An the important part of image-guided radiation therapy or surgery is registration of a three-dimensional (3D) preoperative image to two-dimensional (2D) images images of the patient. It is expected that the accuracy and robustness of a 3D/2D image registration method do not depend of solely on the registration method itself but also on the number and projections (views) of intraoperative images. In this study,patient. we systematically investigate these factors by using registered image data, comprising of CT and X-ray images of a cadaveric lumbar cadaveric spine phantom and the recently proposed 3D/2D registration method. The results indicate that the proportion of successful registrations (robustness) significantly that increases when more X-ray images are used for registration.

Image selection registrations that are based on similarity measures simply adjust the parameters of an appropriate spatial transformation model until the similarity are measure reaches an optimum. The numerous similarity measures that have been proposed in the past are differently sensitive to imaging information modality, image content and differences in the image content, selection of the floating and target image, partial image overlap, etc.modality, In this paper, we evaluate and compare 12 similarity measures for the rigid registration. To study the impact of different have imaging modalities on the behavior of similarity measures, we have used 16 CT/MR and 6 PET/MR image pairs with known content, 'gold standard' registrations. The results for the PET/MR registration and for the registration of CT to both rectified and unrectified we MR images indicate that mutual information, normalized mutual information and the entropy correlation coefficient are the most accurate similarity measures the and have the smallest risk of being trapped in a local optimum. The results of an experiment on the impact have of exchanging the floating and target image indicate that, especially in MR/PET registrations, the behavior of some similarity measures, such the as mutual information, significantly depends on which image is the floating and which is the target.

The used accuracy and robustness of a registration method depend on a number of factors, such as imaging modality, image content and on image degrading effects, the class of spatial transformation used for registration, similarity measure, optimization, and numerous implementation details. The complex and interdependence of these factors makes the assessment of the influence of a particular factor on registration difficult, although it is desirable often desirable to have some estimate of such influences prior to registration. The similarity measure used to create the cost positron function is one of the factors that most influences the quality of registration. Traditionally, limited information on the behavior of measure a similarity measure is obtained either by studying the quality of the final registration or by drawing plots of similarity on measure values obtained by translating or rotating one image relative to the "gold standard." In this paper, we present a a protocol for a more thorough, optimization-independent, and systematic statistical evaluation of similarity measures. This protocol estimates a similarity measure's capture positron range, the number, location and extent of local optima, and the accuracy and distinctiveness of the global optimum. To show and that the proposed evaluation protocol is viable, we have conducted several experiments with nine similarity measures and real computed tomography degrading and magnetic resonance (MR) images of a spine phantom, MR brain images, and MR and positron emission tomography brain images,factors, for which "gold standard" registrations were available. We have also studied the impact of histogram bin size on the behavior of of nine similarity measures. The proposed evaluation protocol is useful for selecting the best similarity measure and corresponding optimization method is for a particular application, as well as for studying the influence of sampling, interpolation, histogram bin size, partial image overlap,of and image degradation, such as noise, intensity inhomogeneity, and geometrical distortions on the behavior of a similarity measure.

In spatial this paper we present a novel 3D/2D registration method, where first, a 3D image is reconstructed from a few 2D the X-ray images and next, the preoperative 3D image is brought into the best possible spatial correspondence with the reconstructed image methodology by optimizing a similarity measure. Because the quality of the reconstructed image is generally low, we introduce a novel asymmetric is mutual information similarity measure, which is able to cope with low image quality as well as with different imaging modalities.and The novel 3D/2D registration method has been evaluated using standardized evaluation methodology and publicly available 3D CT, 3DRX, and MR spatial and 2D X-ray images of two spine phantoms, for which gold standard registrations were known. In terms of robustness, reliability measure. and capture range the proposed method outperformed the gradient-based method and the method based on digitally reconstructed radiographs (DRRs).

In similarity image-guided therapy, high-quality preoperative images serve for planning and simulation, and intraoperatively as "background", onto which models of surgical instruments 2-D or radiation beams are projected. The link between a preoperative image and intraoperative physical space of the patient is established standard by image-to-patient registration. In this paper, we present a novel 3-D/2-D registration method. First, a 3-D image is reconstructed from image a few 2-D X-ray images and next, the preoperative 3-D image is brought into the best possible spatial correspondence with rate, the reconstructed image by optimizing a similarity measure (SM). Because the quality of the reconstructed image is generally low, we optimizing introduce a novel SM, which is able to cope with low image quality as well as with different imaging modalities.low The novel 3-D/2-D registration method has been evaluated and compared to the gradient-based method (GBM) using standardized evaluation methodology and from publicly available 3-D computed tomography (CT), 3-D rotational X-ray (3DRX), and magnetic resonance (MR) and 2-D X-ray images of two rate, spine phantoms, for which gold standard registrations were known. For each of the 3DRX, CT, or MR images and each more set of X-ray images, 1600 registrations were performed from starting positions, defined as the mean target registration error (mTRE), randomly projected. generated and uniformly distributed in the interval of -20 mm around the gold standard. The capture range was defined as onto the distance from gold standard for which the final TRE was less than 2 mm in at least 95% of method all cases. In terms of success rate, as the function of initial misalignment and capture range the proposed method outperformed images the GBM. TREs of the novel method and the GBM were approximately the same. For the registration of 3DRX and preoperative CT images to X-ray images as few as 2-3 X-ray views were sufficient to obtain approximately .4 mm TREs, 7-9 images mm capture range, and 80%-90% of successful registrations. To obtain similar results for MR to X-ray registrations, an image, reconstructed between from at least 11 X-ray images was required. Reconstructions from more than 11 images had no effect on the registration of results.

Evaluation reliable and comparison of registration techniques for image-guided surgery is an important problem that has received little attention in the literature.paper In this paper we address the challenging problem of generating reliable "gold standard" data for use in evaluating the accuracy are of 3D/2D registrations. We have devised a cadaveric lumbar spine phantom with fiducial markers and established highly accurate correspondences between the 3D CT and MR images and 18 2D X-ray images. The expected target registration errors for target points on the and pedicles are less than .26 mm for CT-to-X-ray registration and less than .42 mm for MR-to-X-ray registration. As such, the reliable "gold standard" data, which has been made publicly available on the Internet (http://lit.fe.uni-lj.si/Downloads/downloads.asp), is useful for evaluation and comparison of accuracy 3D/2D image registration methods.

A fiducial crucial part of image-guided therapy is registration of preoperative and intraoperative images, by which the precise position and orientation of X-ray the patient's anatomy is determined in three dimensions. This paper presents a novel approach to register three-dimensional (3-D) computed tomography accuracy (CT) or magnetic resonance (MR) images to one or more two-dimensional (2-D) X-ray images. The registration is based solely on on the information present in 2-D and 3-D images. It does not require fiducial markers, intraoperative X-ray image segmentation, or timely X-ray construction of digitally reconstructed radiographs. The originality of the approach is in using normals to bone surfaces, preoperatively defined in require 3-D MR or CT data, and gradients of intraoperative X-ray images at locations defined by the X-ray source and 3-D normals surface points. The registration is concerned with finding the rigid transformation of a CT or MR volume, which provides the more best match between surface normals and back projected gradients, considering their amplitudes and orientations. We have thoroughly validated our registration X-ray method by using MR, CT, and X-ray images of a cadaveric lumbar spine phantom for which "gold standard" registration was and established by means of fiducial markers, and its accuracy assessed by target registration error. Volumes of interest, containing single vertebrae the L1-L5, were registered to different pairs of X-ray images from different starting positions, chosen randomly and uniformly around the "gold images, standard" position. CT/X-ray (MR/ X-ray) registration, which is fast, was successful in more than 91%(82% except for L1) of X-ray) trials if started from the "gold standard" translated or rotated for less than 6 mm or 17 degrees (3 mm of or 8.6 degrees), respectively. Root-mean-square target registration errors were below .5 mm for the CT to X-ray registration and below determined 1.4 mm for MR to X-ray registration.

One proposed of the most important technical challenges in image-guided intervention is to obtain a precise transformation between the intrainterventional patient's anatomy them and corresponding preinterventional 3-D image on which the intervention was planned. This goal can be achieved by acquiring intrainterventional 2-D X-ray, images and matching them to the preinterventional 3-D image via 3-D/2-D image registration. A novel 3-D/2-D registration method is proposed via in this paper. The method is based on robustly matching 3-D preinterventional image gradients and coarsely reconstructed 3-D gradients from evaluation the intrainterventional 2-D images. To improve the robustness of finding the correspondences between the two sets of gradients, hypothetical correspondences is are searched for along normals to anatomical structures in 3-D images, while the final correspondences are established in an iterative reconstructed process, combining the robust random sample consensus algorithm (RANSAC) and a special gradient matching criterion function. The proposed method was images evaluated using the publicly available standardized evaluation methodology for 3-D/2-D registration, consisting of 3-D rotational X-ray, computed tomography, magnetic resonance evaluation (MR), and 2-D X-ray images of two spine segments, and standardized evaluation criteria. In this way, the proposed method could which be objectively compared to the intensity, gradient, and reconstruction-based registration methods. The obtained results indicate that the proposed method performs the favorably both in terms of registration accuracy and robustness. The method is especially superior when just a few X-ray images is and when MR preinterventional images are used for registration, which are important advantages for many clinical applications.

In measured the past, a number of methods were proposed for quantitative assessment of vertebral rotation from three-dimensional (3D) images. However, these anatomical methods were based on manual identification of distinctive anatomical landmarks, required manual determination of cross-sections from 3D images, and measured and only axial vertebral rotation instead of the rotation in 3D. In this paper, we propose an automated method for quantitative determination assessment of vertebral rotation in 3D that is based on finding the planes of vertebral symmetry by matching image intensity of gradients on both sides of each plane. The method was evaluated on 28 images of normal and pathological vertebrae, obtained measured by computed tomography (CT) and magnetic resonance (MR). For each vertebra, final angle displacements of 200 initial angle displacements, uniformly this distributed within 30 degrees from manually obtained reference angles, were obtained. The results show that by the proposed method, vertebral distinctive rotation can be successfully estimated in 3D with an average accuracy of 1. degrees and precision of .5 degrees.

In of this paper, a novel method for EEG to MRI registration is proposed. Initial registration is achieved by extracting and matching by symmetry planes of MRI and EEG data, followed by iterative registration based on minimizing a cost function. Comparison of the incorporating intensity distributions of the whole MR image and MRI voxels around a head surface point yields global similarities, while the on comparison of intensity distributions of MRI voxels around corresponding EEG points, which reflects the head's sagittal symmetry, yields local similarities.divergences Therefore, when the EEG points are registered to the MR image, maximal global and local similarities should be obtained. The of cost function, incorporating global and local similarities, was the sum of Kullback-Leibler divergences between corresponding intensity distributions. The proposed method around was evaluated on clinical MRI data with simulated EEG data, yielding mean registration error of .48 +/- .33 mm, while data, with real EEG data an average root-mean-square point-to-surface error of 2.27 +/- .02 mm was obtained.

A the novel similarity measure for registering magnetic resonance (MR) and computed tomography (CT) images has been designed and built. MR-CT registration between methods often rely on the statistical intensity relationship between the images. The proposed similarity measure instead depends on the statistical relationships. relationship between the complex phase order between the images. By utilizing the complex phase order likelihood (CPOL) as a similarity proposed measure, structural relationships instead of intensity relationships are explicitly used. This approach can be advantageous for MR-CT registration, where the MR-CT intensities of the CT imagery have highly complex and nonlinear relationships with the intensities of corresponding MR imagery but simpler the linear structural relationships. This new similarity measure has been tested on real MR-CT 3D volumes and has been evaluated based utilizing on fiducial registration error to determine alignment accuracy. Quantitative results show that CPOL is capable of achieving comparable alignment accuracy intensity when compared to normalized mutual information, while being more robust to imaging artifacts such as noise.

We (2) evaluated 4 volume-based automatic image registration algorithms from 2 commercially available treatment planning systems (Philips Syntegra and BrainScan). The algorithms mutual based on cross correlation (CC), local correlation (LC), normalized mutual information (NMI), and BrainScan mutual information (BSMI) were evaluated with:anatomical (1) the synthetic computed tomography (CT) images,(2) the CT and magnetic resonance (MR) phantom images, and (3) the CT with: and MR head image pairs from 12 patients with brain tumors. For the synthetic images, the registration results were compared a with known transformation parameters, and all algorithms achieved accuracy of submillimeter in translation and subdegree in rotation. For the phantom images, images, the registration results were compared with those provided by frame and marker-based manual registration. For the patient images, the MR results were compared with anatomical landmark-based manual registration to qualitatively determine how the results were close to a clinically acceptable and registration. NMI and LC outperformed CC and BSMI, with the sense of being closer to a clinically acceptable result. As a for the robustness, NMI and BSMI outperformed CC and LC. A guideline of image registration in our institution was given,is and final visual assessment is necessary to guarantee reasonable results.

In basic this article, the authors evaluate a merit function for 2D/3D registration called stochastic rank correlation (SRC). SRC is characterized by advantages the fact that differences in image intensity do not influence the registration result; it therefore combines the numerical advantages of improvement cross correlation (CC)-type merit functions with the flexibility of mutual-information-type merit functions. The basic idea is that registration is achieved functions on a random subset of the image, which allows for an efficient computation of Spearman's rank correlation coefficient. This measure was is, by nature, invariant to monotonic intensity transforms in the images under comparison, which renders it an ideal solution for The intramodal images acquired at different energy levels as encountered in intrafractional kV imaging in image-guided radiotherapy. Initial evaluation was undertaken for using a 2D/3D registration reference image dataset of a cadaver spine. Even with no radiometric calibration, SRC shows a significant the improvement in robustness and stability compared to CC. Pattern intensity, another merit function that was evaluated for comparison, gave rather was poor results due to its limited convergence range. The time required for SRC with 5% image content compares well to in the other merit functions; increasing the image content does not significantly influence the algorithm accuracy. The authors conclude that SRC correlation is a promising measure for 2D/3D registration in IGRT and image-guided therapy in general.

We which propose a novel Bayesian registration formulation in which image location is represented as a latent random variable. Location is marginalized the to determine the maximum a priori (MAP) transform between images, which results in registration that is more robust than the informative alternatives of omitting locality (i.e. global registration) or jointly maximizing locality and transform (i.e. iconic registration). A mathematical link is priori established between the Bayesian registration formulation and the mutual information (MI) similarity measure. This leads to a novel technique for MI selecting informative image regions for registration, based on the MI of image intensity and spatial location. Experimental results demonstrate the which effectiveness of the marginalization formulation and the MI-based region selection technique for ultrasound (US) to magnetic resonance (MR) registration in the an image-guided neurosurgical application.

We distortions introduce a new measure of image similarity called the Complex Wavelet Structural Similarity (CW-SSIM) index and show its applicability as index. a general purpose image similarity index. The key idea behind CW-SSIM is that certain image distortions lead to consistent phase a changes in the local wavelet coefficients, and that a consistent phase shift of the coefficients does not change the structural idea content of the image. By conducting four case studies, we have demonstrated the superiority of the CW-SSIM index against other that indices (e.g., Dice, Hausdorff distance) commonly used for assessing the similarity of a given pair of images. In addition, we image show that the CW-SSIM index has a number of advantages. It is robust to small rotations and translations. It provides coefficients, useful comparisons even without a pre-processing image registration step, which is essential for other indices. Moreover, it is computationally less image expensive.

Image-guidance concept in maxillofacial surgery is based predominantly on computed tomographic (CT) images. Its main disadvantage is the considerable amount of radiation metal to which the patient is exposed, and dental metal artefacts. Recently, a new class of devices based on the concept alternative of cone-beam computed tomography (CBCT) has been introduced for maxillofacial imaging, which we have investigated. In a clinical study, the new first seven patients to be operated using a navigation system based on CBCT images, were evaluated. In all cases patient costs to image recording was uneventful and the surgical objective was reached. The guidance given by the navigation system was helpful.the CBCT is an alternative to conventional CT, gives a lower dose of radiation, and costs less. Limitations in the quality which of the images and the size of the field of view may restrict its use. It is suitable for image-guided and surgery using a navigation system as long as the images show enough of the relevant anatomy and pathology.

Dynamic interpret cardiac magnetic resonance imaging (MR) and computed tomography (CT) provide cardiologists and cardiac surgeons with high-quality four-dimensional (4D) images for is diagnosis and therapy, yet the effective use of these high-quality anatomical models remains a challenge. Ultrasound (US) is a flexible mutual imaging tool, but the US images produced are often difficult to interpret unless they are placed within their proper three-dimensional but (3D) anatomical context. The ability to correlate real-time three-dimensional US volumes (RT3D US) with dynamic MR/CT images would offer a our significant contribution to improve the quality of cardiac procedures. In this work, we present a rapid two-step method for registering to RT3D US to high-quality dynamic 3D MR/CT images of the beating heart. This technique overcomes some major limitations of image correlate registration (such as the correct registration result not necessarily occurring at the maximum of the mutual information (MI) metric) using Ultrasound the MI metric. We demonstrate the effectiveness of our method in a dynamic heart phantom (DHP) study and a human our subject study. The achieved mean target registration error of CT+US images in the phantom study is 2.59 mm. Validation using the human MR/US volumes shows a target registration error of 1.76 mm. We anticipate that this technique will substantially improve the with quality of cardiac diagnosis and therapies.

Spatial for alignment of image data is a common task in computer vision and medical imaging. This should preferentially be done with theory minimal intervention of an operator. Similarity measures with origin in the information theory such as mutual information (MI) have proven adequate to be robust registration criteria for this purpose. Intra-oral radiographs can be considered images of piecewise rigid objects. Teeth and (MI) jaws are rigid but can be displaced with respect to each other. Therefore MI criteria combined with affine deformations tend We to fail, when teeth and jaws move with respect to each other between image acquisitions. In this paper, we consider criteria a focused weighing of pixels in the reference image. The resulting criterion, focused mutual information (FMI) is an adequate tool Teeth for the registration of rigid parts of a scene. We also show that the use of FMI is more robust the for the subtraction of lateral radiographs of teeth, than MI confined to a region of interest. Furthermore, the criterion allows also the follow-up of small carious lesions when upper and lower jaw moved between the acquisition of test and reference image.acquisition

The to procedures we propose make possible the mapping of two-dimensional (2-D) bioluminescence image (BLI) data onto a skin surface derived from (MR) a three-dimensional (3-D) anatomical modality [magnetic resonance (MR) or computed tomography (CT)] dataset. This mapping allows anatomical information to be determined incorporated into bioluminescence tomography (BLT) reconstruction procedures and, when applied using sources visible to both optical and anatomical modalities, can (CT)] be used to evaluate the accuracy of those reconstructions. Our procedures, based on immobilization of the animal and a priori measure determined fixed projective transforms, should be more robust and accurate than previously described efforts, which rely on a poorly constrained to retrospectively determined warping of the 3-D anatomical information. Experiments conducted to measure the accuracy of the proposed registration procedure found using it to have a mean error of .36+/- .23 mm. Additional experiments highlight some of the confounds that are often overlooked resonance in the BLT reconstruction process, and for two of these confounds, simple corrections are proposed.

The with aim of this study was to present a new approach to acquire a three-dimensional virtual skull model appropriate for orthognathic was surgery planning without the use of plaster dental models and without deformation of the facial soft-tissue mask. A "triple" cone-beam was computed tomography (CBCT) scan procedure with triple voxel-based rigid registration was evaluated and validated on 10 orthognathic patients. First, the in patient was scanned vertically with a wax bite wafer in place (CBCT scan N degrees 1). Second, a limited dose scan scan of the patient with a Triple Tray AlgiNot impression in place was carried out (CBCT scan N degrees 2).patient Finally, a high-resolution scan of the Triple Tray AlgiNot impression was done (CBCT scan N degrees 3). Sequential and semiautomatic AlgiNot triple voxel-based rigid registration (RN degrees 1-RN degrees 3) was performed to augment the patient's skull model with accurate occlusal patients. and intercuspidation data (Maxilim, version 2.1.1., Medicim NV, Mechelen, Belgium). All registrations were based on the Maximisation of Mutual Information scan registration algorithm. Because the accuracy and stability of the voxel-based registration (RN degrees 1) between the Triple Tray AlgiNot impression valid scan and the limited low-dose patient scan were not known, this particular registration step needed to be validated. The accuracy the of registration was measured on a synthetic skull and showed to be highly accurate. A volume overlap of 98.1% was three-dimensional found for registered impression scan N degrees 1. The mean distance between registered impression scan N degrees 1 and registered concerned, impression scan N degrees 2 was .08 +/- .03 mm (range, .04- .11 mm). As far as the stability of registration .1 was concerned, successful registration with a stable optimal position was obtained with a maximum variability of less than .1 mm.models The results of this study showed that semiautomatic sequential triple voxel-based rigid registration of the triple CBCT scans augmented the virtual 3-D virtual skull model with detailed occlusal and intercuspidation data in a highly accurate and robust way. The method is plaster therefore appropriate and valid for 3-D virtual orthognathic surgery planning in the clinical routine.