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Fusion of pre-interventional three-dimensional (3D) image to live two-dimensional (2D) image can facilitate minimally invasive image-guided interventions. For this purpose a number of 3D-2D registration methods related to different clinical contexts were proposed, however, their translation into clinical theater is still limited by lack of reliable and automatic detection of 3D-2D misalignment. In this paper, we presented a novel approach for verifying 3D-2D misalignment based on learned a priori knowledge using arbitrary similarity measure (SM) and single synthetic image (DRR). First, positions of local optima of SM using DRR image were found and characterized. On live 2D image, the local optima of SM were comparatively examined at the expected, previously learned positions. The approach was tested on publicly available image database of lumbar spine using state-of-the-art back-projection gradient-based SM. The results indicate that proposed approach successfully discriminated the ˵correct″ from ˵poor″ and ˵wrong″ 3D-2D alignments in 100 % of cases. [ABSTRACT FROM AUTHOR]

Before an image registration method can be used in the medical theater a rigorous performance assessment of the registration method must be performed. In this paper, a new image database with a reference-based standardized evaluation methodology for objective evaluation and comparison of 3D/2D registration methods has been introduced. CT images of a female from the Visible Human Project® were used and 15 sub-volumes each containing one of the vertebrae T3-T12 and L1-L5, and the pelvis were defined. Three pairs of lateral and anterior-posterior 2D fluoroscopic X-ray images were rendered from the CT data. Ray-casting algorithm with an energy conversion function was used to generate realistic fluoroscopic-like DRR images. Furthermore, outliers similar to medical intervention tools were also simulated on the 2D images. The assessment protocol to evaluate four criteria: accuracy, reliability, robustness and algorithm complexity, was defined. The proposed image database with the standardized evaluation methodology comprising ground truth registrations, displacements from the ground truth and target points is available upon request from the authors. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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° 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° and precision of 0.5°. [ABSTRACT FROM AUTHOR]

In this paper we propose a protocol for the evaluation of similarity measures for non-rigid registration. The protocol is feasible for the evaluation of non-rigid registration in which the deformation model is based on a set of regularly or irregularly distributed corresponding point pairs. The proposed protocol is able to deduce five properties of the similarity measure for each point pair of the deformation model, so that local or global estimation of the similarity measure properties can be derived. The feasibility of the proposed protocol was demonstrated on a popular deformation model based on B-splines, on six similarity measures, and on the “gold standard” CT and MR images of three spine vertebrae and three MR T1 and T2 images of the head. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this paper we present a novel 3D/2D registration method, where first, a 3D image is reconstructed from a few 2D X-ray images and next, the preoperative 3D image is brought into the best possible spatial correspondence with the reconstructed image by optimizing a similarity measure. Because the quality of the reconstructed image is generally low, we introduce a novel asymmetric mutual information similarity measure, which is able to cope with low image quality as well as with different imaging modalities. The novel 3D/2D registration method has been evaluated using standardized evaluation methodology and publicly available 3D CT, 3DRX, and MR and 2D X-ray images of two spine phantoms [1], for which gold standard registrations were known. In terms of robustness, reliability and capture range the proposed method outperformed the gradient-based method [2] and the method based on digitally reconstructed radiographs (DRRs). [ABSTRACT FROM AUTHOR]