Automatic registration and precise tumour localization method for robot-assisted puncture procedure under inconsistent breath-holding conditions.

Multisensor systems with optical 3D sensors are frequently employed to capture complete surface information by measuring workpieces from different views. During coarse and fine registration the resulting datasets are afterward transformed into one common coordinate system. Automatic fine registration methods are well established in dimensional metrology, whereas there is a deficit in automatic coarse registration methods. The advantage of a fully automatic registration procedure is twofold: it enables a fast and contact-free alignment and further a flexible application to datasets of any kind of optical 3D sensor. In this paper, an algorithm adapted for a robust automatic coarse registration is presented. The method was originally developed for the field of object reconstruction or localization. It is based on a segmentation of planes in the datasets to calculate the transformation parameters. The rotation is defined by the normals of three corresponding segmented planes of two overlapping datasets, while the translation is calculated via the intersection point of the segmented planes. First results have shown that the translation is strongly shape dependent: 3D data of objects with non-orthogonal planar flanks cannot be registered with the current method. In the novel supplement for the algorithm, the translation is additionally calculated via the distance between centroids of corresponding segmented planes, which results in more than one option for the transformation. A newly introduced measure considering the distance between the datasets after coarse registration evaluates the best possible transformation. Results of the robust automatic registration method are presented on the example of datasets taken from a cutting tool with a fringe-projection system and a focus-variation system. The successful application in dimensional metrology is proven with evaluations of shape parameters based on the registered datasets of a calibrated workpiece.

Automatic Registration between Real-Time Ultrasonography and Pre-Procedural Magnetic Resonance Images: A Prospective Comparison between Two Registration Methods by Liver Surface and Vessel and by Liver Surface Only

Fine registration is a fundamental step for further application of remote sensing images. Focused on deficiencies in traditional manual registration, this paper presents a new method for automatic fine registration of multi-spectral images. To make the most of image information, the algorithm detects and matches feature points in the selected bands. Then pick up the common control points which contain more reliability relative to others after eliminating wrong matching points. The last registration model can be built based on common control points and the points selected by common ones. Experimental results with Landsat TM5 images demonstrate that the method is more accurate and suitable for automatic batch processing.

Rationale and Objective: Due to the limited temporal and spatial resolution, coronary CT angiographic image quality is not optimal for robust and accurate stenosis quantification, and plaque differentiation and quantification. By combining the high-resolution IVUS images with CT images, a detailed representation of the coronary arteries can be provided in the CT images. Methods: The two vessel data sets are matched using three steps. First, vessel segments are matched using anatomical landmarks. Second, the landmarks are aligned in cross-sectional vessel images. Third, the semi-automatically detected IVUS lumen contours are matched to the CTA data, using manual interaction and automatic registration methods. Results: The IVUS-CTA fusion tool facilitates the unique combined view of the high-resolution IVUS segmentation of the outer vessel wall and lumen-intima transitions on the CT images. The cylindrical projection of the CMPR image decreases the analysis time with 50 percent. The automatic registration of the cross-vessel views decreases the analyses time with 85 percent. Conclusions: The fusion of IVUS images and their segmentation results with coronary CT angiographic images provide a detailed view of the lumen and vessel wall of coronary arteries. The automatic fusion tool makes such a registration feasible for the development and validation of analysis tools.

To evaluate patient-specific quality assurance (PSQA) of 3 targets in a single delivery using a novel film-based phantom. The phantom was designed to rotate freely as a sphere and could measure 3 targets with film in a single delivery. After identifying the coordinates of 3 targets in the skull, the rotation angles about the equator and meridian were computed for optimal phantom setup, ensuring the film plane intersected the 3 targets. The plans were delivered on the CyberKnife system using fiducial tracking. The irradiated films were scanned and processed. All films were analysed using 3 gamma criteria. Fifteen CyberKnife test plans with 3 different modalities were delivered on the phantom. Both automatic and marker-based registration methods were applied when registering the irradiated film and dose plane. Gamma analysis was performed using a 3%/1 mm, 2%/1 mm, and 1%/1 mm criteria with a 10% threshold. For the automatic registration method, the passing rates were 98.2% ± 1.9%, 94.2% ± 3.7%, and 80.9% ± 6.3%, respectively. For the marker-based registration approach, the passing rates were 96.4% ± 2.7%, 91.7% ± 4.3%, and 78.4% ± 6.2%, respectively. A novel spherical phantom was evaluated for the CyberKnife system and achieved acceptable PSQA passing rates using TG218 recommendations. The phantom can measure true-composite dose and offers high-resolution results for PSQA, making it a valuable device for robotic radiosurgery. This is the first study on PSQA of 3 targets concurrently on the CyberKnife system.

Radiation therapy of prostate cancer requires a high geometric accuracy of dose delivery. Unfortunately, this accuracy can be hardly obtained because of imperfect patients repositioning throughout the preparation and execution of the treatment. In order to correct for such errors we developed methods for online registration of portal images (PI) and digitally reconstructed radiographs (DRR). To improve the preconditions for registration of the Pis and DRRs image preprocessing, such as contrast-limited adaptive histogram equalization (CLAHE) of the Pis, noise and edge filtering of the Pis and the DRRs, has been performed. Then, similarity measures as mean square error, cross correlation and mutual information were investigated. A fully automatic bony-based registration method has been developed. As a reference method a semi-automatic stereometric fiducial-based registration has also been developed. This method uses gold seeds implanted into the prostate as internal fiducial markers for assessment of the similarity measures. Small lead spheres as well were adhered as external fiducial markers on the Alderson phantom. This method was also used for the verification of the automatic bony-based registration. The registration of repositioning was then performed fully automatically in less than 30 s with the Alderson phantom and with the patient data. At the present stage, there remains a registration error of less than 2 mm.

Registration of multiple 3D ultrasound sectors in order to provide an extended field of view is important for the appreciation of larger anatomical structures at high spatial and temporal resolution. In this paper, we present a method for fully automatic spatio-temporal registration between two partially overlapping 3D ultrasound sequences. The temporal alignment is solved by aligning the normalized cross correlation-over-time curves of the sequences. For the spatial alignment, corresponding 3D Scale Invariant Feature Transform (SIFT) features are extracted from all frames of both sequences independently of the temporal alignment. A rigid transform is then calculated by least squares minimization in combination with random sample consensus. The method is applied to 16 echocardiographic sequences of the left and right ventricles and evaluated against manually annotated temporal events and spatial anatomical landmarks. The mean distances between manually identified landmarks in the left and right ventricles after automatic registration were (mean ± SD) 4.3 ± 1.2 mm compared to a reference error of 2.8 ± 0.6 mm with manual registration. For the temporal alignment, the absolute errors in valvular event times were 14.4 ± 11.6 ms for Aortic Valve (AV) opening, 18.6 ± 16.0 ms for AV closing, and 34.6 ± 26.4 ms for mitral valve opening, compared to a mean inter-frame time of 29 ms.

A fast, robust, accurate, and automatic registration technique based on magnetic resonance (MR) active microcoils (active markers) for registration of tracked medical devices to preprocedural MR-images is presented. This allows for a straight-forward integration of position measurement systems into clinical procedures. The presented method is useful for guidance purposes in clinical applications with high demands on accuracy and ease-of-use (e.g., neurosurgical or orthopedic applications). The determination of the positions of the active markers is integrated into the preparation phase of the actual MR imaging scan. The technique features a generic interface using DICOM standards for communication with navigation workstations linked to an MR system. The position of the active markers is fixed with respect to a reference system of an optical positioning measurement system (OPMS) and thus the coregistration of the MR system and the OPMS is established. In a phantom study, a mean overall targeting accuracy of 0.9+/-0.1 mm was achieved and compared favorably to results obtained from manual registration tests (1.8+/-0.3 mm) carried out in parallel. For a test person trained for both registration methods, workflow improvements of 3-6 min per registration step were found. The need for manual interaction is entirely eliminated thus avoiding user-bias, which is advantageous for the usage in clinical routine. The method improves the ease-of-use of tracking equipment during stereotactic guidance. The method is finally demonstrated in a volunteer study using a model of a Mayfield skull clamp with integrated active and optical reference markers.

We propose an automatic segmentation and registration method for matching lung surfaces of temporal CT scans. Our method consists of three steps. First, an automatic segmentation is used for accurately identifying lung surfaces. Second, initial registration using an optimal cube is performed for correcting the gross translational mismatch. Third, the initial alignment is step by step refined by the iterative surface registration. For the fast and robust convergence of the distance measure to the optimal value, a 3D distance map is generated by the narrow band distance propagation. Experimental results show that our segmentation and registration method extracts accurate lung surfaces and aligns them much faster than conventional ones using a distance measure.

Current markerless registration methods for neurosurgical robotics use the facial surface to match the robot space with the image space, and acquisition of the facial surface usually requires manual interaction and constrains the patient to a supine position. To overcome these drawbacks, we propose a registration method that is automatic and does not constrain patient position. An optical camera attached to the robot end effector captures images around the patient's head from multiple views. Then, high coverage of the head surface is reconstructed from the images through multi-view stereo vision. Since the acquired head surface point cloud contains color information, a specific mark that is manually drawn on the patient's head prior to the capture procedure can be extracted to automatically accomplish coarse registration rather than using facial anatomic landmarks. Then, fine registration is achieved by registering the high coverage of the head surface without relying solely on the facial region, thus eliminating patient position constraints. The head surface was acquired by the camera with a good repeatability accuracy. The average target registration error of 8 different patient positions measured with targets inside a head phantom was [Formula: see text], while the mean surface registration error was [Formula: see text]. The method proposed in this paper achieves automatic markerless registration in multiple patient positions and guarantees registration accuracy inside the head. This method provides a new approach for establishing the spatial relationship between the image space and the robot space.

As the need for automating registration techniques is recognized, the authors feel that there is a need to survey all the registration methods which may be applicable to Earth and space science problems and to evaluate their performances on a large variety of existing remote sensing data as well as on simulated data of soon-to-be-flown instruments. The authors present the first steps towards this quantitative evaluation: a few automatic image registration algorithms are described and first results of their evaluation are presented for three different datasets.

An automatic 3D point cloud registration method based on regional curvature maps

Electro-anatomical maps (EAM) and CT surface registration are widely used for catheter navigation in atrial fibrillation ablations. However, few studies have investigated the registration algorithm. Moreover, some of them are semiautomatic, so that physicians must be proficient; some are inaccurate for catheter navigation. A both automatic and accurate registration method is needed. A Hausdorff distance based approach (HD) was proposed for EAM/CT registration. First, using principal axes based registration, EAM/CT pairs were coarsely aligned. Then, using Hausdorff distance as the fine objective function, EAM/CT pairs were finely aligned. Six real EAM/CT pairs were collected from five patients and 38 simulated pairs were generated. Each pair was aligned using Carto-Merge, a stochastic approach (SA) and HD. Considering the balance of operability, accuracy and robustness, HD obtained the best EAM/CT registration results among the three approaches. Experiments validate that the proposed method registers EAM and CT surface both automatically and accurately.

The purpose of this study was to develop a customized framework for evaluating the registration accuracy of four registration techniques and measuring the untouched surface area of canal instrumentation by visually inspecting and calculating the overlapping area of the surfaces. Twenty-one mandibular incisors were scanned by micro-computed tomography before and after instrumentation. Elastix registration, surface registration, manual registration, and DataViewer registration techniques were used to align the pre- and post-operative datasets. The customized MeVisLab framework was created to investigate the registration accuracy by visual inspection and calculating overlapping areas. The canal surfaces were imported into the same framework to measure the untouched surface area and the consistence test was validated. The correlation between registration accuracy and untouched surface area was analyzed. There is a statistically significant difference between manual registration and automatic registration (P < 0.05). There is no statistical difference between the two untouched surface measure methods (P > 0.05). The partial correlation coefficients for the untouched surface area and registration accuracy were 0.45 (P < 0.05). This application framework based on free customizable software, allows a new method to measure registration accuracy and untouched surface area in an efficient and sensitive way. The application of a precise registration method would improve the quality of micro-CT canal instrumentation studies. This study developed a customized framework based on free software for evaluating the registration accuracy of different registration techniques and measuring the untouched surface area of canal instrumentation could help researchers to improve the quality of micro-CT studies of canal instrumentation.

In X-ray fused with MRI, previously gathered roadmap MRI volume images are overlaid on live X-ray fluoroscopy images to help guide the clinician during an interventional procedure. The incorporation of MRI data allows for the visualization of soft tissue that is poorly visualized under X-ray. The widespread clinical use of this technique will require fully automating as many components as possible. While previous use of this method has required time-consuming manual intervention to register the two modalities, in this article, the authors present a fully automatic rigid-body registration method. External fiducial markers that are visible under these two complimentary imaging modalities were used to register the X-ray images with the roadmap MR images. The method has three components: (a) The identification of the 3D locations of the markers from a full 3D MR volume, (b) the identification of the 3D locations of the markers from a small number of 2D X-ray fluoroscopy images, and (c) finding the rigid-body transformation that registers the two point sets in the two modalities. For part (a), the localization of the markers from MR data, the MR volume image was thresholded, connected voxels were segmented and labeled, and the centroids of the connected components were computed. For part (b), the X-ray projection images, produced by an image intensifier, were first corrected for distortions. Binary mask images of the markers were created from the distortion-corrected X-ray projection images by applying edge detection, pattern recognition, and image morphological operations. The markers were localized in the X-ray frame using an iterative backprojection-based method which segments voxels in the volume of interest, discards false positives based on the previously computed edge-detected projections, and calculates the locations of the true markers as the centroids of the clusters of voxels that remain. For part (c), a variant of the iterative closest point method was used to find correspondences between and register the two sets of points computed from MR and X-ray data. This knowledge of the correspondence between the two point sets was used to refine, first, the X-ray marker localization and then the total rigid-body registration between modalities. The rigid-body registration was used to overlay the roadmap MR image onto the X-ray fluoroscopy projections. In 35 separate experiments, the markers were correctly registered to each other in 100% of the cases. When half the number of X-ray projections was used (10 X-ray projections instead of 20), the markers were correctly registered in all 35 experiments. The method was also successful in all 35 experiments when the number of markers was (retrospectively) halved (from 16 to 8). The target registration error was computed in a phantom experiment to be less than 2.4 mm. In two in vivo experiments, targets (interventional devices with pointlike metallic structures) inside the heart were successfully registered between the two modalities. The method presented can be used to automatically register a roadmap MR image to X-ray fluoroscopy using fiducial markers and as few as ten X-ray projections.