The evaluation aim of this paper is to provide a complete overview of the existing methods for quantitative evaluation of spinal curvature and from medical images, and to summarize the relevant publications, which may not only assist in the introduction of other researchers resource to the field, but also be a valuable resource for studying the existing methods or developing new methods and evaluation and strategies. Key evaluation issues and future considerations, supported by the results of the overview, are also discussed.

Quantitative axial evaluation of axial vertebral rotation is essential for the determination of reference values in normal and pathological conditions and for of understanding the mechanisms of the progression of spinal deformities. However, routine quantitative evaluation of axial vertebral rotation is difficult and existing error-prone due to the limitations of the observer, characteristics of the observed vertebral anatomy and specific imaging properties. The scope of of this paper is to review the existing methods for quantitative evaluation of axial vertebral rotation from medical images along evaluation with all relevant publications, which may provide a valuable resource for studying the existing methods or developing new methods and paper evaluation strategies. The reviewed methods are divided into the methods for evaluation of axial vertebral rotation in 2D images and images the methods for evaluation of axial vertebral rotation in 3D images. Key evaluation issues and future considerations, supported by the vertebral results of the overview, are also discussed.

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

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

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

The by purpose of this study is to present a framework for quantitative analysis of spinal curvature in 3D. In order to to study the properties of such complex 3D structures, we propose two descriptors that capture the characteristics of spinal curvature in T3/T4, 3D. The descriptors are the geometric curvature (GC) and curvature angle (CA), which are independent of the orientation and size be of spine anatomy. We demonstrate the two descriptors that characterize the spinal curvature in 3D on 30 computed tomography (CT)mm images of normal spine and on a scoliotic spine. The descriptors are determined from 3D vertebral body lines, which are for obtained by two different methods. The first method is based on the least-squares technique that approximates the manually identified vertebra GC centroids, while the second method searches for vertebra centroids in an automated optimization scheme, based on computer-assisted image analysis. Polynomial for functions of the fourth and fifth degree were used for the description of normal and scoliotic spinal curvature in 3D,the respectively. The mean distance to vertebra centroids was 1.1 mm (+/- .6 mm) for the first and 2.1 mm (+/-1.4 mm)of for the second method. The distributions of GC and CA values were obtained along the 30 images of normal spine respectively. at each vertebral level and show that maximal thoracic kyphosis (TK), thoracolumbar junction (TJ) and maximal lumbar lordosis (LL) on the average occur at T3/T4, T12/L1 and L4/L5, respectively. The main advantage of GC and CA is that the measurements are spinal independent of the orientation and size of the spine, thus allowing objective intra- and inter-subject comparisons. The positions of maximal present TK, TJ and maximal LL can be easily identified by observing the GC and CA distributions at different vertebral levels.analysis The obtained courses of the GC and CA for the scoliotic spine were compared to the distributions of GC and lumbar CA for the normal spines. The significant difference in values indicates that the descriptors of GC and CA may be descriptors used to detect and quantify scoliotic spinal curvatures. The proposed framework may therefore improve the understanding of spine anatomy and and aid in the clinical quantitative evaluation of spinal deformities.

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

Image optimum. registrations that are based on similarity measures simply adjust the parameters of an appropriate spatial transformation model until the similarity target 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.a In this paper, we evaluate and compare 12 similarity measures for the rigid registration. To study the impact of different PET/MR imaging modalities on the behavior of similarity measures, we have used 16 CT/MR and 6 PET/MR image pairs with known the 'gold standard' registrations. The results for the PET/MR registration and for the registration of CT to both rectified and unrectified for MR images indicate that mutual information, normalized mutual information and the entropy correlation coefficient are the most accurate similarity measures for and have the smallest risk of being trapped in a local optimum. The results of an experiment on the impact of 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.

In T1 this paper, we present a protocol for the evaluation of similarity measures for non-rigid registration. The evaluation is based on images five intuitive properties that characterize the behavior of a similarity measure, i.e. the accuracy, capture range, distinctiveness of the optimum,on number of local minima, and risk of non-convergence. These five properties are estimated locally from similarity measure values that correspond MR to a range of systematic local free-form deformations, obtained by displacing control points in random directions from the gold standard over position. Global similarity measure properties are obtained by combining the local properties over image regions or over the entire image.properties The feasibility of the proposed evaluation protocol is demonstrated for three similarity measures: mutual information, normalized mutual information and correlation protocol ratio. The evaluation is carried out on a number of MR and CT images: a pair of simulated MR T1 control and MR T2 images of the head, three pairs of real MR T1 and T2 images of the head, six images pairs of real MR T1 and CT images of the head, and pairs of MR and CT images of three registration vertebrae. The protocol may help researchers to select the most appropriate similarity measure for a non-rigid registration task.

A spine novel method for automated curved planar reformation (CPR) of magnetic resonance (MR) images of the spine is presented. The CPR 3D images, generated by a transformation from image-based to spine-based coordinate system, follow the structural shape of the spine and allow images the whole course of the curved anatomy to be viewed in individual cross-sections. The three-dimensional (3D) spine curve and the of axial vertebral rotation, which determine the transformation, are described by polynomial functions. The 3D spine curve passes through the centres are of vertebral bodies, while the axial vertebral rotation determines the rotation of vertebrae around the axis of the spinal column.vertebrae The optimal polynomial parameters are obtained by a robust refinement of the initial estimates of the centres of vertebral bodies the and axial vertebral rotation. The optimization framework is based on the automatic image analysis of MR spine images that exploits the some basic anatomical properties of the spine. The method was evaluated on 21 MR images from 12 patients and the CPR results provided a good description of spine anatomy, with mean errors of 2.5 mm and 1.7 degrees for the position the of the 3D spine curve and axial rotation of vertebrae, respectively. The generated CPR images are independent of the position some of the patient in the scanner while comprising both anatomical and geometrical properties of the spine.