Single‐scan patient‐specific scatter correction in computed tomography using peripheral detection of scatter and compressed sensing scatter retrieval

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.

The problem we address is the optimization and comparison of window-based scatter correction (SC) methods in SPECT for maximum a posteriori reconstructions. While sophisticated reconstruction-based SC methods are available, the commonly used window-based SC methods are fast, easy to use, and perform reasonably well. Rather than subtracting a scatter estimate from the measured sinogram and then reconstructing, we use an ensemble approach and model the mean scatter sinogram in the likelihood function. This mean scatter sinogram estimate, computed from satellite window data, is itself inexact (noisy). Therefore two sources of noise, that due to Poisson noise of unscattered photons and that due to the model error in the scatter estimate, are propagated into the reconstruction. The optimization and comparison is driven by a figure of merit, the area under the LROC curve (ALROC) that gauges performance in a signal detection plus localization task. We use model observers to perform the task. This usually entails laborious generation of many sample reconstructions, but in this work, we instead develop a theoretical approach that allows one to rapidly compute ALROC given known information about the imaging system and the scatter correction scheme. A critical step in the theory approach is to predict additional (above that due to to the propagated Poisson noise of the primary photons) contributions to the reconstructed image covariance due to scatter (model error) noise. Simulations show that our theory method yields, for a range of search tolerances, LROC curves and ALROC values in close agreement to that obtained using model observer responses obtained from sample reconstruction methods. This opens the door to rapid comparison of different window-based SC methods and to optimizing the parameters (including window placement and size, scatter sinogram smoothing kernel) of the SC method.

Cone-beam computed tomography (CBCT) has the notable features of high efficiency and high precision, and is widely used in areas such as medical imaging and industrial non-destructive testing. However, the presence of the ray scatter reduces the quality of CT images. By referencing the slit collimation approach, a scatter correction method for CBCT based on the interlacing-slit scan is proposed. Firstly, according to the characteristics of CBCT imaging, a scatter suppression plate with interlacing slits is designed and fabricated. Then the imaging of the scatter suppression plate is analyzed, and a scatter correction calculation method for CBCT based on the image fusion is proposed, which can splice out a complete set of scatter suppression projection images according to the interlacing-slit projection images of the left and the right imaging regions in the scatter suppression plate, and simultaneously complete the scatter correction within the flat panel detector (FPD). Finally, the overall process of scatter suppression and correction is provided. The experimental results show that this method can significantly improve the clarity of the slice images and achieve a good scatter correction.

Small calcifications are often the earliest and the main indicator of breast cancer. Dual-energy digital mammography (DEDM) has been considered as a promising technique to improve the detectability of calcifications since it can be used to suppress the contrast between adipose and glandular tissues of the breast. X-ray scatter leads to erroneous calculations of the DEDM image. Although the pinhole-array interpolation method can estimate scattered radiations, it requires extra exposures to measure the scatter and apply the correction. The purpose of this work is to design an algorithmic method for scatter correction in DEDM without extra exposures. In this paper, a scatter correction method for DEDM was developed based on the knowledge that scattered radiation has small spatial variation and that the majority of pixels in a mammogram are noncalcification pixels. The scatter fraction was estimated in the DEDM calculation and the measured scatter fraction was used to remove scatter from the image. The scatter correction method was implemented on a commercial full-field digital mammography system with breast tissue equivalent phantom and calcification phantom. The authors also implemented the pinhole-array interpolation scatter correction method on the system. Phantom results for both methods are presented and discussed. The authors compared the background DE calcification signals and the contrast-to-noise ratio (CNR) of calcifications in the three DE calcification images: image without scatter correction, image with scatter correction using pinhole-array interpolation method, and image with scatter correction using the authors' algorithmic method. The authors' results show that the resultant background DE calcification signal can be reduced. The root-mean-square of background DE calcification signal of 1962 μm with scatter-uncorrected data was reduced to 194 μm after scatter correction using the authors' algorithmic method. The range of background DE calcification signals using scatter-uncorrected data was reduced by 58% with scatter-corrected data by algorithmic method. With the scatter-correction algorithm and denoising, the minimum visible calcification size can be reduced from 380 to 280 μm. When applying the proposed algorithmic scatter correction to images, the resultant background DE calcification signals can be reduced and the CNR of calcifications can be improved. This method has similar or even better performance than pinhole-array interpolation method in scatter correction for DEDM; moreover, this method is convenient and requires no extra exposure to the patient. Although the proposed scatter correction method is effective, it is validated by a 5-cm-thick phantom with calcifications and homogeneous background. The method should be tested on structured backgrounds to more accurately gauge effectiveness.

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.

Applications of volumetric CT (VCT) are hampered by shading and streaking artifacts in the reconstructed images. These artifacts are mainly due to strong x-ray scatter signals accompanied with the large illumination area within one projection, which lead to CT number inaccuracy, image contrast loss and spatial nonuniformity. Although different scatter correction algorithms have been proposed in literature, a standard solution still remains unclear. Measurement-based methods use a beam blocker to acquire scatter samples. These techniques have unrivaled advantages over other existing algorithms in that they are simple and efficient, and achieve high scatter estimation accuracy without prior knowledge of the imaged object. Nevertheless, primary signal loss is inevitable in the scatter measurement, and multiple scans or moving the beam blocker during data acquisition are typically employed to compensate for the missing primary data. In this paper, we propose a new measurement-based scatter correction algorithm without primary compensation for full-fan VCT. An accurate reconstruction is obtained with one single-scan and a stationary x-ray beam blocker, two seemingly incompatible features which enable simple and efficient scatter correction without increase of scan time or patient dose. Based on the CT reconstruction theory, we distribute the blocked data over the projection area where primary signals are considered approximately redundant in a full scan, such that the CT image quality is not degraded even with primary loss. Scatter is then accurately estimated by interpolation and scatter-corrected CT images are obtained using an FDK-based reconstruction algorithm. The proposed method is evaluated using two phantom studies on a tabletop CBCT system. On the Catphan©600 phantom, our approach reduces the reconstruction error from 207 Hounsfield unit (HU) to 9 HU in the selected region of interest, and improves the image contrast by a factor of 2.0 in the high-contrast regions. On an anthropomorphic head phantom, the reconstruction error is reduced from 97 HU to 6 HU in the soft tissue region and image spatial nonuniformity decreases from 27% to 5% after correction. Our method inherits the main advantages of measurement-based methods while avoiding their shortcomings. It has the potential to become a practical scatter correction solution widely implementable on different VCT systems.

In 3D PET scatter degrades image quality and quantification. The currently most popular scatter estimation method is the single scatter simulation (SSS) which accommodates for multiple scattering by scaling the single scatter estimation. However, it has not been clear yet how accurate this approximation is for cases where multiple scatter is significant, raising the specific questions: "How important double scatter correction is, and how accurately do we simulate the total scatter events by appropriate scaling?" This project aims to clarify the improvements in terms of quantification due to scatter correction, using: (i) single scatter events only, (ii) single and double scatter events, (iii) total scatter events, or (iv) scaled single scatter, and evaluate the analytic scatter estimation algorithm as implemented in the open source reconstruction software STIR. The analytic SSS scatter estimation implemented in STIR is compared with the SimSET Monte Carlo package. Scatter correction accuracy is examined for different levels of scattering and scaling approaches. A large anthropomorphic phantom was reconstructed with FBP. The images have been compared quantitatively: Areas with high scatter fraction are compared with single scatter corrected images and show a 50% bias reduction after performing single and double scatter correction. Scaled single scatter correction results are in good agreement with SimSET true events, less than 10% difference. Total-fit and tail-fit scaled single scatter results in approximately equal mean values. SSS correction with tail-fit scaling in STIR is very close with SimSET true events, 10% difference. The results show that multiple scatter correction improves accuracy and scaling single scatter is an efficient method to compensate for multiple scattering for standard PET scanning acquisitions.

Purpose: Scatter contamination in cone-beam computed tomography (CBCT) degrades the image quality by introducing shading artifacts. A moving-blocker-based approach has been proposed to simultaneously estimate scatter and reconstruct the complete volume within field of view (FOV) from a single CBCT scan, where promising results were obtained from simulation studies. In this work, we experimentally demonstrated the effectiveness of the moving-blocker-based scatter correction approach by implementing a moving blocker system on a LINAC on-board kV CBCT imaging system. Methods: A physical attenuator (i.e., “blocker”) consisting equal spaced lead strips was mounted on a linear actuator. A step motor connected to the actuator drove the blocker to move back and forth along gantry rotation axis during the CBCT acquisition. Scatter signal was estimated from the blocked region of imaging panel, and interpolated into the un-blocked region. A sparseness prior based statistical iterative reconstruction algorithm was used to reconstruct CBCT images from unblocked projections after the scatter signal was subtracted. Experimental studies were performed on both a Catphan phantom and an anthropomorphic pelvic phantom to evaluate performance of the moving blocker system. Results: The scatter-induced shading artifacts were substantially reduced in the images acquired with the moving blocker system. CT number error in selected regions of interest of reduced from 318 to 17 and from 23 9 to 10 for the Catphan phantom and pelvic phantom, respectively. Conclusion: We demonstrated for the first time that the moving blocker system can successfully estimate the scatter signal in projection data, reduce the imaging dose and obtain complete volumetric information within the FOV using a single scan.

High resolution PET scanners based on small individual detectors have a low sensitivity resulting from increased inter-detector scatter. It has been postulated that this limitation can be overcome by using enlarged discrimination windows to include more low-energy events and by developing more efficient energy-dependent methods to correct for scatter. In this work, we investigate one such method based on the frame-by-frame scatter correction of multispectral data. Images acquired in the conventional (344-658 keV), broad (129-658 keV) and multispectral (129-658 keV) window modes were processed by stationary and nonstationary convolution-restoration scatter correction methods. Broad and multispectral window acquisition improved counting efficiency relative to the conventional window. The degradation of image quality due to added scatter can be mostly recovered by the subtraction-restoration scatter correction. However, the multispectral frame-by-frame method was found to be more sensitive to the effects of the nonstationary scatter response functions and its performance was not as good as that of the broad window. It is concluded that more sophisticated scatter degradation models and correction methods need to be established to take full advantage of multispectral data.

Validierung einer Streukorrekturmethode zur IMRT-Verifikation mit Hilfe eines Portal-Imaging-Systems

Validation of a TOPAS cone beam computed tomography (CBCT) Monte Carlo model towards personalised CBCT dosimetry in interventional radiology (IR).

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.

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.

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.

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.