A penalized weighted least-squares image reconstruction based on scatter correction methods for X-ray CT

Computed tomography (CT) is of great significance in the field of medical diagnosis. However, metal artifacts in the reconstruction images are disadvantageous for doctors to make a fast and accurate diagnosis when high-density metals present in the scanned location. The spectral CT has excellent performance in metal artifact reduction (MAR) method, which can combinate the prior information can to realize the information complementarity. In this paper, a MAR method based on dual-energy raw data constrained learning is proposed in this paper. The raw projection data of high/low energy and the results of normalized metal artifact reduction (NMAR) are input to the dual-stream U-Net (DSU-Net) for getting the virtual monoenergetic image (VMI) to reduce the secondary artifacts. The experimental results show that the peak signal-to-noise ratio (PSNR) of the output image is up to 49.60, SSIM to 0.997. It is proved that the raw data constrained learning method can suppress the residual artifacts from the traditional information pretreatment method.

Metal artifacts are considered a major challenge in computed tomography (CT) as these adversely affect the diagnosis and treatment of patients. Several approaches have been developed to address this problem. The present study explored the clinical potential of a novel photon-counting detector (PCD) CT system in reducing metal artifacts in head CT scans. In particular, we studied the recovery of an oral tumor region located under metal artifacts after correction. Three energy thresholds were used to group data into three bins (bin 1: low-energy, bin 2: middle-energy, and bin 3: high-energy) in the prototype PCD CT system. Three types of physical phantoms were scanned on the prototype PCD CT system. First, we assessed the accuracy of iodine quantification using iodine phantoms at varying concentrations. Second, we evaluated the performance of material decomposition (MD) and virtual monochromatic images (VMIs) using a multi-energy CT phantom. Third, we designed an ATOM phantom with metal insertions to verify the effect of the proposed metal artifact reduction. In particular, we placed an insertion-mimicking an iodine-enhanced oral tumor in the beam path of metallic objects. Normalized metal artifact reduction (NMAR) was performed for each energy bin image, followed by an image-based MD and VMI reconstruction. Image quality was analyzed quantitatively by contrast-to-noise ratio (CNR) measurements. The results of iodine quantification showed a good match between the true and measured iodine concentrations. Furthermore, as expected, the contrast between iodine and the surrounding material was higher in bin 1 image than in bin 3 image. On the other hand, the bin 3 image of the ATOM phantom showed fewer metal artifacts than the bin 1 image because of the higher photon energy. The result of quantitative assessment demonstrated that the 40-keV VMI (CNR: 20.6 ± 1.2) with NMAR and MD remarkably increased the contrast of the iodine-enhanced region compared with that of the conventional images (CNR: 10.4 ± 0.5) having 30 to 140 keV energy levels. The PCD-based multi-energy CT imaging has immense potential to maximize the contrast of the target tissue and reduce metal artifacts simultaneously. We believe that it would open the door to novel applications for the diagnosis and treatment of several diseases.

Purpose: Metal artifacts generated by dental implants cause dose discrepancy in intensity‐modulated radiation therapy (IMRT) of head‐and‐neck cancer cases due to the inaccurate predicted doses. To determine the effective metal artifact reduction (MAR) method presenting minimum dose errors with delivered dose, dosimetric impact of different MAR methods were evaluated in radiation treatment plans. Methods: MAR was implemented using Euler elastic and curvature based sinogram inpainting (EECSI) algorithm, volume clustering with Hounsfield unit (HU) overriding (VCU), and multimodal image‐based correction (MIC). When delivered doses were calculated on the corrected computed tomography (CT) images by employing the same plan parameters in the reference IMRT plan (Planref), dose errors were analyzed with dose‐volume histograms (DVHs), monitor unit, and dose conformity of target volumes. Dosimetric impacts were also estimated using the biophysical evaluation parameters in three oropharyngeal patients. Results: Optimized fluence in the Planref resulted in overdose up to 5% in the salivary glands and spinal cords on the corrected CT images, while the overdose in target volumes increased tumor control probability of 3–7%. Target doses in peripheral regions affected by the surrounded dark pixels were deformed and decreased conformity. The DVHs of organs at risk (OAR) and target volumes also showed the 5–8% volume gap in the prescription dose range. As the efficient MAR is employed into the head‐and‐neck IMRT plans, more accurate predicted doses of primary structures would be achieved, even if the dose differences of each MAR method are small less than 2–3%. Conclusion: EEECSI and VCU were useful methods to reduce the metal artifacts and to predict more accurate delivered doses. Since dental artifacts cause dose variation in peripheral region of target volumes and OAR, dose calculation on corrected CT images using efficient MAR techniques and dose verification would be encouraged in head‐and‐neck IMRT plans.

Metal Artifact Reduction (MAR) plays an important role in Computed Tomography (CT) research and application because severe artifacts degrade the image quality and diagnosis value if metal objects are present in the field of measurement. Although there are already many works for MAR, these works are for fan beam CT, not for cone beam CT, which is the trend and receiving much research attention. In this paper, we extend the Normalized Metal Artifact Reduction (NMAR) for fan beam CT to NMAR3 for cone beam CT, by replacing the linear interpolation in the NMAR with bi-linear interpolation. Experiments are carried out on 17 sets of spine phantom CT. 15 of them have reference CT as ground truth and 2 ones not. Both quantitative and qualitative results verified that NMAR3 outperforms the baseline method, i.e., bi-linear interpolation based method.

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.

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.

Spectral X-ray computed tomography (SCT) is an emerging method for non-destructive imaging of the inner structure of materials. Compared with the conventional X-ray CT, this technique provides spectral photon energy resolution in a finite number of energy channels, adding a new dimension to the reconstructed volumes and images. While this mitigates energy-dependent distortions such as beam hardening, metal artifacts due to photon starvation effects are still present, especially for low-energy channels where the attenuation coefficients are higher. We present a correction method for metal artifact reduction in SCT that is based on spectral deep learning. The correction efficiently reduces streaking artifacts in all the energy channels measured. We show that the additional information in the energy domain provides relevance for restoring the quality of low-energy reconstruction affected by metal artifacts. The correction method is parameter free and only takes around 15 ms per energy channel, satisfying near-real time requirement of industrial scanners.

A significant and increasing number of patients receiving radiation therapy present with metal objects close to, or even within, the treatment area, resulting in artifacts in computed tomography (CT) imaging, which is the most commonly used imaging method for treatment planning in radiation therapy. In the presence of metal implants, such as dental fillings in treatment of head-and-neck tumors, spinal stabilization implants in spinal or paraspinal treatment or hip replacements in prostate cancer treatments, the extreme photon absorption by the metal object leads to prominent image artifacts. Although current CT scanners include a series of correction steps for beam hardening, scattered radiation and noisy measurements, when metal implants exist within or close to the treatment area, these corrections do not suffice. CT metal artifacts affect negatively the treatment planning of radiation therapy either by causing difficulties to delineate the target volume or by reducing the dose calculation accuracy. Various metal artifact reduction (MAR) methods have been explored in terms of improvement of organ delineation and dose calculation in radiation therapy treatment planning, depending on the type of radiation treatment and location of the metal implant and treatment site. Including a brief description of the available CT MAR methods that have been applied in radiation therapy, this article attempts to provide a comprehensive review on the dosimetric effect of the presence of CT metal artifacts in treatment planning, as reported in the literature, and the potential improvement suggested by different MAR approaches. The impact of artifacts on the treatment planning and delivery accuracy is discussed in the context of different modalities, such as photon external beam, brachytherapy and particle therapy, as well as by type and location of metal implants.

Scatter artifacts impair the CT image quality and the accuracy of CT values. Especially in cases with metal implants and in wide cone-angle flat detector CT scans, scatter artifact removal can be of great value. Typical scatter correction methods try to estimate scattered radiation and subtract the estimated scatter from the uncorrected data. Scatter is found either by time-consuming Monte Carlo-based simulations of the photon trajectories, or by rawdata-based modelling of the scatter content using scatter kernels, whose open parameters have to be determined very accurately and for each scanner and type of object individually, and that sometimes even require a data base of typical objects. The procedures are time-consuming and require intimate knowledge about the scanner, in particular about the spectral properties, for which a correction is designed. We propose an empirical scatter correction (ESC) algorithm which does not need lots of prior knowledge for calibration. ESC assumes that a linear combination of the uncorrected image with various ESC basis images is scatter-free. The coefficients for the linear combination are determined in image domain by maximizing a flatness criterion of the combined volume. Here, we minimized the total variation in soft tissue regions using the gradient descent method with a line search. Simulated data and several patient data sets acquired with a clinical cone-beam spiral CT scanner, where scatter was added using a Monte Carlo scatter calculation algorithm, were used to evaluate ESC. Metal implants were simulated into those data sets, too. Our preliminary results indicate that ESC has the potential to efficiently reduce scatter artifacts in general, and metal artifacts in particular. ESC is computationally inexpensive, highly flexible, and does not require know-how of the scanner properties.

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.

PurposeAs computed tomography (CT) imaging is the most commonly used modality for treatment planning in radiation therapy, metal artifacts in the planning CT images may complicate the target delineation and reduce the dose calculation accuracy. Although current CT scanners do provide certain correction steps, it is a common understanding that there is not a universal solution yet to the metal artifact reduction (MAR) in general. Particularly noting the importance of MAR for radiation treatment planning, we propose a novel MAR method in this work that recruits an additional tilted CT scan and synthesizes nearly metal‐artifact‐free CT images.MethodsThe proposed method is based on the facts that the most pronounced metal artifacts in CT images show up along the x‐ray beam direction traversing multiple metallic objects and that a tilted CT scan can provide complementary information free of such metal artifacts in the earlier scan. Although the tilted CT scan would contain its own metal artifacts in the images, the artifacts may manifest in a different fashion leaving a chance to concatenate the two CT images with the metal artifacts much suppressed. We developed an image processing technique that uses the structural similarity (SSIM) for suppressing the metal artifacts. On top of the additional scan, we proposed to use an existing MAR method for each scan if necessary to further suppress the metal artifacts.ResultsThe proposed method was validated by a simulation study using the pelvic region of an XCAT numerical phantom and also by an experimental study using the head part of the Rando phantom. The proposed method was found to effectively reduce the metal artifacts. Quantitative analyses revealed that the proposed method reduced the mean absolute percentages of the error by up to 86% and 89% in the simulation and experimental studies, respectively.ConclusionsIt was confirmed that the proposed method, using complementary information acquired from an additional tilted CT scan, can provide nearly metal‐artifact‐free images for the treatment planning.

Objective.Metal artifacts seriously deteriorate CT image quality. Current metal artifacts reduction (MAR) methods suffer from insufficient correction or easily introduce secondary artifacts. To better suppress metal artifacts, we propose a sinogram completion approach extracting and utilizing useful information that contained in the corrupted metal trace projections.Approach.Our method mainly contains two stages: sinogram interpolation by an improved normalization technique for initial correction and physics-informed nonlinear sinogram decomposition for further improvement. In the first stage, different from the popular normalized metal artifact reduction method, we propose a more meaningful normalization scheme for the interpolation procedure. In the second stage, instead of performing a linear sinogram decomposition as done in the physics-informed sinogram completion method, we introduce a nonlinear decomposition model that can accurately separate the sinogram into metal and non-metal contributions by better modeling the physical scanning process. The interpolated sinogram and physics-informed correction compensate each other to reach the optimal correction results.Main results.Experimental results on simulated and real data indicate that, in terms of both structures preservation and detail recovery, the proposed physics-informed nonlinear sinogram completion method achieves very competitive performance for MAR compared to existing methods.Significance.According to our knowledge, it is for the first time that a nonlinear sinogram decomposition model is proposed in the literature for metal artifacts correction. It might motivate further research exploring this idea for various sinogram processing tasks.

With the rapid increase of orthopedic metal implants, more and more computed tomography (CT) scans of such patients with metal implantation were conducted. However, CT metal artifact caused by these implants would interfer clinical diagnosis and treatment. In order to solve this problem, large amount of researches had been carried out to reduce the metal artifact in the aspect of engineers and doctors in recent years. In the view of engineers, methods mainly include: transformation of the type and the property of CT machine such as dual-energy CT (DECT) or cone beam CT (CBCT), instead of spiral CT. The methods of doctors mainly include: the transformation of CT scanning parameters, such as tube voltage, field of view (FOV), layer thickness; the CT post-processing software such as Orthopedics Metal Artifact Reduction (O-MAR) which was applied comprehensively in orthopedics in recent years. Domestic and international progress of relevant studies on metal artifact reduction technology will be demonstrated in both aspects of engineers and doctors. Final reference for reduction of metal artifact in orthopedic application will be provided based on the above methods. Key words: Metal implant; Computed tomography; Metal artifact; Orthopedics

Purpose: Artifacts produced by implanted metal can significantly degrade the diagnostic value of CT examinations. The purpose of this work was to develop a projection-based method for reducing metal artifacts in multi-slice helical CT and to evaluate it using clinical data. Method and Materials: The proposed method uses reformatted projections created from helical projection data by combining data at the same angle view over the full longitudinal scan range. After pitch correction, automatic segmentation and removal of the metal were performed on each reformatted projection using edge detection, morphological dilation, and boundary tracing algorithms. 2D interpolation was used to fill remnant voids. The main advantage of this method is that both the segmentation of the metal projections and the interpolations are performed on complete projections with the entire metal present, which is more accurate than those based upon narrow-beam projections in multi-slice CT. Results: The method was evaluated using three separate clinical datasets acquired from a 64-slice CT scanner (Siemens Sensation 64). One case was a quantitative CT study for measuring bone mineral density, in which a calibration phantom with five attenuation standards was placed under the patient. The other two cases were typical abdomen/pelvis exams. In all cases the patient had a dense metal hip implant. For the quantitative CT case, the CT number and standard deviation within the bladder and the five calibration inserts were measured. The metal artifacts were reduced substantially and more accurate CT numbers were obtained after the correction. Conclusion: A projection-based method for metal artifact reduction in multi-slice helical CT was developed and evaluated using patient cases. The results demonstrated that the proposed method can effectively reduce the artifacts caused by dense metal implants. Conflict of Interest: Research is partially supported by Siemens Medical Solutions.