Consistency of MR scan planning is very important for diagnosis, especially in multi-site trials and follow-up studies, where disease progress or response to treatment is evaluated. Accurate manual scan planning is tedious and requires skillful operators. On the other hand, automated scan planning is difficult due to relatively low quality of survey images ("scouts") and strict processing time constraints. This paper presents a novel method for automated planning of MRI scans of the spine. Lumbar and cervical examinations are considered, although the proposed method is extendible to other types of spine examinations, such as thoracic or total spine imaging. The automated scan planning (ASP) system consists of an anatomy recognition part, which is able to automatically detect and label the spine anatomy in the scout scan, and a planning part, which performs scan geometry planning based on recognized anatomical landmarks. A validation study demonstrates the robustness of the proposed method and its feasibility for clinical use.

The detection and extraction of complex anatomical structures usually involves a trade-off between the complexity of local feature extraction and classification, and the complexity and performance of the subsequent structural inference from the viewpoint of combinatorial optimization. Concerning the latter, computationally efficient methods are of particular interest that return the globally-optimal structure. We present an efficient method for part-based localization of anatomical structures which embeds contextual shape knowledge in a probabilistic graphical model. It allows for robust detection even when some of the part detections are missing. The application scenario for our statistical evaluation is spine detection and labeling in magnetic resonance images.

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

We propose a completely automated algorithm for the detection of the spinal centreline and the centres of vertebral bodies and intervertebral discs in images acquired by computed tomography (CT) and magnetic resonance (MR) imaging. The developed methods are based on the analysis of the geometry of spinal structures and the characteristics of CT and MR images and were evaluated on 29 CT and 13 MR images of lumbar spine. The overall mean distance between the obtained and the ground truth spinal centrelines and centres of vertebral bodies and intervertebral discs were 1.8 +/- 1.1 mm and 2.8 +/- 1.9 mm, respectively, and no considerable differences were detected among the results for CT, T(1)-weighted MR and T(2)-weighted MR images. The knowledge of the location of the spinal centreline and the centres of vertebral bodies and intervertebral discs is valuable for the analysis of the spine. The proposed method may therefore be used to initialize the techniques for labelling and segmentation of vertebrae.

In the past, a number of methods were proposed for quantitative assessment of vertebral rotation from three-dimensional (3D) images. However, these methods were based on manual identification of distinctive anatomical landmarks, required manual determination of cross-sections from 3D images, and measured only axial vertebral rotation instead of the rotation in 3D. In this paper, we propose an automated method for quantitative assessment of vertebral rotation in 3D that is based on finding the planes of vertebral symmetry by matching image intensity gradients on both sides of each plane. The method was evaluated on 28 images of normal and pathological vertebrae, obtained by computed tomography (CT) and magnetic resonance (MR). For each vertebra, final angle displacements of 200 initial angle displacements, uniformly distributed within 30 degrees from manually obtained reference angles, were obtained. The results show that by the proposed method, vertebral rotation can be successfully estimated in 3D with an average accuracy of 1.0 degrees and precision of 0.5 degrees.

The purpose of this study is to present a framework for quantitative analysis of spinal curvature in 3D. In order to study the properties of such complex 3D structures, we propose two descriptors that capture the characteristics of spinal curvature in 3D. The descriptors are the geometric curvature (GC) and curvature angle (CA), which are independent of the orientation and size of spine anatomy. We demonstrate the two descriptors that characterize the spinal curvature in 3D on 30 computed tomography (CT) images of normal spine and on a scoliotic spine. The descriptors are determined from 3D vertebral body lines, which are obtained by two different methods. The first method is based on the least-squares technique that approximates the manually identified vertebra centroids, while the second method searches for vertebra centroids in an automated optimization scheme, based on computer-assisted image analysis. Polynomial functions of the fourth and fifth degree were used for the description of normal and scoliotic spinal curvature in 3D, respectively. The mean distance to vertebra centroids was 1.1 mm (+/-0.6 mm) for the first and 2.1 mm (+/-1.4 mm) for the second method. The distributions of GC and CA values were obtained along the 30 images of normal spine at each vertebral level and show that maximal thoracic kyphosis (TK), thoracolumbar junction (TJ) and maximal lumbar lordosis (LL) on average occur at T3/T4, T12/L1 and L4/L5, respectively. The main advantage of GC and CA is that the measurements are independent of the orientation and size of the spine, thus allowing objective intra- and inter-subject comparisons. The positions of maximal TK, TJ and maximal LL can be easily identified by observing the GC and CA distributions at different vertebral levels. The obtained courses of the GC and CA for the scoliotic spine were compared to the distributions of GC and CA for the normal spines. The significant difference in values indicates that the descriptors of GC and CA may be used to detect and quantify scoliotic spinal curvatures. The proposed framework may therefore improve the understanding of spine anatomy and aid in the clinical quantitative evaluation of spinal deformities.

Traditional techniques for analyzing tortuous anatomical structures (e.g. arteries, colon, spine) in the coordinate system of the 3D image generally do not provide sufficient or qualitative enough diagnostic information, because planar cross-sections do not follow curved paths along the structures. To overcome this shortcoming, images in the coordinate system of the structure must be created. We propose a transformation from standard image-based to a novel spine-based coordinate system. The origin and axes of the proposed spine-based coordinate system are determined on the curve that represents the vertebral column, and the rotation of the vertebrae around the spine curve, both of which are described by polynomial models. The optimal polynomial parameters are obtained in an image analysis based optimization framework. The method has been evaluated on five CT spine images.

Traditional techniques for visualizing anatomical structures are based on planar cross-sections from volume images, such as images obtained by computed tomography (CT) or magnetic resonance imaging (MRI). However, planar cross-sections taken in the coordinate system of the 3D image often do not provide sufficient or qualitative enough diagnostic information, because planar cross-sections cannot follow curved anatomical structures (e.g. arteries, colon, spine, etc). Therefore, not all of the important details can be shown simultaneously in any planar cross-section. To overcome this problem, reformatted images in the coordinate system of the inspected structure must be created. This operation is usually referred to as curved planar reformation (CPR). In this paper we propose an automated method for CPR of 3D spine images, which is based on the image transformation from the standard image-based to a novel spine-based coordinate system. The axes of the proposed spine-based coordinate system are determined on the curve that represents the vertebral column, and the rotation of the vertebrae around the spine curve, both of which are described by polynomial models. The optimal polynomial parameters are obtained in an image analysis based optimization framework. The proposed method was qualitatively and quantitatively evaluated on five CT spine images. The method performed well on both normal and pathological cases and was consistent with manually obtained ground truth data. The proposed spine-based CPR benefits from reduced structural complexity in favour of improved feature perception of the spine. The reformatted images are diagnostically valuable and enable easier navigation, manipulation and orientation in 3D space. Moreover, reformatted images may prove useful for segmentation and other image analysis tasks.

In this paper, a novel method for EEG to MRI registration is proposed. Initial registration is achieved by extracting and matching symmetry planes of MRI and EEG data, followed by iterative registration based on minimizing a cost function. Comparison of the intensity distributions of the whole MR image and MRI voxels around a head surface point yields global similarities, while the comparison of intensity distributions of MRI voxels around corresponding EEG points, which reflects the head's sagittal symmetry, yields local similarities. Therefore, when the EEG points are registered to the MR image, maximal global and local similarities should be obtained. The cost function, incorporating global and local similarities, was the sum of Kullback-Leibler divergences between corresponding intensity distributions. The proposed method was evaluated on clinical MRI data with simulated EEG data, yielding mean registration error of 0.48 +/- 0.33 mm, while with real EEG data an average root-mean-square point-to-surface error of 2.27 +/- 0.02 mm was obtained.

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

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

Medical image acquisition devices provide a vast amount of anatomical and functional information, which facilitate and improve diagnosis and patient treatment, especially when supported by modern quantitative image analysis methods. However, modality specific image artifacts, such as the phenomena of intensity inhomogeneity in magnetic resonance images (MRI), are still prominent and can adversely affect quantitative image analysis. In this paper, numerous methods that have been developed to reduce or eliminate intensity inhomogeneities in MRI are reviewed. First, the methods are classified according to the inhomogeneity correction strategy. Next, different qualitative and quantitative evaluation approaches are reviewed. Third, 60 relevant publications are categorized according to several features and analyzed so as to reveal major trends, popularity, evaluation strategies and applications. Finally, key evaluation issues and future development of the inhomogeneity correction field, supported by the results of the analysis, are discussed.

Curved Planar Reformation (CPR) has proved to be a practical and widely used tool for the visualization of curved tubular structures within the human body. It has been useful in medical procedures involving the examination of blood vessels and the spine. However, it is more difficult to use it for large, tubular, structures such as the trachea and the colon because abnormalities may be smaller relative to the size of the structure and may not have such distinct density and shape characteristics.Our new approach improves on this situation by using volume rendering for hollow regions and standard CPR for the surrounding tissue. This effectively combines gray scale contextual information with detailed color information from the area of interest. The approach is successfully used with each of the standard CPR types and the resulting images are promising as an alternative to virtual endoscopy.Because the CPR and the volume rendering are tightly coupled, the projection method used has a significant effect on properties of the volume renderer such as distortion and isometry. We describe and compare the different CPR projection methods and how they affect the volume rendering process.A version of the algorithm is also presented which makes use of importance driven techniques; this ensures the users attention is always focused on the area of interest and also improves the speed of the algorithm.

Recently, extensions to curved planar reformation (CPR) were proposed to improve vascular visualization of medical images. While these projective transformations provide enhanced visualization of vascular trees, non-planar alignment and arbitrary topology can cause visualization artifacts. Vascular trees in medical images are not aligned to planar cross-sections of volumetric image slices and thus aggravate simultaneous visualization of diagnostic features. Complex tree topology and non-planar alignment requires the need for an adaptive projection scheme to prevent visualization artifacts while preserving correctness of anatomical information. In this paper, we present algorithmic details for topological and orientation invariant visualization of vascular trees. Vascular high-level description of the medial axis guides the reformation process by flattening the vascular tree interior to successive image planes for respective radial sampling angles. Tree orientations are estimated from intrinsic shape properties of the vascular tree for rotation invariant projection. Radial sampling planes perpendicular to the medial axis tangents are the basis for topological invariant visualization of complete vascular interiors. We present experimental results on two different vascular tree topologies and demonstrate that our method is able to produce artifact free visualization of vascular interiors.

The 3D reconstruction of the spine in upright posture can be obtained by bi-planar radiographic methods, developed since the 1970s. The principle is to identify 4-25 anatomical landmarks per vertebrae and per images. This identification time is hardly manageable in clinical practice. A semi-automated method is used: 3D standard vertebral models are positioned along with a 3D curve (identified all the way through the vertebral bodies). The silhouettes of the models of C7 and L5 vertebrae are first adjusted and the positions of the other vertebrae are interpolated and optimised. The inter- and intra-operator variabilities and the errors between the semi-automated method and the manual identification of six anatomical landmarks per vertebra are evaluated on 20 pairs of X-ray images of subjects with different spinal deformities. The identification time for the semi-automated method is 5 min. For scolitic subjects, the precision is under 2.2 degrees and the accuracy is under 3.2 degrees for all lateral, sagittal and axial rotations.

Recently, extensions to curved planar reformation (CPR) were proposed to improve vascular visualization of medical images. While these projective transformations provide enhanced visualization of vascular trees, non-planar alignment and arbitrary topology can cause visualization artifacts. Vascular trees in medical images are not aligned to planar cross-sections of volumetric image slices and thus aggravate simultaneous visualization of diagnostic features. Complex tree topology and non-planar alignment requires the need for an adaptive projection scheme to prevent visualization artifacts while preserving correctness of anatomical information. In this paper, we present algorithmic details for topological and orientation invariant visualization of vascular trees. Vascular high-level description of the medial axis guides the reformation process by flattening the vascular tree interior to successive image planes for respective radial sampling angles. Tree orientations are estimated from intrinsic shape properties of the vascular tree for rotation invariant projection. Radial sampling planes perpendicular to the medial axis tangents are the basis for topological invariant visualization of complete vascular interiors. We present experimental results on two different vascular tree topologies and demonstrate that our method is able to produce artifact free visualization of vascular interiors.

Curved multi-planar reformation (curved MPR) is one of the commonly used vascular visualization methods in clinics. It re-samples and visualizes the vascular central axis surface (VCAS), which is a curved surface passing through the vascular central axis (VCA) or vessel centerline. The rotation of the VCAS along the VCA generates a set of 2D images. In this paper, we introduce a 3D curved MPR method, VCAS planar reformation (VPR) by a convex hull, called a biconvex slab. The entire vessel is enclosed within a biconvex slab and rendered in one image by volume rendering, such as MIP or X-ray. The method is applied to computed tomographic angiography (CTA) data sets. The resulting image is clear and free from obstruction by bones and other adjacent organs.

The detection and extraction of complex anatomical structures usually involves a trade-off between the complexity of local feature extraction and classification, and the complexity and performance of the subsequent structural inference from the viewpoint of combinatorial optimization. Concerning the latter, computationally efficient methods are of particular interest that return the globally-optimal structure. We present an efficient method for part-based localization of anatomical structures which embeds contextual shape knowledge in a probabilistic graphical model. It allows for robust detection even when some of the part detections are missing. The application scenario for our statistical evaluation is spine detection and labeling in magnetic resonance images.

Characterization of second order local image structure by a 6D vector (or jet) of Gaussian derivative measurements is considered. We consider the affect on jets of a group of transformations-affine intensity-scaling, image rotation and reflection, and their compositions-that preserve intrinsic image structure. We show how this group stratifies the jet space into a system of orbits. Considering individual orbits as points, a 3D orbifold is defined. We propose a norm on jet space which we use to induce a metric on the orbifold. The metric tensor shows that the orbifold is intrinsically curved. To allow visualization of the orbifold and numerical computation with it, we present a mildly-distorting but volume-preserving embedding of it into euclidean 3-space. We call the resulting shape, which is like a flattened lemon, the second order local-image-structure solid. As an example use of the solid, we compute the distribution of local structures in noise and natural images. For noise images, analytical results are possible and they agree with the empirical results. For natural images, an excess of locally 1D structure is found.

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

We present a novel method for curved planar reformation (CPR) of spine images obtained by magnetic resonance (MR) imaging. CPR images, created via a transformation from image-based to spine-based coordinate system, follow the structural shape of the spine and allow the whole course of the curved structure to be viewed in a single image. The spine-based coordinate system is defined on the 3D spine curve and on the axial vertebral rotation, both described by polynomial models. The 3D spine curve passes through the centers of vertebral bodies, and the axial vertebral rotation determines the rotation of vertebral spinous processes around the spine. The optimal polynomial parameters are found in an optimization framework, based on image analysis. The method was evaluated on 19 MR images of the spine from 10 patients.