A computationally efficient method for automatic registration of orthogonal x-ray images with volumetric CT data

The paper presents a computationally efficient 3D-2D image registration algorithm for automatic pre-treatment validation in radiotherapy. The novel aspects of the algorithm include (a) a hybrid cost function based on partial digitally reconstructed radiographs (DRRs) generated along projected anatomical contours and a level set term for similarity measurement; and (b) a fast search method based on parabola fitting and sensitivity-based search order. Using CT and orthogonal x-ray images from a skull and a pelvis phantom, the proposed algorithm is compared with the conventional ray-casting full DRR based registration method. Not only is the algorithm shown to be computationally more efficient with registration time being reduced by a factor of 8, but also the algorithm is shown to offer 50% higher capture range allowing the initial patient displacement up to 15 mm (measured by mean target registration error). For the simulated data, high registration accuracy with average errors of 0.53 mm +/- 0.12 mm for translation and 0.61 +/- 0.29 degrees for rotation within the capture range has been achieved. For the tested phantom data, the algorithm has also shown to be robust without being affected by artificial markers in the image.     

Matching PET and CT scans of the head and neck area: development of method and validation

Positron emission tomography (PET) provides important information on tumor biology, but lacks detailed anatomical information. Our aim in the present study was to develop and validate an automatic registration method for matching PET and CT scans of the head and neck. Three difficulties in achieving this goal are (1) nonrigid motions of the neck can hamper the use of automatic ridged body transformations; (2) emission scans contain too little anatomical information to apply standard image fusion methods; and (3) no objective way exists to quantify the quality of the match results. These problems are solved as follows: accurate and reproducible positioning of the patient was achieved by using a radiotherapy treatment mask. The proposed method makes use of the transmission rather than the emission scan. To obtain sufficient (anatomical) information for matching, two bed positions for the transmission scan were included in the protocol. A mutual information-based algorithm was used as a registration technique. PET and CT data were obtained in seven patients. Each patient had two CT scans and one PET scan. The datasets were used to estimate the consistency by matching PET to CT1, CT1 to CT2, and CT2 to PET using the full circle consistency test. It was found that using our method, consistency could be obtained of 4 mm and 1.3 degrees on average. The PET voxels used for registration were 5.15 mm, so the errors compared quite favorably with the voxel size. Cropping the images (removing the scanner bed from images) did not improve the consistency of the algorithm. The transmission scan, however, could potentially be reduced to a single position using this approach. In conclusion, the represented algorithm and validation technique has several features that are attractive from both theoretical and practical point of view, it is a user-independent, automatic validation technique for matching CT and PET scans of the head and neck, which gives the opportunity to compare different image enhancements.     

基于傅里叶梅林变换的图像配准方法

目的:提出一种新的配准框架用于图像引导放射治疗系统中的2D/3D图像配准,有效降低传统方法迭代搜索时间,同时保证放射治疗要求的配准精度。方法:利用傅里叶梅林变换方法对正侧位kV图像与对应方位参考CT图像生成的数字重建放射影像（DRR）进行粗配准,根据傅里叶梅林变换计算得到的二维平移向量以及放射治疗系统的机械几何参数反推出参考CT图像的三维空间位置偏差,更新正侧位的DRR图像,最后通过正侧位kV图像与DRR图像的相似度进行精配准达到临床需求。结果:采用临床金标准数据验证方法的配准性能,实验结果表明,配准误差为0.576 5 mm,平均运行时间为3.34 s。结论:该方法鲁棒性强,对图像的噪声不敏感,人工干预少,可满足临床应用的需求。     

基于单幅X线图像和CT数据的2D/3D配准系统

目的建立基于统一计算架构(CUDA)下以单幅X线图像及CT扫描数据为数据源的2D/3D配准系统,并应用于膝关节在体运动及植入假体稳定性研究。方法首先应用张正友标定法对采集X线图像设备进行标定;其次基于CUDA构架利用光线跟踪算法生成数字影像重建图像,以相关性函数为相似性测度计算2D/3D配准参数;最后以三维激光扫描仪所获得的点云数据进行3D/3D配准,以验证2D/3D配准结果。结果以标本整体位置变换进行配准实验,6自由度平均误差中,位移小于1mm,旋转小于1°。结论此2D/3D配准系统达到了运动检测精度的要求,可以作为研究膝关节运动情况和假体在体稳定性研究的计算平台。     

Automated 2D-3D registration of portal images and CT data using line-segment enhancement

In prostate radiotherapy, setup errors with respect to the patient's bony anatomy can be reduced by aligning 2D megavoltage (MV) portal images acquired during treatment to a reference 3D kilovoltage (kV) CT acquired for treatment planning purposes. The purpose of this study was to evaluate a fully automated 2D-3D registration algorithm to quantify setup errors in 3D through the alignment of line-enhanced portal images and digitally reconstructed radiographs computed from the CT. The line-enhanced images were obtained by correlating the images with a filter bank of short line segments, or "sticks" at different orientations. The proposed methods were validated on (1) accurately collected gold-standard data consisting of a 3D kV cone-beam CT scan of an anthropomorphic phantom of the pelvis and 2D MV portal images in the anterior-posterior (AP) view acquired at 15 different poses and (2) a conventional 3D kV CT scan and weekly 2D MV AP portal images of a patient over 8 weeks. The mean (and standard deviation) of the absolute registration error for rotations around the right-lateral (RL), inferior-superior (IS), and posterior-anterior (PA) axes were 0.212 degree (0.214 degree), 0.055 degree (0.033 degree) and 0.041 degree (0.039 degree), respectively. The corresponding registration errors for translations along the RL, IS, and PA axes were 0.161 (0.131) mm, 0.096 (0.033) mm, and 0.612 (0.485) mm. The mean (and standard deviation) of the total registration error was 0.778 (0.543) mm. Registration on the patient images was successful in all eight cases as determined visually. The results indicate that it is feasible to automatically enhance features in MV portal images of the pelvis for use within a completely automated 2D-3D registration framework for the accurate determination of patient setup errors. They also indicate that it is feasible to estimate all six transformation parameters from a 3D CT of the pelvis and a single portal image in the AP view.     

Model-independent, multimodality deformable image registration by local matching of anatomical features and minimization of elastic energy

With respect to the demands of adaptive and 4D-radiotherapy applications, an algorithm is proposed for a fully automatic, multimodality deformable registration that follows the concept of translational relocation of regularly distributed image subvolumes governed by local anatomical features. Thereby, the problem of global deformable registration is broken down to multiple independent local registration steps which allows for straightforward parallelization of the algorithm. In a subsequent step, possible local misregistrations are corrected for by minimization of the elastic energy of the displacement field under consideration of image information. The final displacement field results from interpolation of the subvolume shift vectors. The algorithm can employ as a similarity measure both the correlation coefficient and mutual information. The latter allows the application to intermodality deformable registration problems. The typical calculation time on a modern multiprocessor PC is well below 1 min, which facilitates almost-interactive, "online" usage. CT-to-MRI and CT-to-cone-beam-CT registrations of head-and-neck data sets are presented, as well as inhale-to-exhale registrations of lung CT data sets. For quantitative evaluation of registration accuracy, a virtual thorax phantom was developed; additionally, a landmark-based evaluation on four lung respiratory-correlated CT data sets was performed. This consistently resulted in average registration residuals on the order of the voxel size or less (3D-residuals approximately 1-2 mm). Summarizing, the presented algorithm allows an accurate multimodality deformable registration with calculation times well below 1 min, and thus bears promise as a versatile basic tool in adaptive and 4D-radiotherapy applications.     

2D/3D registration of endoscopic ultrasound to CT volume data

This paper describes a computer-aided navigation system using image fusion to support endoscopic interventions such as the accurate collection of biopsy specimens. An endoscope provides the physician with real-time ultrasound (US) and a video image. An image slice that corresponds to the corresponding image from the US scan head is derived from a preoperative computed tomography (CT) or magnetic resonance image volume data set using oblique reformatting and displayed side by side with the US image. The position of the image acquired by the US scan head is determined by a miniaturized electromagnetic tracking system (EMTS) after calibrating the endoscope's scan head. The transformation between the patient coordinate system and the preoperative data set is calculated using a 2D/3D registration. This is achieved by calibrating an intraoperative interventional CT slice with an optical tracking system (OTS) using the same algorithm as for the US calibration. The slice is then used for 2D/3D registration with the coordinate system of the preoperative volume. The fiducial registration error (FRE) for the US calibration was 2.0 mm +/- 0.4 mm; the interventional CT FRE was 0.36 +/- 0.12 mm; and the 2D/3D registration target registration error (TRE) was 1.8 +/- 0.3 mm. The point-to-point registration between the OTS and the EMTS had an FRE of 0.9 +/- 0.4 mm. Finally, we found an overall TRE for the complete system to be 3.9 +/- 0.6 mm.     

Assessment of rigid multi-modality image registration consistency using the multiple sub-volume registration (MSR) method

Registration of different imaging modalities such as CT, MRI, functional MRI (fMRI), positron (PET) and single photon (SPECT) emission tomography is used in many clinical applications. Determining the quality of any automatic registration procedure has been a challenging part because no gold standard is available to evaluate the registration. In this note we present a method, called the 'multiple sub-volume registration' (MSR) method, for assessing the consistency of a rigid registration. This is done by registering sub-images of one data set on the other data set, performing a crude non-rigid registration. By analysing the deviations (local deformations) of the sub-volume registrations from the full registration we get a measure of the consistency of the rigid registration. Registration of 15 data sets which include CT, MR and PET images for brain, head and neck, cervix, prostate and lung was performed utilizing a rigid body registration with normalized mutual information as the similarity measure. The resulting registrations were classified as good or bad by visual inspection. The resulting registrations were also classified using our MSR method. The results of our MSR method agree with the classification obtained from visual inspection for all cases (p < 0.02 based on ANOVA of the good and bad groups). The proposed method is independent of the registration algorithm and similarity measure. It can be used for multi-modality image data sets and different anatomic sites of the patient.     

A frequency-based approach to locate common structure for 2D-3D intensity-based registration of setup images in prostate radiotherapy

In many radiotherapy clinics, geometric uncertainties in the delivery of 3D conformal radiation therapy and intensity modulated radiation therapy of the prostate are reduced by aligning the patient's bony anatomy in the planning 3D CT to corresponding bony anatomy in 2D portal images acquired before every treatment fraction. In this paper, we seek to determine if there is a frequency band within the portal images and the digitally reconstructed radiographs (DRRs) of the planning CT in which bony anatomy predominates over non-bony anatomy such that portal images and DRRs can be suitably filtered to achieve high registration accuracy in an automated 2D-3D single portal intensity-based registration framework. Two similarity measures, mutual information and the Pearson correlation coefficient were tested on carefully collected gold-standard data consisting of a kilovoltage cone-beam CT (CBCT) and megavoltage portal images in the anterior-posterior (AP) view of an anthropomorphic phantom acquired under clinical conditions at known poses, and on patient data. It was found that filtering the portal images and DRRs during the registration considerably improved registration performance. Without filtering, the registration did not always converge while with filtering it always converged to an accurate solution. For the pose-determination experiments conducted on the anthropomorphic phantom with the correlation coefficient, the mean (and standard deviation) of the absolute errors in recovering each of the six transformation parameters were Theta(x):0.18(0.19) degrees, Theta(y):0.04(0.04) degrees, Theta(z):0.04(0.02) degrees, t(x):0.14(0.15) mm, t(y):0.09(0.05) mm, and t(z):0.49(0.40) mm. The mutual information-based registration with filtered images also resulted in similarly small errors. For the patient data, visual inspection of the superimposed registered images showed that they were correctly aligned in all instances. The results presented in this paper suggest that robust and accurate registration can be achieved with intensity-based methods by focusing on rigid bony structures in the images while diminishing the influence of artifacts with similar frequencies as soft tissue.     

2D-3D registration of coronary angiograms for cardiac procedure planning and guidance

We present a completely automated 2D-3D registration technique that accurately maps a patient-specific heart model, created from preoperative images, to the patient's orientation in the operating room. This mapping is based on the registration of preoperatively acquired 3D vascular data with intraoperatively acquired angiograms. Registration using both single and dual-plane angiograms is explored using simulated but realistic datasets that were created from clinical images. Heart deformations and cardiac phase mismatches are taken into account in our validation using a digital 4D human heart model. In an ideal situation where the pre- and intraoperative images were acquired at identical time points within the cardiac cycle, the single-plane and the dual-plane registrations resulted in 3D root-mean-square (rms) errors of 1.60 +/- 0.21 and 0.53 +/- 0.08 mm, respectively. When a 10% timing offset was added between the pre- and the intraoperative acquisitions, the single-plane registration approach resulted in inaccurate registrations in the out-of-plane axis, whereas the dual-plane registration exhibited a 98% success rate with a 3D rms error of 1.33 +/- 0.28 mm. When all potential sources of error were included, namely, the anatomical background, timing offset, and typical errors in the vascular tree reconstruction, the dual-plane registration performed at 94% with an accuracy of 2.19 +/- 0.77 mm.     

Automatic registration between reference and on-board digital tomosynthesis images for positioning verification

The authors developed a hybrid multiresolution rigid-body registration technique to automatically register reference digital tomosynthesis (DTS) images with on-board DTS images to guide patient positioning in radiation therapy. This hybrid registration technique uses a faster but less accurate static method to achieve an initial registration, followed by a slower but more accurate adaptive method to fine tune the registration. A multiresolution scheme is employed in the registration to further improve the registration accuracy, robustness, and efficiency. Normalized mutual information is selected as the criterion for the similarity measure and the downhill simplex method is used as the search engine. This technique was tested using image data both from an anthropomorphic chest phantom and from eight head-and-neck cancer patients. The effects of the scan angle and the region-of-interest (ROI) size on the registration accuracy and robustness were investigated. The necessity of using the adaptive registration method in the hybrid technique was validated by comparing the results of the static method and the hybrid method. With a 44 degrees scan angle and a large ROI covering the entire DTS volume, the average of the registration capture ranges in single-axis simulations was between -31 and +34 deg for rotations and between -89 and +78 mm for translations in the phantom study, and between -38 and +38 deg for rotations and between -58 and +65 mm for translations in the patient study. Decreasing the DTS scan angle from 44 degrees to 22 degrees mainly degraded the registration accuracy and robustness for the out-of-plane rotations. Decreasing the ROI size from the entire DTS volume to the volume surrounding the spinal cord reduced the capture ranges to between -23 and +18 deg for rotations and between -33 and +43 mm for translations in the phantom study, and between -18 and +25 deg for rotations and between -35 and +39 mm for translations in the patient study. Results also showed that the hybrid registration technique had much larger capture ranges than the static method alone in registering the out-of-plane rotations.     

Patient positioning method based on binary image correlation between two edge images for proton-beam radiation therapy

A new technique based on normalized binary image correlation between two edge images has been proposed for positioning proton-beam radiotherapy patients. A Canny edge detector was used to extract two edge images from a reference x-ray image and a test x-ray image of a patient before positioning. While translating and rotating the edged test image, the absolute value of the normalized binary image correlation between the two edge images is iteratively maximized. Each time before rotation, dilation is applied to the edged test image to avoid a steep reduction of the image correlation. To evaluate robustness of the proposed method, a simulation has been carried out using 240 simulated edged head front-view images extracted from a reference image by varying parameters of the Canny algorithm with a given range of rotation angles and translation amounts in x and y directions. It was shown that resulting registration errors have an accuracy of one pixel in x and y directions and zero degrees in rotation, even when the number of edge pixels significantly differs between the edged reference image and the edged simulation image. Subsequently, positioning experiments using several sets of head, lung, and hip data have been performed. We have observed that the differences of translation and rotation between manual positioning and the proposed method were within one pixel in translation and one degree in rotation. From the results of the validation study, it can be concluded that a significant reduction in workload for the physicians and technicians can be achieved with this method.     

Registration of 2D x-ray images to 3D MRI by generating pseudo-CT data

Spatial and soft tissue information provided by magnetic resonance imaging can be very valuable during image-guided procedures, where usually only real-time two-dimensional (2D) x-ray images are available. Registration of 2D x-ray images to three-dimensional (3D) magnetic resonance imaging (MRI) data, acquired prior to the procedure, can provide optimal information to guide the procedure. However, registering x-ray images to MRI data is not a trivial task because of their fundamental difference in tissue contrast. This paper presents a technique that generates pseudo-computed tomography (CT) data from multi-spectral MRI acquisitions which is sufficiently similar to real CT data to enable registration of x-ray to MRI with comparable accuracy as registration of x-ray to CT. The method is based on a k-nearest-neighbors (kNN)-regression strategy which labels voxels of MRI data with CT Hounsfield Units. The regression method uses multi-spectral MRI intensities and intensity gradients as features to discriminate between various tissue types. The efficacy of using pseudo-CT data for registration of x-ray to MRI was tested on ex vivo animal data. 2D-3D registration experiments using CT and pseudo-CT data of multiple subjects were performed with a commonly used 2D-3D registration algorithm. On average, the median target registration error for registration of two x-ray images to MRI data was approximately 1 mm larger than for x-ray to CT registration. The authors have shown that pseudo-CT data generated from multi-spectral MRI facilitate registration of MRI to x-ray images. From the experiments it could be concluded that the accuracy achieved was comparable to that of registering x-ray images to CT data.     

Dynamic ventilation imaging from four-dimensional computed tomography

A novel method for dynamic ventilation imaging of the full respiratory cycle from four-dimensional computed tomography (4D CT) acquired without added contrast is presented. Three cases with 4D CT images obtained with respiratory gated acquisition for radiotherapy treatment planning were selected. Each of the 4D CT data sets was acquired during resting tidal breathing. A deformable image registration algorithm mapped each (voxel) corresponding tissue element across the 4D CT data set. From local average CT values, the change in fraction of air per voxel (i.e. local ventilation) was calculated. A 4D ventilation image set was calculated using pairs formed with the maximum expiration image volume, first the exhalation then the inhalation phases representing a complete breath cycle. A preliminary validation using manually determined lung volumes was performed. The calculated total ventilation was compared to the change in contoured lung volumes between the CT pairs (measured volume). A linear regression resulted in a slope of 1.01 and a correlation coefficient of 0.984 for the ventilation images. The spatial distribution of ventilation was found to be case specific and a 30% difference in mass-specific ventilation between the lower and upper lung halves was found. These images may be useful in radiotherapy planning.     

An algorithm for mapping the catheter tip position on a fluorograph to the three-dimensional position in magnetic resonance angiography volume data

This paper proposes an algorithm which maps the position of a catheter tip on a fluorograph to the 3D position in magnetic resonance angiography (MRA) data. This algorithm was assessed for its accuracy. We designed an algorithm consisting of a registration step and a recognition step. The registration step registers MRA and fluorography data using a digital subtraction angiography (DSA) image. The recognition step recognizes the position in the MRA data corresponding to the catheter tip position on a fluorograph. We checked the accuracy of the recognition step by employing an artificial data set consisting of 3D image data (64 x 64 x 64 matrix) and its projection image (92 x 92 matrix) and the accuracy of the registration step with the aid of three of the 3D time-of-flight MRA data sets (256 x 256 matrix and 60 slices) and their projection images in the form of DSA images. The accuracy of the recognition step depended upon that of the registration. When there was no misregistration, all of the mean errors were less than 0.2 mm. The mean errors of the registration step were 0.273 mm and 0.226 mm, respectively, for the longitudinal shift along the X and Y axes, 0.478 degrees, 1.203 degrees and 0.208 degrees, respectively, for the rotation angles around the X, Y and Z axes and 0.020 times for the magnification. The mean image error between the projection image of the registered MRA data and that of the MRA data which were employed as the DSA image was 0.034 mm.     

Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information

An automatic method for delineating the prostate (including the seminal vesicles) in three-dimensional magnetic resonance scans is presented. The method is based on nonrigid registration of a set of prelabeled atlas images. Each atlas image is nonrigidly registered with the target patient image. Subsequently, the deformed atlas label images are fused to yield a single segmentation of the patient image. The proposed method is evaluated on 50 clinical scans, which were manually segmented by three experts. The Dice similarity coefficient (DSC) is used to quantify the overlap between the automatic and manual segmentations. We investigate the impact of several factors on the performance of the segmentation method. For the registration, two similarity measures are compared: Mutual information and a localized version of mutual information. The latter turns out to be superior (median DeltaDSC approximately equal 0.02, p < 0.01 with a paired two-sided Wilcoxon test) and comes at no added computational cost, thanks to the use of a novel stochastic optimization scheme. For the atlas fusion step we consider a majority voting rule and the "simultaneous truth and performance level estimation" algorithm, both with and without a preceding atlas selection stage. The differences between the various fusion methods appear to be small and mostly not statistically significant (p > 0.05). To assess the influence of the atlas composition, two atlas sets are compared. The first set consists of 38 scans of healthy volunteers. The second set is constructed by a leave-one-out approach using the 50 clinical scans that are used for evaluation. The second atlas set gives substantially better performance (DeltaDSC=0.04, p < 0.01), stressing the importance of a careful atlas definition. With the best settings, a median DSC of around 0.85 is achieved, which is close to the median interobserver DSC of 0.87. The segmentation quality is especially good at the prostate-rectum interface, where the segmentation error remains below 1 mm in 50% of the cases and below 1.5 mm in 75% of the cases.     

Fusion of coregistered cross-modality images using a temporally alternating display method

As an aid to the interpretation of functional images, cross-modality coregistration of functional and anatomical images has grown rapidly. Various ways of easily interpreting and visualising coregistered images have previously been investigated; for their display, an intensity-weighted temporally alternating method is used. For brain images, geometric registration involves the automatic alignment method, using the head scalp boundary extracted from the sinogram of a PET emission scan and a surface-matching algorithm; images of the chest or abdomen are registered semi-automatically using a paired point matching algorithm. For the simultaneous display of geometrically registered images, rapid image switching is applied; both images are written with independent colour scales. The rapidly alternating display of two images, synchronised with monitor scanning, induces the fusion of images in the human visual perception system. The accuracy of registration of PET and MRI images is within 2 mm for two point sets. A resulting image is intensified by weighting the display time and/or controlling the intensity map of each image with the degree of interest. This method may be useful for the interpretation and visualisation of coregistered images.     

Accuracy of treatment planning based on stereolithography in computer assisted surgery

Three-dimensional stereolithographic models (SL models), made of solid acrylic resin derived from computed-tomography (CT) data, are an established tool for preoperative treatment planning in numerous fields of medicine. An innovative approach, combining stereolithography with computer-assisted point-to-point navigation, can support the precise surgical realization of a plan that has been defined on an SL model preoperatively. The essential prerequisites for the application of such an approach are: (1) The accuracy of the SL models (including accuracy of the CT scan and correspondence of the model with the patient's anatomy) and (2) the registration method used for the transfer of the plan from the SL model to the patient (i.e., whether the applied registration markers can be added to the SL model corresponding to the markers at the patient with an accuracy that keeps the "cumulative error" at the end of the chain of errors, in the order of the accuracy of contemporary navigation systems). In this study, we focus on these two topics: By applying image-matching techniques, we fuse the original CT data of the patient with the corresponding CT data of the scanned SL model, and measure the deviations of defined parameter (e.g., distances between anatomical points). To evaluate the registration method used for the planning transfer, we apply a point-merge algorithm, using four marker points that should be located at exactly corresponding positions at the patient and at connective bars that are added to the surface of the SL model. Again, deviations at defined anatomical structures are measured and analyzed statistically. Our results prove sufficient correspondence of the two data sets and accuracy of the registration method for routine clinical application. The evaluation of the SL model accuracy revealed an arithmetic mean of the relative deviations from 0.8% to 5.4%, with an overall mean deviation of 2.2%. Mean deviations of the investigated anatomical structures ranged from 0.8 mm to 3.2 mm. An overall mean (comprising all structures) of 2.5 mm was found. The fiducial registration error of the point-merge algorithm ranged from 1.0 mm to 1.4 mm. The evaluated chain of errors showed a mean deviation of 2.5 mm. This study verifies that preoperative planning on SL models and intraoperative transfer of this plan with computer assisted navigation is a suitable and sufficiently reliable method for clinical applications.     

A Numerical Preoperative Planning Model to Predict Arterial Deformations in Endovascular Aortic Aneurysm Repair

Endovascular aneurysm repair is rapidly emerging as the primary preferred method for treating abdominal aortic aneurysm. In this image-guided interventional procedure, to obtain the roadmap and decrease contrast injections, preoperative CT images are overlaid onto live fluoroscopy images using various 2D/3D image fusion techniques. However, the structural changes due to the insertion of stiff tools degrade the fusion accuracy. To correct the mismatch and quantify the intraoperative deformations, we present a patient-specific biomechanical model of the aorto-iliac structure and its surrounding tissues. The predictive capability of the model was evaluated against intraoperative data for a group of four patients. Incorporating the perivascular tissues into the model significantly improved the results and the mean distance between the real and simulated endovascular tools was 2.99?±?1.78 mm on the ipsilateral side and 4.59?±?3.25 mm on the contralateral side. Moreover, the distance between the deformed iliac ostia and their corresponding landmarks on intraoperative images was 2.99?±?2.48 mm.     

Automatic 3D-to-2D registration for CT and dual-energy digital radiography for calcification detection

We are investigating three-dimensional (3D) to two-dimensional (2D) registration methods for computed tomography (CT) and dual-energy digital radiography (DEDR). CT is an established tool for the detection of cardiac calcification. DEDR could be a cost-effective alternative screening tool. In order to utilize CT as the "gold standard" to evaluate the capability of DEDR images for the detection and localization of calcium, we developed an automatic, intensity-based 3D-to-2D registration method for 3D CT volumes and 2D DEDR images. To generate digitally reconstructed radiography (DRR) from the CT volumes, we developed several projection algorithms using the fast shear-warp method. In particular, we created a Gaussian-weighted projection for this application. We used normalized mutual information (NMI) as the similarity measurement. Simulated projection images from CT values were fused with the corresponding DEDR images to evaluate the localization of cardiac calcification. The registration method was evaluated by digital phantoms, physical phantoms, and clinical data sets. The results from the digital phantoms show that the success rate is 100% with a translation difference of less than 0.8 mm and a rotation difference of less than 0.2 degrees. For physical phantom images, the registration accuracy is 0.43 +/- 0.24 mm. Color overlay and 3D visualization of clinical images show that the two images registered well. The NMI values between the DRR and DEDR images improved from 0.21 +/- 0.03 before registration to 0.25 +/- 0.03 after registration. Registration errors measured from anatomic markers decreased from 27.6 +/- 13.6 mm before registration to 2.5 +/- 0.5 mm after registration. Our results show that the automatic 3D-to-2D registration is accurate and robust. This technique can provide a useful tool for correlating DEDR with CT images for screening coronary artery calcification.