University of Ljubljana, Faculty of Electrical Engineering, Trzaska 25, 1000 Ljubljana, Slovenia. dejan.tomazevic@fe.uni-lj.si
In image-guided therapy, high-quality preoperative images serve for planning and simulation, and intraoperatively as "background", onto which models of surgical instruments or radiation beams are projected. The link between a preoperative image and intraoperative physical space of the patient is established 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 a few 2-D X-ray images and next, the preoperative 3-D image is brought into the best possible spatial correspondence with the reconstructed image by optimizing a similarity measure (SM). Because the quality of the reconstructed image is generally low, we introduce a novel SM, which is able to cope with low image quality as well as with different imaging modalities. The novel 3-D/2-D registration method has been evaluated and compared to the gradient-based method (GBM) using standardized evaluation methodology and 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 spine phantoms, for which gold standard registrations were known. For each of the 3DRX, CT, or MR images and each set of X-ray images, 1600 registrations were performed from starting positions, defined as the mean target registration error (mTRE), randomly generated and uniformly distributed in the interval of -20 mm around the gold standard. The capture range was defined as the distance from gold standard for which the final TRE was less than 2 mm in at least 95% of all cases. In terms of success rate, as the function of initial misalignment and capture range the proposed method outperformed the GBM. TREs of the novel method and the GBM were approximately the same. For the registration of 3DRX and CT images to X-ray images as few as 2-3 X-ray views were sufficient to obtain approximately .4 mm TREs, 7-9 mm capture range, and 80%-90% of successful registrations. To obtain similar results for MR to X-ray registrations, an image, reconstructed from at least 11 X-ray images was required. Reconstructions from more than 11 images had no effect on the registration results.
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Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv. 2007 ;10 (Pt 1):450-7 18051090 (P,S,G,E,B)
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.
Department of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia.
Purpose: A promising patient positioning technique is based on registering computed tomographic (CT) or magnetic resonance (MR) images to cone-beam CT images (CBCT). The extra radiation dose delivered to the patient can be substantially reduced by using fewer projections. This approach results in lower quality CBCT images. The purpose of this study is to evaluate a number of similarity measures (SMs) suitable for registration of CT or MR images to low-quality CBCTs. Methods and Materials: Using the recently proposed evaluation protocol, we evaluated nine SMs with respect to pretreatment imaging modalities, number of two-dimensional (2D) images used for reconstruction, and number of reconstruction iterations. The image database consisted of 100 X-ray and corresponding CT and MR images of two vertebral columns. Results: Using a higher number of 2D projections or reconstruction iterations results in higher accuracy and slightly lower robustness. The similarity measures that behaved the best also yielded the best registration results. The most appropriate similarity measure was the asymmetric multi-feature mutual information (AMMI). Conclusions: The evaluation protocol proved to be a valuable tool for selecting the best similarity measure for the reconstruction-based registration. The results indicate that accurate and robust CT/CBCT or even MR/CBCT registrations are possible if the AMMI similarity measure is used.
Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv. 2005 ;8 (Pt 2):231-8 16685964 (P,S,G,E,B)
University of Ljubljana, Faculty of Electrical Engineering, Trzaska 25, 1000 Ljubljana, Slovenia. dejan.tomazevic@fe.uni-lj.si
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, for which gold standard registrations were known. In terms of robustness, reliability and capture range the proposed method outperformed the gradient-based method and the method based on digitally reconstructed radiographs (DRRs).
Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia.
Evaluation and comparison of registration techniques for image-guided surgery is an important problem that has received little attention in the literature. In this paper we address the challenging problem of generating reliable "gold standard" data for use in evaluating the accuracy of 3D/2D registrations. We have devised a cadaveric lumbar spine phantom with fiducial markers and established highly accurate correspondences between 3D CT and MR images and 18 2D X-ray images. The expected target registration errors for target points on the 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 "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 3D/2D image registration methods.
University of Ljubljana, Faculty of Electrical Engineering, Trzaska 25, 1000 Ljubljana, Slovenia. dejan.tomazevic@fe.uni-lj.si
A crucial part of image-guided therapy is registration of preoperative and intraoperative images, by which the precise position and orientation of the patient's anatomy is determined in three dimensions. This paper presents a novel approach to register three-dimensional (3-D) computed tomography (CT) or magnetic resonance (MR) images to one or more two-dimensional (2-D) X-ray images. The registration is based solely on the information present in 2-D and 3-D images. It does not require fiducial markers, intraoperative X-ray image segmentation, or timely construction of digitally reconstructed radiographs. The originality of the approach is in using normals to bone surfaces, preoperatively defined in 3-D MR or CT data, and gradients of intraoperative X-ray images at locations defined by the X-ray source and 3-D surface points. The registration is concerned with finding the rigid transformation of a CT or MR volume, which provides the best match between surface normals and back projected gradients, considering their amplitudes and orientations. We have thoroughly validated our registration method by using MR, CT, and X-ray images of a cadaveric lumbar spine phantom for which "gold standard" registration was established by means of fiducial markers, and its accuracy assessed by target registration error. Volumes of interest, containing single vertebrae L1-L5, were registered to different pairs of X-ray images from different starting positions, chosen randomly and uniformly around the "gold standard" position. CT/X-ray (MR/ X-ray) registration, which is fast, was successful in more than 91%(82% except for L1) of trials if started from the "gold standard" translated or rotated for less than 6 mm or 17 degrees (3 mm or 8.6 degrees), respectively. Root-mean-square target registration errors were below .5 mm for the CT to X-ray registration and below 1.4 mm for MR to X-ray registration.
One of the most important technical challenges in image-guided intervention is to obtain a precise transformation between the intrainterventional patient's anatomy and corresponding preinterventional 3-D image on which the intervention was planned. This goal can be achieved by acquiring intrainterventional 2-D 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 in this paper. The method is based on robustly matching 3-D preinterventional image gradients and coarsely reconstructed 3-D gradients from the intrainterventional 2-D images. To improve the robustness of finding the correspondences between the two sets of gradients, hypothetical correspondences are searched for along normals to anatomical structures in 3-D images, while the final correspondences are established in an iterative process, combining the robust random sample consensus algorithm (RANSAC) and a special gradient matching criterion function. The proposed method was 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 (MR), and 2-D X-ray images of two spine segments, and standardized evaluation criteria. In this way, the proposed method could be objectively compared to the intensity, gradient, and reconstruction-based registration methods. The obtained results indicate that the proposed method performs favorably both in terms of registration accuracy and robustness. The method is especially superior when just a few X-ray images and when MR preinterventional images are used for registration, which are important advantages for many clinical applications.
Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv. 2008 ;11 (Pt 1):942-50 18979836 (P,S,G,E,B)
University of Ljubljana, Faculty of Electrical Engineering, Slovenia. tomaz.vrtovec@fe.uni-lj.si
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. degrees and precision of .5 degrees.
Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv. 2008 ;11 (Pt 1):762-70 18979815 (P,S,G,E,B)
Faculty of Electrical Engineering, University of Ljubljana, Slovenia. ziga.spiclin@fe.uni-lj.si
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 .48 +/- .33 mm, while with real EEG data an average root-mean-square point-to-surface error of 2.27 +/- .02 mm was obtained.
University of Ljubljana, Faculty of Electrical Engineering, Trzaska 25, SI-1000 Ljubljana, Slovenia.
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 (+/- .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.
Comparative evaluation of similarity measures for the rigid registration of multi-modal head images.
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.
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Biomedical Sciences Group, Department of Dentistry, Oral Pathology and Maxillofacial Surgery, Section Forensic Odontology, Katholieke Universiteit Leuven, Kapucijnenvoer 7, B-3000 Leuven, Belgium; Radiology Department, Faculty of Dentistry, Chulalongkorn University, Henri-Dunant Road, Bangkok 10330, Thailand.
Recently, different portable hand-held and battery-powered dental X-ray units have become available. Especially for forensic odontological purposes, they offer diverse advantages such as for use in disaster areas and crime-scene locations as also in autopsy rooms and mortuaries. For any application, the most important feature of these hand-held devices is the delivered image quality. The aim of this study is to evaluate the radiographic image quality acquired by two portable X-ray devices in combination with two types of image receptors and to compare the findings with the image quality of a standard intra-oral X-ray device. Eleven samples consisting of eight teeth, two dry skeletal specimens and one formalin-fixed mandible part were mounted on blocks for standardised (re)positioning. Radiological images were acquired with two hand-held (AnyRay((R)) 60kVp, .02-4.00mAs and NOMAD((R)) 60kVp, .023-2.277mAs) and one wall-mounted (MinRay((R)) 60/70kVp .14-22.4mAs) X-ray device combined with two image receptor systems (VistaScan((R)) phosphor storage plate (PSP) and SIGMA((R)) M CMOS Active Pixel technology sensor). The effect of X-ray source-to-object distance (SOD) was checked at 20cm in conjunction with object to image receptor distances (OIDs) of .8 and 2.5cm. For each parameter setup, the exposure times were run from low till high. An expert consent statement was achieved by agreement of four expert observers selecting the optimal images based on a developed four point quality rating system. Next, a selection of the images was assembled in a set of 198 observation screens and scored by seven observers. The observation screens were designed to compare observer scores, relations between devices, receptors and OIDs and images obtained from the different devices at equal exposure levels (mAs). All results were statistically analysed. Radiological image quality was significantly higher for phosphor plate compared with the CMOS digital receptor system (p< .0001). Furthermore, a significantly superior image quality was obtained for OID= .8 than for OID=2.5 (p= .039). A significant difference in image quality between the three devices was also established (p= .02). The present study demonstrated the feasibility of portable X-ray systems for forensic odontological applications based on rendering optimal image quality, provided an in vitro guideline of optimal parameter settings and offered a radiological image database usable in further research.
A hybrid imaging system for simultaneous fluorescence tomography and X-ray computed tomography of small animals has been developed and presented. The system capitalizes on the imaging power of a 360x-projection free-space fluorescence tomography system, implemented within a micro-CT scanner. Image acquisition is based on techniques that automatically adjust a series of imaging parameters to offer a high dynamic range data set. Image segmentation further allows the incorporation of structural priors in the optical reconstruction problem to improve the imaging performance. The functional system characteristics are showcased and images from a brain imaging study are shown, reconstructed using X-ray CT derived priors into the optical forward problem.
ICP, Universität Stuttgart, Pfaffenwaldring 27, 70569 Stuttgart, Germany.
Transport properties of a multiscale carbonate rock are predicted from pore scale models, reconstructed using a continuum geometrical modeling technique. The method combines crystallite information from two-dimensional high-resolution images with sedimentary correlations from a three-dimensional low-resolution microcomputed tomography ( micro-CT) image to produce a rock sample with calibrated porosity, structural correlation, and transport properties at arbitrary resolutions. Synthetic micro-CT images of the reconstructed model match well with experimental micro-CT images at different resolutions, making it possible to predict physical transport parameters at higher resolutions.
School of Computing Science, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada, yta19@cs.sfu.ca.
In current radiologists' workstations, a scroll mouse is typically used as the primary input device for navigating image slices and conducting operations on an image. Radiological analysis and diagnosis rely on careful observation and annotation of medical images. During analysis of 3D MRI and CT volumes, thousands of mouse clicks are performed everyday, which can cause wrist fatigue. This paper presents a dynamic control-to-display (C-D) gain mouse movement method, controlled by an eyegaze tracker as the target predictor. By adjusting the C-D gain according to the distance to the target, the mouse click targeting time is reduced. Our theoretical and experimental studies show that the mouse movement time to a known target can be reduced by up to 15%. We also present an experiment with 12 participants to evaluate the role of eyegaze targeting in the realistic situation of unknown target positions. These results indicate that using eyegaze to predict the target position, the dynamic C-D gain method can improve pointing performance by 8% and reduce the error rate over traditional mouse movement.
ICT Research Center, Key Laboratory of Optoelectronic Technology and System of the Education Ministry of China, Chongqing University, Chongqing 400030, China. shenk@cqu.edu.cn
Multiresolution Analysis (MRA) plays an important role in image and signal processing fields, and it can extract information at different scales. Image fusion is a process of combining two or more images into an image, which extracts features from source images and provides more information than one image. The research presented in this article is aimed at the development of an automated imaging enhancement system in digital radiography (DR) images, which can clearly display all the defects in one image and don't bring blocking effect. In terms of characteristic of the collected radiographic signals, in the proposed scheme the subsection of signals is mapped to -255 gray scale to form several gray images and then these images are fused to form a new enhanced image. This article focuses on comparing the discriminating power of several multiresolution images decomposing methods using contrast pyramid, wavelet, and ridgelet respectively. The algorithms are extensively tested and the results are compared with standard image enhancement algorithms. Tests indicate that the fused images present a more detailed representation of the x-ray image. Detection, recognition, and search tasks may therefore benefit from this.
Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada N2L 3G1.
A novel similarity measure for registering magnetic resonance (MR) and computed tomography (CT) images has been designed and built. MR-CT registration methods often rely on the statistical intensity relationship between the images. The proposed similarity measure instead depends on the statistical relationship between the complex phase order between the images. By utilizing the complex phase order likelihood (CPOL) as a similarity measure, structural relationships instead of intensity relationships are explicitly used. This approach can be advantageous for MR-CT registration, where the intensities of the CT imagery have highly complex and nonlinear relationships with the intensities of corresponding MR imagery but simpler linear structural relationships. This new similarity measure has been tested on real MR-CT 3D volumes and has been evaluated based on fiducial registration error to determine alignment accuracy. Quantitative results show that CPOL is capable of achieving comparable alignment accuracy when compared to normalized mutual information, while being more robust to imaging artifacts such as noise.
In this paper, a novel multiresolution algorithm for registering multimodal images using an adaptive Monte Carlo scheme is presented. At each iteration, random solution candidates are generated from a multidimensional solution space of possible geometric transformations using an adaptive sampling approach. The generated solution candidates are evaluated based on the Pearson Type VII error between the phase moments of the images to determine the solution candidate with the lowest error residual. The multidimensional sampling distribution is refined with each iteration to produce increasingly more plausible solution candidates for the optimal alignment between the images. The proposed algorithm is efficient, robust to local optima, and does not require manual initialization or prior information about the images. Experimental results based on various real-world medical images show that the proposed method is capable of achieving higher registration accuracy than existing multimodal registration algorithms for situations where little to no overlapping regions exist.
Imaging & Visualization Department, Siemens Corporate Research, Princeton, NJ, 08540, USA, rui.liao@siemens.com.
To present an efficient and robust method for 3-D reconstruction of the coronary artery tree from multiple ECG-gated views of an X-ray angiography. 2-D coronary artery centerlines are extracted automatically from X-ray projection images using an enhanced multi-scale analysis. For the difficult data with low vessel contrast, a semi-automatic tool based on fast marching method is implemented to allow manual correction of automatically-extracted 2-D centerlines. First, we formulate the 3-D symbolic reconstruction of coronary arteries from multiple views as an energy minimization problem incorporating a soft epipolar line constraint and a smoothness term evaluated in 3-D. The proposed formulation results in the robustness of the reconstruction to the imperfectness in 2-D centerline extraction, as well as the reconstructed coronary artery tree being inherently smooth in 3-D. We further propose to solve the energy minimization problem using alpha-expansion moves of Graph Cuts, a powerful optimization technique that yields a local minimum in a strong sense at a relatively low computational complexity. We show experimental results on a synthetic coronary phantom, a porcine data set and 11 patient data sets. For the coronary phantom, results obtained using different number of views are presented. 3-D reconstruction error evaluated by the mean plus one standard deviation is below one millimeter when 4 or more views are used. For real data, reconstruction using 4 to 5 views and 256 depth labels averaged around 12 s on a computer with 2.13 GHz Intel Pentium M and achieves a mean 2-D back-projection error of 1.18 mm (ranging from .84 to 1.71 mm) in 12 cases. The accuracy for multi-view reconstruction of the coronary artery tree as reported from the phantom and patient studies is promising, and the efficiency is significantly improved compared to other approaches reported in the literature, which range from a few to tens of minutes. Visually good and smooth reconstruction is demonstrated.
Yunkai Zhang,
James C H Chu,
Wenchien Hsi,
Atif J Khan,
Parthiv S Mehta,
Damian B Bernard,
Ross A Abrams
Department of Radiation Oncology, Rush University Medical Center, Chicago, IL.
We evaluated 4 volume-based automatic image registration algorithms from 2 commercially available treatment planning systems (Philips Syntegra and BrainScan). The algorithms based on cross correlation (CC), local correlation (LC), normalized mutual information (NMI), and BrainScan mutual information (BSMI) were evaluated with: (1) the synthetic computed tomography (CT) images,(2) the CT and magnetic resonance (MR) phantom images, and (3) the CT and MR head image pairs from 12 patients with brain tumors. For the synthetic images, the registration results were compared with known transformation parameters, and all algorithms achieved accuracy of submillimeter in translation and subdegree in rotation. For the phantom images, the registration results were compared with those provided by frame and marker-based manual registration. For the patient images, the results were compared with anatomical landmark-based manual registration to qualitatively determine how the results were close to a clinically acceptable registration. NMI and LC outperformed CC and BSMI, with the sense of being closer to a clinically acceptable result. As for the robustness, NMI and BSMI outperformed CC and LC. A guideline of image registration in our institution was given, and final visual assessment is necessary to guarantee reasonable results.
