Metal Artifacts In Computed Tomography Images Research Articles

Metal Artifact Correction in Industrial CT Images Based on a Dual-Domain Joint Deep Learning Framework

Industrial computed tomography (CT) images reconstructed directly from projection data using the filtered back projection (FBP) method exhibit strong metal artifacts due to factors such as beam hardening, scatter, statistical noise, and deficiencies in the reconstruction algorithms. Traditional correction approaches, confined to either the projection domain or the image domain, fail to fully utilize the rich information embedded in the data. To leverage information from both domains, we propose a joint deep learning framework that integrates UNet and ResNet architectures for the correction of metal artifacts in CT images. Initially, the UNet network is employed to correct the imperfect projection data (sinograms), the output of which serves as the input for the CT image reconstruction unit. Subsequently, the reconstructed CT images are fed into the ResNet, with both networks undergoing a joint training process to optimize image quality. We take the projection data obtained by analytical simulation as the data set. The resulting optimized industrial CT images show a significant reduction in metal artifacts, with the average Peak Signal-to-Noise Ratio (PSNR) reaching 36.13 and the average Structural Similarity Index (SSIM) achieving 0.953. By conducting simultaneous correction in both the projection and image domains, our method effectively harnesses the complementary information from both, exhibiting a marked improvement in correction results over the deep learning-based single-domain corrections. The generalization capability of our proposed method is further verified in ablation experiments and multi-material phantom CT artifact correction.

An Innovative Metal Artifact Reduction Algorithm based on Res-U-Net GANs

During X-ray computed tomography (CT) scans, the metal implants in the patient's body will produce severe artifacts, which reduce the image quality and interferes with the doctor's judgment. Therefore, it is necessary to develop an algorithm for removing metal artifacts in CT images and reconstructing high-quality images. In this paper, we proposed a generative adversarial networks (GANs)-based metal artifact reduction algorithm for the image domain, Res-U-Net GANs. This method can effectively suppress noise and remove metal artifacts in CT images. Our new approach includes a generator and a discriminator. The generator contains several residual blocks, a U-Net structure and skip connections. And a weighted joint loss function is also used for training. These structures can reduce metal artifacts in images, improve image quality, and restore implant details. We use SSIM, PSNR and RMSE to evaluate the performance of the proposed method. The mean SSIM, PSNR and RMSE of the testing set images are 0.977, 39.044 and 0.011, respectively. And the trained model which is compiled and encapsulated, also show excellent performance in processing clinical data sets, which can remove metal artifacts in clinical CT images. We consider that the proposed algorithm can remove metal artifacts in CT images and restore image details, which is very helpful for radiologists.

Effect of metal implants and metal artifacts on back-projected two-dimensional entrance fluence determined by EPID dosimetry.

To evaluate the errors caused by metal implants and metal artifacts in the two-dimensional entrance fluences reconstructed using the back-projection algorithm based on electronic portal imaging device (EPID) images. The EPID in the Varian VitalBeam accelerator was used to acquire portal dose images (PDIs), and then commercial EPID dosimetry software was employed to reconstruct the two-dimensional entrance fluences based on computed tomography (CT) images of the head phantoms containing interchangeable metal-free/titanium/aluminum round bars. The metal-induced errors in the two-dimensional entrance fluences were evaluated by comparing the γ results and the pixel value errors in the metal-affected regions. We obtained metal-artifact-free CT images by replacing the voxel values of non-metal inserts with those of metal inserts in metal-free CT images to evaluate the metal-artifact-induced errors. The γ passing rates (versus PDIs obtained without a phantom in the beam field (PDIair ), 2%/2mm) for the back-projected two-dimensional entrance fluences of phantoms containing titanium or aluminum (BPTi /BPAl ) were reduced from 92.4% to 90.5% and 90.6%, respectively, relative to the metal-free phantom (BPmetal-free ). Titanium causes more severe metal artifacts in CT images than aluminum, and its removal resulted in a 0.0022 CU (median) reduction in the pixel value of BPTi artifact-free relative to BPTi in the metal-affected region. Moreover, the mean absolute error (MAE) and root mean square error (RMSE) decreased from 0.0050 CU and 0.0063 CU to 0.0034 CU and 0.0040 CU, respectively (vs. BPmetal-free ). Metal implants increase the errors in back-projected two-dimensional entrance fluences, and metals with higher electron densities cause more errors. For high-electron-density metal implants that produce severe metal artifacts (e.g., titanium), removing metal artifacts from the CT images can improve the accuracy of the two-dimensional entrance fluences reconstructed by back-projection algorithms.

A Simple Method for Computationally Generating Metal Artifacts in CT Images for Treatment Planning: A Pilot Phantom Study

In treatment planning for radiation therapy, the use of computed tomography (CT) images including metal artifacts causes a reduction in the dose calculation accuracy. In clinical practice, the artifacts are manually contoured and assigned an appropriate fixed CT number. To validate the procedure, images taken before and after metal insertion into a patient are required, which may be impractical. We propose a simple method for computationally generating metal artifacts in clinical images. In the proposed method, a clinical image free of metal artifacts is used. To simulate metal inside a patient, CT numbers of a region in the image are replaced with a fixed extremely high value. A sinogram is created by the forward projection of the image. Data values of the sinogram in the metal region are converted into smaller values. From the sinogram, an image including artifacts is reconstructed with the filtered back projection. The simulated artifacts consisted of dark and bright bands and were observed to be similar to the actual metal artifacts. CT numbers in multiple small regions of interest in the image obtained by the proposed method showed a good agreement with those in the actual image. The proposed method was demonstrated to generate the metal artifacts additionally on the clinical images. The method would be potentially applicable to a validation study for the clinical procedure of manually contouring and assigning CT numbers to metal artifacts.

An additional tilted-scan-based CT metal-artifact-reduction method for radiation therapy planning.

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.

Metal artifact reduction in CT, a phantom study: subjective and objective evaluation of four commercial metal artifact reduction algorithms when used on three different orthopedic metal implants.

Background Metal implants may introduce severe artifacts in computed tomography (CT) images. Over the last few years dedicated algorithms have been developed in order to reduce metal artifacts in CT images. Purpose To investigate and compare metal artifact reduction algorithms (MARs) from four different CT vendors when imaging three different orthopedic metal implants. Material and Methods Three clinical metal implants were attached to the leg of an anthropomorphic phantom: cobalt-chrome; stainless steel; and titanium. Four commercial MARs were investigated: SmartMAR (GE); O-MAR (Philips); iMAR (Siemens); and SEMAR (Toshiba). The images were evaluated subjectively by three observers and analyzed objectively by calculating the fraction of pixels with CT number above 500 HU in a region of interest around the metal. The average CT number and image noise were also measured. Results Both subjective evaluation and objective analysis showed that MARs reduced metal artifacts and improved the image quality for CT images containing metal implants of steel and cobalt-chrome. When using MARs on titanium, all MARs introduced new visible artifacts. Conclusion The effect of MARs varied between CT vendors and different metal implants used in orthopedic surgery. Both in subjective evaluation and objective analysis the effect of applying MARs was most obvious on steel and cobalt-chrome implants when using SEMAR from Toshiba followed by SmartMAR from GE. However, MARs may also introduce new image artifacts especially when used on titanium implants. Therefore, it is important to reconstruct all CT images containing metal with and without MARs.

Contouring and dose calculation in head and neck cancer radiotherapy after reduction of metal artifacts in CT images

Background: Delineation accuracy of the gross tumor volume (GTV) in radiotherapy planning for head and neck (H&N) cancer is affected by computed tomography (CT) artifacts from metal implants which obscure identification of tumor as well as organs at risk (OAR). This study investigates the impact of metal artifact reduction (MAR) in H&N patients in terms of delineation consistency and dose calculation precision in radiation treatment planning.Material and methods: Tumor and OAR delineations were evaluated in planning CT scans of eleven oropharynx patients with streaking artifacts in the tumor region preceding curative radiotherapy (RT). The GTV-tumor (GTV-T), GTV-node and parotid glands were contoured by four independent observers on standard CT images and MAR images. Dose calculation was evaluated on thirty H&N patients with dental implants near the treated volume. For each patient, the dose derived from the clinical treatment plan using the standard image set was compared with the recalculated dose on the MAR image dataset.Results: Reduction of metal artifacts resulted in larger volumes of all delineated structures compared to standard reconstruction. The GTV-T and the parotids were on average 22% (p < 0.06) and 7% larger (p = 0.005), respectively, in the MAR image plan compared to the standard image plan. Dice index showed reduced inter-observer variations after reduction of metal artifacts for all structures. The average surface distance between contours of different observers improved using the MAR images for GTV and parotids (p = 0.04 and p = 0.01). The median volume receiving a dose difference larger than ±3% was 2.3 cm3 (range 0–32 cm3).Conclusions: Delineation of structures in the head and neck were affected by metal artifacts and volumes were generally larger and more consistent after reduction of metal artifacts, however, only small changes were observed in the dose calculations.

Localization of deep brain stimulation electrodes via metal artifacts in CT images.

In Deep Brain Stimulation (DBS), the location of implanted electrodes in the brain has direct influence on the therapeutic effect of the treatment. This work deals with estimating the position of the implanted DBS electrodes from the images registered by X-ray Computed Tomography (CT) scanners. A technique named junction method that takes advantage of the streak artifacts created by the metal parts of the electrodes in CT images is proposed for this purpose. To start with, the brain image is extracted by defining a brain mask. Next, the edges are intensified by applying a Gaussian convolution operator followed by a measure of the second derivative of the image along all directions in the image plane. Criteria of adjacency and length are applied to the lines detected by the Hough transform to distinguish between tracks of streak artifacts and the brain structure. At some points, straight lines are distorted by noise. To handle this issue, all lines that fit same line equation are merged. The horizontal line connecting the two DBS electrodes (one in each cerebral hemisphere) is called electrode line. To specify the electrodes position, intersections of the electrode line with every other line are marked. Finally, to obtain the vertical position estimate, the above algorithm is applied to the image stack.

Metal artifact reduction algorithm based on model images and spatial information

Computed tomography (CT) has become one of the most favorable choices for diagnosis of trauma. However, high-density metal implants can induce metal artifacts in CT images, compromising image quality. In this study, we proposed a model-based metal artifact reduction (MAR) algorithm. First, we built a model image using the k-means clustering technique with spatial information and calculated the difference between the original image and the model image. Then, the projection data of these two images were combined using an exponential weighting function. At last, the corrected image was reconstructed using the filter back-projection algorithm. Two metal-artifact contaminated images were studied. For the cylindrical water phantom image, the metal artifact was effectively removed. The mean CT number of water was improved from −28.95±97.97 to −4.76±4.28. For the clinical pelvic CT image, the dark band and the metal line were removed, and the continuity and uniformity of the soft tissue were recovered as well. These results indicate that the proposed MAR algorithm is useful for reducing metal artifact and could improve the diagnostic value of metal-artifact contaminated CT images.