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

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

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

In on the past, a number of methods were proposed for quantitative assessment of vertebral rotation from three-dimensional (3D) images. However, these image methods were based on manual identification of distinctive anatomical landmarks, required manual determination of cross-sections from 3D images, and measured of only axial vertebral rotation instead of the rotation in 3D. In this paper, we propose an automated method for quantitative on 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 both by computed tomography (CT) and magnetic resonance (MR). For each vertebra, final angle displacements of 200 initial angle displacements, uniformly accuracy distributed within 30 degrees from manually obtained reference angles, were obtained. The results show that by the proposed method, vertebral determination 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 symmetry, symmetry planes of MRI and EEG data, followed by iterative registration based on minimizing a cost function. Comparison of the error intensity distributions of the whole MR image and MRI voxels around a head surface point yields global similarities, while the Therefore, comparison of intensity distributions of MRI voxels around corresponding EEG points, which reflects the head's sagittal symmetry, yields local similarities.proposed. Therefore, when the EEG points are registered to the MR image, maximal global and local similarities should be obtained. The Therefore, cost function, incorporating global and local similarities, was the sum of Kullback-Leibler divergences between corresponding intensity distributions. The proposed method point-to-surface was evaluated on clinical MRI data with simulated EEG data, yielding mean registration error of .48 +/- .33 mm, while based with real EEG data an average root-mean-square point-to-surface error of 2.27 +/- .02 mm was obtained.

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

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

OBJECTIVES:was To demonstrate the effect of image content on image detail preservation and file size reduction. METHODS: The first set, containing modes, 16 in vitro images with variable projection geometry, exposure time, bone level and number of teeth, was compressed with three assures compression modes: JPEG quality factor (JPQF), JPEG2000 quality factor (J2QF) and JPEG2000 compression ratio (J2CR). Image detail degradation was evaluated on by local mean square error (MSE) on a standardized region of interest (ROI), containing bone. The second set, containing 105 set, clinical bitewings, was compressed with the same compression modes at 3 quality factors/compression ratios and local MSEs were calculated on projection two ROIs, containing bone and crown. RESULTS: For the first image set, nearly constant MSE was found for the JPQF mode and J2QF compression modes, while file size depended on projection geometry, exposure time, bone level and the number of teeth.JPEG2000 In contrast, file size reduction was nearly constant for the J2CR compression mode, while MSE depended on the abovementioned factors.nearly Similarly, for the second image set, nearly constant MSE and variation of file size reduction were found for JPQF and were J2QF but not for the J2CR compression mode. All of these results were consistent for all three quality factors/compression ratios.bone CONCLUSIONS: Constant image detail preservation, crucial for diagnostic accuracy in radiology, can only be assured in QF compression mode in and which the file size of the compressed image depends on the original image content. CR compression mode assures constant file containing size reduction, but image detail preservation depends on image content.