Automated 2D-3D registration of a radiograph and a cone beam CT using line-segment enhancement

The objective of this study was to develop a fully automated two-dimensional (2D)-three-dimensional (3D) registration framework to quantify setup deviations in prostate radiation therapy from cone beam CT (CBCT) data and a single AP radiograph. A kilovoltage CBCT image and kilovoltage AP radiograph of an anthropomorphic phantom of the pelvis were acquired at 14 accurately known positions. The shifts in the phantom position were subsequently estimated by registering digitally reconstructed radiographs (DRRs) from the 3D CBCT scan to the AP radiographs through the correlation of enhanced linear image features mainly representing bony ridges. Linear features were enhanced by filtering the images with "sticks," short line segments which are varied in orientation to achieve the maximum projection value at every pixel in the image. The mean (and standard deviations) of the absolute errors in estimating translations along the three orthogonal axes in millimeters were 0.134 (0.096) AP(out-of-plane), 0.021 (0.023) ML and 0.020 (0.020) SI. The corresponding errors for rotations in degrees were 0.011 (0.009) AP, 0.029 (0.016) ML (out-of-plane), and 0.030 (0.028) SI (out-of-plane). Preliminary results with megavoltage patient data have also been reported. The results suggest that it may be possible to enhance anatomic features that are common to DRRs from a CBCT image and a single AP radiography of the pelvis for use in a completely automated and accurate 2D-3D registration framework for setup verification in prostate radiotherapy. This technique is theoretically applicable to other rigid bony structures such as the cranial vault or skull base and piecewise rigid structures such as the spine.     

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.     

Registration of electronic portal images for patient set-up verification

Images acquired from an electronic portal imaging device are aligned with digitally reconstructed radiographs (DRRs) or other portal images to verify patient positioning during radiation therapy. Most of the currently available computer aided registration methods are based on the manual placement of corresponding landmarks. The purpose of the paper is twofold: (a) the establishment of a methodology for patient set-up verification during radiotherapy based on the registration of electronic portal images, and (b) the evaluation of the proposed methodology in a clinical environment. The estimation of set-up errors, using the proposed methodology, can be accomplished by matching the portal image of the current fraction of the treatment with the portal image of the baseline treatment (reference portal image) using a nearly automated technique. The proposed registration method is tested on a number of phantom data as well as on data from four patients. The phantom data included portal images that corresponded to various positions of the phantom on the treatment couch. For each patient, a set of 30 portal images was used. For the phantom data (for both transverse and lateral portal images), the maximum absolute deviations of the translational shifts were within 1.5 mm, whereas the in-plane rotation angle error was less than 0.5 degrees. The two-way Anova revealed no statistical significant variability both within observer and between-observer measurements (P > 0.05). For the patient data, the mean values obtained with manual and the proposed registration methods were within 0.5 mm. In conclusion, the proposed registration method has been incorporated within a system, called ESTERR-PRO. Its image registration capability achieves high accuracy and both intra- and inter-user reproducibility. The system is fully operational within the Radiotherapy Department of 'HYGEIA' Hospital in Athens and it could be easily installed in any other clinical environment since it requires standardized hardware specifications and minimal human intervention.     

Phantom validation of coregistration of PET and CT for image-guided radiotherapy

Radiotherapy treatment planning integrating positron emission tomography (PET) and computerized tomography (CT) is rapidly gaining acceptance in the clinical setting. Although hybrid systems are available, often the planning CT is acquired on a dedicated system separate from the PET scanner. A limiting factor to using PET data becomes the accuracy of the CT/PET registration. In this work, we use phantom and patient validation to demonstrate a general method for assessing the accuracy of CT/PET image registration and apply it to two multi-modality image registration programs. An IAEA (International Atomic Energy Association) brain phantom and an anthropomorphic head phantom were used. Internal volumes and externally mounted fiducial markers were filled with CT contrast and 18F-fluorodeoxyglucose (FDG). CT, PET emission, and PET transmission images were acquired and registered using two different image registration algorithms. CT/PET Fusion (GE Medical Systems, Milwaukee, WI) is commercially available and uses a semi-automated initial step followed by manual adjustment. Automatic Mutual Information-based Registration (AMIR), developed at our institution, is fully automated and exhibits no variation between repeated registrations. Registration was performed using distinct phantom structures; assessment of accuracy was determined from registration of the calculated centroids of a set of fiducial markers. By comparing structure-based registration with fiducial-based registration, target registration error (TRE) was computed at each point in a three-dimensional (3D) grid that spans the image volume. Identical methods were also applied to patient data to assess CT/PET registration accuracy. Accuracy was calculated as the mean with standard deviation of the TRE for every point in the 3D grid. Overall TRE values for the IAEA brain phantom are: CT/PET Fusion = 1.71 +/- 0.62 mm, AMIR = 1.13 +/- 0.53 mm; overall TRE values for the anthropomorphic head phantom are: CT/PET Fusion = 1.66 +/- 0.53 mm, AMIR = 1.15 +/- 0.48 mm. Precision (repeatability by a single user) measured for CT/PET Fusion: IAEA phantom = 1.59 +/- 0.67 mm and anthropomorphic head phantom = 1.63 +/- 0.52 mm. (AMIR has exact precision and so no measurements are necessary.) One sample patient demonstrated the following accuracy results: CT/PET Fusion = 3.89 +/- 1.61 mm, AMIR = 2.86 +/- 0.60 mm. Semi-automatic and automatic image registration methods may be used to facilitate incorporation of PET data into radiotherapy treatment planning in relatively rigid anatomic sites, such as head and neck. The overall accuracies in phantom and patient images are < 2 mm and < 4 mm, respectively, using either registration algorithm. Registration accuracy may decrease, however, as distance from the initial registration points (CT/PET fusion) or center of the image (AMIR) increases. Additional information provided by PET may improve dose coverage to active tumor subregions and hence tumor control. This study shows that the accuracy obtained by image registration with these two methods is well suited for image-guided radiotherapy.     

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.     

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.     

Optimization of slice sensitivity profile for radiographic tomosynthesis

Similar to other tomographic imaging modalities, the slice sensitivity profile (SSP) is an important image quality metric for radiographic tomosynthesis. In this study, the relationship between the acquisition angular range (Theta) and the SSP for the linear trajectory system was carefully investigated from both theoretical and experimental perspectives. A mathematical SSP model was derived for arbitrary points in the reconstructed volume. We used a newly developed flat-panel tomosynthesis prototype system to experimentally validate the mathematical model from 20 degrees (+/-10 degrees) to 60 degrees (+/-30 degrees) angular ranges. The SSP was measured by imaging an edge phantom placed at an angle with respect to the detector plane using the modulation transfer function degradation (MTF-d) method. In addition to the experiments, computer simulations were performed to investigate the relationship in a wider angular range (2.5 degrees to 60 degrees). Furthermore, image data from an anthropomorphic phantom were collected to corroborate the system analysis. All the images in this study were constructed using a 3D view-weighted cone-beam filtered backprojection algorithm (3D VW CB-FBP). The theoretical analysis reveals that the SSP of linear trajectory tomosynthesis is inversely proportional to tan(Theta/2). This theory was supported by both simulation (chi2=1.415, DF=7, p =0.985) and phantom experiment (r=0.999, p < 0.001) and was further confirmed by an analysis of the reconstructed images of an anthropomorphic phantom. The results imply that the benefit of narrower SSP by increasing angular range quickly diminishes once beyond 40 degrees. The advantages of the MTF-d method were also demonstrated.     

Progressive attenuation fields: fast 2D-3D image registration without precomputation

Computation of digitally reconstructed radiograph (DRR) images is the rate-limiting step in most current intensity-based algorithms for the registration of three-dimensional (3D) images to two-dimensional (2D) projection images. This paper introduces and evaluates the progressive attenuation field (PAF), which is a new method to speed up DRR computation. A PAF is closely related to an attenuation field (AF). A major difference is that a PAF is constructed on the fly as the registration proceeds; it does not require any precomputation time, nor does it make any prior assumptions of the patient pose or limit the permissible range of patient motion. A PAF effectively acts as a cache memory for projection values once they are computed, rather than as a lookup table for precomputed projections like standard AFs. We use a cylindrical attenuation field parametrization, which is better suited for many medical applications of 2D-3D registration than the usual two-plane parametrization. The computed attenuation values are stored in a hash table for time-efficient storage and access. Using clinical gold-standard spine image data sets from five patients, we demonstrate consistent speedups of intensity-based 2D-3D image registration using PAF DRRs by a factor of 10 over conventional ray casting DRRs with no decrease of registration accuracy or robustness.     

Automatic localization of vertebral levels in x-ray fluoroscopy using 3D-2D registration: a tool to reduce wrong-site surgery

Surgical targeting of the incorrect vertebral level (wrong-level surgery) is among the more common wrong-site surgical errors, attributed primarily to the lack of uniquely identifiable radiographic landmarks in the mid-thoracic spine. The conventional localization method involves manual counting of vertebral bodies under fluoroscopy, is prone to human error and carries additional time and dose. We propose an image registration and visualization system (referred to as LevelCheck), for decision support in spine surgery by automatically labeling vertebral levels in fluoroscopy using a GPU-accelerated, intensity-based 3D-2D (namely CT-to-fluoroscopy) registration. A gradient information (GI) similarity metric and a CMA-ES optimizer were chosen due to their robustness and inherent suitability for parallelization. Simulation studies involved ten patient CT datasets from which 50?000 simulated fluoroscopic images were generated from C-arm poses selected to approximate the C-arm operator and positioning variability. Physical experiments used an anthropomorphic chest phantom imaged under real fluoroscopy. The registration accuracy was evaluated as the mean projection distance (mPD) between the estimated and true center of vertebral levels. Trials were defined as successful if the estimated position was within the projection of the vertebral body (namely mPD <5?mm). Simulation studies showed a success rate of 99.998% (1 failure in 50?000 trials) and computation time of 4.7?s on a midrange GPU. Analysis of failure modes identified cases of false local optima in the search space arising from longitudinal periodicity in vertebral structures. Physical experiments demonstrated the robustness of the algorithm against quantum noise and x-ray scatter. The ability to automatically localize target anatomy in fluoroscopy in near-real-time could be valuable in reducing the occurrence of wrong-site surgery while helping to reduce radiation exposure. The method is applicable beyond the specific case of vertebral labeling, since any structure defined in pre-operative (or intra-operative) CT or cone-beam CT can be automatically registered to the fluoroscopic scene.     

Effects of x-ray and CT image enhancements on the robustness and accuracy of a rigid 3D/2D image registration

A rigid body three-dimensional/two-dimensional (3D/2D) registration method has been implemented using mutual information, gradient ascent, and 3D texturemap-based digitally reconstructed radiographs. Nine combinations of commonly used x-ray and computed tomography (CT) image enhancement methods, including window leveling, histogram equalization, and adaptive histogram equalization, were examined to assess their effects on accuracy and robustness of the registration method. From a set of experiments using an anthropomorphic chest phantom, we were able to draw several conclusions. First, the CT and x-ray preprocessing combination with the widest attraction range was the one that linearly stretched the histograms onto the entire display range on both CT and x-ray images. The average attraction ranges of this combination were 71.3 mm and 61.3 deg in the translation and rotation dimensions, respectively, and the average errors were 0.12 deg and 0.47 mm. Second, the combination of the CT image with tissue and bone information and the x-ray images with adaptive histogram equalization also showed subvoxel accuracy, especially the best in the translation dimensions. However, its attraction ranges were the smallest among the examined combinations (on average 36 mm and 19 deg). Last the bone-only information on the CT image did not show convergency property to the correct registration.     

Evaluation of three types of reference image data for external beam radiotherapy target localization using digital tomosynthesis (DTS)

Digital tomosynthesis (DTS) is a fast, low-dose three-dimensional (3D) imaging approach which yields slice images with excellent in-plane resolution, though low plane-to-plane resolution. A stack of DTS slices can be reconstructed from a single limited-angle scan, with typical scan angles ranging from 10 degrees to 40 degrees and acquisition times of less than 10 s. The resulting DTS slices show soft tissue contrast approaching that of full cone-beam CT. External beam radiotherapy target localization using DTS requires the registration of on-board DTS images with corresponding reference image data. This study evaluates three types of reference volume: original reference CT, exact reference DTS (RDTS), and a more computationally efficient approximate reference DTS (RDTSapprox), as well as three different DTS scan angles (22 degrees, 44 degrees, and 65 degrees) for the DTS target localization task. Three-dimensional mutual information (MI) shared between reference and onboard DTS volumes was computed in a region surrounding the spine of a chest phantom, as translations spanning +/-5 mm and rotations spanning +/-5 degrees were simulated along each dimension in the reference volumes. The locations of the MI maxima were used as surrogates for registration accuracy, and the width of the MI peaks were used to characterize the registration robustness. The results show that conventional treatment planning CT volumes are inadequate reference volumes for direct registration with on-board DTS data. The efficient RDTSapprox method also appears insufficient for MI-based registration without further refinement of the technique, though it may be suitable for manual registration performed by a human observer. The exact RDTS volumes, on the other hand, delivered a 3D DTS localization accuracy of 0.5 mm and 0.50 along each axis, using only a single 44 degrees coronal on-board DTS scan of the chest phantom.     

Wobbled splatting--a fast perspective volume rendering method for simulation of x-ray images from CT

3D/2D registration, the automatic assignment of a global rigid-body transformation matching the coordinate systems of patient and preoperative volume scan using projection images, is an important topic in image-guided therapy and radiation oncology. A crucial part of most 3D/2D registration algorithms is the fast computation of digitally rendered radiographs (DRRs) to be compared iteratively to radiographs or portal images. Since registration is an iterative process, fast generation of DRRs-which are perspective summed voxel renderings-is desired. In this note, we present a simple and rapid method for generation of DRRs based on splat rendering. As opposed to conventional splatting, antialiasing of the resulting images is not achieved by means of computing a discrete point spread function (a so-called footprint), but by stochastic distortion of either the voxel positions in the volume scan or by the simulation of a focal spot of the x-ray tube with non-zero diameter. Our method generates slightly blurred DRRs suitable for registration purposes at framerates of approximately 10 Hz when rendering volume images with a size of 30 MB.     

2D/3D刚性图像配准在肿瘤放疗计划中的应用

目的:在肿瘤放疗中,2D/3D刚性图像配准技术是精确定位病人重要保证。方法:数字重建放射影像技术是2D/3D配准中最为关键的部分,一定程度上影响着配准效率。在对数字重建放射影像进行重点研究的基础上,实现了一种基于Bresenham方法的快速数字影像重建算法,并利用腹部CT体数据进行了2D/3D配准实验。实验以互信息、模式强度和梯度差分为相似性测度并采用了Powell-Brent优化算法。结果:在数字重建放射图像算法实验中,Bresemham方法相比于另外两种光线跟踪算法,时间仅需0.467 s。在2D/3D配准实验中,对三种相似性测度的实验结果进行了比较,互信息和梯度差分有较好的配准结果,模式强度仍存在不少问题。结论:实验结果表明,利用Bresenham方法产生的数字重建影像能使得配准具有较高精度,但配准时间仍然较长。     

Novel image registration quality evaluator (RQE) with an implementation for automated patient positioning in cranial radiation therapy

In external beam radiation therapy, digitally reconstructed radiographs (DRRs) and portal images are used to verify patient setup based either on a visual comparison or, less frequently, with automated registration algorithms. A registration algorithm can be trapped in local optima due to irregularity of patient anatomy, image noise and artifacts, and/or out-of-plane shifts, resulting in an incorrect solution. Thus, human observation, which is subjective, is still required to check the registration result. We propose to use a novel image registration quality evaluator (RQE) to automatically identify misregistrations as part of an algorithm-based decision-making process for verification of patient positioning. A RQE, based on an adaptive pattern classifier, is generated from a pair of reference and target images to determine the acceptability of a registration solution given an optimization process. Here we applied our RQE to patient positioning for cranial radiation therapy. We constructed two RQEs-one for the evaluation of intramodal registrations (i.e., portal-portal); the other for intermodal registrations (i.e., portal-DRR). Mutual information, because of its high discriminatory ability compared with other measures (i.e., correlation coefficient and partitioned intensity uniformity), was chosen as the test function for both RQEs. We adopted 1 mm translation and 1 degree rotation as the maximal acceptable registration errors, reflecting desirable clinical setup tolerances for cranial radiation therapy. Receiver operating characteristic analysis was used to evaluate the performance of the RQE, including computations of sensitivity and specificity. The RQEs showed very good performance for both intramodal and intermodal registrations using simulated and phantom data. The sensitivity and the specificity were 0.973 and 0.936, respectively, for the intramodal RQE using phantom data. Whereas the sensitivity and the specificity were 0.961 and 0.758, respectively, for the intermodal RQE using phantom data. Phantom experiments also indicated our RQEs detected out-of-plane deviations exceeding 2.5 mm and 2.50. A preliminary retrospective clinical study of the RQE on cranial portal imaging also yielded good sensitivity > or = 0.857) and specificity (> or = 0.987). Clinical implementation of a RQE could potentially reduce the involvement of the human observer for routine patient positioning verification, while increasing setup accuracy and reducing setup verification time.     

3D interfractional patient position verification using 2D-3D registration of orthogonal images

Reproducible positioning of the patient during fractionated external beam radiation therapy is imperative to ensure that the delivered dose distribution matches the planned one. In this paper, we expand on a 2D-3D image registration method to verify a patient's setup in three dimensions (rotations and translations) using orthogonal portal images and megavoltage digitally reconstructed radiographs (MDRRs) derived from CT data. The accuracy of 2D-3D registration was improved by employing additional image preprocessing steps and a parabolic fit to interpolate the parameter space of the cost function utilized for registration. Using a humanoid phantom, precision for registration of three-dimensional translations was found to be better than 0.5 mm (1 s.d.) for any axis when no rotations were present. Three-dimensional rotations about any axis were registered with a precision of better than 0.2 degrees (1 s.d.) when no translations were present. Combined rotations and translations of up to 4 degrees and 15 mm were registered with 0.4 degrees and 0.7 mm accuracy for each axis. The influence of setup translations on registration of rotations and vice versa was also investigated and mostly agrees with a simple geometric model. Additionally, the dependence of registration accuracy on three cost functions, angular spacing between MDRRs, pixel size, and field-of-view, was examined. Best results were achieved by mutual information using 0.5 degrees angular spacing and a 10 x 10 cm2 field-of-view with 140 x 140 pixels. Approximating patient motion as rigid transformation, the registration method is applied to two treatment plans and the patients' setup errors are determined. Their magnitude was found to be < or = 6.1 mm and < or = 2.7 degrees for any axis in all of the six fractions measured for each treatment plan.     

A feasibility study of mutual information based setup error estimation for radiotherapy.

We have investigated a fully automatic setup error estimation method that aligns DRRs (digitally reconstructed radiographs) from a three-dimensional planning computed tomography image onto two-dimensional radiographs that are acquired in a treatment room. We have chosen a MI (mutual information)-based image registration method, hoping for robustness to intensity differences between the DRRs and the radiographs. The MI-based estimator is fully automatic since it is based on the image intensity values without segmentation. Using 10 repeated scans of an anthropomorphic chest phantom in one position and two single scans in two different positions, we evaluated the performance of the proposed method and a correlation-based method against the setup error determined by fiducial marker-based method. The mean differences between the proposed method and the fiducial marker-based method were smaller than 1 mm for translational parameters and 0.8 degree for rotational parameters. The standard deviations of estimates from the proposed method due to detector noise were smaller than 0.3 mm and 0.07 degree for the translational parameters and rotational parameters, respectively.     

Registration of DRRs and portal images for verification of stereotactic body radiotherapy: a feasibility study in lung cancer treatment

Image guidance has become a pre-requisite for hypofractionated radiotherapy where the applied dose per fraction is increased. Particularly in stereotactic body radiotherapy (SBRT) for lung tumours, one has to account for set-up errors and intrafraction tumour motion. In our feasibility study, we compared digitally reconstructed radiographs (DRRs) of lung lesions with MV portal images (PIs) to obtain the displacement of the tumour before irradiation. The verification of the tumour position was performed by rigid intensity based registration and three different merit functions such as the sum of squared pixel intensity differences, normalized cross correlation and normalized mutual information. The registration process then provided a translation vector that defines the displacement of the target in order to align the tumour with the isocentre. To evaluate the registration algorithms, 163 test images were created and subsequently, a lung phantom containing an 8 cm(3) tumour was built. In a further step, the registration process was applied on patient data, containing 38 tumours in 113 fractions. To potentially improve registration outcome, two filter types (histogram equalization and display equalization) were applied and their impact on the registration process was evaluated. Generated test images showed an increase in successful registrations when applying a histogram equalization filter whereas the lung phantom study proved the accuracy of the selected algorithms, i.e. deviations of the calculated translation vector for all test algorithms were below 1 mm. For clinical patient data, successful registrations occurred in about 59% of anterior-posterior (AP) and 46% of lateral projections, respectively. When patients with a clinical target volume smaller than 10 cm(3) were excluded, successful registrations go up to 90% in AP and 50% in lateral projection. In addition, a reliable identification of the tumour position was found to be difficult for clinical target volumes at the periphery of the lung, close to backbone or diaphragm. Moreover, tumour movement during shallow breathing strongly influences image acquisition for patient positioning. Recapitulating, 2D/3D image registration for lung tumours is an attractive alternative compared to conventional CT verification of the tumour position. Nevertheless, size and location of the tumour are limiting parameters for an accurate registration process.     

Performance of a feature-based algorithm for 3D-3D registration of CT angiography to cone-beam CT for endovascular repair of complex abdominal aortic aneurysms

Background

A crucial step in image fusion for intraoperative guidance during endovascular procedures is the registration of preoperative computed tomography angiography (CTA) with intraoperative Cone Beam CT (CBCT). Automatic tools for image registration facilitate the 3D image guidance workflow. However their performance is not always satisfactory. The aim of this study is to assess the accuracy of a new fully automatic, feature-based algorithm for 3D3D registration of CTA to CBCT.

Methods

The feature-based algorithm was tested on clinical image datasets from 14 patients undergoing complex endovascular aortic repair. Deviations in Euclidian distances between vascular as well as bony landmarks were measured and compared to an intensity-based, normalized mutual information algorithm.

Results

The results for the feature-based algorithm showed that the median 3D registration error between the anatomical landmarks of CBCT and CT images was less than 3 mm. The feature-based algorithm showed significantly better accuracy compared to the intensity-based algorithm (p?<?0.001).

Conclusion

A feature-based algorithm for 3D image registration is presented.

     

Development of a patient-specific two-compartment anthropomorphic breast phantom

The purpose of this paper is to develop a technique for the construction of a two-compartment anthropomorphic breast phantom specific to an individual patient's pendant breast anatomy. Three-dimensional breast images were acquired on a prototype dedicated breast computed tomography (bCT) scanner as part of an ongoing IRB-approved clinical trial of bCT. The images from the breast of a patient were segmented into adipose and glandular tissue regions and divided into 1.59?mm thick breast sections to correspond to the thickness of polyethylene stock. A computer-controlled water-jet cutting machine was used to cut the outer breast edge and the internal regions corresponding to glandular tissue from the polyethylene. The stack of polyethylene breast segments was encased in a thermoplastic 'skin' and filled with water. Water-filled spaces modeled glandular tissue structures and the surrounding polyethylene modeled the adipose tissue compartment. Utility of the phantom was demonstrated by inserting 200?μm microcalcifications as well as by measuring point dose deposition during bCT scanning. Affine registration of the original patient images with bCT images of the phantom showed similar tissue distribution. Linear profiles through the registered images demonstrated a mean coefficient of determination (r(2)) between grayscale profiles of 0.881. The exponent of the power law describing the anatomical noise power spectrum was identical in the coronal images of the patient's breast and the phantom. Microcalcifications were visualized in the phantom at bCT scanning. The real-time air kerma rate was measured during bCT scanning and fluctuated with breast anatomy. On average, point dose deposition was 7.1% greater than the mean glandular dose. A technique to generate a two-compartment anthropomorphic breast phantom from bCT images has been demonstrated. The phantom is the first, to our knowledge, to accurately model the uncompressed pendant breast and the glandular tissue distribution for a specific patient. The modular design of the phantom allows for studies of a single breast segment and the entire breast volume. Insertion of other devices, materials and tissues of interest into the phantom provide a robust platform for future breast imaging and dosimetry studies.