A comparison of a similarity-based and a feature-based 2-D-3-D registration method for neurointerventional use.

Two-dimensional (2-D)-to-three-dimensional (3-D) registration can improve visualization which may aid minimally invasive neurointerventions. Using clinical and phantom studies, two state-of-the-art approaches to rigid registration are compared quantitatively: an intensity-based algorithm using the gradient difference similarity measure; and an iterative closest point (ICP)-based algorithm. The gradient difference approach was found to be more accurate, with an average registration accuracy of 1.7 mm for clinical data, compared to the ICP-based algorithm with an average accuracy of 2.8 mm. In phantom studies, the ICP-based algorithm proved more reliable, but with more complicated clinical data, the gradient difference algorithm was more robust. Average computation time for the ICP-based algorithm was 20 s per registration, compared with 14 min and 50 s for the gradient difference algorithm.     

A comparison of similarity measures for use in 2-D-3-D medicalimage registration

A comparison of six similarity measures for use in intensity-based two-dimensional-three-dimensional (2-D-3-D) image registration is presented. The accuracy of the similarity measures are compared to a “gold-standard” registration which has been accurately calculated using fiducial markers. The similarity measures are used to register a computed tomography (CT) scan of a spine phantom to a fluoroscopy image of the phantom. The registration is carried out within a region-of-interest in the fluoroscopy image which is user defined to contain a single vertebra. Many of the problems involved in this type of registration are caused by features which were not modeled by a phantom image alone. More realistic “gold-standard” data sets were simulated using the phantom image with clinical image features overlaid. Results show that the introduction of soft-tissue structures and interventional instruments into the phantom image can have a large effect on the performance of some similarity measures previously applied to 2-D-3-D image registration. Two measures were able to register accurately and robustly even when soft-tissue structures and interventional instruments were present as differences between the images. These measures were pattern intensity and gradient difference. Their registration accuracy, for all the rigid-body parameters except for the source to film translation, was within a root-mean-square (rms) error of 0.53 mm or degrees to the “gold-standard” values. No failures occurred while registering using these measures     

Practical aspects of a data-driven motion correction approach for brain SPECT

Patient motion can cause image artifacts in single photon emission computed tomography despite restraining measures. Data-driven detection and correction of motion can be achieved by comparison of acquired data with the forward projections. This enables the brain locations to be estimated and data to be correctly incorporated in a three-dimensional (3-D) reconstruction algorithm. Digital and physical phantom experiments were performed to explore practical aspects of this approach. METHODS: Noisy simulation data modeling multiple 3-D patient head movements were constructed by projecting the digital Hoffman brain phantom at various orientations. Hoffman physical phantom data incorporating deliberate movements were also gathered. Motion correction was applied to these data using various regimes to determine the importance of attenuation and successive iterations. Studies were assessed visually for artifact reduction, and analyzed quantitatively via a mean registration error (MRE) and mean square difference measure (MSD). RESULTS: Artifacts and distortion in the motion corrupted data were reduced to a large extent by application of this algorithm. MRE values were mostly well within 1 pixel (4.4 mm) for the simulated data. Significant MSD improvements (>2) were common. Inclusion of attenuation was unnecessary to accurately estimate motion, doubling the efficiency and simplifying implementation. Moreover, most motion-related errors were removed using a single iteration. The improvement for the physical phantom data was smaller, though this may be due to object symmetry. CONCLUSION: These results provide the basis of an implementation protocol for clinical validation of the technique.     

Nonrigid registration of 3-D free-hand ultrasound images of the breast

Three-dimensional (3-D) ultrasound imaging of the breast enables better assessment of diseases than conventional two-dimensional (2-D) imaging. Free-hand techniques are often used for generating 3-D data from a sequence of 2-D slice images. However, the breast deforms substantially during scanning because it is composed primarily of soft tissue. This often causes tissue mis-registration in spatial compounding of multiple scan sweeps. To overcome this problem, in this paper, instead of introducing additional constraints on scanning conditions, we use image processing techniques. We present a fully automatic algorithm for 3-D nonlinear registration of free-hand ultrasound data. It uses a block matching scheme and local statistics to estimate local tissue deformation. A Bayesian regularization method is applied to the sample displacement field. The final deformation field is obtained by fitting a B-spline approximating mesh to the sample displacement field. Registration accuracy is evaluated using phantom data and similar registration errors are achieved with (0.19 mm) and without (0.16 mm) gaps in the data. Experimental results show that registration is crucial in spatial compounding of different sweeps. The execution time of the method on moderate hardware is sufficiently fast for fairly large research studies.     

Markerless real-time 3-D target region tracking by motion backprojection from projection images

Accurate and fast localization of a predefined target region inside the patient is an important component of many image-guided therapy procedures. This problem is commonly solved by registration of intraoperative 2-D projection images to 3-D preoperative images. If the patient is not fixed during the intervention, the 2-D image acquisition is repeated several times during the procedure, and the registration problem can be cast instead as a 3-D tracking problem. To solve the 3-D problem, we propose in this paper to apply 2-D region tracking to first recover the components of the transformation that are in-plane to the projections. The 2-D motion estimates of all projections are backprojected into 3-D space, where they are then combined into a consistent estimate of the 3-D motion. We compare this method to intensity-based 2-D to 3-D registration and a combination of 2-D motion backprojection followed by a 2-D to 3-D registration stage. Using clinical data with a fiducial marker-based gold-standard transformation, we show that our method is capable of accurately tracking vertebral targets in 3-D from 2-D motion measured in X-ray projection images. Using a standard tracking algorithm (hyperplane tracking), tracking is achieved at video frame rates but fails relatively often (32% of all frames tracked with target registration error (TRE) better than 1.2 mm, 82% of all frames tracked with TRE better than 2.4 mm). With intensity-based 2-D to 2-D image registration using normalized mutual information (NMI) and pattern intensity (PI), accuracy and robustness are substantially improved. NMI tracked 82% of all frames in our data with TRE better than 1.2 mm and 96% of all frames with TRE better than 2.4 mm. This comes at the cost of a reduced frame rate, 1.7 s average processing time per frame and projection device. Results using PI were slightly more accurate, but required on average 5.4 s time per frame. These results are still substantially faster than 2-D to 3-D registration. We conclude that motion backprojection from 2-D motion tracking is an accurate and efficient method for tracking 3-D target motion, but tracking 2-D motion accurately and robustly remains a challenge.     

Iterative principal axes registration method for analysis of MR-PETbrain images

Computerized automatic registration of MR-PET images of the brain is of significant interest for multimodality brain image analysis. Here, the authors discuss the principal axes transformation for registration of three-dimensional MR and PET images. A new brain phantom designed to test MR-PET registration accuracy determines that the principal axes registration (PAR) method is accurate to within an average of 1.37 mm with a standard deviation of 0.78 mm. Often the PET scans are not complete in the sense that the PET volume does not match the respective MR volume. The authors have developed an iterative PAR (IPAR) algorithm for such cases. Partial volumes of PET can be accurately registered to the complete MR volume using the new iterative algorithm. The quantitative and qualitative analyses of MR-PET image registration are presented and discussed. Results show that the new PAR algorithm is accurate and practical in MR-PET correlation studies     

Intensity-based 2-D-3-D registration of cerebral angiograms

We propose a new method for aligning three-dimensional (3-D) magnetic resonance angiography (MRA) with 2-D X-ray digital subtraction angiograms (DSA). Our method is developed from our algorithm to register computed tomography volumes to X-ray images based on intensity matching of digitally reconstructed radiographs (DRRs). To make the DSA and DRR more similar, we transform the MRA images to images of the vasculature and set to zero the contralateral side of the MRA to that imaged with DSA. We initialize the search for a match on a user defined circular region of interest. We have tested six similarity measures using both unsegmented MRA and three segmentation variants of the MRA. Registrations were carried out on images of a physical neuro-vascular phantom and images obtained during four neuro-vascular interventions. The most accurate and robust registrations were obtained using the pattern intensity, gradient difference, and gradient correlation similarity measures, when used in conjunction with the most sophisticated MRA segmentations. Using these measures, 95% of the phantom start positions and 82% of the clinical start positions were successfully registered. The lowest root mean square reprojection errors were 1.3 mm (standard deviation 0.6) for the phantom and 1.5 mm (standard deviation 0.9) for the clinical data sets. Finally, we present a novel method for the comparison of similarity measure performance using a technique borrowed from receiver operator characteristic analysis.     

Surgical navigation by autostereoscopic image overlay of integral videography

This paper describes an autostereoscopic image overlay technique that is integrated into a surgical navigation system to superimpose a real three-dimensional (3-D) image onto the patient via a half-silvered mirror. The images are created by employing a modified version of integral videography (IV), which is an animated extension of integral photography. IV records and reproduces 3-D images using a microconvex lens array and flat display; it can display geometrically accurate 3-D autostereoscopic images and reproduce motion parallax without the need for special devices. The use of semitransparent display devices makes it appear that the 3-D image is inside the patient's body. This is the first report of applying an autostereoscopic display with an image overlay system in surgical navigation. Experiments demonstrated that the fast IV rendering technique and patient-image registration method produce an average registration accuracy of 1.13 mm. Experiments using a target in phantom agar showed that the system can guide a needle toward a target with an average error of 2.6 mm. Improvement in the quality of the IV display will make this system practical and its use will increase surgical accuracy and reduce invasiveness.     

Registration of head volume images using implantable fiducialmarkers

Describes an extrinsic-point-based, interactive image-guided neurosurgical system designed at Vanderbilt University, Nashville, TN, as part of a collaborative effort among the Departments of Neurological Surgery, Computer Science, and Biomedical Engineering. Multimodal image-to-image (II) and image-to-physical (IP) registration is accomplished using implantable markers. Physical space tracking is accomplished with optical triangulation. The authors investigate the theoretical accuracy of point-based registration using numerical simulations, the experimental accuracy of their system using data obtained with a phantom, and the clinical accuracy of their system using data acquired in a prospective clinical trial by 6 neurosurgeons at 4 medical centers from 158 patients undergoing craniotomies to respect cerebral lesions. The authors can determine the position of their markers with an error of approximately 0.4 mm in X-ray computed tomography (CT) and magnetic resonance (MR) images and 0.3 mm in physical space. The theoretical registration error using 4 such markers distributed around the head in a configuration that is clinically practical is approximately 0.5-0.6 mm. The mean CT-physical registration error for the: phantom experiments is 0.5 mm and for the clinical data obtained with rigid head fixation during scanning is 0.7 mm. The mean CT-MR registration error for the clinical data obtained without rigid head fixation during scanning is 1.4 mm, which is the highest mean error that the authors observed. These theoretical and experimental findings indicate that this system is an accurate navigational aid that can provide real-time feedback to the surgeon about anatomical structures encountered in the surgical field     

3-D/2-D registration of CT and MR to X-ray images

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 0.5 mm for the CT to X-ray registration and below 1.4 mm for MR to X-ray registration.     

Elastic registration of fMRI data using Bézier-spline transformations

A three-dimensional (3-D) elastic registration algorithm has been developed to find a veridical transformation that maps activation patterns from functional magnetic resonance imaging (fMRI) experiments onto a 3-D high-resolution anatomical dataset. The proposed algorithm uses trilinear Bézier-splines and a 3-D voxel-based optimization technique to determine the transformation that maps the functional data onto the coordinate system of the anatomical dataset. Simple conditions are presented which guarantee that the data are mapped one-to-one on each other. Two voxel-based similarity measures, the linear correlation coefficient and the entropy correlation coefficient, are used. Their performance with respect to the registration of fMRI data is compared. Tests on simulated and real data have been performed to evaluate the accuracy of the method. Our results demonstrate that subvoxel accuracy can be achieved even for noisy low-resolution multislice datasets with local distortions up to 10 mm. Although the method is optimized for the registration of functional and anatomical MR images, it can also be used for solving other elastic registration problems.     

Guide wire reconstruction and visualization in 3DRA using monoplane fluoroscopic imaging

A method has been developed that, based on the guide wire position in monoplane fluoroscopic images, visualizes the approximate guide wire position in the three-dimensional (3-D) vasculature, that is obtained prior to the intervention with 3-D rotational X-ray angiography (3DRA). The method assumes the position of the guide wire in the fluoroscopic images is known. A two-dimensional feature image is determined from the 3DRA data. In this feature image, the guide wire position is determined in a two-step approach: a mincost algorithm is used to determine a suitable position for the guide wire, and subsequently a snake optimization technique is applied to move the guide wire to a better position. The resulting guide wire can then be visualized in 3-D in combination with the 3DRA dataset. The reconstruction accuracy of the method has been evaluated using a 3DRA image of a vascular phantom filled with contrast, and monoplane fluoroscopic images of the same phantom without contrast and with a guide wire inserted. The evaluation has been performed for different projection angles, and with different parameters for the method. The final result does not appear to be very sensitive to the parameters of the method. The average mean error of the estimated 3-D guide wire position is 1.5 mm, and the average tip distance is 2.3 mm. The effect of inaccurate C-arm geometry information is also investigated. Small errors in geometry information (up to 1 degrees) will slightly decrease the 3-D reconstruction accuracies, with an error of at most 1 mm. The feasibility of this approach on clinical data is demonstrated.     

Estimation of mouse organ locations through registration of a statistical mouse atlas with micro-CT images

Micro-CT is widely used in preclinical studies of small animals. Due to the low soft-tissue contrast in typical studies, segmentation of soft tissue organs from noncontrast enhanced micro-CT images is a challenging problem. Here, we propose an atlas-based approach for estimating the major organs in mouse micro-CT images. A statistical atlas of major trunk organs was constructed based on 45 training subjects. The statistical shape model technique was used to include inter-subject anatomical variations. The shape correlations between different organs were described using a conditional Gaussian model. For registration, first the high-contrast organs in micro-CT images were registered by fitting the statistical shape model, while the low-contrast organs were subsequently estimated from the high-contrast organs using the conditional Gaussian model. The registration accuracy was validated based on 23 noncontrast-enhanced and 45 contrast-enhanced micro-CT images. Three different accuracy metrics (Dice coefficient, organ volume recovery coefficient, and surface distance) were used for evaluation. The Dice coefficients vary from 0.45 ± 0.18 for the spleen to 0.90 ± 0.02 for the lungs, the volume recovery coefficients vary from 0.96 ± 0.10 for the liver to 1.30 ± 0.75 for the spleen, the surface distances vary from 0.18 ± 0.01 mm for the lungs to 0.72 ± 0.42 mm for the spleen. The registration accuracy of the statistical atlas was compared with two publicly available single-subject mouse atlases, i.e., the MOBY phantom and the DIGIMOUSE atlas, and the results proved that the statistical atlas is more accurate than the single atlases. To evaluate the influence of the training subject size, different numbers of training subjects were used for atlas construction and registration. The results showed an improvement of the registration accuracy when more training subjects were used for the atlas construction. The statistical atlas-based registration was also compared with the thin-plate spline based deformable registration, commonly used in mouse atlas registration. The results revealed that the statistical atlas has the advantage of improving the estimation of low-contrast organs.     

Seed localization in Ultrasound and Registration to C-Arm Fluoroscopy Using Matched Needle Tracks for Prostate Brachytherapy

We propose a novel fiducial-free approach for the registration of C-arm fluoroscopy to 3-D ultrasound images of prostate brachytherapy implants to enable dosimetry. The approach involves the reliable detection of a subset of radioactive seeds from 3-D ultrasound, and the use of needle tracks in both ultrasound and fluoroscopy for registration. Seed detection in ultrasound is achieved through template matching in 3-D radio frequency ultrasound signals, followed by thresholding and spatial filtering. The resulting subset of seeds is registered to the complete reconstruction of the brachytherapy implant from multiple C-arm fluoroscopy views. To compensate for the deformation caused by the ultrasound probe, simulated warping is applied to the seed cloud from fluoroscopy. The magnitude of the applied warping is optimized within the registration process. The registration is performed in two stages. First, the needle track projections from fluoroscopy and ultrasound are matched. Only the seeds in the matched needles are then used as fiducials for point-based registration. We report results from a physical phantom with a realistic implant (average postregistration seed distance of 1.6 ± 1.2?mm) and from five clinical patient datasets (average error: 2.8 ± 1.5?mm over 128 detected seeds). We conclude that it is feasible to use RF ultrasound data, template matching, and spatial filtering to detect a reliable subset of brachytherapy seeds from ultrasound to enable registration to fluoroscopy for dosimetry.     

Rapid elastic image registration for 3-D ultrasound

A Subvolume-based algorithm for elastic Ultrasound REgistration (SURE) was developed and evaluated. Designed primarily to improve spatial resolution in three-dimensional compound imaging, the algorithm registers individual image volumes nonlinearly before combination into compound volumes. SURE works in one or two stages, optionally using MIAMI Fuse software first to determine a global affine registration before iteratively dividing the volume into subvolumes and computing local rigid registrations in the second stage. Connectivity of the entire volume is ensured by global interpolation using thin-plate splines after each iteration. The performance of SURE was quantified in 20 synthetically deformed in vivo ultrasound volumes, and in two phantom scans, one of which was distorted at acquisition by placing an aberrating layer in the sound path. The aberrating layer was designed to induce beam aberrations reported for the female breast. Synthetic deformations of 1.5-2.5 mm were reduced by over 85% when SURE was applied to register the distorted image volumes with the original ones. Registration times were below 5 min on a 500-MHz CPU for an average data set size of 13 MB. In the aberrated phantom scans, SURE reduced the average deformation between the two volumes from 1.01 to 0.30 mm. This was a statistically significant (P = 0.01) improvement over rigid and affine registration transformations, which produced reductions to 0.59 and 0.50 mm, respectively.     

Robust Gradient-Based 3-D/2-D Registration of CT and MR to X-Ray Images

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.      

A method to track cortical surface deformations using a laser range scanner

This paper reports a novel method to track brain shift using a laser-range scanner (LRS) and nonrigid registration techniques. The LRS used in this paper is capable of generating textured point-clouds describing the surface geometry/intensity pattern of the brain as presented during cranial surgery. Using serial LRS acquisitions of the brain's surface and two-dimensional (2-D) nonrigid image registration, we developed a method to track surface motion during neurosurgical procedures. A series of experiments devised to evaluate the performance of the developed shift-tracking protocol are reported. In a controlled, quantitative phantom experiment, the results demonstrate that the surface shift-tracking protocol is capable of resolving shift to an accuracy of approximately 1.6 mm given initial shifts on the order of 15 mm. Furthermore, in a preliminary in vivo case using the tracked LRS and an independent optical measurement system, the automatic protocol was able to reconstruct 50% of the brain shift with an accuracy of 3.7 mm while the manual measurement was able to reconstruct 77% with an accuracy of 2.1 mm. The results suggest that a LRS is an effective tool for tracking brain surface shift during neurosurgery.     

Accuracy of automatically determined borders in digital two-dimensional echocardiography using a cardiac phantom

Although two-dimensional echocardiography (2-D echo) is a useful technique for evaluation of global and regional left ventricular function, the main limitation is the inability to easily extract reliable and accurate quantitative information throughout all phases of the cardiac cycle. We sought to develop suitable automated techniques for the objective determination of endocardial and epicardial borders in two-dimensional echocardiographic images. To test algorithms for the automatic detection of myocardial borders we constructed a cardiac ultrasound phantom consisting of 16 echogenic annuli of known dimensions embedded in a material of low echogenicity which allowed imaging without partial volume effects. An algorithm based on Gaussian filtering followed by a difference gradient operator was applied to detect edges in the 2-D echo images of these annuli. The radii of the automatically determined inner borders were within 0.44 mm root meansquared error over a range of 15-25 mm true radius. This lower boundary for the error in our approach to automatic placement of myocardial borders in 2-D echocardiograms suggests the potential to extract more information concerning left ventricular function than is available with current techniques.     

Super-resolution in PET imaging

This paper demonstrates a super-resolution method for improving the resolution in clinical positron emission tomography (PET) scanners. Super-resolution images were obtained by combining four data sets with spatial shifts between consecutive acquisitions and applying an iterative algorithm. Super-resolution attenuation corrected PET scans of a phantom were obtained using the two-dimensional and three-dimensional (3-D) acquisition modes of a clinical PET/computed tomography (CT) scanner (Discovery LS, GEMS). In a patient study, following a standard 18F-FDG PET/CT scan, a super-resolution scan around one small lesion was performed using axial shifts without increasing the patient radiation exposure. In the phantom study, smaller features (3 mm) could be resolved axially with the super-resolution method than without (6 mm). The super-resolution images had better resolution than the original images and provided higher contrast ratios in coronal images and in 3-D acquisition transaxial images. The coronal super-resolution images had superior resolution and contrast ratios compared to images reconstructed by merely interleaving the data to the proper axial location. In the patient study, super-resolution reconstructions displayed a more localized 18F-FDG uptake. A new approach for improving the resolution of PET images using a super-resolution method has been developed and experimentally confirmed, employing a clinical scanner. The improvement in axial resolution requires no changes in hardware.     

A "twisting and bending" model-based nonrigid image registration technique for 3-D ultrasound carotid images

Atherosclerosis at the carotid bifurcation resulting in cerebral emboli is a major cause of ischemic stroke. Most strokes associated with carotid atherosclerosis can be prevented by lifestyle/dietary changes and pharmacological treatments if identified early by monitoring carotid plaque changes. Registration of 3-D ultrasound (US) images of carotid plaque obtained at different time points is essential for sensitive monitoring of plaque changes in volume and surface morphology. This registration technique should be nonrigid, since different head positions during image acquisition sessions cause relative bending and torsion in the neck, producing nonlinear deformations between the images. We modeled the movement of the neck using a “twisting and bending” model with only six parameters for nonrigid registration. We evaluated the algorithm using 3-D US carotid images acquired at two different head positions to simulate images acquired at different times. We calculated the mean registration error (MRE) between the segmented vessel surfaces in the target image and the registered image using a distance-based error metric after applying our “twisting and bending” model-based nonrigid registration algorithm. We achieved an average registration error of $0.80 pm 0.26$ mm using our nonrigid registration technique, which was a significant improvement in registration accuracy over rigid registration, even with reduced degrees-of-freedom compared to the other nonrigid registration algorithms.