Scatter Correction For Cone-beam CT Research Articles

A rotating beam-blocker method for cone beam CT scatter correction.

Cone beam CT (CBCT) is widely utilized in clinics. However, the scatter artifact degrades the CBCT image quality, hampering the expansion of CBCT applications. Recently, beam-blocker methods have been used for CBCT scatter correction and proved their high cost-effectiveness. A rotating beam-blocker (RBB) method for CBCT scatter correction was proposed to complete scatter correction and image reconstruction within a single scan in both full- and half-fan scan scenarios. The RBB consisted of two open regions and two blocked regions, and was designed as a centrosymmetric structure. The open and blocked projections could be alternatively obtained within one single rotation. The open projections were corrected with the scatter signal calculated from the blocked projections, and then used to reconstruct the 3D image via the Feldkamp-Davis-Kress algorithm. The performance of the RBB method was evaluated on head and pelvis phantoms in scenarios with and without a bowtie filter. The images obtained from nine repeated scans in each scenario were used to calculate the evaluation metrics including the CT number error, spatial nonuniformity (SNU) and contrast-to-noise ratio (CNR). For the head phantom, the CT number error was decreased to<5 after scatter correction from>200 HU before correction when scanned without a bowtie filter, and to<4 from>160 HU when scanned with a full bowtie filter. For the pelvis phantom, the CT number error was reduced to<12 after scatter correction from>250 HU before correction when scanned without a bowtie filter, and to<10 from>190 HU when scanned with a half bowtie filter. After scatter correction, the uniformity and contrast were both improved, resulting in an SNU of>79% decrease and CNR of>2 times increase, respectively. High-quality CBCT images could be obtained in a single scan after using the proposed RBB method for scatter correction, enabling more accurate image guidance for surgery and radiation therapy applications. With almost no time delay between the successive open and blocked projections, the RBB method could eliminate the motion-induced anatomical mismatches between the corresponding open and blocked projections and could find particular usefulness in thoracic and abdominal imaging.

A rotating beam-blocker method for cone beam CT scatter correction.

Cone beam CT (CBCT) is widely utilized in clinics. However, the scatter artifact degrades the CBCT image quality, hampering the expansion of CBCT applications. Recently, beam-blocker methods have been used for CBCT scatter correction and proved their high cost-effectiveness. A rotating beam-blocker (RBB) method for CBCT scatter correction was proposed to complete scatter correction and image reconstruction within a single scan in both full- and half-fan scan scenarios. The RBB consisted of two open regions and two blocked regions, and was designed as a centrosymmetric structure. The open and blocked projections could be alternatively obtained within one single rotation. The open projections were corrected with the scatter signal calculated from the blocked projections, and then used to reconstruct the 3D image via the Feldkamp-Davis-Kress algorithm. The performance of the RBB method was evaluated on head and pelvis phantoms in scenarios with and without a bowtie filter. The images obtained from nine repeated scans in each scenario were used to calculate the evaluation metrics including the CT number error, spatial nonuniformity (SNU) and contrast-to-noise ratio (CNR). For the head phantom, the CT number error was decreased to<5 after scatter correction from>200 HU before correction when scanned without a bowtie filter, and to<4 from>160 HU when scanned with a full bowtie filter. For the pelvis phantom, the CT number error was reduced to<12 after scatter correction from>250 HU before correction when scanned without a bowtie filter, and to<10 from>190 HU when scanned with a half bowtie filter. After scatter correction, the uniformity and contrast were both improved, resulting in an SNU of>79% decrease and CNR of>2 times increase, respectively. High-quality CBCT images could be obtained in a single scan after using the proposed RBB method for scatter correction, enabling more accurate image guidance for surgery and radiation therapy applications. With almost no time delay between the successive open and blocked projections, the RBB method could eliminate the motion-induced anatomical mismatches between the corresponding open and blocked projections and could find particular usefulness in thoracic and abdominal imaging.

Scatter correction for cone-beam CT via scatter kernel superposition-inspired convolutional neural network

Objective. X-ray scatter leads to signal bias and degrades the image quality in Computed Tomography imaging. Conventional real-time scatter estimation and correction methods include the scatter kernel superposition (SKS) methods, which approximate x-ray scatter field as a convolution of the scatter sources and scatter propagation kernels to reflect the spatial spreading of scatter x-ray photons. SKS methods are fast to implement but generally suffer from low accuracy due to the difficulties in determining the scatter kernels. Approach. To address such a problem, this work describes a new scatter estimation and correction method by combining the concept of SKS methods and convolutional neural network. Unlike conventional SKS methods which estimate the scatter amplitude and the scatter kernel based on the value of an individual pixel, the proposed method generates the scatter amplitude maps and the scatter width maps from projection images through a neural network, from which the final estimated scatter field is calculated based on a convolution process. Main Results. By incorporating physics in the network design, the proposed method requires fewer trainable parameters compared with another deep learning-based method (Deep Scatter Estimation). Both numerical simulations and physical experiments demonstrate that the proposed SKS-inspired convolutional neural network outperforms the conventional SKS method and other deep learning-based methods in both qualitative and quantitative aspects. Significance. The proposed method can effectively correct the scatter-related artifacts with a SKS-inspired convolutional neural network design.

Image-based scatter correction for cone-beam CT using flip swin transformer U-shape network.

Cone beam computed tomography (CBCT) plays an increasingly important role in image-guided radiation therapy. However, the image quality of CBCT is severely degraded by excessive scatter contamination, especially in the abdominal region, hindering its further applications in radiation therapy. To restore low-quality CBCT images contaminated by scatter signals, a scatter correction algorithm combining the advantages of convolutional neural networks (CNN) and Swin Transformer is proposed. In this paper a scatter correction model for CBCT image, the Flip Swin Transformer U-shape network (FSTUNet) model, is proposed. In this model, the advantages of CNN in texture detail and Swin Transformer in global correlation are used to accurately extract shallow and deep features, respectively. Instead of using the original Swin Transformer tandem structure, we build the Flip Swin Transformer Block to achieve a more powerful inter-window association extraction. The validity and clinical relevance of the method is demonstrated through extensive experiments on a Monte Carlo (MC) simulation dataset and frequency split dataset generated by a validated method, respectively. Experimental results on the MC simulated dataset show that the root mean square error of images corrected by the method is reduced from over 100 HU to about 7 HU. Both the structural similarity index measure (SSIM) and the universal quality index (UQI) are close to 1. Experimental results on the frequency split dataset demonstrate that the method not only corrects shading artifacts but also exhibits a high degree of structural consistency. In addition, comparison experiments show that FSTUNet outperforms UNet, Deep Residual Convolutional Neural Network (DRCNN), DSENet, Pix2pixGAN, and 3DUnet methods in both qualitative and quantitative metrics. Accurately capturing the features at different levels is greatly beneficial for reconstructing high-quality scatter-free images. The proposed FSTUNet method is an effective solution to CBCT scatter correction and has the potential to improve the accuracy of CBCT image-guided radiation therapy.

A practical and robust method for beam blocker-based cone beam CT scatter correction

Objective. In the traditional beam-blocker based cone beam CT (CBCT) scatter correction, the scatter measured in the region shaded by lead strips was multiplied by a correction factor to directly represent the scatter in the unblocked region. The correction factor optimization is a tedious process and lacks an objective stop criterion. To skip the optimization process, an indirect scatter estimation method was developed and validated in phantom imaging. Approach. A beam-blocker made of lead strips was mounted between the x-ray source and object for scatter estimation. The primary signal between lead strips in the blocked region was first calculated by subtracting the measured scatter, and then used to calculate the scatter signal in the unblocked region corresponding to the same attenuation path. The calculated scatter signal was smoothed via local filtration and used to correct the measured projection in the unblocked region. Finally, the CBCT was reconstructed via Feldkamp–Davis–Kress algorithm. A Catphan and a head phantom were used to verify the performance of the proposed method in both full- and half-blocker scenarios, and with and without a bow-tie filter. Main Results. For scans without the bow-tie filter, the CT number error was reduced to 3.97 2.27 and 5.51 3.90 HU in the full- and half-blocker scenarios, respectively, for the Catphan, and to 4.01 2.18 and 7.97 ± 4.05 HU for the head phantom. When the bow-tie filter was applied, the CT number error was reduced to 2.29 1.42 and 6.72 0.77 HU in the full- and half-blocker scenarios, respectively, for the Catphan, and 2.35 1.25 and 4.96 ± 1.89 HU for the head phantom. Significance. The proposed method effectively avoids the influence of the inserted beam blocker itself on the scatter intensity estimation, and proves a more practical and robust way for the beam-blocker based scatter correction in CBCT scanning.

A generalized image quality improvement strategy of cone-beam CT using multiple spectral CT labels in Pix2pix GAN

Objective. The quantitative and routine imaging capabilities of cone-beam CT (CBCT) are hindered from clinical applications due to the severe shading artifacts of scatter contamination. The scatter correction methods proposed in the literature only consider the anatomy of the scanned objects while disregarding the impact of incident x-ray energy spectra. The multiple-spectral model is in urgent need for CBCT scatter estimation. Approach. In this work, we incorporate the multiple spectral diagnostic multidetector CT labels into the pixel-to-pixel (Pix2pix) GAN to estimate accurate scatter distributions from CBCT projections acquired at various imaging volume sizes and x-ray energy spectra. The Pix2pix GAN combines the residual network as the generator and the PatchGAN as the discriminator to construct the correspondence between the scatter-contaminated projection and scatter distribution. The network architectures and loss function of Pix2pix GAN are optimized to achieve the best performance on projection-to-scatter transition. Results. The CBCT data of a head phantom and abdominal patients are applied to test the performance of the proposed method. The error of the corrected CBCT image using the proposed method is reduced from over 200 HU to be around 20 HU in both phantom and patient studies. The mean structural similarity index of the CT image is improved from 0.2 to around 0.9 after scatter correction using the proposed method compared with the MC-simulation method, which indicates a high similarity of the anatomy in the images before and after the proposed correction. The proposed method achieves higher accuracy of scatter estimation than using the Pix2pix GAN with the U-net generator. Significance. The proposed scheme is an effective solution to the multiple spectral CBCT scatter correction. The scatter-correction software using the proposed model will be available at: https://github.com/YangkangJiang/Cone-beam-CT-scatter-correction-tool.

Planning CT-guided robust and fast cone-beam CT scatter correction using a local filtration technique.

Cone-beam CT (CBCT) has been widely utilized in image-guided radiotherapy. Planning CT (pCT)-aided CBCT scatter correction could further enhance image quality and extend CBCT application to dose calculation and adaptive planning. Nevertheless, existing pCT-based approaches demand accurate registration between pCT and CBCT, leading to limited imaging performance and increased computational cost when large anatomical discrepancies exist. In this work, we proposed a robust and fast CBCT scatter correction method using local filtration technique and rigid registration between pCT and CBCT (LF-RR). First of all, the pCT was rigidly registered with CBCT, then forward projection was performed on registered pCT to create scatter-free projections. The raw scatter signals were obtained via subtracting the scatter-free projections from the measured CBCT projections. Based on frequency and intensity threshold criteria, reliable scatter signals were selected from the raw scatter signals, and further filtered for global scatter estimation via local filtration technique. Finally, corrected CBCT was reconstructed with the projections generated by subtracting the scatter estimation from the raw CBCT projections using FDK algorithm. The LF-RR method was evaluated via comparison with another pCT-based scatter correction method based on Median and Gaussian filters (MG method). Proposed method was first validated on an anthropomorphic pelvis phantom, and showed satisfied performance on scatter removal even when anatomical mismatches were intentionally created on pCT. The quantitative analysis was further performed on four clinical CBCT images. Compared with the uncorrected CBCT, CBCT corrected by MG with rigid registration (MG-RR), MG with deformable registration (MG-DR), and LF-RR reduced the CT number error from to , and HU for adipose and from to , , 3 HU for muscle, respectively. After correction, the spatial non-uniformity (SNU) of CBCT corrected with MG-RR, MG-DR and LF-RR was , , and HU for adipose, and , , and HU for muscle, respectively. Meanwhile, the contrast-to-noise ratio (CNR) between muscle and adipose was increased by a factor of 2.70, 2.89 and 2.56, respectively. Using the LF-RR method, the scatter correction of 656 projections can be finished within 10 s and the corrected volumetric images (200 slices) can be obtained within 2 min. We developed a fast and robust pCT-based CBCT scatter correction method which exploits the local-filtration technique to promote the accuracy of scatter estimation and is resistant to pCT-to-CBCT registration uncertainties. Both phantom and patient studies showed the superiority of the proposed correction in imaging accuracy and computational efficiency, indicating promisingfuture clinical application.

Scatter correction based on adaptive photon path-based Monte Carlo simulation method in Multi-GPU platform

Monte Carlo (MC)-based simulation is the most precise method in scatter correction for Cone-beam CT (CBCT). Nonetheless, the existing MC methods cannot be fully applied in clinical due to its low efficiency. The traditional MC simulations perform calculations via a particle-by-particle scheme, which leads to high computation costs because abundant photons do not reach the X-ray detector in transport. The conventional approaches cannot control where the particle ends. Hence, it unavoidably waste lots of time in transporting numerous photons that have no contribution to the signal at the detector, yielding a low computational efficiency. To solve the problem, an innovative GPU-based Metropolis MC (gMMC) method was proposed. Compared with the traditional ones, the Metropolis based algorithm utilizes a path-by-path sampling method. The method can automatically control each particle path and eventually accelerate the convergence. In this paper, we firstly take planning CT image as prior information because of its precise CT value, and utilize gMMC to estimate scatter signal. Then the scatter signals are removed from the raw CBCT projections. Afterwards, FDK reconstruction is performed to obtain the corrected image,some accelerating strategies including reducing photon history number, pixels sampling, projection angles sampling and reconstructed image down-sampling achieve adaptive fast CBCT image reconstruction. For having high computational efficiency, we implemented the whole workflow on a 4-GPU workstation. In order to verify the feasibility of the the method, the experiment of several cases are conducted including simulation, phantom, and real patient cases. Results indicate that the image contrast becomes better, the scatter artifacts are eliminated. The maximum error (emax), the minimum error (emin), the 95th percentile error (e95%), average error (¯e) are reduced from 264, 56, 14 and 21 HU to 28, 10, 3 and 7 HU in full-fan case, and from 387, 5, 19 and 95 HU to 39, 2, 2 and 6 HU in the half-fan case. In terms of computation time, the MC simulation time of all cases is within 2.5 seconds, and the total time is within 15 seconds.

Scatter correction of cone-beam CT using a deep residual convolution neural network (DRCNN)

Scatter correction is an essential technique to improve the image quality of cone-beam CT (CBCT). Although different scatter correction methods have been proposed in the literature, a standard solution is still being studied due to the limitations including accuracy, computation efficiency and generalization. In this paper, we propose a novel scatter correction scheme for CBCT using a deep residual convolution neural network (DRCNN) to overcome the limitations. The proposed method combines the deep convolution neural network (CNN) and the residual learning framework (RLF) to train the mapping function from the uncorrected image to the corrected image. Two residual network modules (RNMs) are built based on the RLF to improve the accuracy of the mapping function by strengthening the propagation of the gradient. The dropout operations are applied as the regularizer of the network to avoid the overfitting problem. The RMSE of the corrected images reconstructed using the DRCNN is reduced from over 200 HU to be about 20 HU. The structural similarity (SSIM) is slightly increased from 0.95 to 0.99, indicating that the proposed scheme maintains the anatomical structure. The proposed DRCNN has a higher accuracy of scatter correction than the networks without the RLF or the dropout operations. The proposed network is effective, efficient and robust as a solution to the CBCT scatter correction.

Water equivalent path length calculations using scatter-corrected head and neck CBCT images to evaluate patients for adaptive proton therapy

Proton therapy has dosimetric advantages due to the well-defined range of the proton beam over photon radiotherapy. When the proton beams, however, are delivered to the patient in fractionated radiation treatment, the treatment outcome is affected by delivery uncertainties such as anatomic change in the patient and daily patient setup error. This study aims at establishing a method to evaluate the dosimetric impact of the anatomic change and patient setup error during head and neck proton therapy. Range variations due to the delivery uncertainties were assessed by calculating water equivalent path length (WEPL) to the distal edge of tumor volume using planning CT and weekly treatment cone-beam CT (CBCT) images. Specifically, mean difference and root mean squared deviation (RMSD) of the distal WEPLs were calculated as the weekly range variations. To accurately calculate the distal WEPLs, an existing CBCT scatter correction algorithm was used. An automatic rigid registration was used to align the planning CT and treatment CBCT images, simulating a six degree-of-freedom couch correction at treatments. The authors conclude that the dosimetric impact of the anatomic change and patient setup error was reasonably captured in the differences of the distal WEPL variation with a range calculation uncertainty of 2%. The proposed method to calculate the distal WEPL using the scatter-corrected CBCT images can be an essential tool to decide the necessity of re-planning in adaptive proton therapy.

Correction of scattering effects in cone beam CT

Introduction Cone Beam CT (CBCT) is becoming widely used in Radiation Therapy, not only to verify set-up accuracy but more and more to recalculate treatment plans according to changes indicated at time of set-up (e.g. tumor shrinking, anatomy changes etc.). The problem here is incorrect Hounsfield numbers due to scatter effects inherent to CBCT. Purpose This works aims at a first approximation for scatter corrections in CBCT. Material and methods First a method was developed to measure scatter spectra of a white beam incident on water and emitted by a commercial X-ray tube. From these spectra scattering cross sections were derived. Further experimental results were then utilized to derive scattering cross sections based on semi-theoretical approaches originally put forward by Morin and Hubbel. After comparison and verification the cross sections were eventually fed into algorithms specifically developed to correct for scatter perturbations in CBCT. Results A method has been established to derive scattering cross sections of a white beam emitted by an industrial X-ray tube. These results can then be used in a second step (algorithm) to correct for scatter perturbations in CBCT. Conclusions The work presented shows the feasibility of scatter corrections in water for a commercial CBCT. The used semi-theoretical approach can be extended and applied to other materials eventually leading to a data base applicable to a full-fledged scatter correction in CBCT.

MO-FG-CAMPUS-JeP1-05: Water Equivalent Path Length Calculations Using Scatter-Corrected Head and Neck CBCT Images to Evaluate Patients for Adaptive Proton Therapy

Purpose: To establish a method to evaluate the dosimetric impact of anatomic changes in head and neck patients during proton therapy by using scatter-corrected cone-beam CT (CBCT) images. Methods: The water equivalent path length (WEPL) was calculated to the distal edge of PTV contours by using tomographic images available for six head and neck patients received photon therapy. The proton range variation was measured by calculating the difference between the distal WEPLs calculated with the planning CT and weekly treatment CBCT images. By performing an automatic rigid registration, six degrees-of-freedom (DOF) correction was made to the CBCT images to account for the patient setup uncertainty. For accurate WEPL calculations, an existing CBCT scatter correction algorithm, whose performance was already proven for phantom images, was calibrated for head and neck patient images. Specifically, two different image similarity measures, mutual information (MI) and mean square error (MSE), were tested for the deformable image registration (DIR) in the CBCT scatter correction algorithm. Results: The impact of weight loss was reflected in the distal WEPL differences with the aid of the automatic rigid registration reducing the influence of patient setup uncertainty on the WEPL calculation results. The WEPL difference averaged over distal area was 2.9 ± 2.9 (mm) across all fractions of six patients and its maximum, mostly found at the last available fraction, was 6.2 ± 3.4 (mm). The MSE-based DIR successfully registered each treatment CBCT image to the planning CT image. On the other hand, the MI-based DIR deformed the skin voxels in the planning CT image to the immobilization mask in the treatment CBCT image, most of which was cropped out of the planning CT image. Conclusion: The dosimetric impact of anatomic changes was evaluated by calculating the distal WEPL difference with the existing scatter-correction algorithm appropriately calibrated. Jihun Kim, Yang-Kyun Park, Gregory Sharp, and Brian Winey have received grant support from the NCI Federal Share of program income earned by Massachusetts General Hospital on C06 CA059267, Proton Therapy Research and Treatment Center.

TH‐A‐18C‐04: Ultrafast Cone‐Beam CT Scatter Correction with GPU‐Based Monte Carlo Simulation

Purpose:Scatter artifacts severely degrade image quality of cone‐beam CT (CBCT). We present an ultrafast scatter correction framework by using GPU‐based Monte Carlo (MC) simulation and prior patient CT image, aiming at automatically finish the whole process including both scatter correction and reconstructions within 30 seconds.Methods:The method consists of six steps: 1) FDK reconstruction using raw projection data; 2) Rigid Registration of planning CT to the FDK results; 3) MC scatter calculation at sparse view angles using the planning CT; 4) Interpolation of the calculated scatter signals to other angles; 5) Removal of scatter from the raw projections; 6) FDK reconstruction using the scatter‐corrected projections. In addition to using GPU to accelerate MC photon simulations, we also use a small number of photons and a down‐sampled CT image in simulation to further reduce computation time. A novel denoising algorithm is used to eliminate MC scatter noise caused by low photon numbers. The method is validated on head‐and‐neck cases with simulated and clinical data.Results:We have studied impacts of photo histories, volume down sampling factors on the accuracy of scatter estimation. The Fourier analysis was conducted to show that scatter images calculated at 31 angles are sufficient to restore those at all angles with &lt;0.1% error. For the simulated case with a resolution of 512×512×100, we simulated 10M photons per angle. The total computation time is 23.77 seconds on a Nvidia GTX Titan GPU. The scatter‐induced shading/cupping artifacts are substantially reduced, and the average HU error of a region‐of‐interest is reduced from 75.9 to 19.0 HU. Similar results were found for a real patient case.Conclusion:A practical ultrafast MC‐based CBCT scatter correction scheme is developed. The whole process of scatter correction and reconstruction is accomplished within 30 seconds.This study is supported in part by NIH (1R01CA154747‐01), The Core Technology Research in Strategic Emerging Industry, Guangdong, China (2011A081402003).

Ultrafast cone-beam CT scatter correction with GPU-based Monte Carlo simulation

Purpose: Scatter artifacts severely degrade image quality of cone-beam CT (CBCT). We present an ultrafast scatter correction framework by using GPU-based Monte Carlo (MC) simulation and prior patient CT image, aiming at automatically finish the whole process including both scatter correction and reconstruction within 30 seconds. Methods : The method consists of six steps: 1) FDK reconstruction using raw projection data; 2) Rigid Registration of planning CT to the FDK results; 3) MC scatter calculation at sparse view angles using the planning CT; 4) Interpolation of the calculated scatter signals to other angles; 5) Removal of scatter from the raw projections; 6) FDK reconstruction using the scatter-corrected projections. In addition to using GPU to accelerate MC photon simulations, we also use a small number of photons and a down-sampled CT image in simulation to further reduce computation time. A novel denoising algorithm is used to eliminate MC noise from the simulated scatter images caused by low photon numbers. The method is validated on one simulated head-and-neck case with 364 projection angles. Results: We have examined variation of the scatter signal among projection angles using Fourier analysis. It is found that scatter images at 31 angles are sufficient to restore those at all angles with &lt; 0.1% error. For the simulated patient case with a resolution of 512 × 512 × 100, we simulated 5 × 10 6 photons per angle. The total computation time is 20.52 seconds on a Nvidia GTX Titan GPU, and the time at each step is 2.53, 0.64, 14.78, 0.13, 0.19, and 2.25 seconds, respectively. The scatter-induced shading/cupping artifacts are substantially reduced, and the average HU error of a region-of-interest is reduced from 75.9 to 19.0 HU. Conclusion : A practical ultrafast MC-based CBCT scatter correction scheme is developed. It accomplished the whole procedure of scatter correction and reconstruction within 30 seconds. ---------------------------- Cite this article as : Xu Y, Bai T, Yan H, Ouyang L, Wang J, Pompos A, Zhou L, Jiang SB, Jia X. Ultrafast cone-beam CT scatter correction with GPU-based Monte Carlo simulation. Int J Cancer Ther Oncol 2014; 2(2):020245. DOI: 10.14319/ijcto.0202.45

A novel method for megavoltage scatter correction in cone-beam CT acquired concurrent with rotational irradiation

Background and purposeAcquisition of cone-beam CT (CBCT) concurrent with VMAT results in scatter of the megavoltage (MV) beam onto the kilovoltage (kV) detector deteriorating CBCT image quality. The aim of this paper is to develop a method to estimate and correct for MV scatter reaching the kV panel. Material and methodsThe correction method is based on an alternating pulse sequence such that the kV source is enabled in only every other frame. MV scatter, noise and artifacts in the kV-unexposed frames are separated based on their spatial and temporal frequency characteristics and subsequently subtracted from the exposed frames. The method was tested on a phantom and one lung cancer patient scanned on an Elekta Synergy system simultaneous with a 9Gy VMAT delivery. ResultsWithout correction, acquisition during VMAT leads to significant cupping and loss of bone contrast. The novel method removes the signal due to MV scatter. Compared to acquisition without MV beam, the reconstruction quality is identical except for some unavoidable noise due to the scattered MV radiation in the exposed frames. Correction is performed in-line with acquisition without introducing delays. ConclusionsA novel, efficient and effective method was developed to correct for MV scatter in CBCT scans acquired concurrent with rotational radiotherapy.

TH-D-351-05: Scatter Correction for Cone-Beam CT in Radiation Therapy

Purpose: Cone-beam CT (CBCT) is being increasingly used in radiation therapy. However, as compared to conventional CT, the degraded image quality of CBCT hampers its applications. Due to the large volume of x-ray illumination in CBCT, scatter is considered as one of the fundamental limitations of CBCT image quality. Many scatter correction algorithms have been developed, while drawbacks still exist. Here, we propose a new scatter correction method which is particularly useful in radiation therapy. Method and Materials: Since the same patient is scanned repetitively during one radiation treatment course, we measure the scatter distributions in one scan, and use the measured scatter distributions to estimate and correct scatter in the following scans. A partially blocked CBCT is used in the scatter measurement scan. The x-ray beam blocker has a strip pattern, such that the whole-field scatter distribution can be estimated from the detected signals in the shadow region and the patient rigid transformation can be determined from the reconstructed image using the illuminated detector projection data. From the derived patient transformation, the measured scatter is then modified and used for scatter correction in the following regular CBCT scans. Results: The proposed method has been evaluated using Monte Carlo simulations and physical experiments on an anthropomorphic chest phantom. The results show a significant suppression of scatter artifacts using the proposed method. Using the reconstruction in a narrow collimator geometry as a reference, the comparison also shows that the proposed method reduces reconstruction error from 13.2% to 0.8%. Conclusion: This work indicates that much improved CBCT image quality is achievable using the proposed scatter correction method in radiation therapy. Our method is very attractive in applications where high CBCT reconstruction accuracy is critical, for example, dose calculation in adaptive radiation therapy.