Clinical Cone Beam Computed Tomography Images Research Articles

Sex dependent differences of temporomandibular condylar bone mineral density distribution

ABSTRACT Objective The objective of this study was to examine whether bone mineral density (BMD) distribution in the mandibular condyle and facial morphology are associated with temporomandibular joint osteoarthritis (TMJ OA) using clinical cone beam computed tomography (CBCT) images. Methods CBCT images of 35 adults (16 male and 19 female) were examined to obtain TMJ OA counts, cephalometric analyses, and histograms of gray values that are proportional to BMD. Mean, standard deviation (SD), and low and high gray values at the 5th and 95th percentiles (Low5 and High5) of the histograms were measured. Results The female group had significantly higher values of TMJ OA counts, mean, and SD on the right mandibular condyle, High5 on both sides, and all gray value parameters for total (right + left) than the male group. Conclusion Comprehensive analysis of BMD distribution in the mandibular condyle can provide useful information for prognosis of TMJ OA.

Combining physics-based models with deep learning image synthesis and uncertainty in intraoperative cone-beam CT of the brain.

Image-guided neurosurgery requires high localization and registration accuracy to enable effective treatment and avoid complications. However, accurate neuronavigation based on preoperative magnetic resonance (MR) or computed tomography (CT) images is challenged by brain deformation occurring during the surgical intervention. To facilitate intraoperative visualization of brain tissues and deformable registration with preoperative images, a 3D deep learning (DL) reconstruction framework (termed DL-Recon) was proposed for improved intraoperative cone-beam CT (CBCT) image quality. The DL-Recon framework combines physics-based models with deep learning CT synthesis and leverages uncertainty information to promote robustness to unseen features. A 3D generative adversarial network (GAN) with a conditional loss function modulated by aleatoric uncertainty was developed for CBCT-to-CT synthesis. Epistemic uncertainty of the synthesis model was estimated via Monte Carlo (MC) dropout. Using spatially varying weights derived from epistemic uncertainty, the DL-Recon image combines the synthetic CT with an artifact-corrected filtered back-projection (FBP) reconstruction. In regions of high epistemic uncertainty, DL-Recon includes greater contribution from the FBP image. Twenty paired real CT and simulated CBCT images of the head were used for network training and validation, and experiments evaluated the performance of DL-Recon on CBCT images containing simulated and real brain lesions not present in the training data. Performance among learning- and physics-based methods was quantified in terms of structural similarity (SSIM) of the resulting image to diagnostic CT and Dice similarity metric (DSC) in lesion segmentation compared to ground truth. A pilot study was conducted involving seven subjects with CBCT images acquired during neurosurgery to assess the feasibility of DL-Recon in clinical data. CBCT images reconstructed via FBP with physics-based corrections exhibited the usual challenges to soft-tissue contrast resolution due to image non-uniformity, noise, and residual artifacts. GAN synthesis improved image uniformity and soft-tissue visibility but was subject to error in the shape and contrast of simulated lesions that were unseen in training. Incorporation of aleatoric uncertainty in synthesis loss improved estimation of epistemic uncertainty, with variable brain structures and unseen lesions exhibiting higher epistemic uncertainty. The DL-Recon approach mitigated synthesis errors while maintaining improvement in image quality, yielding 15%-22% increase in SSIM (image appearance compared to diagnostic CT) and up to 25% increase in DSC in lesion segmentation compared to FBP. Clear gains in visual image quality were also observed in real brain lesions and in clinical CBCT images. DL-Recon leveraged uncertainty estimation to combine the strengths of DL and physics-based reconstruction and demonstrated substantial improvements in the accuracy and quality of intraoperative CBCT. The improved soft-tissue contrast resolution could facilitate visualization of brain structures and support deformable registration with preoperative images, further extending the utility of intraoperative CBCT in image-guided neurosurgery.

Assessment of Bone Height Changes Based on the Cone-Beam Computed Tomography Following Intentional Replantation for Periodontally Compromised Teeth.

Background and Objectives; This study aimed to evaluate the clinical outcomes and bone changes before and after intentional replantation (IR) for periodontally compromised teeth by using cone-beam computed tomography (CBCT). Materials and Methods; Fourteen periodontally involved teeth were selected for IR. A preoperative orthodontic procedure was performed to apply luxation, and the tooth was then gently extracted. Retrograde filling of the root-end canal was performed. Once the tooth was repositioned in the socket, it was splinted with the adjacent tooth. After three months, prosthetic restoration was performed. Results; Clinical parameters and CBCT images were obtained before and after the IR procedure. The height of the alveolar bone was measured on the CBCT images by using software. Most preoperative symptoms, including pain, mobility, probing depth (PD) and bleeding on probing (BOP), significantly decreased after IR (pain: 4.71 to 1.00; mobility: 1.36 to 0.29; PD: 5.60 to 2.85; BOP: 3.50 to 0.79). CBCT analysis indicated an increase in bone height after IR (the amount of change: maxilla, 4.00; mandible, 1.95). Conclusions; A previous study reported that IR for periodontally involved teeth is quite limited. However, in this study, IR of periodontally compromised teeth showed favorable results in clinical and radiographic evaluations, suggesting that IR may be an alternative to extraction of teeth with periodontal disease.

Aging Alters Cervical Vertebral Bone Density Distribution: A Cross-Sectional Study

Osteoporosis reduces bone mineral density (BMD) with aging. The incidence of cervical vertebral injuries for the elderly has increased in the last decade. Thus, the objective of the current study was to examine whether dental cone beam computed tomography (CBCT) can identify age and sex effects on volumetric BMD and morphology of human cervical vertebrae. A total of 136 clinical CBCT images were obtained from 63 male and 73 female patients (20 to 69 years of age). Three-dimensional images of cervical vertebral bodies (C2 and C3) were digitally isolated. A gray level, which is proportional to BMD, was obtained and its distribution was analyzed in each image. Morphology, including volume, heights, widths, and concavities, was also measured. Most of the gray level parameters had significantly higher values of C2 and C3 in females than in males for all age groups (p < 0.039). The female 60-age group had significant lower values of Mean and Low5 of C2 and C3 than both female 40- and 50-age groups (p < 0.03). The reduced BMD of the female 60-age group likely resulted from postmenopausal demineralization of bone. Current findings suggest that dental CBCT can detect age-dependent changes of cervical vertebral BMD, providing baseline information to develop an alternative tool to diagnose osteoporosis.

Atlas-based segmentation of cochlear microstructures in cone beam CT.

To develop an automated segmentation approach for cochlear microstructures [scala tympani (ST), scala vestibuli (SV), modiolus (Mod), mid-modiolus (Mid-Mod), and round window membrane (RW)] in clinical cone beam computed tomography (CBCT) images of the temporal bone for use in surgical simulation software and for preoperative surgical evaluation. This approach was developed using the publicly available OpenEar (OE) Library that includes temporal bone specimens with spatially registered CBCT and 3D micro-slicing images. Five of these datasets were spatially aligned to our internal OSU atlas. An atlas of cochlear microstructures was created from one of the OE datasets. An affine registration of this atlas to the remaining OE CBCT images was used for automatically segmenting the cochlear microstructures. Quantitative metrics and visual review were used for validating the automatic segmentations. The average DICE metrics were 0.77 and 0.74 for the ST and SV, respectively. The average Hausdorff distance (AVG HD) was 0.11mm and 0.12mm for both scalae. The mean distance between the centroids for the round window was 0.32mm, and the mean AVG HD was 0.09mm. The mean distance and angular rotation between the mid-modiolar axes were 0.11mm and 9.8 degrees, respectively. Visually, the segmented structures were accurate and similar to that manually traced by an expert observer. An atlas-based approach using 3D micro-slicing data and affine spatial registration in the cochlear region was successful in segmenting cochlear microstructures of temporal bone anatomy for use in simulation software and potentially for pre-surgical planning and rehearsal.

Quantifying Subresolution 3D Morphology of Bone with Clinical Computed Tomography

The aim of this study was to quantify sub-resolution trabecular bone morphometrics, which are also related to osteoarthritis (OA), from clinical resolution cone beam computed tomography (CBCT). Samples (n = 53) were harvested from human tibiae (N = 4) and femora (N = 7). Grey-level co-occurrence matrix (GLCM) texture and histogram-based parameters were calculated from CBCT imaged trabecular bone data, and compared with the morphometric parameters quantified from micro-computed tomography. As a reference for OA severity, histological sections were subjected to OARSI histopathological grading. GLCM and histogram parameters were correlated to bone morphometrics and OARSI individually. Furthermore, a statistical model of combined GLCM/histogram parameters was generated to estimate the bone morphometrics. Several individual histogram and GLCM parameters had strong associations with various bone morphometrics (|r| > 0.7). The most prominent correlation was observed between the histogram mean and bone volume fraction (r = 0.907). The statistical model combining GLCM and histogram-parameters resulted in even better association with bone volume fraction determined from CBCT data (adjusted R2 change = 0.047). Histopathology showed mainly moderate associations with bone morphometrics (|r| > 0.4). In conclusion, we demonstrated that GLCM- and histogram-based parameters from CBCT imaged trabecular bone (ex vivo) are associated with sub-resolution morphometrics. Our results suggest that sub-resolution morphometrics can be estimated from clinical CBCT images, associations becoming even stronger when combining histogram and GLCM-based parameters.

Efficacy of Cone-Beam Computed Tomography in Evaluating Bone Quality for Optimum Implant Treatment Planning.

This study examined (1) if cone-beam computed tomography (CBCT) can determine relative differences in bone mineral density distribution using clinical images of patients' mandibular bone and (2) if the relative differences can be used to detect the effects of sex and age on bone mineral density distribution. Sixty-six clinical CBCT images from patients (36 females and 30 males) of 3 age groups (40, 50, and 60 years) were identified. Alveolar (AB) and basal cortical bone (CB) regions were digitally isolated. A histogram of gray levels, which are proportional to degrees of bone mineralization, was obtained from each region. Mean, variability (SD and coefficient of variation), and percentage differences of gray level parameters between AB and basal CBs were computed. Significant sex differences in gray level variability were observed within the postmenopausal age group (P < 0.042). These findings suggest that clinical CBCT images can be a valuable tool in providing information on bone quality, which is an important criterion for optimum planning for dental implant placement.

Lung diaphragm tracking in CBCT images using spatio-temporal MRF

In EBRT in order to monitor the intra fraction motion of thoracic and abdominal tumors, one of the standard approaches is to use the lung diaphragm apex as an internal marker. However, tracking the position of the apex from image based observations is a challenging problem, as it undergoes both position and shape variation. The purpose of this paper is to propose an alternative method for tracking the ipsi-lateral hemidiaphragm apex (IHDA) position on Cone Beam Computed Tomography (CBCT) projection images. A hierarchical method is proposed to track the IHDA position across the frames. The diaphragm state is modeled as a spatio-temporal Markov Random Field (MRF). The likelihood function is derived from the votes based on 4D-Hough space. The optimal state of the diaphragm is obtained by solving the associated energy minimization problem using graph-cuts. A heterogeneous GPU implementation is provided for the method using CUDA framework and the performance is compared with that of CPU implementation. The method was tested using 15 clinical CBCT images. The results demonstrate that the MRF formulation outperforms the full search method in terms of accuracy. The GPU based heterogeneous implementation of the proposed algorithm takes about 25s, which is 16% improvement over the existing benchmark. The proposed MRF formulation considers all the possible combinations from the 4D-Hough space and therefore results in better tracking accuracy. The GPU based implementation exploits the inherent parallelism in our algorithm to accelerate the performance thereby increasing the viability of the approach for clinical use.

Implementation of an efficient Monte Carlo calculation for CBCT scatter correction: phantom study.

Cone‐beam computed tomography (CBCT) images suffer from poor image quality, in a large part due to contamination from scattered X‐rays. In this work, a Monte Carlo (MC)‐based iterative scatter correction algorithm was implemented on measured phantom data acquired from a clinical on‐board CBCT scanner. An efficient EGSnrc user code (egs_cbct) was used to transport photons through an uncorrected CBCT scan of a Catphan 600 phantom. From the simulation output, the contribution from primary and scattered photons was estimated in each projection image. From these estimates, an iterative scatter correction was performed on the raw CBCT projection data. The results of the scatter correction were compared with the default vendor reconstruction. The scatter correction was found to reduce the error in CT number for selected regions of interest, while improving contrast‐to‐noise ratio (CNR) by 18%. These results demonstrate the performance of the proposed scatter correction algorithm in improving image quality for clinical CBCT images.PACS numbers: 87.10.Rt, 87.57.Q‐

Can dental cone beam computed tomography assess bone mineral density?

Mineral density distribution of bone tissue is altered by active bone modeling and remodeling due to bone complications including bone disease and implantation surgery. Clinical cone beam computed tomography (CBCT) has been examined whether it can assess oral bone mineral density (BMD) in patient. It has been indicated that CBCT has disadvantages of higher noise and lower contrast than conventional medical computed tomography (CT) systems. On the other hand, it has advantages of a relatively lower cost and radiation dose but higher spatial resolution. However, the reliability of CBCT based mineral density measurement has not yet been fully validated. Thus, the objectives of this review are to discuss 1) why assessment of BMD distribution is important and 2) whether the clinical CBCT can be used as a potential tool to measure the BMD. Brief descriptions of image artefacts associated with assessment of gray value, which has been used to account for mineral density, in CBCT images are provided. Techniques to correct local and conversion errors in obtaining the gray values in CBCT images are also introduced. This review can be used as a quick reference for users who may encounter these errors during analysis of CBCT images.

Patient-specific scatter correction in clinical cone beam computed tomography imaging made possible by the combination of Monte Carlo simulations and a ray tracing algorithm

Purpose. Cone beam computed tomography (CBCT) image quality is limited by scattered photons. Monte Carlo (MC) simulations provide the ability of predicting the patient-specific scatter contamination in clinical CBCT imaging. Lengthy simulations prevent MC-based scatter correction from being fully implemented in a clinical setting. This study investigates the combination of using fast MC simulations to predict scatter distributions with a ray tracing algorithm to allow calibration between simulated and clinical CBCT images. Material and methods. An EGSnrc-based user code (egs_cbct), was used to perform MC simulations of an Elekta XVI CBCT imaging system. A 60keV x-ray source was used, and air kerma scored at the detector plane. Several variance reduction techniques (VRTs) were used to increase the scatter calculation efficiency. Three patient phantoms based on CT scans were simulated, namely a brain, a thorax and a pelvis scan. A ray tracing algorithm was used to calculate the detector signal due to primary photons. A total of 288 projections were simulated, one for each thread on the computer cluster used for the investigation. Results. Scatter distributions for the brain, thorax and pelvis scan were simulated within 2% statistical uncertainty in two hours per scan. Within the same time, the ray tracing algorithm provided the primary signal for each of the projections. Thus, all the data needed for MC-based scatter correction in clinical CBCT imaging was obtained within two hours per patient, using a full simulation of the clinical CBCT geometry. Conclusions. This study shows that use of MC-based scatter corrections in CBCT imaging has a great potential to improve CBCT image quality. By use of powerful VRTs to predict scatter distributions and a ray tracing algorithm to calculate the primary signal, it is possible to obtain the necessary data for patient specific MC scatter correction within two hours per patient.

Identification of occlusal prematurity by clinical examination and cone-beam computed tomography

The aim of this study was to evaluate the ability to identify occlusal prematurity by images from paraxial slices of cone-beam computed tomography (CBCT). For such purpose, a pilot study was performed in which 16 asymptomatic young patients were subjected to a clinical examination, including a careful occlusal analysis and then individual deprogrammer devices ("Lucia's JIG") were fabricated. Premature contacts were clinically identified in centric relation (CR) for each patient by jaw manipulation and interocclusal marking with articulating paper (Accufilm). Subsequently, these devices were adjusted in CR and used during the tomographic exams in such a way that CBCT in CR could be obtained. After routine processing, the images were analyzed in order to identify occlusal prematurity on the displayed images by 30 professionals divided according to areas of activity (occlusion specialist, general practitioner and radiologist; n=10 per area) and time of professional experience (less than 5, between 5 and 10, and over 10 years). By comparing the premature contacts identified in the clinical analysis and CBCT images, an agreement index between these two variables was calculated. Data were analyzed statistically by ANOVA and Scott-Knott test (α=0.05). The results showed that the identification of occlusal prematurity by paraxial CBCT slices was proven to be a method of average reliability.

Physical phantom studies of helical cone‐beam CT with exact reconstruction

Onboard cone-beam computed tomography (CBCT) connected to radiotherapy linear accelerators suffers CT number consistency and uniformity problems in addition to limited longitudinal coverage. Such problems have prevented CBCT from being fully utilized in many quantitative applications including tumor response evaluation and daily radiation dose computation. This paper presents a feasibility study on the helical CBCT scan with exact reconstruction that could be a potential solution. A Varian TrueBeam treatment machine was programmed in the research mode to accomplish helical scans that required synchronized gantry circular rotation and couch table linear motion. Two physical phantoms were scanned in both 360° and 720° helical trajectories. A Katsevich exact reconstruction algorithm was implemented and tested with digital phantom simulations. It was further optimized to account for mechanical instabilities of both gantry rotation and couch table motion from the physical phantom measurements. Preprocessing was employed to correct photon scattering, beam hardening, and bowtie filtration. The reconstructed images were compared to those reconstructed from the FDK approximate reconstruction algorithm using the same phantom projections. Comparisons have also been made with the clinical circular CBCT images and the diagnostic helical CT images of the same physical phantoms. Satisfactory reconstruction results were obtained for the Katsevich algorithm in digital phantom study. Physical phantom results demonstrated that a 360° helical scan could provide up to 19 cm longitudinal coverage, which could be increased to 54 cm with a 720° helical scan. Image spatial resolution and soft tissue contrast were sufficient. The Q-value, which combined the spatial frequency response (modulation transfer function) and the image noise, was calculated, and suggested that the Katsevich algorithm was superior to the FDK algorithm. A helical CBCT scan is useful to extend the longitudinal coverage. The Katsevich exact reconstruction algorithm could provide additional advantages in image qualities over the traditional FDK approximate algorithm. The combination of helical CBCT scan with exact reconstruction was proved feasible and would render CBCT more useful in image-guided radiation therapy.

Cone beam computed tomography imaging as a diagnostic tool in determining root fracture in endodontically treated teeth

Introduction: Vertical root fractures (VRFs) of endodontically treated teeth are serious complications that are associated with a poor survival prognosis. The diagnosis of VRFs is difficult, because the clinical signs and symptoms are often obscure and periapical radiographs (PAs) provide limited diagnostic information. Recently, Cone Beam Computed Tomography (CBCT) has been used to address complicated cases of VRFs with an inconclusive diagnosis. Aim: To determine the clinical diagnostic accuracy of CBCT in detecting VRFs in complex failing cases of endodontically treated teeth, as an adjunct to PAs. Materials and Methods: Evaluating the diagnostic accuracy of CBCT in a series of clinical cases of suspected VRFs, presented via clinical photographs, PAs, and CBCT images. Results: CBCT imaging significantly aided the differential diagnosis in most cases. Conclusions: CBCT imaging provides a 3D image and information in many cases of VRF that 2-dimensional PAs fail to generate. The slice thickness and interslice interval of the CBCT image influence the perception of the normal anatomy and pathoses.

A novel image-domain-based cone-beam computed tomography enhancement algorithm

Kilo-voltage (kV) cone-beam computed tomography (CBCT) plays an important role in image-guided radiotherapy. However, due to a large cone-beam angle, scatter effects significantly degrade the CBCT image quality and limit its clinical application. The goal of this study is to develop an image enhancement algorithm to reduce the low-frequency CBCT image artifacts, which are also called the bias field. The proposed algorithm is based on the hypothesis that image intensities of different types of materials in CBCT images are approximately globally uniform (in other words, a piecewise property). A maximum a posteriori probability framework was developed to estimate the bias field contribution from a given CBCT image. The performance of the proposed CBCT image enhancement method was tested using phantoms and clinical CBCT images. Compared to the original CBCT images, the corrected images using the proposed method achieved a more uniform intensity distribution within each tissue type and significantly reduced cupping and shading artifacts. In a head and a pelvic case, the proposed method reduced the Hounsfield unit (HU) errors within the region of interest from 300 HU to less than 60 HU. In a chest case, the HU errors were reduced from 460 HU to less than 110 HU. The proposed CBCT image enhancement algorithm demonstrated a promising result by the reduction of the scatter-induced low-frequency image artifacts commonly encountered in kV CBCT imaging.

A New Scatter Correction Method for KV and MV Cone Beam CT

Kilo-voltage(KV) and mega-voltage(MV) cone beam computed tomography (CBCT) play an important role in image-guided adaptive radiotherapy. However, the cupping effect, introduced by Compton scatter contribution due to a large cone angle of CBCT acquisition, significantly degrades the CBCT image quality and hampers its clinical application. The objective of this study is to develop a new CBCT scatter correction method in the image domain. The hypotheses of the proposed method are that the image intensity of different kind of materials in CBCT image should be globally uniform and the cupping effect can be described as a bias field, a low frequency signal across the whole 3-D images. Based on above assumptions, we proposed a maximum a posteriori probability (MAP) framework to estimate the bias field contribution. The well-known fuzzy C-mean model is extended to define the likelihood function of the CBCT image. A markov random field model is used to describe the prior probability of the mixture defined in FCM model. The bias field is characterized by a Gaussian prior probability with a banded covariance matrix representing a low-pass filter. An iterated conditional mode (ICM)-like approach is utilized to minimize the objective function. The corrected CBCT image can be obtained by subtracting the bias field from original CBCT image. The performance of correcting the cupping effect of CBCT image with the proposed MAP framework was tested using both contrast phantoms and clinical CBCT images. In the cylindrical shape-contrast phantom the higher density material is surrounded by the lower density material. In the contrast phantom study, the intensities of higher density material in both KV and MV CBCT images are even lower than that of lower density material due to the cupping effect. The corrected CBCT images showed almost same intensity distribution as the original contrast phantom images. In a real patient study, it is clearly shown that there is a shadow inside the brain soft tissue in the original brain KV and MV CBCT images. The corrected CBCT images significantly improved the uniformity of intensity distribution of brain soft tissue. The proposed image-based scatter correction method showed a promising result to reduce the cupping effect commonly encountered in KV and MV CBCT image.

SU‐GG‐I‐18: An Image‐Domain Based Cone Beam CT Scatter Correction Method

Purpose: Due to a large cone angle, scatter contribution significantly degrades the image quality of cone beam computed tomography (CBCT) by introducing the cupping effect. The objective of this study is to develop a new CBCT scatter correction method in image domain. Method and Materials: The hypotheses of the proposed method are that the image intensity of different kind of materials in CBCT image is globally uniform and the cupping effect can be described as a bias field, a low frequency signal across the whole 3‐D images. Based on above assumptions, we proposed a maximum a posteriori probability (MAP) framework to estimate the bias field contribution. The well‐known fuzzy C‐mean model is extended to define the likelihood function of the CBCT image. A markov random field model is used to describe the prior probability of the mixture defined in FCM model. The bias field is characterized by a zero mean Gaussian prior probability. An iterated conditional mode‐like approach is utilized to maximize the objective function. The corrected CBCT image can be obtained by subtracting the bias field from original CBCT image. Results: The performance of correcting the cupping effect of CBCT image with the proposed MAP framework was tested using both digital phantoms and clinical CBCT images. In the phantom studies the intensity of higher density material in the simulated CBCT images of contrast phantoms is even lower that that of lower density material due to cupping effect. The corrected CBCT image showed almost same intensity distribution as the original contrast phantom images. For real patient study, compared to the original brain CBCT image, the corrected CBCT image significantly improved the uniformity of intensity distribution of brain soft tissue. Conclusion: The proposed image‐based scatter correction method showed a promising result to reduce the cupping effect commonly encountered in CBCT image.

Quantitative evaluation of a cone‐beam computed tomography–planning computed tomography deformable image registration method for adaptive radiation therapy

Deformable (non‐rigid) registration is an essential tool in both adaptive radiation therapy and image‐guided radiation therapy to account for soft‐tissue changes during the course of treatment. The evaluation method most commonly used to assess the accuracy of deformable image registration is qualitative human evaluation. Here, we propose a method for systematically measuring the accuracy of an algorithm in recovering artificially introduced deformations in cases of rigid geometry, and we use that method to quantify the ability of a modified basis spline (B‐Spline) registration algorithm to recover artificially introduced deformations. The evaluation method is entirely computer‐driven and eliminates biased interpretation associated with human evaluation; it can be applied to any chosen method of image registration.Our method involves using planning computed tomography (PCT) images acquired with a conventional CT simulator and cone‐beam computed tomography (CBCT) images acquired daily by a linear accelerator–mounted kilovoltage image system in the treatment delivery room. The deformation that occurs between the PCT and daily CBCT images is obtained using a modified version of the B‐Spline deformable model designed to overcome the low soft‐tissue contrast and the artifacts and distortions observed in CBCT images. Clinical CBCT images and contours of phantom and central nervous system cases were deformed (warped) with known random deformations. In registering the deformed with the non‐deformed image sets, we tracked the algorithm's ability to recover the original, non‐deformed set. Registration error was measured as the mean and maximum difference between the original and the registered surface contours from outlined structures. Using this approach, two sets of tests can be devised. To measure the residual error related to the optimizer's convergence performance, the warped CBCT image is registered to the unwarped version of itself, eliminating unknown factors such as noise and positioning errors. To study additional errors introduced by artifacts and noise in the CBCT image, the warped CBCT image is registered to the original PCT image.Using a B‐Spline deformable image registration algorithm, mean residual error introduced by the algorithm's performance on noise‐free images was less than 1 mm, with a maximum of 2 mm. The chosen deformable image registration model was capable of accommodating significant variability in structures over time, because the artificially introduced deformation magnitude did not significantly influence the residual error. On the second type of test, noise and artifacts reduced registration accuracy to a mean of 1.33 mm and a maximum of 4.86 mm.The accuracy of deformable image registration can be easily and consistently measured by evaluating the algorithm's ability to recover artificially introduced deformations in rigid cases in which the true solution is known a priori. The method is completely automated, applicable to any chosen registration algorithm, and does not require user interaction of any kind.PACS numbers: 87.57.Gg, 87.57.Ce, 87.62.+n

SU‐FF‐I‐04: A Fast Variable‐Intensity Ring Suppression Algorithm

Purpose: Gain drifts and nonlinearities in amorphous silicon flat‐panel x‐ray detectors can produce ring artifacts in reconstructed cone‐beam computed tomography (CBCT) images. We have found that the magnitude of these artifacts can exceed 50 HU in clinical situations, and that the intensity of a given ring may not be uniform throughout an image. In some cases (e.g. half‐fan pelvic scans), discrete arcs may be produced. The goal of this study was to develop a post‐processing algorithm to efficiently suppress such variable‐intensity rings in axial slices. Method and Materials: Our approach builds upon the work of Sijbers and Postnov who showed that constant‐intensity rings can be estimated via radial median filtering of the input image after its transformation to polar coordinates. To characterize variable‐intensity rings and arcs, we developed a 2‐D estimation technique that uses a combination of row‐based (radial) and column‐based (angular) filters operating in the polar domain. The 2‐D estimates were transformed back to Cartesian space for subtraction from the original image. The new algorithm was implemented in C++ and tested on clinical and phantom CBCT images acquired using a Varian 4030CB detector. Results: Correction times (3.2GHz Intel Pentium4 processor), including coordinate transformations, averaged 55 msec/slice for 512×512 matrix sizes. Rings and arcs were reduced in intensity by more than an order of magnitude to levels well below the background noise intensity. By subtracting ring estimates in Cartesian space, the polar matrix size could be reduced without sacrificing spatial resolution in the final image. This permitted for a 4× reduction in execution time compared to the original Sijbers‐Postnov approach where subtraction occurs in polar space. Conclusion: The Sijbers‐Postnov algorithm ring suppression algorithm was modified to provide improved image quality and fast execution times suitable for clinical implementation. Conflict of Interest: Funding provided by Varian Medical Systems.