Automatic three-dimensional inspection of patient setup in radiation therapy using portal images, simulator images, and computed tomography data

Fast digitally reconstructed radiograph generation using particle-based statistical shape and intensity model

Purpose

Statistical shape and intensity models (SSIMs) and digitally reconstructed radiographs (DRRs) were introduced for non-rigid 2D-3D registration and skeletal geometry/density reconstruction studies. The computation of DRRs takes most of the time during registration or reconstruction. The goal of this study is to propose a particle-based method for composing an SSIM and a DRR image generation scheme and analyze the quality of the images compared with previous DRR generation methods.

Approach

Particle-based SSIMs consist of densely scattered particles on the surface and inside of an object, with each particle having an intensity value. Generating the DRR resembles ray tracing, which counts the particles that are binned with each ray and calculates the radiation attenuation. The distance between adjacent particles was considered to be the radiologic path during attenuation integration, and the mean linear attenuation coefficient of the two particles was multiplied. The proposed method was compared with the DRR of CT projection. The mean squared error and peak signal-to-noise ratio (PSNR) were calculated between the DRR images from the proposed method and those of existing methods of projecting tetrahedral-based SSIMs or computed tomography (CT) images to verify the accuracy of the proposed scheme.

Results

The suggested method was about 600 times faster than the tetrahedral-based SSIM without using the hardware acceleration technique. The PSNR was 37.59 dB, and the root mean squared error of the normalized pixel intensities was 0.0136.

Conclusions

The proposed SSIM and DRR generation procedure showed high temporal performance while maintaining image quality, and particle-based SSIM is a feasible form for representing a 3D volume and generating the DRR images.

Comparison of Similarity Measures for Rigid-body CT/Dual X-ray Image Registrations

A set of experiments were conducted to evaluate six similarity measures for intensity-based rigid-body 3D/2D image registration. Similarity measure is an index that measures the similarity between a digitally reconstructed radiograph (DRR) and an x-ray planar image. The registration is accomplished by maximizing the sum of the similarity measures between biplane x-ray images and the corresponding DRRs in an iterative fashion. We have evaluated the accuracy and attraction ranges of the registrations using six different similarity measures on phantom experiments for head, thorax, and pelvis. The images were acquired using Varian Medial System On-Board Imager. Our results indicated that normalized cross correlation and entropy of difference showed a wide attraction range (62 deg and 83 mm mean attraction range, ωmean), but the worst accuracy (4.2 mm maximum error, emax). The gradient-based similarity measures, gradient correlation and gradient difference, and the pattern intensity showed sub-millimeter accuracy, but narrow attraction ranges ( ωmean=29 deg, 31 mm). Mutual information was in-between of these two groups ( emax=2.5 mm, ωmean= 48 deg, 52 mm). On the data of 120 x-ray pairs from eight IRB approved prostate patients, the gradient difference showed the best accuracy. In the clinical applications, registrations starting with the mutual information followed by the gradient difference may provide the best accuracy and the most robustness.

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

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

New therapeutics in spine metastases

The number of patients who will develop metastatic spinal tumors is estimated to be between 5 and 10% of all cancer patients. As the therapy for systemic cancer improves, the number of patients developing symptomatic spinal tumors that require local therapy will increase. Over the last 10 years there has been a dramatic evolution in our ability to treat spinal tumors. These advances have not only been created by improvements in surgical techniques and instrumentation, but also developments in radiographic imaging, radiation therapy and chemotherapy. It is important for spine surgeons, radiologists, and radiation and medical oncologists to continue developing techniques for spinal salvage that will improve pain relief, achieve mechanical stability, improve or maintain neurologic function and sustain local tumor control. The evolution of these technologies will help to provide palliation and improve quality of life for patients with metastatic disease.

Fast reconstructed radiographs from octree-compressed volumetric data

PURPOSE

Simulated 2D X-ray images called digitally reconstructed radiographs (DRRs) have important applications within medical image registration frameworks where they are compared with reference X-rays or used in implementations of digital tomosynthesis (DTS). However, rendering DRRs from a CT volume is computationally demanding and relatively slow using the conventional ray-casting algorithm. Image-guided radiation therapy systems using DTS to verify target location require a large number DRRs to be precomputed since there is insufficient time within the automatic image registration procedure to generate DRRs and search for an optimal pose.

METHOD

DRRs were rendered from octree-compressed CT data. Previous work showed that octree-compressed volumes rendered by conventional ray casting deliver a registration with acceptable clinical accuracy, but efficiently rendering the irregular grid of an octree data structure is a challenge for conventional ray casting. We address this by using vertex and fragment shaders of modern graphics processing units (GPUs) to directly project internal spaces of the octree, represented by textured particle sprites, onto the view plane. The texture is procedurally generated and depends on the CT pose.

RESULTS

The performance of this new algorithm was found to be 4 times faster than that of a ray-casting algorithm implemented using NVIDIA™Compute Unified Device Architecture (CUDA™) on an equivalent GPU (~95 % octree compression). Rendering artifacts are apparent (consistent with other splatting algorithm), but image quality tends to improve with compression and fewer particles are needed. A peak signal-to-noise ratio analysis confirmed that the images rendered from compressed volumes were of marginally better quality to those rendered using Gaussian footprints.

CONCLUSIONS

Using octree-encoded DRRs within a 2D/3D registration framework indicated the approach may be useful in accelerating automatic image registration.

A feasibility study of a molecular-based patient setup verification method using a parallel-plane PET system

A feasibility study of a novel PET-based molecular image guided radiation therapy (m-IGRT) system was conducted by comparing PET-based digitally reconstructed planar image (PDRI) registration with radiographic registration. We selected a pair of opposing parallel-plane PET systems for the practical implementation of this system. Planar images along the in-plane and cross-plane directions were reconstructed from the parallel-plane PET data. The in-plane and cross-plane FWHM of the profile of 2 mm diameter sources was approximately 1.8 and 8.1 mm, respectively. Therefore, only the reconstructed in-plane image from the parallel-plane PET data was used in the PDRI registration. In the image registration, five different sizes of (18)F cylindrical sources (diameter: 8, 12, 16, 24, 32 mm) were used to determine setup errors. The data acquisition times were 1, 3 and 5 min. Image registration was performed by five observers to determine the setup errors from PDRI registration and radiographic registration. The majority of the mean registration errors obtained from the PDRI registration were not significantly different from those obtained from the radiographic registration. Acquisition time did not appear to result in significant differences in the mean registration error. The mean registration error for the PDRI registration was found to be 0.93 ± 0.33 mm. This is not statistically different from the radiographic registration which had a mean registration error of 0.92 ± 0.27 mm. Our results suggest that m-IGRT image registration using PET-based reconstructed planar images along the in-plane direction is feasible for clinical use if PDRI registration is performed at two orthogonal gantry angles.

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.

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.

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.

Edge detection of the radiation field in double exposure portal images using a curve propagation algorithm

An accurate detection of the radiation field is crucial to 3D conformal radiotherapy (3D‐CRT). Automated techniques to detect the field edges on double exposure portal images have previously focused on thresholding techniques. In this paper, we present a new approach based on a curve propagation technique (the Fast Marching method) which proves to be more effective at detecting the radiation field than its thresholding counterpart. The comparison of both techniques in terms of computational speed and effectiveness of the detection is presented using complex images with non‐homogeneous intensity levels inside the radiation field, and gradual variations in intensity level at the field boundaries. Results show that our Fast Marching method is easier to automate, and converges faster to the boundaries of the segmented radiation field. The computation time of the Fast Marching technique is five times faster in typical portal images.

PACS numbers: 87.53.Oq, 87.57.Nk, 87.57.‐s.

Quality assurance for kilo- and megavoltage in-room imaging and localization for off- and online setup error correction

In-room radiography is not a new concept for image-guided radiation therapy. Rapid advances in technology, however, have made this positioning method convenient, and thus radiograph-based positioning has propagated widely. The paradigms for quality assurance of radiograph-based positioning include imager performance, systems integration, infrastructure, procedure documentation and testing, and support for positioning strategy implementation.

Evaluation of Image Enhancement Method on Target Registration Using Cone Beam CT in Radiation Therapy

An intensity based six-degree image registration algorithm between cone-beam CT (CBCT) and planning CT has been developed for image-guided radiation therapy (IGRT). CT images of an anthropomorphic chest phantom were acquired using conventional CT scanner and corresponding CBCT was reconstructed based on projection images acquired by an on-board imager (OBI). Both sets of images were initially registered to each other using attached fudicial markers to achieve a golden standard registration. Starting from this point, an offset was applied to one set of images, and the matching result was found by a gray-value based registration method. Finally, The registration error was evaluated by comparing the detected shifts with the known shift. Three window-level (WL) combinations commonly used for image enhancement were examined to investigate the effect of anatomical information of Bony only (B), Bone+Tissue (BT), and Bone+Tissue+Air (BTA) on the accuracy and robustness of gray-value based registration algorithm. Extensive tests were performed in searching for the attraction range of registration algorithm. The widest attraction range was achieved with the WL combination of BTA. The average attraction ranges of this combination were 73.3 mm and 81.6 degree in the translation and rotation dimensions, respectively, and the average registration errors were 0.15 mm and 0.32 degree. The WL combination of BT shows the secondary largest attraction ranges. The WL combination of B shows limited convergence property and its attraction range was the smallest among the three examined combinations (on average 33.3 mm and 25.0 degree). If two sets of 3D images in original size (512 × 512) were used, registration could be accomplished within 10~20 minutes by current algorithm, which is only acceptable for off-line reviewing purpose. As the size of image set reduced by a factor of 2~4, the registration time would be 2~4 minutes which is feasible for on-line target localization.

Different styles of image-guided radiotherapy

To account for geometric uncertainties during radiotherapy, safety margins are applied. In many cases, these margins overlap organs at risk, thereby limiting dose escalation. The aim of image-guided radiotherapy is to improve the accuracy by imaging tumors and critical structures on the machine just before irradiation. The availability of high-quality imaging systems and automatic image registration on the machine leads to many new clinical applications, such as high-precision hypofractionated treatments of brain metastases and solitary long tumors with online tumor position corrections. In this review, the prerequisites for image guidance in terms of planning, image acquisition, and processing are first described. Then, the various methods of correction are discussed such as table shifts and rotation and direct adaptation of machine parameters. Then, online, offline, and intrafraction correction strategies are discussed. Finally, some imaging dose issues are discussed showing that kilovoltage cone-beam computed tomography guidance has a net positive impact on the integral dose; the gain caused by margin reduction is larger than the image dose. We can conclude that image-guided radiotherapy is very much a clinical reality and that the development of optimal clinical protocols should currently be the focus of research.

The management of imaging dose during image-guided radiotherapy: report of the AAPM Task Group 75

Radiographic image guidance has emerged as the new paradigm for patient positioning, target localization, and external beam alignment in radiotherapy. Although widely varied in modality and method, all radiographic guidance techniques have one thing in common--they can give a significant radiation dose to the patient. As with all medical uses of ionizing radiation, the general view is that this exposure should be carefully managed. The philosophy for dose management adopted by the diagnostic imaging community is summarized by the acronym ALARA, i.e., as low as reasonably achievable. But unlike the general situation with diagnostic imaging and image-guided surgery, image-guided radiotherapy (IGRT) adds the imaging dose to an already high level of therapeutic radiation. There is furthermore an interplay between increased imaging and improved therapeutic dose conformity that suggests the possibility of optimizing rather than simply minimizing the imaging dose. For this reason, the management of imaging dose during radiotherapy is a different problem than its management during routine diagnostic or image-guided surgical procedures. The imaging dose received as part of a radiotherapy treatment has long been regarded as negligible and thus has been quantified in a fairly loose manner. On the other hand, radiation oncologists examine the therapy dose distribution in minute detail. The introduction of more intensive imaging procedures for IGRT now obligates the clinician to evaluate therapeutic and imaging doses in a more balanced manner. This task group is charged with addressing the issue of radiation dose delivered via image guidance techniques during radiotherapy. The group has developed this charge into three objectives: (1) Compile an overview of image-guidance techniques and their associated radiation dose levels, to provide the clinician using a particular set of image guidance techniques with enough data to estimate the total diagnostic dose for a specific treatment scenario, (2) identify ways to reduce the total imaging dose without sacrificing essential imaging information, and (3) recommend optimization strategies to trade off imaging dose with improvements in therapeutic dose delivery. The end goal is to enable the design of image guidance regimens that are as effective and efficient as possible.

Detection of treatment setup errors between two CT scans for patients with head and neck cancer

Accuracy of treatment setup for head and neck patients undergoing intensity-modulated radiation therapy is of paramount importance. The conventional method using orthogonal portal images can only detect translational setup errors while the most frequent setup errors for head and neck patients could be rotational errors. With the rapid development of image-guided radiotherapy, three-dimensional images are readily acquired and can be used to detect both translational and rotational setup errors. The purpose of this study is to determine the significance of rotational variations between two planning CT scans acquired for each of eight head and neck patients, who experienced substantial weight loss or tumor shrinkage. To this end, using a rigid body assumption, we developed an in-house computer program that utilizes matrix transformations to align point bony landmarks with an incremental best-fit routine. The program returns the quantified translational and rotational shifts needed to align the scans of each patient. The program was tested using a phantom for a set of known translational and rotational shifts. For comparison, a commercial treatment planning system was used to register the two CT scans and estimate the translational errors for these patients. For the eight patients, we found that the average magnitudes and standard deviations of the rotational shifts about the transverse, anterior-posterior, and longitudinal axes were 1.7 +/- 2.3 degrees, 0.8 +/- 0.7 degrees, and 1.8 +/- 1.1 degrees, respectively. The average magnitudes and standard deviations of the translational shifts were 2.5 +/- 2.6 mm, 2.9 +/- 2.8 mm, 2.7 +/- 1.7 mm while the differences detected between our program and the CT-CT fusion method were 1.8 +/- 1.3 mm, 3.3 +/- 5.4 mm, and 3.0 +/- 3.4 mm in the left-right, anterior-posterior, and superior-inferior directions, respectively. A trend of larger rotational errors resulting in larger translational differences between the two methods was observed. In conclusion, conventional methods used for verifying patient positioning may misinterpret rotational shifts as translational shifts, and our study demonstrated that rotational errors may be significant in the treatment of head and neck cancer.

Analysis of the rigid and deformable component of setup inaccuracies on portal images in head and neck radiotherapy

The issue of setup errors consisting of translation, rotation and deformation components in head and neck radiotherapy is addressed with a piecewise registration of small independent regions on a portal image to their reference position. These rectangular regions are termed featurelets as they contain relevant anatomical features. The resulting displacement vectors of each featurelet reflect both the center-of-mass (COM), i.e. the rigid, and the non-rigid component of the setup error. The displacement vectors of a series of daily portal images were subjected to a principal component analysis. In addition to the mean, systematic displacement of each featurelet, this analysis yields correlated patterns of anatomical deformations. Hence, the physiological movements of an individual patient can be obtained without a biomechanical model. It is shown that in the presence of setup errors that are due to rotations or deformations a correction by the COM displacement may deteriorate the error of parts of the anatomy further. The featurelet analysis can be used to refine setup correction protocols, tune spatially variable setup margins in treatment planning and optimize patient immobilization devices.

Concise matrix analysis of point-based prostate targeting for intensity modulated radiation therapy

Intensity Modulated Radiation Therapy (IMRT) has recently emerged as an effective clinical treatment tool to treat various types of cancers by limiting the external beam dose to the surrounding normal tissue. However, the process of limiting external radiation dose to the tissue surrounding the tumor volume is not a trivial task. Several parameters including tumor volume and inhomogeneity, position and shape of the tumor volume, and the geometrical distribution of the radiation beams directly affect the determination of the external radiation dose. In addition, a major variable in effective delivery of the radiation dose is "set-up error" caused by the changes in patient position. Any changes in the position of the patient affect the geometrical location of the tumor volume and, therefore, need to be accommodated in the delivery of radiation beams during the treatment. This work presents a complete matrix representation required to calculate the three-dimensional rigid body homogeneous transformation matrices corresponding to external beam radiotherapy setup error and subsequent corrections in treatment beam parameters. A new concise orthogonal rotation solution is presented for use with clinical noisy data. Monte Carlo simulations prove the new matrix results are consistently better than the standard inverse solution. The required corrections in beam table, gantry, and collimator angles as function of the planned beam gantry angle are derived. For transformations that include a rotation on the sagittal plane, the required offsets to beam parameters are complex functions of the planned gantry angle but are clearly documented graphically for clinical use. A case study is presented with an error analysis that supports the use of the presented method in a clinical environment. Clinical implementation and evaluation of the presented method with patient data is also included in the paper.

Geometric uncertainty of 2D projection imaging in monitoring 3D tumor motion

The purpose of this study was to investigate the accuracy of two-dimensional (2D) projection imaging methods in three-dimensional (3D) tumor motion monitoring. Many commercial linear accelerator types have projection imaging capabilities, and tumor motion monitoring is useful for motion inclusive, respiratory gated or tumor tracking strategies. Since 2D projection imaging is limited in its ability to resolve the motion along the imaging beam axis, there is unresolved motion when monitoring 3D tumor motion. From the 3D tumor motion data of 160 treatment fractions for 46 thoracic and abdominal cancer patients, the unresolved motion due to the geometric limitation of 2D projection imaging was calculated as displacement in the imaging beam axis for different beam angles and time intervals. The geometric uncertainty to monitor 3D motion caused by the unresolved motion of 2D imaging was quantified using the root-mean-square (rms) metric. Geometric uncertainty showed interfractional and intrafractional variation. Patient-to-patient variation was much more significant than variation for different time intervals. For the patient cohort studied, as the time intervals increase, the rms, minimum and maximum values of the rms uncertainty show decreasing tendencies for the lung patients but increasing for the liver and retroperitoneal patients, which could be attributed to patient relaxation. Geometric uncertainty was smaller for coplanar treatments than non-coplanar treatments, as superior-inferior (SI) tumor motion, the predominant motion from patient respiration, could be always resolved for coplanar treatments. Overall rms of the rms uncertainty was 0.13 cm for all treatment fractions and 0.18 cm for the treatment fractions whose average breathing peak-trough ranges were more than 0.5 cm. The geometric uncertainty for 2D imaging varies depending on the tumor site, tumor motion range, time interval and beam angle as well as between patients, between fractions and within a fraction.

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.

Repositioning accuracy of two different mask systems—3D revisited: Comparison using true 3D/3D matching with cone-beam CT

PURPOSE

The repositioning accuracy of mask-based fixation systems has been assessed with two-dimensional/two-dimensional or two-dimensional/three-dimensional (3D) matching. We analyzed the accuracy of commercially available head mask systems, using true 3D/3D matching, with X-ray volume imaging and cone-beam CT.

METHODS AND MATERIALS

Twenty-one patients receiving radiotherapy (intracranial/head-and-neck tumors) were evaluated (14 patients with rigid and 7 with thermoplastic masks). X-ray volume imaging was analyzed online and offline separately for the skull and neck regions. Translation/rotation errors of the target isocenter were analyzed. Four patients were treated to neck sites. For these patients, repositioning was aided by additional body tattoos. A separate analysis of the setup error on the basis of the registration of the cervical vertebra was performed. The residual error after correction and intrafractional motility were calculated.

RESULTS

The mean length of the displacement vector for rigid masks was 0.312 +/- 0.152 cm (intracranial) and 0.586 +/- 0.294 cm (neck). For the thermoplastic masks, the value was 0.472 +/- 0.174 cm (intracranial) and 0.726 +/- 0.445 cm (neck). Rigid masks with body tattoos had a displacement vector length in the neck region of 0.35 +/- 0.197 cm. The intracranial residual error and intrafractional motility after X-ray volume imaging correction for rigid masks was 0.188 +/- 0.074 cm, and was 0.134 +/- 0.14 cm for thermoplastic masks.

CONCLUSIONS

The results of our study have demonstrated that rigid masks have a high intracranial repositioning accuracy per se. Given the small residual error and intrafractional movement, thermoplastic masks may also be used for high-precision treatments when combined with cone-beam CT. The neck region repositioning accuracy was worse than the intracranial accuracy in both cases. However, body tattoos and image guidance improved the accuracy. Finally, the combination of both mask systems with 3D image guidance has the potential to replace therapy simulation and intracranial stereotaxy.

Developments in electronic portal imaging systems

Verification of geometric accuracy at the time of treatment delivery has always been a necessary part of the radiotherapy process. Since the introduction of conformal and intensity-modulated radiotherapy, the consequences of patient positioning errors are more serious. Portal imaging has played a large part in fulfilling the need for improved geometric accuracy. This review examines how portal imaging has progressed through the development and evolution of electronic portal imaging devices (EPIDs). Changes in technology, including the current commercial systems, and how image quality has changed are presented. The clinical usage of EPIDs and the technological innovations being devised for further improvements in image quality and systems are considered.

Patient position verification using small IMRT fields

A commonly used approach to quantify and minimize patient setup errors is by using electronic portal imaging devices (EPIDs). The position of the tumor can be verified indirectly by matching the bony anatomy to a reference image containing the same structures. In this paper we present two off-line methods for detecting the position of the bony anatomy automatically, even if every single portal image of each segment of an IMRT treatment beam contains insufficient matching information. Extra position verification fields will no longer be necessary, which reduces the total dose to the patient. The first method, the stack matching method (SMM), stacks the portal image of each segment of a beam to a three dimensional (3D) volume, and this volume is subsequently used during the matching phase. The second method [the averaged projection matching method (APMM)], is a simplification of the first one, since the initially created volume is reduced again to a 2D artificial image, which speeds up the matching procedure considerably, without a significant loss of accuracy. Matching is based on normalized mutual information. We demonstrate our methods by comparing them to existing matching routines, such as matching based on the largest segment. Both phantom and patient experiments show that our methods are comparable with the results obtained from standard position verification methods. The matches are verified by means of visual inspection. Furthermore, we show that when a distinct area of 40-60 cm2 of the EPID is exposed during one treatment beam, both SMM and APMM are able to deliver a good matching result.

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.

Magnitude and clinical relevance of translational and rotational patient setup errors: A cone-beam CT study

PURPOSE

To establish volume imaging using an on-board cone-beam CT (CB-CT) scanner for evaluation of three-dimensional patient setup errors.

METHODS AND MATERIALS

The data from 24 patients were included in this study, and the setup errors using 209 CB-CT studies and 148 electronic portal images were analyzed and compared. The effect of rotational errors alone, translational errors alone, and combined rotational and translational errors on target coverage and sparing of organs at risk was investigated.

RESULTS

Translational setup errors using the CB-CT scanner and an electronic portal imaging device differed <1 mm in 70.7% and <2 mm in 93.2% of the measurements. Rotational errors >2 degrees were recorded in 3.7% of pelvic tumors, 26.4% of thoracic tumors, and 12.4% of head-and-neck tumors; the corresponding maximal rotational errors were 5 degrees , 8 degrees , and 6 degrees . No correlation between the magnitude of translational and rotational setup errors was observed. For patients with elongated target volumes and sharp dose gradients to adjacent organs at risk, both translational and rotational errors resulted in considerably decreased target coverage and highly increased doses to the organs at risk compared with the initial treatment plan.

CONCLUSIONS

The CB-CT scanner has been successfully established for the evaluation of patient setup errors, and its feasibility in day-to-day clinical practice has been demonstrated. Our results have indicated that rotational errors are of clinical significance for selected patients receiving high-precision radiotherapy.

Evaluation of similarity measures for reconstruction-based registration in image-guided radiotherapy and surgery

PURPOSE

A promising patient positioning technique is based on registering computed tomographic (CT) or magnetic resonance (MR) images to cone-beam CT images (CBCT). The extra radiation dose delivered to the patient can be substantially reduced by using fewer projections. This approach results in lower quality CBCT images. The purpose of this study is to evaluate a number of similarity measures (SMs) suitable for registration of CT or MR images to low-quality CBCTs.

METHODS AND MATERIALS

Using the recently proposed evaluation protocol, we evaluated nine SMs with respect to pretreatment imaging modalities, number of two-dimensional (2D) images used for reconstruction, and number of reconstruction iterations. The image database consisted of 100 X-ray and corresponding CT and MR images of two vertebral columns.

RESULTS

Using a higher number of 2D projections or reconstruction iterations results in higher accuracy and slightly lower robustness. The similarity measures that behaved the best also yielded the best registration results. The most appropriate similarity measure was the asymmetric multi-feature mutual information (AMMI).

CONCLUSIONS

The evaluation protocol proved to be a valuable tool for selecting the best similarity measure for the reconstruction-based registration. The results indicate that accurate and robust CT/CBCT or even MR/CBCT registrations are possible if the AMMI similarity measure is used.

A gradient feature weighted Minimax algorithm for registration of multiple portal images to 3DCT volumes in prostate radiotherapy

PURPOSE

To develop an accurate, fast, and robust algorithm for registering portal and computed tomographic (CT) images for radiotherapy using a combination of sparse and dense field data that complement each other.

METHODS AND MATERIALS

Gradient Feature Weighted Minimax (GFW Minimax) method was developed to register multiple portal images to three-dimensional CT images. Its performance was compared with that of three others: Minimax, Mutual Information, and Gilhuijs' method. Phantom and prostate cancer patient images were used. Effects of registration errors on tumor control probability (TCP) and normal tissue complication probability (NTCP) were investigated as a relative measure.

RESULTS

Registration of four portals to CTs resulted in 30% lower error when compared with registration with two portals. Computation time increased by nearly 50%. GFW Minimax performed the best, followed by Gilhuijs' method, the Minimax method, and Mutual Information.

CONCLUSIONS

Using four portals instead of two lowered the registration error. Reduced fields of view images with full feature sets gave similar results in shorter times as full fields of view images. In clinical situations where soft tissue targets are of importance, GFW Minimax algorithm was significantly more accurate and robust. With registration errors lower than 1 mm, margins may be scaled down to 4 mm without adversely affecting TCP and NTCP.

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 novel method for automatic detection of patient out-of-plane rotation by comparing a single portal image to a reference image

A novel method for detecting out-of-plane patient rotation by comparing a single portal image to its reference image is presented. Out-of-plane rotation results in an apparent distortion of the anatomy in a portal image. This distortion can be mathematically predicted with the magnification varying at each point in the image. While scaling of points at equal depth is invariant under in-plane rotation or translation, and changes equally in both dimensions for an axial shift of the patient, a change of scaling in only one dimension can be ascribed to an out-of-plane rotation. For the two conditions that are used in this study, it is shown that out-of-plane rotation yields a different scaling of the image in two perpendicular directions and therefore it is feasible to calculate the scale factors as a function of out-of-plane rotation. Conversely the recovery of scale factors in two different directions at the same time would enable the magnitude of the out-of-plane rotation to be recovered. The properties of the Fourier transform of the image are used to align the portal image with the reference image (a simulator image or first approved portal image) prior to the recovery of the scale factors. Correlating the Fourier transform of the portal image on a log-scale with that of the reference image enables the scale factors to be automatically extracted from a single portal image. In the two approaches investigated, out-of-plane rotations of up to 41 degrees and 20 degrees (respectively) have been recovered with a maximum error of 2.4 degrees. This technique could be used to automatically detect patient roll or tilt prior to or during a treatment session.

Development of computerized patient setup verification and correction system in radiotherapy

Visual comparison of a reference image with a verification image is commonly used for setup verification in external beam radiation therapy. However, it sometimes lacks reproducibility and provides insufficient quantitative evidence. The present study was performed to develop computerized methods for determining landmarks to verify a portal image with digital reconstruction radiograph (DRR), and to investigate the clinical effectiveness of our method. Our computer algorithm consisted of three main procedures--preprocessing, determination of landmarks, and verification--none of which required manual operation. Finally, our system indicated the distance for setup correction. We evaluated the accuracy of our system using pelvic phantom images, and the maximum magnitude of error was shown to be 1.12 (n=9). The results indicated that the error range of our system was sufficiently small to examine patient positioning error, which should be less than 5 mm, as described in AAPM report TG40. Our system will aid operators in positioning patients accurately for external radiation therapy.

Performance evaluation of an automated image registration algorithm using an integrated kilovoltage imaging and guidance system

Image-guided radiation therapy delivery may be used to assess the position of the tumor and anatomical structures within the body as opposed to relying on external marks. The purpose of this manuscript is to evaluate the performance of the image registration software for automatically detecting and repositioning a 3D offset of a phantom using a kilovoltage onboard imaging system. Verification tests were performed on both a geometric rigid phantom and an anthropomorphic head phantom containing a humanoid skeleton to assess the precision and accuracy of the automated positioning system. From the translation only studies, the average deviation between the detected and known offset was less than 0.75 mm for each of the three principal directions, and the shifts did not show any directional sensitivity. The results are given as the measurement with standard deviation in parentheses. The combined translations and rotations had the greatest average deviation in the lateral, longitudinal, and vertical directions. For all dimensions, the magnitude of the deviation does not appear to be correlated with the magnitude of the actual translation introduced. The On-Board Imager (OBI) system has been successfully integrated into a feasible online radiotherapy treatment guidance procedure. Evaluation of each patient's resulting automatch should be performed by therapists before each treatment session for adequate clinical oversight.

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.

3-D/2-D registration by integrating 2-D information in 3-D

In image-guided therapy, high-quality preoperative images serve for planning and simulation, and intraoperatively as "background", onto which models of surgical instruments or radiation beams are projected. The link between a preoperative image and intraoperative physical space of the patient is established by image-to-patient registration. In this paper, we present a novel 3-D/2-D registration method. First, a 3-D image is reconstructed from a few 2-D X-ray images and next, the preoperative 3-D image is brought into the best possible spatial correspondence with the reconstructed image by optimizing a similarity measure (SM). Because the quality of the reconstructed image is generally low, we introduce a novel SM, which is able to cope with low image quality as well as with different imaging modalities. The novel 3-D/2-D registration method has been evaluated and compared to the gradient-based method (GBM) using standardized evaluation methodology and publicly available 3-D computed tomography (CT), 3-D rotational X-ray (3DRX), and magnetic resonance (MR) and 2-D X-ray images of two spine phantoms, for which gold standard registrations were known. For each of the 3DRX, CT, or MR images and each set of X-ray images, 1600 registrations were performed from starting positions, defined as the mean target registration error (mTRE), randomly generated and uniformly distributed in the interval of 0-20 mm around the gold standard. The capture range was defined as the distance from gold standard for which the final TRE was less than 2 mm in at least 95% of all cases. In terms of success rate, as the function of initial misalignment and capture range the proposed method outperformed the GBM. TREs of the novel method and the GBM were approximately the same. For the registration of 3DRX and CT images to X-ray images as few as 2-3 X-ray views were sufficient to obtain approximately 0.4 mm TREs, 7-9 mm capture range, and 80%-90% of successful registrations. To obtain similar results for MR to X-ray registrations, an image, reconstructed from at least 11 X-ray images was required. Reconstructions from more than 11 images had no effect on the registration results.

A robust technique for 2D-3D registration

A robust 2D-3D registration method with a wide capture range is presented. The method registers pre-operatively collected 3D computed tomography (CT) data sets of a single bone fragment to its intra-operative fluoroscope images. The registration technique relies on hardware rendering of CT data on consumer-grade graphics cards to generate digitally reconstructed radiographs (DRRs) in real time. We also employ unscented Kalman filter to solve for the non-linear dynamics governing this 2D-3D registration problem. The method is validated on phantom models of three different anatomies, namely scaphoid, pelvis and femur. We show that, under the same testing conditions, our proposed technique outperforms the conventional simplex-based method in capture range and robustness while providing comparable accuracy and computation time.

Comparison of simulator-CT versus simulator fluoroscopy versus surface marking based radiation treatment planning: A prospective study by three-dimensional evaluation

BACKGROUND AND PURPOSE

Field placement for Radiation Treatment Planning can be done based on the surface markings or simulator fluoroscopy or simulator with CT facilities. A prospective study was carried out to compare these three techniques of radiation treatment planning to quantitatively find out the difference in normal tissue dosages and target volume coverage in the three groups after three-dimensional evaluation.

PATIENTS AND METHODS

The CT scans of 30 patients in the treatment position, taken on a Shimadzu SCT-3000 TF scanner at 1cm intervals, were transferred to Theraplan-500 three-dimensional radiation treatment planning computer. The normal tissues and target volumes (GTV and CTV) were outlined on all the CT slices as per (ICRU) Report no. 50. Three types of radiation treatment planning was done sequentially: Plan I-based on the surface markings alone, Plan II-based on simulator-fluoroscopy, and Plan III-based on Simulator-CT.

RESULTS

The mean dose to 95% of the clinical target volume (D95) was increased by 4.4 and 6.4% by Plans II and III as compared with Plan I. The mean dose to 3/3rd (D(3/3)) to all the critical organs was decreased by 6.6 and 8.4% by Plans II and III as compared to Plan I. The mean time, in simulator room, for field placement for Plans I-III was 6.2, 14.6 and 44 min, respectively.

CONCLUSIONS

Thus for adequate coverage of target volumes and sparing normal tissues, Simulator-CT based radiation treatment planning is the best method of radiation treatment planning though it is more time consuming.

Clinical use of electronic portal imaging

Accurate and routine target localization is necessary for successful outcome in radiation therapy treatments. Electronic portal imaging devices (EPIDs) provide an advanced tool with digital technology to improve target localization and maintain clinical efficiency. EPIDs are ubiquitous in the radiation therapy clinic, and they provide a powerful and flexible tool to collect and process data in a quantitative manner to improve treatment accuracy for virtually any treatment site. This manuscript provides an overview of the clinical implementation process for effective use of EPIDs. It continues with a review of correction strategies and finally highlights numerous examples of effective clinical application of EPID.

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.