 2D C-Arm and 3D CT Images for a C-Arm Image-Assisted Navigation System for Spinal Surgery.

C-Arm image-assisted surgical navigation system has been broadly applied to spinal surgery. However, accurate path planning on the C-Arm AP-view image is difficult. This research studies 2D-3D image registration methods to obtain the optimum transformation matrix between C-Arm and CT image frames. Through the transformation matrix, the surgical path planned on preoperative CT images can be transformed and displayed on the C-Arm images for surgical guidance. The positions of surgical instruments will also be displayed on both CT and C-Arm in the real time. Five similarity measure methods of 2D-3D image registration including Normalized Cross-Correlation, Gradient Correlation, Pattern Intensity, Gradient Difference Correlation, and Mutual Information combined with three optimization methods including Powell's method, Downhill simplex algorithm, and genetic algorithm are applied to evaluate their performance in converge range, efficiency, and accuracy. Experimental results show that the combination of Normalized Cross-Correlation measure method with Downhill simplex algorithm obtains maximum correlation and similarity in C-Arm and Digital Reconstructed Radiograph (DRR) images. Spine saw bones are used in the experiment to evaluate 2D-3D image registration accuracy. The average error in displacement is 0.22 mm. The success rate is approximately 90% and average registration time takes 16 seconds.

Perfil de pacientes em lista de espera para transplante renal

Aim: to identify the profile of patients on the waiting list for kidney transplantation. Method: descriptive and quantitative study performed from May 2010 to May 2012, with 55 patients followed at the outpatient unit of nephrology in Natal/RN, Brazil. Results: of those surveyed, 63,6% were males, 39,9 (±12,2) years median age, coming from the country (56,4%), married (60,0%), with children (50,9%). Elementary School was the predominant level of schooling (41,8%) and the majority of patients (89,1%) did not exercise any working activity. Most had monthly income up to two minimum wages (67,3%). The majority of patients in the unique waiting list were submitted to hemodialysis (98,2%), were hypertensive (81,8%) and with an average registration time of 1,9 (±1,9) years. Conclusion: to know the profile of patients is essential to plan health actions, in order to minimize the morbidity and mortality on the waiting list.

Fast rigid registration of pre-operative magnetic resonance images to intra-operative ultrasound for neurosurgery based on high confidence gradient orientations

We present a novel approach for the registration of pre-operative magnetic resonance images to intra-operative ultrasound images for the context of image-guided neurosurgery. Our technique relies on the maximization of gradient orientation alignment in a reduced set of high confidence locations of interest and allows for fast, accurate, and robust registration. Performance is compared with multiple state-of-the-art techniques including conventional intensity-based multi-modal registration strategies, as well as other context-specific approaches. All methods were evaluated on fourteen clinical neurosurgical cases with brain tumors, including low-grade and high-grade gliomas, from the publicly available MNI BITE dataset. Registration accuracy of each method is evaluated as the mean distance between homologous landmarks identified by two or three experts. We provide an analysis of the landmarks used and expose some of the limitations in validation brought forward by expert disagreement and uncertainty in identifying corresponding points. The proposed approach yields a mean error of 2.57 mm across all cases (the smallest among all evaluated techniques). Additionally, it is the only evaluated technique that resolves all cases with a mean distance of less than 1 mm larger than the theoretical minimal mean distance when using a rigid transformation. Finally, our proposed method provides reduced processing times with an average registration time of 0.76 s in a GPU-based implementation, thereby facilitating its integration into the operating room.

Image-guided liver surgery: intraoperative projection of computed tomography images utilizing tracked ultrasound

BackgroundUltrasound (US) is the most commonly used form of image guidance during liver surgery. However, the use of navigation systems that incorporate instrument tracking and three-dimensional visualization of preoperative tomography is increasing. This report describes an initial experience using an image-guidance system with navigated US. MethodsAn image-guidance system was used in a total of 50 open liver procedures to aid in localization and targeting of liver lesions. An optical tracking system was employed to localize surgical instruments. Customized hardware and calibration of the US transducer were required. The results of three procedures are highlighted in order to illustrate specific navigation techniques that proved useful in the broader patient cohort. ResultsOver a 7-month span, the navigation system assisted in completing 21 (42%) of the procedures, and tracked US alone provided additional information required to perform resection or ablation in six procedures (12%). Average registration time during the three illustrative procedures was <1min. Average set-up time was approximately 5min per procedure. ConclusionsThe Explorer™ Liver guidance system represents novel technology that continues to evolve. This initial experience indicates that image guidance is valuable in certain procedures, specifically in cases in which difficult anatomy or tumour location or echogenicity limit the usefulness of traditional guidance methods.

An approach for rigid image registration based on wavelet decomposition and modified particle swarm optimisation

An intelligent approach based on modified particle swarm optimisation and wavelet decomposition was proposed for rigid image registration. In the phase of approximate registration, the modified particle swarm optimisation (PSO), more efficient optimisation without the velocity item in the evolutionary equation, was employed to optimise the solution responding to the mutual information value calculated from the approximate sub-images extracted from the original images by wavelet decomposition. And in the phase of fine registration, cyclic variable method was employed to search maximal mutual information value calculated from the original images. The experimental result showed that the average total registration time was reduced greatly by wavelet decomposition and precision was improved by the modified PSO.

Automated registration of hip and spine for longitudinal QCT studies: Integration with 3D densitometric and structural analysis

To eliminate user interaction in longitudinal quantitative computed tomography (QCT) measurements of bone mineral density (BMD) and geometry, we have developed and optimized an automated registration algorithm for QCT images of the hip and spine and integrated it with a previously developed 3D densitometric and structural analysis program. With registration, the follow-up images are automatically aligned with respect to the baseline scans, and the bone quantification of the aligned follow-up scan is initiated based on the bone morphometric features defined on the baseline scan. To validate the algorithm, we analyzed 20 pairs of repeat QCT images (10 hip pairs and 10 spine pairs) acquired on a modern multi-slice CT scanner, with repositioning between each scan pair to simulate repeat visits. Bone measurements obtained with automatic registration achieved comparable or improved precision errors compared to those obtained by careful manual analysis of the follow-up scans. The algorithm we have developed was based on the mutual information approach, with simplex optimization under a multi-resolution scheme. The average registration time was 2.3 min for a hip pair and 1.1 min for a vertebra pair using a standard desktop computer. Based on the reduced user interaction, high degree of precision, and short execution time, this is a promising technique for monitoring therapy in patients and clinical trials.

Evaluation of voxel-based registration of 3-D power Doppler ultrasound and 3-D magnetic resonance angiographic images of carotid arteries

Spatial registration and fusion of ultrasound (US) images with other modalities may aid clinical interpretation. We implemented and evaluated on patient data an automated retrospective registration of magnetic resonance angiography (MRA) carotid bifurcation images with 3-D power Doppler ultrasound (PD US) and indirectly with 3-D B-mode US. Volumes were initially thresholded to reduce the uncorrelated noise signals. The registration algorithm subsequently maximized the mutual information measure between the PD US and 3-D MRA via iterative simplex search to find best “rigid body” transformation. We rated the performance of the algorithm visually on ( n = 5) clinical MRA and 3-D PD US datasets. We also evaluated quantitatively the effect of thresholding, initial misalignment of the paired volumes and the reproducibility registration. We investigated the effect of image artefacts by simulation experiments. Preregistration misalignments of up to 5 mm in the transaxial plane, up to 10 mm along the axis of the carotids and up to 40 ° resulted in 107 of 110 successful registrations, with translational and rotational errors of 0.32 mm ± 0.3 mm and 1.6 ± 2.1°. The algorithm was not affected by missing arterial segments of up to 8 mm in length. The average registration time was 4 min. We conclude that the algorithm could be applied to 3-D US PD and MRA data for automated multimodality registration of carotid vessels without the use of fiducials. (E-mail:psomka@irus.rri.on.ca)