Scatter correction method for cone-beam CT based on interlacing-slit scan

X-ray scatter results in a significant degradation of image quality in computed tomography (CT), representing a major limitation in cone-beam CT (CBCT) and large field-of-view diagnostic scanners. In this work, a novel scatter estimation and correction technique is proposed that utilizes peripheral detection of scatter during the patient scan to simultaneously acquire image and patient-specific scatter information in a single scan, and in conjunction with a proposed compressed sensing scatter recovery technique to reconstruct and correct for the patient-specific scatter in the projection space. The method consists of the detection of patient scatter at the edges of the field of view (FOV) followed by measurement based compressed sensing recovery of the scatter through-out the projection space. In the prototype implementation, the kV x-ray source of the Varian TrueBeam OBI system was blocked at the edges of the projection FOV, and the image detector in the corresponding blocked region was used for scatter detection. The design enables image data acquisition of the projection data on the unblocked central region of and scatter data at the blocked boundary regions. For the initial scatter estimation on the central FOV, a prior consisting of a hybrid scatter model that combines the scatter interpolation method and scatter convolution model is estimated using the acquired scatter distribution on boundary region. With the hybrid scatter estimation model, compressed sensing optimization is performed to generate the scatter map by penalizing the L1 norm of the discrete cosine transform of scatter signal. The estimated scatter is subtracted from the projection data by soft-tuning, and the scatter-corrected CBCT volume is obtained by the conventional Feldkamp-Davis-Kress algorithm. Experimental studies using image quality and anthropomorphic phantoms on a Varian TrueBeam system were carried out to evaluate the performance of the proposed scheme. The scatter shading artifacts were markedly suppressed in the reconstructed images using the proposed method. On the Catphan©504 phantom, the proposed method reduced the error of CT number to 13 Hounsfield units, 10% of that without scatter correction, and increased the image contrast by a factor of 2 in high-contrast regions. On the anthropomorphic phantom, the spatial nonuniformity decreased from 10.8% to 6.8% after correction. A novel scatter correction method, enabling unobstructed acquisition of the high frequency image data and concurrent detection of the patient-specific low frequency scatter data at the edges of the FOV, is proposed and validated in this work. Relative to blocker based techniques, rather than obstructing the central portion of the FOV which degrades and limits the image reconstruction, compressed sensing is used to solve for the scatter from detection of scatter at the periphery of the FOV, enabling for the highest quality reconstruction in the central region and robust patient-specific scatter correction.

In recent years, cone-beam computed tomography (CBCT) has played an important role in medical imaging. However, the applications of CBCT are limited due to the severe scatter contamination. Conventional Monte Carlo (MC) simulation can provide accurate scatter estimation for scatter correction, but the expensive computational cost has always been the bottleneck of MC method in clinical application. In this work, an MC simulation method combined with a variance reduction technique called correlated sampling is proposed for fast iterative scatter correction. Correlated sampling exploits correlation between similar simulation systems to reduce the variance of interest quantities. Specifically, conventional MC simulation is first performed on the scatter-contaminated CBCT to generate the initial scatter signal. In the subsequent correction iterations, scatter estimation is then updated by applying correlated MC sampling to the latest corrected CBCT images by reusing the random number sequences of the task-related photons in conventional MC. Afterward, the corrected projections obtained by subtracting the scatter estimation from raw projections are utilized for FDK reconstruction. These steps are repeated until an adequate scatter correction is obtained. The performance of the proposed framework is evaluated by the accuracy of the scatter estimation, the quality of corrected CBCT images and efficiency. Overall, the difference in mean absolute percentage error between scatter estimation with and without correlated sampling is 0.25% for full-fan case and 0.34% for half-fan case, respectively. In simulation studies, scatter artifacts are substantially eliminated, where the mean absolute error value is reduced from 15 to 2HU in full-fan case and from 53 to 13HU in half-fan case. Scatter-to-primary ratio is reduced to 0.02 for full-fan and 0.04 for half-fan, respectively. In phantom study, the contrast-to-noise ratio (CNR) is increased by a factor of 1.63, and the contrast is increased by a factor of 1.77. As for clinical studies, the CNR is improved by 11% and 14% for half-fan and full-fan, respectively. The contrast after correction is increased by 19% for half-fan and 44% for full-fan. Furthermore, root mean square error is also effectively reduced, especially from 78 to 4HU for full-fan. Experimental results demonstrate that the figure of merit is improved between 23 and 43 folds when using correlated sampling. The proposed method takes less than 25s for the whole iterative scatter correction process. The proposed correlated sampling-based MC simulation method can achieve fast and accurate scatter correction for CBCT, making it suitable for real-time clinical use.

Purpose: X‐ray scatter poses a severe physical limitation to image quality in cone‐beam CT (CBCT), resulting in contrast reduction, image artifacts, and lack of CT number accuracy. We report and demonstrate the performance of a novel scatter correction method in which scatter fluence is estimated directly in each projection from pixel values at the edge of the detector behind the collimator leaves. Method and Materials: The algorithm operates on the assumption that signal behind the collimator leaves is attributable to x‐ray scatter. The 2D scatter fluence is estimated by interpolating between pixel values measured along the top and bottom edges of the detector behind the collimator leaves. The resulting scatter fluence is subtracted from each projection to yield primary‐only images for CBCT reconstruction. Performance was investigated in phantom experiments on an experimental CBCT benchtop, and the effect on image quality was demonstrated in patient images (head, chest, and pelvis sites) obtained on a preclinical system for CBCT‐guided radiation therapy. Results: The algorithm provides significant reduction in scatter artifacts without compromise in contrast‐to‐noise ratio (CNR) — e.g., Head: cupping reduced from 10% to 2%, while breast‐to‐water CNR improved from 5.2% to 7.8%; Body: cupping reduced from 42% to 26% without change in CNR. Patient images demonstrate increased uniformity, accuracy, and contrast, with slightly increased noise (net increase in CNR) in all of the sites investigated. Qualitative evaluation illustrates that soft‐tissue structures that are otherwise undetectable are clearly delineated in scatter‐corrected reconstructions. Conclusion: The algorithm provides a robust method for x‐ray scatter fluence estimation and correction in CBCT. Operating from a single assumption without prior information, analytical modeling, or Monte Carlo, the technique is easily incorporated as a preprocessing step in CBCT reconstruction. Quantitative evaluation in phantoms and pre‐clinical evaluation in patients demonstrates significant artifact reduction without degradation in CNR.

Scatter contamination constitutes a dominant source of degradation of image quality in cone-beam computed tomography (CBCT). We have recently developed an analytic image reconstruction method with a scatter correction capability from the partially blocked cone-beam data out of a single scan. Despite its easy implementation and its computational efficiency, the developed method may result in additional image artifacts for a large cone angle geometry due to data inconsistency. To improve the image quality at a large cone angle, we propose a weighted rebinned backprojection-filtration (wrBPF) algorithm in conjunction with a hybrid type scatter correction approach. The proposed method uses a beam-blocker array that provides partial data for scatter correction and image reconstruction and that only blocks the beam within a limited cone angle. Thisdesign allows a chance to keep the image quality at larger cone angles by use of data redundancy since the projection data corresponding to larger cone angles are not blocked. However, the scatter correction would not be straightforward. In order to correct for the scatter in the projections at larger cone angles, we propose a novel scatter correction method combining a measurement-based and a convolution-based method. We first estimated the scatter signal using a measurement-based method in the partially beam-blocked regions, and then optimized the fitting parameters of a convolution-kernel that can be used for scatter correction in the projections at larger cone angles. For image reconstruction, we developed a wrBPF with butterfly filtering. We have conducted an experimental study to validate the proposed algorithm for image reconstruction and scatter correction. The experimental results revealed that the developed reconstruction method makes full use of the benefits of partial beam-blocking for scatter correction and image reconstruction and at the same time enhances image quality at larger cone angles by use of an optimized convolution-based scatter correction. The proposed method that enjoys the advantages of both measurement-based and convolution-based methods for scatter correction has successfully demonstrated its capability of reconstructing accurate images out of a single scan in circular CBCT.

Due to the increased axial coverage of multislice computed tomography (CT) and the introduction of flat detectors, the size of x-ray illumination fields has grown dramatically, causing an increase in scatter radiation. For CT imaging, scatter is a significant issue that introduces shading artifact, streaks, as well as reduced contrast and Hounsfield Units (HU) accuracy. The purpose of this work is to provide a fast and accurate scatter artifacts correction algorithm for cone beam CT (CBCT) imaging. The method starts with an estimation of coarse scatter profiles for a set of CBCT data in either image domain or projection domain. A denoising algorithm designed specifically for Poisson signals is then applied to derive the final scatter distribution. Qualitative and quantitative evaluations using thorax and abdomen phantoms with Monte Carlo (MC) simulations, experimental Catphan phantom data, and in vivo human data acquired for a clinical image guided radiation therapy were performed. Scatter correction in both projection domain and image domain was conducted and the influences of segmentation method, mismatched attenuation coefficients, and spectrum model as well as parameter selection were also investigated. Results show that the proposed algorithm can significantly reduce scatter artifacts and recover the correct HU in either projection domain or image domain. For the MC thorax phantom study, four-components segmentation yields the best results, while the results of three-components segmentation are still acceptable. The parameters (iteration number K and weight β) affect the accuracy of the scatter correction and the results get improved as K and β increase. It was found that variations in attenuation coefficient accuracies only slightly impact the performance of the proposed processing. For the Catphan phantom data, the mean value over all pixels in the residual image is reduced from -21.8 to -0.2 HU and 0.7 HU for projection domain and image domain, respectively. The contrast of the in vivo human images is greatly improved after correction. The software-based technique has a number of advantages, such as high computational efficiency and accuracy, and the capability of performing scatter correction without modifying the clinical workflow (i.e., no extra scan/measurement data are needed) or modifying the imaging hardware. When implemented practically, this should improve the accuracy of CBCT image quantitation and significantly impact CBCT-based interventional procedures and adaptive radiation therapy.

The recently introduced robotic x-ray systems provide the flexibility to acquire cone-beam computed tomography (CBCT) data using customized, application-specific source-detector trajectories. We exploit this capability to mitigate the effects of x-ray scatter and noise in CBCT imaging of weight-bearing foot and cervical spine (C-spine) using scan orbits with a tilted rotation axis. We used an advanced CBCT simulator implementing accurate models of x-ray scatter, primary attenuation, and noise to investigate the effects of the orbital tilt angle in upright foot and C-spine imaging. The system model was parameterized using a laboratory version of a three-dimensional (3D) robotic x-ray system (Multitom RAX, Siemens Healthineers). We considered a generalized tilted axis scan configuration, where the detector remained parallel to patient's long body axis during the acquisition, but the elevation of source and detector was changing. A modified Feldkamp-Davis-Kress (FDK) algorithm was developed for reconstruction in this configuration, which departs from the FDK assumption of a detector that is perpendicular to the scan plane. The simulated foot scans involved source-detector distance (SDD) of 1386mm, orbital tilt angles ranging 10° to 40°, and 400 views at 1mAs/view and 0.5° increment; the C-spine scans involved -25° to -45° tilt angles, SDD of 1090mm, and 202 views at 1.3mAs and 1° increment The imaging performance was assessed by projection-domain measurements of the scatter-to-primary ratio (SPR) and by reconstruction-domain measurements of contrast, noise and generalized contrast-to-noise ratio (gCNR, accounting for both image noise and background nonuniformity) of the metatarsals (foot imaging) and cervical vertebrae (spine imaging). The effects of scatter correction were also compared for horizontal and tilted scans using an ideal Monte Carlo (MC)-based scatter correction and a frame-by-frame mean scatter correction. The proposed modified FDK, involving projection resampling, mitigated streak artifacts caused by the misalignment between the filtering direction and the detector rows. For foot imaging (no grids), an optimized 20° tilted orbit reduced the maximum SPR from ~1.5 in a horizontal scan to <0.5. The gCNR of the second metatarsal was enhanced twofold compared to a horizontal orbit. For the C-spine (with vertical grids), imaging with a tilted orbit avoided highly attenuating x-ray paths through the lower cervical vertebrae and shoulders. A -35° tilted orbit yielded improved image quality and visualization of the lower cervical spine: the SPR of lower cervical vertebrae was reduced from ~10 (horizontal orbit) to <6 (tilted orbit), and the gCNR for C5-C7 increased by a factor of 2. Furthermore, tilted orbits showed potential benefits over horizontal orbits by enabling scatter correction with a simple frame-by-frame mean correction without substantial increase in noise-induced artifacts after the correction. Tilted scan trajectories, enabled by the emerging robotic x-ray system technology, were optimized for CBCT imaging of foot and cervical spine using an advanced simulation framework. The results demonstrated the potential advantages of tilted axis orbits in mitigation of scatter artifacts and improving contrast-to-noise ratio in CBCT reconstructions.

The moving blocker method has been demonstrated to be effective for scatter correction (SC) of cone-beam computed tomography (CBCT). However, a fixed thresholding was usually used to detect the blocker edge in the projection images, which only works in high-contrast uniform regions. The edge detection errors, caused by low-contrast and non-uniform intensity, can lead to significant artifacts in the reconstructed images. In this work, we propose to use the projection images at multiple adjacent views to estimate the scatter signal directly without identifying the blocked regions. Subsequently, the scatter signal can serve as an adaptive threshold to locate the unblocked regions for the estimation of the primary signal. The experimental results using an anthropomorphic pelvis phantom CBCT data show that the proposed method can robustly estimate the scatter and primary signals by avoiding blocker edge detection errors and lead to much improved reconstruction images.

For metal artifact reduction (MAR) in X-ray cone-beam computed tomography (CBCT), we propose a penalized weighted least-squares (PWLS) image reconstruction method [1] based on scatter correction (SC) method. Our main contribution is to incorporate the inaccuracy of the scatter correction data in PWLS method, and to accelerate the PWLS image reconstruction with a preconditionned conjugate gradient (PCG) method [2]. The reconstruction results of both the simulation and experimental data show that our proposed method effectively reduces the metal artifacts (MA). Compared to the state of art, normalized metal artifact reduction (NMAR) method [3], our method prevents the introduction of some new artifacts due to an inapporiate interpolation possible used in NMAR.

Cone beam computed tomography (CBCT) with a flat panel detector can provide more precise and fine images compared to that with an image intensifier which have been used previously. Measurement of the length clearly varies among different measurement regions on the images of CBCT with an image intensifier. However, little is known whether measurement of the length on the images of CBCT with a flat panel detector varies as well or uniform. The objective of this study was, therefore, to analyze the homogeneity of the measurement of the length on the images of the CBCT with a flat panel detector. The results demonstrated that both short- and long-term reproducibility of measurement of the length on the CBCT images of the x-ray mesh gauge was less than 1.5%. The non-uniformity of measurement of the length on the CBCT images was less than 2% between any of two regions. The non-uniformity of measurement of the length on the CBCT images with φ60mm×h60mm exposed area was almost constant at any regions independent of the tube current. That on the CBCT images with φ40mm×h40mm exposed area varied randomly. The non-uniformity of measurement of the length varied randomly on the CBCT images with small exposed area; however, this was considerably small and negligible. The results suggest that measurement of the length on the images of CBCT with a flat panel detector may be substantially uniform at any regions in comparison with that on the images of CBCT with an image intensifier.

In this work Monte Carlo (MC) simulations are used to correct kilovoltage (kV) cone-beam computed tomographic (CBCT) projections for scatter radiation. All images were acquired using a kV CBCT bench-top system composed of an x-ray tube, a rotation stage and a flat-panel imager. The EGSnrc MC code was used to model the system. BEAMnrc was used to model the x-ray tube while a modified version of the DOSXYZnrc program was used to transport the particles through various phantoms and score phase space files with identified scattered and primary particles. An analytical program was used to read the phase space files and produce image files. The scatter correction was implemented by subtracting Monte Carlo predicted scatter distribution from measured projection images; these projection images were then reconstructed. Corrected reconstructions showed an important improvement in image quality. Several approaches to reduce the simulation time were tested. To reduce the number of simulated scatter projections, the effect of varying the projection angle on the scatter distribution was evaluated for different geometries. It was found that the scatter distribution does not vary significantly over a 30-degree interval for the geometries tested. It was also established that increasing the size of the voxels in the voxelized phantom does not affect the scatter distribution but reduces the simulation time. Different techniques to smooth the scatter distribution were also investigated.

Purpose:Cone beam computed tomography (CBCT) imaging is on increasing demand for high‐performance image‐guided radiotherapy such as online tumor delineation and dose calculation. However, the current CBCT imaging has severe scatter artifacts and its current clinical application is therefore limited to patient setup based mainly on the bony structures. This study's purpose is to develop a CBCT artifact correction method.Methods:The proposed scatter correction method utilizes the planning CT to improve CBCT image quality. First, an image registration is used to match the planning CT with the CBCT to reduce the geometry difference between the two images. Then, the planning CT‐based prior information is entered into the Bayesian deconvolution framework to iteratively perform a scatter artifact correction for the CBCT mages. This technique was evaluated using Catphan phantoms with multiple inserts. Contrast‐to‐noise ratios (CNR) and signal‐to‐noise ratios (SNR), and the image spatial nonuniformity (ISN) in selected volume of interests (VOIs) were calculated to assess the proposed correction method.Results:Post scatter correction, the CNR increased by a factor of 1.96, 3.22, 3.20, 3.46, 3.44, 1.97 and 1.65, and the SNR increased by a factor 1.05, 2.09, 1.71, 3.95, 2.52, 1.54 and 1.84 for the Air, PMP, LDPE, Polystryrene, Acrylic, Delrin and Teflon inserts, respectively. The ISN decreased from 21.1% to 4.7% in the corrected images. All values of CNR, SNR and ISN in the corrected CBCT image were much closer to those in the planning CT images. The results demonstrated that the proposed method reduces the relevant artifacts and recovers CT numbers.Conclusion:We have developed a novel CBCT artifact correction method based on CT image, and demonstrated that the proposed CT‐guided correction method could significantly reduce scatter artifacts and improve the image quality. This method has great potential to correct CBCT images allowing its use in adaptive radiotherapy.

Kilovoltage (kV) cone beam computed tomography (CBCT) images suffer from a substantial scatter contribution. In this study, Monte Carlo (MC) simulations are used to evaluate the scattered radiation present in projection images. These predicted scatter distributions are also used as a scatter correction technique. Images were acquired using a kV CBCT bench top system. The EGSnrc MC code was used to model the flat panel imager, the phantoms, and the x-ray source. The x-ray source model was validated using first and second half-value layers (HVL) and profile measurements. The HVLs and the profile were found to agree within 3% and 6%, respectively. MC simulated and measured projection images for a cylindrical water phantom and for an anthropomorphic head phantom agreed within 8% and 10%. A modified version of the DOSXYZnrc MC code was used to score phase space files with identified scattered and primary particles behind the phantoms. The cone angle, the source-to-detector distance, the phantom geometry, and the energy were varied to determine their effect on the scattered radiation distribution. A scatter correction technique was developed in which the MC predicted scatter distribution is subtracted from the projections prior to reconstruction. Preliminary testing of the procedure was done with an anthropomorphic head phantom and a contrast phantom. Contrast and profile measurements were obtained for the scatter corrected and noncorrected images. An improvement of 3% for contrast between solid water and a liver insert and 11% between solid water and a Teflon insert were obtained and a significant reduction in cupping and streaking artifacts was observed.

Due to the increased cone beam coverage and the introduction of flat panel detector, the size of X-ray illumination fields has grown dramatically in Cone Beam Computed Tomography (CBCT), causing an increase in scatter radiation. Existing reconstruction algorithms do not model the scatter radiation, so scatter artifacts appear in the reconstruction images. The contribution of scattering of photons inside the detector itself becomes prominent and challenging in case of X-ray source of high energy (over a few 100 keV) which is used in typical industrial Non Destructive Testing (NDT). In this paper, comprehensive evaluation of contribution of detector scatter is performed using continuously thickness-adapted kernels. A separation of scatter due to object and the detector is presented using a four-Gaussian model. The results obtained prove that the scatter correction only due to the object is not sufficient to obtain reconstruction image free from artifacts as the detector also scatters considerably. The obtained results are also validated experimentally using a collimator to remove the contribution of object scatter.

Scatter radiation in contrast-enhanced digital breast tomosynthesis (CEDBT) reduces the image quality and iodinated lesion contrast. Monte Carlo simulation can provide accurate scatter estimation at the cost of computational burden. A model-based convolutional method trades off accuracy for processing speed. The purpose of this study is to develop a fast and robust deep-learning (DL) convolutional neural network (CNN)-based scatter correction method for CEDBT. Projection images and scatter maps of digital anthropomorphic breast phantoms were generated using Monte Carlo simulations. Experiments were conducted to validate the simulated scatter-to-primary ratio (SPR) at different locations of a breast phantom. Simulated projection images were used for CNN training and testing. Two separate U-Nets [low-energy (LE)-CNN and high-energy (HE)-CNN] were trained for LE and HE spectrum, respectively. CNN-based scatter correction was applied to a clinical case with a malignant iodinated mass to evaluate the influence on the lesion detection. The average and standard deviation of mean absolute percentage error of LE-CNN and HE-CNN estimated scatter map are and , respectively. For clinical cases, the lesion signal difference to noise ratio average improvement was 190% after CNN-based scatter correction. To conduct scatter correction on clinical CEDBT images, the whole process of loading CNNs parameters and scatter correction for LE and HE images took , with 9GB GPU computational cost. The SPR variation across the breast agrees between experimental measurements and Monte Carlo simulations. We developed a CNN-based scatter correction method for CEDBT in both CC view and mediolateral-oblique view with high accuracy and fast speed.

In flat-panel detector-based cone beam CT breast imaging (CBCTBI) systems, scattering is an important factor that degrades image quality. It is not practical to measure the scattering profiles of a breast for all view angles in a patient study, but it is possible to develop a method to estimate the scattering profiles based on information acquired from breast phantom studies. A new scattering correction method is proposed for clinical CBCTBI in this study. The scattering profiles of three anthropomorphic uncompressed breast phantoms of different sizes were thoroughly investigated, and the results indicated that though phantom size differed, the scattering profiles were mainly determined by local breast diameters, which are the approximate diameters of coronal slices that are perpendicular to the nipple-to-chestwall direction. Thus for scattering correction purposes it is possible to establish a relationship between location breast diameters and local scattering profiles, namely the fitted smooth curves of scatter-to-primary ratios (SPR) and normalized scattered radiations (NSR). In clinical CBCTBI studies, after the local breast diameters are sampled and measured on projection images, the scattering image for every projection image can be generated based on the established relationship, and the projection images can be corrected using either the SPR based method or the NSR based method. Phantom studies and clinical studies showed that both the SPR and NSR methods are able to correct cupping artifacts and reduce reconstruction error. The SPR method does not increase tissue contrast or noise while the NSR method increases both.