Validation of a two- to three-dimensional registration algorithm for aligning preoperative CT images and intraoperative fluoroscopy images

X23D-Intraoperative 3D Lumbar Spine Shape Reconstruction Based on Sparse Multi-View X-ray Data

Visual assessment based on intraoperative 2D X-rays remains the predominant aid for intraoperative decision-making, surgical guidance, and error prevention. However, correctly assessing the 3D shape of complex anatomies, such as the spine, based on planar fluoroscopic images remains a challenge even for experienced surgeons. This work proposes a novel deep learning-based method to intraoperatively estimate the 3D shape of patients' lumbar vertebrae directly from sparse, multi-view X-ray data. High-quality and accurate 3D reconstructions were achieved with a learned multi-view stereo machine approach capable of incorporating the X-ray calibration parameters in the neural network. This strategy allowed a priori knowledge of the spinal shape to be acquired while preserving patient specificity and achieving a higher accuracy compared to the state of the art. Our method was trained and evaluated on 17,420 fluoroscopy images that were digitally reconstructed from the public CTSpine1K dataset. As evaluated by unseen data, we achieved an 88% average F1 score and a 71% surface score. Furthermore, by utilizing the calibration parameters of the input X-rays, our method outperformed a counterpart method in the state of the art by 22% in terms of surface score. This increase in accuracy opens new possibilities for surgical navigation and intraoperative decision-making solely based on intraoperative data, especially in surgical applications where the acquisition of 3D image data is not part of the standard clinical workflow.

Realistic C-arm to pCT registration for vertebral localization in spine surgery

Abstract

Spine surgeries are vulnerable to wrong-level surgeries and postoperative complications because of their complex structure. Unavailability of the 3D intraoperative imaging device, low-contrast intraoperative X-ray images, variable clinical and patient conditions, manual analyses, lack of skilled technicians, and human errors increase the chances of wrong-site or wrong-level surgeries. State of the art work refers 3D-2D image registration systems and other medical image processing techniques to address the complications associated with spine surgeries. Intensity-based 3D-2D image registration systems had been widely practiced across various clinical applications. However, these frameworks are limited to specific clinical conditions such as anatomy, dimension of image correspondence, and imaging modalities. Moreover, there are certain prerequisites for these frameworks to function in clinical application, such as dataset requirement, speed of computation, requirement of high-end system configuration, limited capture range, and multiple local maxima. A simple and effective registration framework was designed with a study objective of vertebral level identification and its pose estimation from intraoperative fluoroscopic images by combining intensity-based and iterative control point (ICP)–based 3D-2D registration. A hierarchical multi-stage registration framework was designed that comprises coarse and finer registration. The coarse registration was performed in two stages, i.e., intensity similarity-based spatial localization and source-to-detector localization based on the intervertebral distance correspondence between vertebral centroids in projected and intraoperative X-ray images. Finally, to speed up target localization in the intraoperative application, based on 3D-2D vertebral centroid correspondence, a rigid ICP-based finer registration was performed. The mean projection distance error (mPDE) measurement and visual similarity between projection image at finer registration point and intraoperative X-ray image and surgeons’ feedback were held accountable for the quality assurance of the designed registration framework. The average mPDE after peak signal to noise ratio (PSNR)–based coarse registration was 20.41mm. After the coarse registration in spatial region and source to detector direction, the average mPDE reduced to 12.18mm. On finer ICP-based registration, the mean mPDE was finally reduced to 0.36 mm. The approximate mean time required for the coarse registration, finer registration, and DRR image generation at the final registration point were 10 s, 15 s, and 1.5 min, respectively. The designed registration framework can act as a supporting tool for vertebral level localization and its pose estimation in an intraoperative environment. The framework was designed with the future perspective of intraoperative target localization and its pose estimation irrespective of the target anatomy.

Graphical abstract

Registration of IRT and visible light images in neurosurgery: analysis and comparison of automatic intensity-based registration approaches

PURPOSE

The purpose of this study is to analyze and compare six automatic intensity-based registration methods for intraoperative infrared thermography (IRT) and visible light imaging (VIS/RGB). The practical requirement is to get a good performance of Euclidean distance between manually set landmarks in reference and target images as well as to achieve a high structural similarity index metric (SSIM) and peak signal-to-noise ratio (PSNR) with respect to the reference image.

METHODS

In this study, preprocessing is applied to bring both image types to a similar intensity. Similarity transformation is employed to align roughly IRT and visible light images. Two optimizers and two measures are used in this process. Thereafter, due to locally different displacement of the brain surface through respiration and heartbeat, two non-rigid transformations are applied, and finally, a bicubic interpolation is carried out to compensate for the resulting estimated transformation. Performance was assessed using eleven image datasets. The registration accuracy of the different computational approaches was assessed based on SSIM and PSNR. Additionally, five concise landmarks for each dataset were selected manually in reference and target images and the Euclidean distance between the corresponding landmarks was compared.

RESULTS

The results are showing that the combination of normalized intensity, mutual information measure with one-plus-one evolutionary optimizer in combination with Demon registration results in improved accuracy and performance as compared to all other methods tested here. Furthermore, the obtained results led to [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] registrations for datasets 1, 2, 5, 7, and 8 with respect to the second best result by calculating the mean Euclidean distance of five landmarks.

CONCLUSIONS

We conclude that the mutual information measure with one-plus-one evolutionary optimizer in combination with Demon registration can achieve better accuracy and performance to those other methods mentioned here for automatic registration of IRT and visible light images in neurosurgery.

Three-Dimensional Subject-Specific Knee Shape Reconstruction with Asynchronous Fluoroscopy Images Using Statistical Shape Modeling

Background and objectives: Statistical shape modeling (SSM) based on computerized tomography (CT) datasets has enabled reasonably accurate reconstructions of subject-specific 3D bone morphology from one or two synchronous radiographs for clinical applications. Increasing the number of radiographic images may increase the reconstruction accuracy, but errors related to the temporal and spatial asynchronization of clinical alternating bi-plane fluoroscopy may also increase. The current study aimed to develop a new approach for subject-specific 3D knee shape reconstruction from multiple asynchronous fluoroscopy images from 2, 4, and 6 X-ray detector views using a CT-based SSM model; and to determine the optimum number of planar images for best accuracy via computer simulations and in vivo experiments.

Methods: A CT-based SSM model of the knee was established from 60 training models in a healthy young Chinese male population. A new two-phase optimization approach for 3D subject-specific model reconstruction from multiple asynchronous clinical fluoroscopy images using the SSM was developed, and its performance was evaluated via computer simulation and in vivo experiments using one, two and three image pairs from an alternating bi-plane fluoroscope.

Results: The computer simulation showed that subject-specific 3D shape reconstruction using three image pairs had the best accuracy with RMSE of 0.52 ± 0.09 and 0.63 ± 0.085 mm for the femur and tibia, respectively. The corresponding values for the in vivo study were 0.64 ± 0.084 and 0.69 ± 0.069 mm, respectively, which was significantly better than those using one image pair (0.81 ± 0.126 and 0.83 ± 0.108 mm). No significant differences existed between using two and three image pairs.

Conclusion: A new two-phase optimization approach was developed for SSM-based 3D subject-specific knee model reconstructions using more than one asynchronous fluoroscopy image pair from widely available alternating bi-plane fluoroscopy systems in clinical settings. A CT-based SSM model of the knee was also developed for a healthy young Chinese male population. The new approach was found to have high mode reconstruction accuracy, and those for both two and three image pairs were much better than for a single image pair. Thus, two image pairs may be used when considering computational costs and radiation dosage. The new approach will be useful for generating patient-specific knee models for clinical applications using multiple asynchronous images from alternating bi-plane fluoroscopy widely available in clinical settings. The current SSM model will serve as a basis for further inclusion of training models with a wider range of sizes and morphological features for broader applications.

A new 2D-3D registration gold-standard dataset for the hip joint based on uncertainty modeling

Purpose

Estimation of the accuracy of 2D‐3D registration is paramount for a correct evaluation of its outcome in both research and clinical studies. Publicly available datasets with standardized evaluation methodology are necessary for validation and comparison of 2D‐3D registration techniques. Given the large use of 2D‐3D registration in biomechanics, we introduced the first gold standard validation dataset for computed tomography (CT)‐to‐x‐ray registration of the hip joint, based on fluoroscopic images with large rotation angles. As the ground truth computed with fiducial markers is affected by localization errors in the image datasets, we proposed a new methodology based on uncertainty propagation to estimate the accuracy of a gold standard dataset.

Methods

The gold standard dataset included a 3D CT scan of a female hip phantom and 19 2D fluoroscopic images acquired at different views and voltages. The ground truth transformations were estimated based on the corresponding pairs of extracted 2D and 3D fiducial locations. These were assumed to be corrupted by Gaussian noise, without any restrictions of isotropy. We devised the multiple projective points criterion (MPPC) that jointly optimizes the transformations and the noisy 3D fiducial locations for all views. The accuracy of the transformations obtained with the MPPC was assessed in both synthetic and real experiments using different formulations of the target registration error (TRE), including a novel formulation of the TRE (uTRE) derived from the uncertainty analysis of the MPPC.

Results

The proposed MPPC method was statistically more accurate compared to the validation methods for 2D‐3D registration that did not optimize the 3D fiducial positions or wrongly assumed the isotropy of the noise. The reported results were comparable to previous published works of gold standard datasets. However, a formulation of the TRE commonly found in these gold standard datasets was found to significantly miscalculate the true TRE computed in synthetic experiments with known ground truths. In contrast, the uncertainty‐based uTRE was statistically closer to the true TRE.

Conclusions

We proposed a new gold standard dataset for the validation of CT‐to‐X‐ray registration of the hip joint. The gold standard transformations were derived from a novel method modeling the uncertainty in extracted 2D and 3D fiducials. Results showed that considering possible noise anisotropy and including corrupted 3D fiducials in the optimization resulted in improved accuracy of the gold standard. A new uncertainty‐based formulation of the TRE also appeared as a good alternative to the unknown true TRE that has been replaced in previous works by an alternative TRE not fully reflecting the gold standard accuracy.

Measuring three-dimensional tibiofemoral kinematics using dual-slice real-time magnetic resonance imaging

PURPOSE

The purpose of this study is to propose and evaluate a slice-to-volume registration (SVR) method integrating an advanced dual-slice real-time magnetic resonance image (MRI) and three-dimensional (3D) MRI volume of the tibiofemoral joint for determining their 3D kinematics.

METHODS

The real-time and 3D MRI of the knee were collected from 12 healthy adults at 5 static flexion positions and during dynamic flexion/extension movement. The 3D positions and orientations of the femur and tibia were obtained by registering their volumetric models constructed from the 3D MRI to dual-slice real-time MRI using an optimization process. The proposed method was quantitatively evaluated for its performance in terms of the robustness and measurement accuracy, and compared to those of a single-slice SVR method. Its repeatability in measuring knee kinematics during flexion/extension movement was also determined.

RESULTS

In comparison to the single-slice SVR method, the dual-slice method was significantly superior, giving a successful registration rate > 95%, a bias less than 0.5 mm in translations and 0.6° in rotations and a precision <0.7 mm in translations and 0.9° in rotations for determining the 3D tibiofemoral poses. For repeatability of the dual-slice SVR in measuring tibiofemoral kinematics during dynamic flexion/extension, the means of the time-averaged standard deviations were <0.9° for joint angles and 0.5 mm for joint translations.

CONCLUSION

A dual-slice SVR method in conjunction with real-time MRI has been developed and evaluated for its performance in measuring 3D kinematics of the tibiofemoral joint in 12 young adults in terms of the accuracy, robustness, and repeatability. The proposed MRI-based 3D measurement method provides a noninvasive and ionizing radiation-free approach for 3D kinematic measurement of the tibiofemoral joint, which will be helpful for future academic and clinical applications.

Divided-volume matching technique for volume displacement estimation of patient positioning in radiation therapy

PURPOSE

We propose the Divided-Volume Matching (DVM) technique to visualize and estimate three-dimensional (3D) displacements of internal structures to enable more accurate patient positioning for radiation therapy.

METHODS

A CT volume is divided into a volume of interest (VOI) and a base volume (BV); 2D-3D matching is achieved using digital radiography (DR) images and digitally reconstructed radiographs (DRRs), where the DRRs are iteratively generated by changing the 3D positions and rotation angles of the separate volumes independently to identify the best match with the DR images. We demonstrate this technique with two phantom and two clinical cases.

RESULTS

3D displacements of the VOIs could be estimated independently and simultaneously with those of the BVs, with accuracies comparable to those of the conventional 2D-3D matching. The proposed technique yielded more suitable matching results when internal displacements occurred in the regions of interest (ROIs). The best matches were found when the ROI was confined to the focused structure, initial displacement values were coarsely adjusted, one volume was matched while the other was fixed, or any combination thereof.

CONCLUSIONS

The proposed technique can be used effectively for independent displacement estimations of VOIs and BVs for patient positioning in radiation therapy.

A model-based tracking method for measuring 3D dynamic joint motion using an alternating biplane x-ray imaging system

PURPOSES

To propose a new model-based tracking method for measuring three-dimensional (3D) dynamic joint kinematics using a clinical alternating biplane x-ray imaging system; and to quantify in vitro its errors in measuring ankle and knee motions at different motion speeds.

METHODS

A new model-based tracking method based on motion component partition and interpolation (MCPI) was developed for measuring 3D dynamic joint kinematics based on a clinical alternating biplane x-ray imaging system. Two detectors of the biplane imaging system placed perpendicular to each other were operated to collect alternating fluoroscopic images of the targeted joint during tasks. The CT data of the joint were also acquired for the reconstruction of volumetric and surface models of each of the associated bones. The CT-based models of the bones were first registered to the alternating images using a model-to-single-plane fluoroscopic image registration method, and the resulting bone poses were then refined using a two-level optimization with motion component partition and model vertex trajectory interpolation. The MCPI method was evaluated in vitro for measurement errors for an ankle and a knee specimen moving at different speeds against a standard reference provided by a highly accurate motion capture system. The positional and rotational errors of the measured bone poses were quantified in terms of the bias, precision, and root-mean-squared errors (RMSE), as well as the mean target registration error (mTRE), a final mTRE less than 2.5 mm indicating a successful registration.

RESULTS

The new method was found to have RMSE of bone pose measurements of less than 0.18 mm for translations and 0.72° for rotations for the ankle, and 0.33 mm and 0.74° for the knee with a high successful registration rate (>97%), and did not appear to be affected by joint motion speeds. Given the same alternating fluoroscopic images, the MCPI method outperformed the typical biplane analysis method assuming zero time offset between the two fluoroscopic views. The differences in performance between the methods were increased with increased joint motion speed. With the accurate bone pose data, the new method enabled talocrural, subtalar, and tibiofemoral kinematics measurements with submillimeter and subdegree accuracy, except for an RMSE of 1.04° for the internal/external rotation of the talocrural joint.

CONCLUSIONS

A new model-based tracking method based on MCPI has been developed for measuring dynamic joint motions using an alternating biplane x-ray imaging system widely available in medical centers. The MCPI method has been demonstrated in vitro to be highly accurate in determining the 3D kinematics of the bones of both the ankle joint complex and the knee. The current results suggest that the MCPI method would be an effective approach for measuring in vivo 3D kinematics of dynamic joint motion in a clinical setting equipped with an alternating biplane x-ray imaging system.

Reliable landmarks for precise topographical analyses of pathological structural changes of the ovine tibial plateau in 2-D and 3-D subspaces

Selecting identical topographical locations to analyse pathological structural changes of the osteochondral unit in translational models remains difficult. The specific aim of the study was to provide objectively defined reference points on the ovine tibial plateau based on 2-D sections of micro-CT images useful for reproducible sample harvesting and as standardized landmarks for landmark-based 3-D image registration. We propose 5 reference points, 11 reference lines and 12 subregions that are detectable macroscopically and on 2-D micro-CT sections. Their value was confirmed applying landmark-based rigid and affine 3-D registration methods. Intra- and interobserver comparison showed high reliabilities, and constant positions (standard errors < 1%). Spatial patterns of the thicknesses of the articular cartilage and subchondral bone plate were revealed by measurements in 96 individual points of the tibial plateau. As a case study, pathological phenomena 6 months following OA induction in vivo such as osteophytes and areas of OA development were mapped to the individual subregions. These new reference points and subregions are directly identifiable on tibial plateau specimens or macroscopic images, enabling a precise topographical location of pathological structural changes of the osteochondral unit in both 2-D and 3-D subspaces in a region-appropriate fashion relevant for translational investigations.

A Comparison of Accuracy of Image- versus Hardware-based Tracking Technologies in 3D Fusion in Aortic Endografting

OBJECTIVES

Fusion of three-dimensional (3D) computed tomography and intraoperative two-dimensional imaging in endovascular surgery relies on manual rigid co-registration of bony landmarks and tracking of hardware to provide a 3D overlay (hardware-based tracking, HWT). An alternative technique (image-based tracking, IMT) uses image recognition to register and place the fusion mask. We present preliminary experience with an agnostic fusion technology that uses IMT, with the aim of comparing the accuracy of overlay for this technology with HWT.

METHOD

Data were collected prospectively for 12 patients. All devices were deployed using both IMT and HWT fusion assistance concurrently. Postoperative analysis of both systems was performed by three blinded expert observers, from selected time-points during the procedures, using the displacement of fusion rings, the overlay of vascular markings and the true ostia of renal arteries. The Mean overlay error and the deviation from mean error was derived using image analysis software. Comparison of the mean overlay error was made between IMT and HWT. The validity of the point-picking technique was assessed.

RESULTS

IMT was successful in all of the first 12 cases, whereas technical learning curve challenges thwarted HWT in four cases. When independent operators assessed the degree of accuracy of the overlay, the median error for IMT was 3.9 mm (IQR 2.89-6.24, max 9.5) versus 8.64 mm (IQR 6.1-16.8, max 24.5) for HWT (p = .001). Variance per observer was 0.69 mm(2) and 95% limit of agreement ±1.63.

CONCLUSION

In this preliminary study, the error of magnitude of displacement from the "true anatomy" during image overlay in IMT was less than for HWT. This confirms that ongoing manual re-registration, as recommended by the manufacturer, should be performed for HWT systems to maintain accuracy. The error in position of the fusion markers for IMT was consistent, thus may be considered predictable.

Fully automated 2D-3D registration and verification

Clinical application of 2D-3D registration technology often requires a significant amount of human interaction during initialisation and result verification. This is one of the main barriers to more widespread clinical use of this technology. We propose novel techniques for automated initial pose estimation of the 3D data and verification of the registration result, and show how these techniques can be combined to enable fully automated 2D-3D registration, particularly in the case of a vertebra based system. The initialisation method is based on preoperative computation of 2D templates over a wide range of 3D poses. These templates are used to apply the Generalised Hough Transform to the intraoperative 2D image and the sought 3D pose is selected with the combined use of the generated accumulator arrays and a Gradient Difference Similarity Measure. On the verification side, two algorithms are proposed: one using normalised features based on the similarity value and the other based on the pose agreement between multiple vertebra based registrations. The proposed methods are employed here for CT to fluoroscopy registration and are trained and tested with data from 31 clinical procedures with 417 low dose, i.e. low quality, high noise interventional fluoroscopy images. When similarity value based verification is used, the fully automated system achieves a 95.73% correct registration rate, whereas a no registration result is produced for the remaining 4.27% of cases (i.e. incorrect registration rate is 0%). The system also automatically detects input images outside its operating range.

Finite-element-based matching of pre- and intraoperative data for image-guided endovascular aneurysm repair

Endovascular repair of abdominal aortic aneurysms is a well-established technique throughout the medical and surgical communities. Although increasingly indicated, this technique does have some limitations. Because intervention is commonly performed under fluoroscopic control, 2-D visualization of the aneurysm requires the injection of a contrast agent. The projective nature of this imaging modality inevitably leads to topographic errors, and does not give information on arterial wall quality at the time of deployment. A specially adapted intraoperative navigation interface could increase deployment accuracy and reveal such information, which preoperative 3-D imaging might otherwise provide. One difficulty is the precise matching of preoperative data (images and models) and intraoperative observations affected by anatomical deformations due to tool-tissue interactions. Our proposed solution involves a finite-element-based preoperative simulation of tool-tissue interactions, its adaptive tuning regarding patient specific data, and the matching with intraoperative data. The biomechanical model was first tuned on a group of ten patients and assessed on a second group of eight patients.

Increasing the automation of a 2D-3D registration system

Routine clinical use of 2D-3D registration algorithms for Image Guided Surgery remains limited. A key aspect for routine clinical use of this technology is its degree of automation, i.e., the amount of necessary knowledgeable interaction between the clinicians and the registration system. Current image-based registration approaches usually require knowledgeable manual interaction during two stages: for initial pose estimation and for verification of produced results. We propose four novel techniques, particularly suited to vertebra-based registration systems, which can significantly automate both of the above stages. Two of these techniques are based upon the intraoperative "insertion" of a virtual fiducial marker into the preoperative data. The remaining two techniques use the final registration similarity value between multiple CT vertebrae and a single fluoroscopy vertebra. The proposed methods were evaluated with data from 31 operations (31 CT scans, 419 fluoroscopy images). Results show these methods can remove the need for manual vertebra identification during initial pose estimation, and were also very effective for result verification, producing a combined true positive rate of 100% and false positive rate equal to zero. This large decrease in required knowledgeable interaction is an important contribution aiming to enable more widespread use of 2D-3D registration technology.

Electromagnetic tracking for registration and navigation in endovascular aneurysm repair: a phantom study

OBJECTIVE

To assess the feasibility of using an electromagnetic tracking for both registration and navigation in endovascular aneurysm repair.

MATERIALS AND METHODS

A registration process was implemented to align computed tomography (CT) data and electromagnetic tracking data. Two abdominal aortic aneurysm (AAA) phantoms were used, a rigid plastic AAA model (phantom A) and a soft silicon AAA model (phantom B). A pre-procedural CT volume was acquired for each phantom. Intra-operative simulation was performed by placing each phantom in the magnetic field of the tracking device. Using a modified electromagnetic catheter, a set of three-dimensional positions was acquired in the phantom's aortic lumen. Pre-procedural CT images and intra-procedural tracked positions were registered. Four reference points were used to calculate the registration accuracy of phantom A. Three surgeons simulated catheterisation of the left renal artery with phantom B using only image-guided procedure software.

RESULTS

The mean registration error was 1.3 mm (range 0.88-1.89). The median time for left renal catheterisation was 22 s (range 15-59).

CONCLUSION

Registration of CT data and electromagnetic tracking data is feasible using catheter positions in the aorto-iliac structure as landmark. This navigation system could reduce X-ray exposure time and the use of contrast medium injections.

Inferring 3D kinematics of carpal bones from single view fluoroscopic sequences

We present a novel framework for inferring 3D carpal bone kinematics and bone shapes from a single view fluoroscopic sequence. A hybrid statistical model representing both the kinematics and shape variation of the carpal bones is built, based on a number of 3D CT data sets obtained from different subjects at different poses. Given a fluoroscopic sequence, the wrist pose, carpal bone kinematics and bone shapes are estimated iteratively by matching the statistical model with the 2D images. A specially designed cost function enables smoothed parameter estimation across frames. We have evaluated the proposed method on both simulated data and real fluoroscopic sequences. It was found that the relative positions between carpal bones can be accurately estimated, which is potentially useful for detection of conditions such as scapholunate dissociation.

Validation of 3D multimodality roadmapping in interventional neuroradiology

Three-dimensional multimodality roadmapping is entering clinical routine utilization for neuro-vascular treatment. Its purpose is to navigate intra-arterial and intra-venous endovascular devices through complex vascular anatomy by fusing pre-operative computed tomography (CT) or magnetic resonance (MR) with the live fluoroscopy image. The fused image presents the real-time position of the intra-vascular devices together with the patient's 3D vascular morphology and its soft-tissue context. This paper investigates the effectiveness, accuracy, robustness and computation times of the described methods in order to assess their suitability for the intended clinical purpose: accurate interventional navigation. The mutual information-based 3D-3D registration proved to be of sub-voxel accuracy and yielded an average registration error of 0.515 mm and the live machine-based 2D-3D registration delivered an average error of less than 0.2 mm. The capture range of the image-based 3D-3D registration was investigated to characterize its robustness, and yielded an extent of 35 mm and 25° for >80% of the datasets for registration of 3D rotational angiography (3DRA) with CT, and 15 mm and 20° for >80% of the datasets for registration of 3DRA with MR data. The image-based 3D-3D registration could be computed within 8 s, while applying the machine-based 2D-3D registration only took 1.5 µs, which makes them very suitable for interventional use.

Validation for 2D/3D registration. II: The comparison of intensity- and gradient-based merit functions using a new gold standard data set

PURPOSE

A new gold standard data set for validation of 2D/3D registration based on a porcine cadaver head with attached fiducial markers was presented in the first part of this article. The advantage of this new phantom is the large amount of soft tissue, which simulates realistic conditions for registration. This article tests the performance of intensity- and gradient-based algorithms for 2D/3D registration using the new phantom data set.

METHODS

Intensity-based methods with four merit functions, namely, cross correlation, rank correlation, correlation ratio, and mutual information (MI), and two gradient-based algorithms, the backprojection gradient-based (BGB) registration method and the reconstruction gradient-based (RGB) registration method, were compared. Four volumes consisting of CBCT with two fields of view, 64 slice multidetector CT, and magnetic resonance-T1 weighted images were registered to a pair of kV x-ray images and a pair of MV images. A standardized evaluation methodology was employed. Targets were evenly spread over the volumes and 250 starting positions of the 3D volumes with initial displacements of up to 25 mm from the gold standard position were calculated. After the registration, the displacement from the gold standard was retrieved and the root mean square (RMS), mean, and standard deviation mean target registration errors (mTREs) over 250 registrations were derived. Additionally, the following merit properties were computed: Accuracy, capture range, number of minima, risk of nonconvergence, and distinctiveness of optimum for better comparison of the robustness of each merit.

RESULTS

Among the merit functions used for the intensity-based method, MI reached the best accuracy with an RMS mTRE down to 1.30 mm. Furthermore, it was the only merit function that could accurately register the CT to the kV x rays with the presence of tissue deformation. As for the gradient-based methods, BGB and RGB methods achieved subvoxel accuracy (RMS mTRE down to 0.56 and 0.70 mm, respectively). Overall, gradient-based similarity measures were found to be substantially more accurate than intensity-based methods and could cope with soft tissue deformation and enabled also accurate registrations of the MR-T1 volume to the kV x-ray image.

CONCLUSIONS

In this article, the authors demonstrate the usefulness of a new phantom image data set for the evaluation of 2D/3D registration methods, which featured soft tissue deformation. The author's evaluation shows that gradient-based methods are more accurate than intensity-based methods, especially when soft tissue deformation is present. However, the current nonoptimized implementations make them prohibitively slow for practical applications. On the other hand, the speed of the intensity-based method renders these more suitable for clinical use, while the accuracy is still competitive.

Robust initialization of 2D-3D image registration using the projection-slice theorem and phase correlation

PURPOSE

The image registration literature comprises many methods for 2D-3D registration for which accuracy has been established in a variety of applications. However, clinical application is limited by a small capture range. Initial offsets outside the capture range of a registration method will not converge to a successful registration. Previously reported capture ranges, defined as the 95% success range, are in the order of 4-11 mm mean target registration error. In this article, a relatively computationally inexpensive and robust estimation method is proposed with the objective to enlarge the capture range.

METHODS

The method uses the projection-slice theorem in combination with phase correlation in order to estimate the transform parameters, which provides an initialization of the subsequent registration procedure.

RESULTS

The feasibility of the method was evaluated by experiments using digitally reconstructed radiographs generated from in vivo 3D-RX data. With these experiments it was shown that the projection-slice theorem provides successful estimates of the rotational transform parameters for perspective projections and in case of translational offsets. The method was further tested on ex vivo ovine x-ray data. In 95% of the cases, the method yielded successful estimates for initial mean target registration errors up to 19.5 mm. Finally, the method was evaluated as an initialization method for an intensity-based 2D-3D registration method. The uninitialized and initialized registration experiments had success rates of 28.8% and 68.6%, respectively.

CONCLUSIONS

The authors have shown that the initialization method based on the projection-slice theorem and phase correlation yields adequate initializations for existing registration methods, thereby substantially enlarging the capture range of these methods.

Method comparison of automated matching software-assisted cone-beam CT and stereoscopic kilovoltage x-ray positional verification image-guided radiation therapy for head and neck cancer: a prospective analysis

We sought to characterize interchangeability and agreement between cone-beam computed tomography (CBCT) and digital stereoscopic kV x-ray (KVX) acquisition, two methods of isocenter positional verification currently used for IGRT of head and neck cancers (HNC). A cohort of 33 patients were near-simultaneously imaged by in-room KVX and CBCT. KVX and CBCT shifts were suggested using manufacturer software for the lateral (X), vertical (Y) and longitudinal (Z) dimensions. Intra-method repeatability, systematic and random error components were calculated for each imaging modality, as were recipe-based PTV expansion margins. Inter-method agreement in each axis was compared using limits of agreement (LOA) methodology, concordance analysis and orthogonal regression. 100 daily positional assessments were performed before daily therapy in 33 patients with head and neck cancer. Systematic error was greater for CBCT in all axes, with larger random error components in the Y- and Z-axis. Repeatability ranged from 9 to 14 mm for all axes, with CBCT showing greater repeatability in 2/3 axes. LOA showed paired shifts to agree 95% of the time within +/-11.3 mm in the X-axis, +/-9.4 mm in the Y-axis and +/-5.5 mm in the Z-axis. Concordance ranged from 'mediocre' to 'satisfactory'. Proportional bias was noted between paired X- and Z-axis measures, with a constant bias component in the Z-axis. Our data suggest non-negligible differences in software-derived CBCT and KVX image-guided directional shifts using formal method comparison statistics.

Vesselness-based 2D-3D registration of the coronary arteries

PURPOSE

Robust and accurate automated co-registration of the coronary arteries in 3D CTA and 2D X-ray angiography during percutaneous coronary interventions (PCI), in order to present a fused visualization.

METHODS

A novel vesselness-based similarity measure was developed, that avoids an explicit segmentation of the X-ray image. A stochastic optimizer searches the optimal registration using the similarity measure.

RESULTS

Both simulated data and clinical data were used to investigate the accuracy and capture range of the proposed method. The experiments show that the proposed method outperforms the iterative closest point method in terms of accuracy (average residual error of 0.42 mm vs. 1.44 mm) and capture range (average 71.1 mm/20.3 degrees vs. 14.1 mm/5.2 degrees ).

CONCLUSION

The proposed method has proven to be accurate and the capture range is ample for usage in PCI. Especially the absence of an explicit segmentation of the interventionally acquired X-ray images considerably aids the robustness of the method.

Patient specific 4D coronary models from ECG-gated CTA data for intra-operative dynamic alignment of CTA with X-ray images

We present an approach to derive patient specific coronary models from ECG-gated CTA data and their application for the alignment of CTA with mono-plane X-ray imaging during interventional cardiology. A 4D (3D+t) deformation model of the coronary arteries is derived by (i) extraction of a 3D coronary model at an appropriate cardiac phase and (ii) non-rigid registration of the CTA images at different ECG phases to obtain a deformation model. The resulting 4D coronary model is aligned with the X-ray data using a novel 2D+t/3D+t registration approach. Model consistency and accuracy is evaluated using manually annotated coronary centerlines at systole and diastole as reference. Improvement of registration robustness by using the 2D+t/3D+t registration is successfully demonstrated by comparison of the actual X-ray cardiac phase with the automatically determined best matching phase in the 4D coronary model.

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.

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.

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.

Cardiac image integration implications for atrial fibrillation ablation

Cardiac image registration using computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and fluoroscopy is currently being investigated and clinically used for atrial fibrillation (AF) ablation. Cardiac image registration, in the context of left atrium, is intermodal, with the acquired image and the real-time reference image residing in different image spaces, and involves optimization, where one image space is transformed into the other. Geometry-based methods, which include using fiducial points and/or surface-based techniques, are usually used for cardiac image registration. During fiducial point registration, fiducial points are either external skin markers or manually determined by marking anatomical landmarks, using mapping catheters. Usually, a minimum of three non collinear points are needed for optimal registration. Recently, a catheter placed inside the coronary sinus has also been used as a fiducial marker for the purpose of registration. During surface registration, the process involves characterizing surfaces in each of the images and deriving the best transformation between them. Unlike rigid body registration, such as has been extensively used in imaging the brain, cardiac image registration is unique and challenging. In addition to the errors inherent in intermodal registration, such as errors in pixel and voxel dimension and errors due to fiducial point selection, there are errors specific to cardiac image registration, i.e., errors due to cardiac motion during the cardiac cycle and due to respiration. This review addresses the basic principles of registration and the inherent registration errors as they relate to cardiac imaging and registration.

Automatic registration of CT volumes and dual-energy digital radiography for detection of cardiac and lung diseases

We are investigating image processing and analysis techniques to improve the ability of dual-energy digital radiography (DR) for the detection of cardiac calcification. Computed tomography (CT) is an established tool for the diagnosis of coronary artery diseases. Dual-energy digital radiography could be a cost-effective alternative. In this study, we use three-dimensional (3D) CT images as the "gold standard" to evaluate the DR X-ray images for calcification detection. To this purpose, we developed an automatic registration method for 3D CT volumes and two-dimensional (2D) X-ray images. We call this 3D-to-2D registration. We first use a 3D CT image volume to simulate X-ray projection images and then register them with X-ray images. The registered CT projection images are then used to aid the interpretation dual-energy X-ray images for the detection of cardiac calcification. We acquired both CT and X-ray images from patients with coronary artery diseases. Experimental results show that the 3D-to-2D registration is accurate and useful for this new application.

Postoperative Calculation of Acetabular Cup Position Using 2-D–3-D Registration

A method to accurately measure the position and orientation of an acetabular cup implant from postoperative X-rays has been designed and validated. The method uses 2-D-3-D registration to align both the prosthesis and the preoperative computed tomography (CT) volume to the X-ray image. This allows the position of the implant to be calculated with respect to a CT-based surgical plan. Experiments have been carried out using ten sets of patient data. A conventional plain-film measurement technique was also investigated. A gold standard implant position and orientation was calculated using postoperative CT. Results show our method to be significantly more accurate than the plain-film method for calculating cup anteversion. Cup orientation and position could be measured to within a mean absolute error of 1.4 mm or degrees.

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.

A Study of the Accuracy of CyberKnife Spinal Radiosurgery Using Skeletal Structure Tracking

Objective:

New technology has enabled the increasing use of radiosurgery to ablate spinal lesions. The first generation of the CyberKnife (Accuray, Inc., Sunnyvale, CA) image-guided radiosurgery system required implanted radiopaque markers (fiducials) to localize spinal targets. A recently developed and now commercially available spine tracking technology called Xsight (Accuray, Inc.) tracks skeletal structures and eliminates the need for implanted fiducials. The Xsight system localizes spinal targets by direct reference to the adjacent vertebral elements. This study sought to measure the accuracy of Xsight spine tracking and provide a qualitative assessment of overall system performance.

Methods:

Total system error, which is defined as the distance between the centroids of the planned and delivered dose distributions and represents all possible treatment planning and delivery errors, was measured using a realistic, anthropomorphic head-and-neck phantom. The Xsight tracking system error component of total system error was also computed by retrospectively analyzing image data obtained from eleven patients with a total of 44 implanted fiducials who underwent CyberKnife spinal radiosurgery.

Results:

The total system error of the Xsight targeting technology was measured to be 0.61 mm. The tracking system error component was found to be 0.49 mm.

Conclusion:

The Xsight spine tracking system is practically important because it is accurate and eliminates the use of implanted fiducials. Experience has shown this technology to be robust under a wide range of clinical circumstances.

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.

Accuracy evaluation of direct navigation with an isocentric 3D rotational X-ray system

Minimally invasive interventions are often performed under fluoroscopic guidance. Drawbacks of fluoroscopic guidance are the fact that the presented images are 2D projections and that both the patient and the clinician are exposed to radiation. Image-guided navigation using pre-interventionally acquired 3D MR or CT data is an alternative. However, this often requires invasive anatomical landmark-based, marker-based or surface-based image-to-patient registration. In this paper, a coupling between an image-guided navigation system and an intraoperative C-arm X-ray device with 3D imaging capabilities (3D rotational X-ray (3DRX) system) that enables direct navigation without invasive image-to-patient registration on 3DRX volumes, is described and evaluated. The coupling is established in a one-time preoperative calibration procedure. The individual steps in the registration procedure are explained and evaluated. The acquired navigation accuracy using this coupling is approximately one millimeter.

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 of portal images and volumetric CT for patient positioning in radiation therapy

The efficacy of radiation therapy treatment depends on the patient setup accuracy at each daily fraction. A significant problem is reproducing the patient position during treatment planning for every fraction of the treatment process. We propose and evaluate an intensity based automatic registration method using multiple portal images and the pre-treatment CT volume. We perform both geometric and radiometric calibrations to generate high quality digitally reconstructed radiographs (DRRs) that can be compared against portal images acquired right before treatment dose delivery. We use a graphics processing unit (GPU) to generate the DRRs in order to gain computational efficiency. We also perform a comparative study on various similarity measures and optimization procedures. Simple similarity measure such as local normalized correlation (LNC) performs best as long as the radiometric calibration is carefully done. Using the proposed method, we achieved better than 1mm average error in repositioning accuracy for a series of phantom studies using two open field (i.e., 41 cm2) portal images with 90 degrees vergence angle.

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.

Fast generation of digitally reconstructed radiographs using attenuation fields with application to 2D-3D image registration

Generation of digitally reconstructed radiographs (DRRs) is computationally expensive and is typically the rate-limiting step in the execution time of intensity-based two-dimensional to three-dimensional (2D-3D) registration algorithms. We address this computational issue by extending the technique of light field rendering from the computer graphics community. The extension of light fields, which we call attenuation fields (AFs), allows most of the DRR computation to be performed in a preprocessing step; after this precomputation step, DRRs can be generated substantially faster than with conventional ray casting. We derive expressions for the physical sizes of the two planes of an AF necessary to generate DRRs for a given X-ray camera geometry and all possible object motion within a specified range. Because an AF is a ray-based data structure, it is substantially more memory efficient than a huge table of precomputed DRRs because it eliminates the redundancy of replicated rays. Nonetheless, an AF can require substantial memory, which we address by compressing it using vector quantization. We compare DRRs generated using AFs (AF-DRRs) to those generated using ray casting (RC-DRRs) for a typical C-arm geometry and computed tomography images of several anatomic regions. They are quantitatively very similar: the median peak signal-to-noise ratio of AF-DRRs versus RC-DRRs is greater than 43 dB in all cases. We perform intensity-based 2D-3D registration using AF-DRRs and RC-DRRs and evaluate registration accuracy using gold-standard clinical spine image data from four patients. The registration accuracy and robustness of the two methods is virtually identical whereas the execution speed using AF-DRRs is an order of magnitude faster.

Anatomic feature-based registration for patient set-up in head and neck cancer radiotherapy

Modern radiotherapy equipment is capable of delivering high precision conformal dose distributions relative to isocentre. One of the barriers to precise treatments is accurate patient re-positioning before each fraction of treatment. At Massachusetts General Hospital, we perform daily patient alignment using radiographs, which are captured by flat panel imaging devices and sent to an analysis program. A trained therapist manually selects anatomically significant features in the skeleton, and couch movement is computed based on the image coordinates of the features. The current procedure takes about 5 to 10 min and significantly affects the efficiency requirement in a busy clinic. This work presents our effort to develop an improved, semi-automatic procedure that uses the manually selected features from the first treatment fraction to automatically locate the same features on the second and subsequent fractions. An implementation of this semi-automatic procedure is currently in clinical use for head and neck tumour sites. Radiographs collected from 510 patient set-ups were used to test this algorithm. A mean difference of 1.5 mm between manual and automatic localization of individual features and a mean difference of 0.8 mm for overall set-up were seen.