Computed Tomography Metal Artifact Research Articles

Unsupervised CT Metal Artifact Reduction by Plugging Diffusion Priors in Dual Domains.

During the process of computed tomography (CT), metallic implants often cause disruptive artifacts in the reconstructed images, impeding accurate diagnosis. Many supervised deep learning-based approaches have been proposed for metal artifact reduction (MAR). However, these methods heavily rely on training with paired simulated data, which are challenging to acquire. This limitation can lead to decreased performance when applying these methods in clinical practice. Existing unsupervised MAR methods, whether based on learning or not, typically work within a single domain, either in the image domain or the sinogram domain. In this paper, we propose an unsupervised MAR method based on the diffusion model, a generative model with a high capacity to represent data distributions. Specifically, we first train a diffusion model using CT images without metal artifacts. Subsequently, we iteratively introduce the diffusion priors in both the sinogram domain and image domain to restore the degraded portions caused by metal artifacts. Besides, we design temporally dynamic weight masks for the image-domian fusion. The dual-domain processing empowers our approach to outperform existing unsupervised MAR methods, including another MAR method based on diffusion model. The effectiveness has been qualitatively and quantitatively validated on synthetic datasets. Moreover, our method demonstrates superior visual results among both supervised and unsupervised methods on clinical datasets. Codes are available in github.com/DeepXuan/DuDoDp-MAR.

Dosimetric impact of CT metal artifact reduction for spinal implants in stereotactic body radiotherapy planning.

Metal artifacts due to spinal implants may affect the accuracy of dose calculation for radiotherapy. However, the dosimetric impact of metal artifact reduction (MAR) for spinal implants in stereotactic body radiotherapy (SBRT) plans has not been well studied. The objective of this study was to evaluate the dosimetric impact of MAR in spinal SBRT planning with three clinically common dose calculation algorithms. Gammex phantom and 10 patients' computed tomography (CT) images were studied to investigate the effects of titanium implants. A commercial orthopedic MAR algorithm was employed to reduce artifacts. Dose calculations for SBRT were conducted on both artifact-corrected and uncorrected images using three commercial algorithms [analytical anisotropic algorithm (AAA), Acuros XB (AXB), and Monte Carlo (MC)]. Dose discrepancies between artifact-corrected and uncorrected cases were appraised using a dose-volume histogram (DVH) and 3-dimensional (3D) gamma analysis with different distance to agreement (DTA) and dose difference criteria. The gamma agreement index (GAI) was denoted as G(∆D, DTA). Statistical analysis of t-test was utilized to evaluate the dose differences of different algorithms. The phantom study demonstrated that titanium metal artifacts can be effectively reduced. The patient cases study showed that dose differences between the artifact-corrected and uncorrected datasets were small evaluated by gamma index and DVH. Gamma analysis found that even the strict criterion local G(1,1) had average values ≥93.9% for the three algorithms. For all DVH metrics, average differences did not exceed 0.7% in planning target volume (PTV) and 2.1% in planning risk volume of spinal cord (PRV-SC). Statistical analysis showed that the observed dose differences of MC method were significantly larger than those of AAA (P<0.01 for D98% of PTV and P<0.001 for D0.1cc of spinal cord) and AXB methods (P<0.001 for D98% and P<0.0001 for D0.1cc). Dosimetric impact of artifacts caused by titanium implants is not significant in spinal SBRT planning, which indicates that dose calculation algorithms might not be very sensitive to CT number variation caused by titanium inserts.

PDS-MAR: a fine-grained projection-domain segmentation-based metal artifact reduction method for intraoperative CBCT images with guidewires

Objective. Since the invention of modern Computed Tomography (CT) systems, metal artifacts have been a persistent problem. Due to increased scattering, amplified noise, and limited-angle projection data collection, it is more difficult to suppress metal artifacts in cone-beam CT, limiting its use in human- and robot-assisted spine surgeries where metallic guidewires and screws are commonly used. Approach. To solve this problem, we present a fine-grained projection-domain segmentation-based metal artifact reduction (MAR) method termed PDS-MAR, in which metal traces are augmented and segmented in the projection domain before being inpainted using triangular interpolation. In addition, a metal reconstruction phase is proposed to restore metal areas in the image domain. Main results. The proposed method is tested on both digital phantom data and real scanned cone-beam computed tomography (CBCT) data. It achieves much-improved quantitative results in both metal segmentation and artifact reduction in our phantom study. The results on real scanned data also show the superiority of this method. Significance. The concept of projection-domain metal segmentation would advance MAR techniques in CBCT and has the potential to push forward the use of intraoperative CBCT in human-handed and robotic-assisted minimal invasive spine surgeries.

Evaluation of computed tomography metal artifact and CyberKnife fiducial recognition for novel size fiducial markers.

This study aimed to compare fiducial markers used in CyberKnife treatment in terms of metal artifact intensity observed in CT images and fiducial recognition in the CyberKnife system affected by patient body thickness and type of marker. Five markers, ACCULOC 0.9mm×3mm, Ball type Gold Anchor (GA) 0.28mm×10mm, 0.28mm×20mm, and novel size GA 0.4mm×10mm, 0.4mm×20mm were evaluated. To evaluate metal artifacts of CT images, two types of CT images of water-equivalent gels with each marker were acquired using Aquilion LB CT scanner, one applied SEMAR (SEMAR-on) and the other did not apply this technique (SEMAR-off). The evaluation metric of artifact intensity (MSD ) which represents a variation of CT values were compared for each marker. Next, 5, 15, and 20cm thickness of Tough Water (TW) was placed on the gel under the condition of overlapping the vertebral phantom in the Target Locating System, and the live image of each marker was acquired to compare fiducial recognition. The mean MSD of SEMAR-off was 78.80, 74.50, 97.25, 83.29, and 149.64 HU for ACCULOC, GA0.28mm×10mm, 20mm, and 0.40mm×10mm, 20mm, respectively. In the same manner, that of SEMAR-on was 23.52, 20.26, 26.76, 24.89, and 33.96 HU, respectively. Fiducial recognition decreased in the order of 5, 15, and 20cm thickness, and GA 0.4×20mm showed the best recognition at thickness of 20cm TW. We demonstrated the potential to reduce metal artifacts in the CT image to the same level for all the markers we evaluated by applying SEMAR. Additionally, the fiducial recognition of each marker may vary depending on the thickness of the patient's body. Particularly, we showed that GA 0.40×20mm may have more optimal recognition for CyberKnife treatment in cases of high bodily thickness in comparison to the other markers.

Experimental and clinical validation of an artificial intelligence metal artifact correction algorithm for low-dose following up CT of percutaneous vertebroplasty.

Low-dose following up computed tomography (CT) of percutaneous vertebroplasty (PVP) that involves the use of bone cement usually suffers from lightweight metal artifacts, where conventional techniques for CT metal artifact reduction are often not sufficiently effective. This study aimed to validate an artificial intelligence (AI)-based metal artifact correction (MAC) algorithm for use in low-dose following up CT for PVP. In experimental validation, an ovine vertebra phantom was designed to simulate the clinical scenario of PVP. With routine-dose images acquired prior to the cement introduction as the reference, low-dose CT scans were taken on the cemented phantom and processed with conventional MAC and AI-MAC. The resulting image quality was compared in CT attenuation, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR), followed by a quantitative evaluation of the artifact correction accuracy based on adaptive segmentation of the paraspinal muscle. In clinical validation, ten cases of low-dose following up CT after PVP were enrolled to test the performance of diagnosing sarcopenia with measured CT attenuation per cemented vertebral segment, via receiver operating characteristic (ROC) analysis. With respect to the reference image, no significant difference was found for AI-MAC in CT attenuation, image noise, SNRs, and CNR (all P>0.05). The paraspinal muscle segmented on the AI-MAC image was 18.6% and 8.3% more complete to uncorrected and MAC images. Higher area under the curve (AUC) of the ROC analysis was found for AI-MAC (AUC =0.92) compared to the uncorrected (AUC =0.61) and MAC images (AUC =0.70). In low-dose following up CT for PVP, the AI-MAC has been fully validated for its superior ability compared to conventional MAC in suppressing artifacts and may be a reliable alternative for diagnosing sarcopenia.

Influence of CT metal artifact reduction on SPECT/CT quantification of bone scintigraphy – Retrospective study for selected types of metal implants

Implanted metal prostheses can cause severe artifacts in reconstructed computed tomography (CT) images. To reduce the diagnostic impact of these artifacts and improve attenuation correction in single photon emission computed tomography (SPECT), an algorithm of iterative metal artifact reduction (iMAR) for SPECT/CT systems was developed. The aims of this study were (a) to assess the difference in visual image quality by comparing CT and SPECT images reconstructed with and without iMAR and (b) to determine the influence of iMAR on quantitative 99mTc-uptake in SPECT/CT. This retrospective study includes 21 patients with implanted metal prostheses who underwent SPECT/CT bone scintigraphy. CT data were reconstructed with iMAR and without (noMAR) and were used for attenuation correction of SPECT data for xSPECT Quant and xSPECT Bone reconstruction. The effect of iMAR on image quality was evaluated by visual analysis and the effect on quantitative SPECT/CT was assessed by measuring HU values and absolute uptake values (kBq/mL) in volumes of interest (VOIs). There was a significant reduction of visible metal artifacts with iMAR (p<0.01) in the CT images, but visual differences in the SPECT images were minor. The values of quantitative tracer uptake in VOIs near metal implants were lower for iMAR vs. noMAR xSPECT Quant (p<0.01). Only VOIs near metal showed significant differences in HU values, which were 14.6% lower for iMAR CT (p<0.01). The use of iMAR reduces metal artifacts in CT and improves the perceived image quality. Although in some cases a significant difference in the quantitative evaluation of SPECT/CT was observed, the influence of iMAR can be considered small in relation to other factors in the clinical setting.

Impact of computed tomography metal artifact reduction protocol on periprosthetic tissue characterization after total hip arthroplasty:A cadaveric study.

Metal artifact reduction (MAR) has improved computed tomography (CT) imaging of total hip arthroplasty (THA) but the assessment of osteolysis and implant to bone contact relies on the accurate depiction of bone defects, cancellous bone, and cement. This study evaluates the impact of available single and dual-energy protocols on periprosthetic tissue characterization in a cadaveric phantom. Bilateral THA was performed on a fresh frozen cadaveric pelvis with simulated osteolytic cavities. CT acquisitions with projection-basedMAR and noise equivalence were performed using single energy 140 kVp, single energy 150 kVp with 0.6 mm tin filtration, and dual-energy at 100/150 kVp with 0.6 mm tin filtration, from which simulated energies were extracted. Image subtraction, segmentation, region of interest histograms, and line profiles were used to characterize tissue density and separation. Tissue densities were heavily dependent on the energy profile of the protocol. Cancellous bone ranged from 182to 45 HU and cement from 1012to 131 HU using 140 kVp compared to dual-energy with weighted high energy tube, respectively. Spectral separation between cancellous bone, osteolytic defect, and cement was reduced for all protocols compared with 140 kVp. Spectral overlap was most severe using dual-energy with heavily weighted high-energy tubes. Dual-energy algorithms reduced trabecular contrast within the cancellous bone and cortical edge response. Althoughthe dual-energy acquisition has been proposed as an additive to projection-based MAR techniques in THA, reduced density and contrast in clinically relevant periprosthetic tissue compared to 140 kVp single energy may limit its use in characterizing periprosthetic tissues.

A semi-supervised learning method of latent features based on convolutional neural networks for CT metal artifact reduction.

X-ray computed tomography (CT) has become a convenient and efficient clinical medical technique. However, in the presence of metal implants, CT images may be corrupted by metal artifacts. The metal artifact reduction (MAR) methods based on deep learning are mostly supervised methods trained with labeled synthetic-artifact CT images. However, this causes the neural network to be biased toward learning specific synthetic-artifact patterns and leads to a poor generalization for unlabeled real-artifact CT images. In this study, a semi-supervised learning method of latent features based on convolutional neural networks (SLF-CNN) is developed to remove metal artifacts while ensuring a good generalization ability for real-artifact CT images. The proposed semi-supervised method extracts CT image features in alternate iterations of a synthetic-artifact learning stage and a real-artifact learning stage. In the synthetic-artifact learning stage, SLF-CNN is fed with paired synthetic-artifact CT images and is constrained using mean-squared-error (MSE) loss and perceptual loss in a supervised learning fashion. In the real-artifact learning stage, the network weight is updated by minimizing the error between the pseudo-ground truths and the predicted latent features. The feature level pseudo-ground truths are obtained by modeling latent features using the Gaussian process. The overall framework of SLF-CNN adopts an encoder-decoder structure. The encoder is composed of artifact information collection groups to map the input artifact-affected synthetic-artifact CT images and real-artifact CT images into latent features. The decoder is composed of stacked ResNeXt blocks and is responsible for decoding latent features with high-level semantic information to reconstruct artifact-free CT images. The performance of the proposed method is evaluated through contrast experiments and ablation experiments. The contrast experimental results indicate that the artifact-free CT images obtained by SLF-CNN have good metrics values, which are close to or better than those of typical supervised MAR methods. The metal artifacts in artifact-affected CT images are eliminated and the tissue structure details are preserved using SLF-CNN. The ablation experiment shows that adding real-artifact CT images greatly improves the generalization ability of the network. The proposed semi-supervised learning method of latent features for MAR effectively suppresses metal artifacts and improves the generalization ability of the network.

Addressing CT metal artifacts using photon-counting detectors and one-step spectral CT image reconstruction.

The constrained one-step spectral CT image reconstruction (cOSSCIR) algorithm with a nonconvex alternating direction method of multipliers optimizer is proposed for addressing computed tomography (CT) metal artifacts caused by beam hardening, noise, and photon starvation. The quantitative performance of cOSSCIR is investigated through a series of photon-counting CT simulations. cOSSCIR directly estimates basis material maps from photon-counting data using a physics-based forward model that accounts for beam hardening. The cOSSCIR optimization framework places constraints on the basis maps, which we hypothesize will stabilize the decomposition and reduce streaks caused by noise and photon starvation. Another advantage of cOSSCIR is that the spectral data need not be registered, so that a ray can be used even if some energy window measurements are unavailable. Photon-counting CT acquisitions of a virtual pelvic phantom with low-contrast soft tissue texture and bilateral hip prostheses were simulated. Bone and water basis maps were estimated using the cOSSCIR algorithm and combined to form a virtual monoenergetic image for the evaluation of metal artifacts. The cOSSCIR images were compared to a "two-step" decomposition approach that first estimated basis sinograms using a maximum likelihood algorithm and then reconstructed basis maps using an iterative total variation constrained least-squares optimization (MLE+TV ). Images were also compared to a nonspectral TV reconstruction of the total number of counts detected for each ray with and without normalized metal artifact reduction (NMAR) applied. The simulated metal density was increased to investigate the effects of increasing photon starvation. The quantitative error and standard deviation in regions of the phantom were compared across the investigated algorithms. The ability of cOSSCIR to reproduce the soft-tissue texture, while reducing metal artifacts, was quantitativelyevaluated. Noiseless simulations demonstrated the convergence of the cOSSCIR and MLE+TV algorithms to the correct basis maps in the presence of beam-hardening effects. When noise was simulated, cOSSCIR demonstrated a quantitative error of -1 HU, compared to 2 HU error for the MLE+TV algorithm and -154 HU error for the nonspectral TV +NMAR algorithm. For the cOSSCIR algorithm, the standard deviation in the central iodine region of interest was 20 HU, compared to 299 HU for the MLE+TV algorithm, 41 HU for the MLE+TV +Mask algorithm that excluded rays through metal, and 55 HU for the nonspectral TV +NMAR algorithm. Increasing levels of photon starvation did not impact the bias or standard deviation of the cOSSCIR images. cOSSCIR was able to reproduce the soft-tissue texture when an appropriate regularization constraint value wasselected. By directly inverting photon-counting CT data into basis maps using an accurate physics-based forward model and a constrained optimization algorithm, cOSSCIR avoids metal artifacts due to beam hardening, noise, and photon starvation. The cOSSCIR algorithm demonstrated improved stability and accuracy compared to a two-step method of decomposition followed byreconstruction.

Multi-modal feature-fusion for CT metal artifact reduction using edge-enhanced generative adversarial networks

Computed Tomography (CT) imaging is one of the most widely-used and cost-effective technology for organ screening and diseases diagnosis. Because of existence of metallic implants in some patients, the CT images acquired from these patients are often corrupted by undesirable metal artifacts, which causes severe problem of metal artifact. Although there have been proposed many methods to reduce metal artifact, reduction is still challenging and inadequate, and results are suffering from symptom variance, second artifact and poor subjective evaluation. To address these problems, we propose a novel metal artifact reduction method based on generative adversarial networks to simultaneously reduce metal artifacts and enhance texture structure of corrected CT images. Specifically, we firstly incorporate interactive information (text) and imaging CT (image) into a comprehensive feature to yield multi-modal feature-fusion representation, which overcomes the representative ability limitation of single-modal data. The incorporation of interaction information constrains the feature generation to ensure symptom consistency between corrected and target CT. Then, we design an edge-enhance sub-network to avoid second artifact and suppress noise. Besides, we invite three professional physicians to evaluate corrected CT image subjectively. In this paper, We achieved average increment of 11.3% PSNR and 12.1% SSIM on DeepLesion dataset. The subjective evaluations by physicians show that ours outperforms over 6.3%, 7.1%, 5.50% and 6.9% in term of sharpness, resolution, invariance and acceptability, respectively. Our proposed method can achieve high-quality metal artifact reduction results.

A Deep Recurrent Neural Network With FISTA Optimization for CT Metal Artifact Reduction

Metal Artifact Reduction (MAR) is one of the most challenging problems in Computed Tomography (CT) imaging. In CT imaging, metal implants in patient's bodies cause artifacts due to several factors, such as beam hardening effects, statistical property changes of the X-ray beams, and the shapes of metal implants. Although some promising results have been achieved by previously proposed model-based iterative reconstruction (IR) techniques, there is still much room for improvement. One of the problems is that the image prior models used in the IR techniques are too simple to capture the truly complex nature of the CT images. Recent advances in neural network deep learning (DL) can help address this problem and potentially improve MAR results significantly. In this work, we describe a novel DL-based technique for CT MAR. In this technique, we introduce a novel deep neural network based on an IR formulation and a convex optimization technique known as the FISTA (Fast Iterative Shrinkage-Thresholding Algorithm). The neural network, called the RNN-MAR, is an RNN (Recurrent Neural Network) composed of a set of proposed RFUs (Recurrent FISTA Units). While the structure of the RFU has some connections to the GRU (Gated Recurrent Unit), it is specifically designed for CT MAR. The RNN-MAR conducts dual-domain learning (image and sinogram) but can do this using only one objective function. Furthermore, unlike previous CT MAR techniques, the RNN-MAR does not use a binary metal trace. Instead, we used a novel real-valued sinogram domain confidence map, leading to smoother edges. Results from extensive experiments indicate that our RNN-MAR outperforms state-of-the-art DL MAR techniques in terms of the Root Mean Squared Error (RMSE), Peak Signal-to-Noise Ratio (PSNR), and Structure Similarity (SSIM) as well as in terms of visual appearance.

Unsupervised CT Metal Artifact Learning Using Attention-Guided β-CycleGAN.

Metal artifact reduction (MAR) is one of the most important research topics in computed tomography (CT). With the advance of deep learning approaches for image reconstruction, various deep learning methods have been suggested for metal artifact reduction, among which supervised learning methods are most popular. However, matched metal-artifact-free and metal artifact corrupted image pairs are difficult to obtain in real CT acquisition. Recently, a promising unsupervised learning for MAR was proposed using feature disentanglement, but the resulting network architecture is so complicated that it is difficult to handle large size clinical images. To address this, here we propose a simple and effective unsupervised learning method for MAR. The proposed method is based on a novel β -cycleGAN architecture derived from the optimal transport theory for appropriate feature space disentanglement. Moreover, by adding the convolutional block attention module (CBAM) layers in the generator, we show that the metal artifacts can be more focused so that it can be effectively removed. Experimental results confirm that we can achieve improved metal artifact reduction that preserves the detailed texture of the original image.

Inner-ear augmented metal artifact reduction with simulation-based 3D generative adversarial networks

Metal Artifacts creates often difficulties for a high quality visual assessment of post-operative imaging in computed tomography (CT). A vast body of methods have been proposed to tackle this issue, but these methods were designed for regular CT scans and their performance is usually insufficient when imaging tiny implants. In the context of post-operative high-resolution CT imaging, we propose a 3D metal artifact reduction algorithm based on a generative adversarial neural network. It is based on the simulation of physically realistic CT metal artifacts created by cochlea implant electrodes on preoperative images. The generated images serve to train a 3D generative adversarial networks for artifacts reduction. The proposed approach was assessed qualitatively and quantitatively on clinical conventional and cone beam CT of cochlear implant postoperative images. These experiments show that the proposed method outperforms other general metal artifact reduction approaches.

DAN-Net: Dual-domain adaptive-scaling non-local network for CT metal artifact reduction

Metallic implants can heavily attenuate x-rays in computed tomography (CT) scans, leading to severe artifacts in reconstructed images, which significantly jeopardize image quality and negatively impact subsequent diagnoses and treatment planning. With the rapid development of deep learning in the field of medical imaging, several network models have been proposed for metal artifact reduction (MAR) in CT. Despite the encouraging results achieved by these methods, there is still much room to further improve performance. In this paper, a novel dual-domain adaptive-scaling non-local network (DAN-Net) is proposed for MAR. We correct the corrupted sinogram using adaptive scaling first to preserve more tissue and bone details. Then, an end-to-end dual-domain network is adopted to successively process the sinogram and its corresponding reconstructed image is generated by the analytical reconstruction layer. In addition, to better suppress the existing artifacts and restrain the potential secondary artifacts caused by inaccurate results of the sinogram-domain network, a novel residual sinogram learning strategy and non-local module are leveraged in the proposed network model. Experiments demonstrate the performance of the proposed DAN-Net is competitive with several state-of-the-art MAR methods in both qualitative and quantitative aspects.

Photon-counting normalized metal artifact reduction (NMAR) in diagnostic CT.

Metal artifacts can drastically reduce the diagnostic value of computed tomography (CT) images. Even the state-of-the-art algorithms cannot remove them completely. Photon-counting CT inherently provides spectral information, similar to dual-energy CT. Many applications, such as material decomposition, are not possible when metal artifacts are present. Our aim is to develop a prior-based metal artifact reduction specifically for photon-counting CT that can correct each bin image individually or in their combinations. Photon-counting CT sorts incoming photons into several energy bins, producing bin and threshold images containing spectral information. We use this spectral information to obtain a better prior image for the state-of-the-art metal artifact reduction algorithm FSNMAR. First, we apply a non-linear transformation to the bin images to obtain bone-emphasized images. Subsequently, we forward-project the bin images and bone-emphasized images and multiply the resulting sinograms with each other element-wise to mimic beam hardening effects. These sinograms are reconstructed and linearly combined to produce an artifact-reduced image. The coefficients of this linear combination are automatically determined by minimizing a threshold-based cost function in the image domain. After thresholding, we obtain the prior image for FSNMAR, which is applied to the individual bin images and the lowest threshold image. We test our photon-counting normalized metal artifact reduction (PCNMAR) on forensic CT data and compare it to conventional FSNMAR, where the prior is generated via linear sinogram inpainting. For numerical analysis, we compute both the standard deviation in an ROI with metal artifacts and the CNR of soft tissue and fat. PCNMAR can effectively reduce metal artifacts without sacrificing the overall image quality. Compared to FSNMAR, our method produces fewer secondary artifacts and is more consistent with the measurements. Areas that contain metal, air, and soft tissue are more accurate in PCNMAR. In some cases, the standard deviation in the artifact ROI is reduced by more than 50% relative to FSNMAR, while the CNR values are similar. If extreme artifacts are present, PCNMAR is unable to outperform FSNMAR. Using either two, four, or only the highest energy bin to produce the prior image yielded comparable results. PCNMAR is an effective method of reducing metal artifacts in photon-counting CT. The spectral information available in photon-counting CT is highly beneficial for metal artifact reduction, especially the high-energy bin, which inherently contains fewer artifacts. While scanning with four instead of two bins does not provide a better artifact reduction, it allows for more freedom in the selection of energy thresholds.

Cine Cardiac MRI Motion Artifact Reduction Using a Recurrent Neural Network.

Cine cardiac magnetic resonance imaging (MRI) is widely used for the diagnosis of cardiac diseases thanks to its ability to present cardiovascular features in excellent contrast. As compared to computed tomography (CT), MRI, however, requires a long scan time, which inevitably induces motion artifacts and causes patients' discomfort. Thus, there has been a strong clinical motivation to develop techniques to reduce both the scan time and motion artifacts. Given its successful applications in other medical imaging tasks such as MRI super-resolution and CT metal artifact reduction, deep learning is a promising approach for cardiac MRI motion artifact reduction. In this paper, we propose a novel recurrent generative adversarial network model for cardiac MRI motion artifact reduction. This model utilizes bi-directional convolutional long short-term memory (ConvLSTM) and multi-scale convolutions to improve the performance of the proposed network, in which bi-directional ConvLSTMs handle long-range temporal features while multi-scale convolutions gather both local and global features. We demonstrate a decent generalizability of the proposed method thanks to the novel architecture of our deep network that captures the essential relationship of cardiovascular dynamics. Indeed, our extensive experiments show that our method achieves better image quality for cine cardiac MRI images than existing state-of-the-art methods. In addition, our method can generate reliable missing intermediate frames based on their adjacent frames, improving the temporal resolution of cine cardiac MRI sequences.

Evaluation of image quality of a novel computed tomography metal artifact management technique on an anthropomorphic head and neck phantom.

Background and purposeArtefacts caused by dental amalgam implants present a common challenge in computed tomography (CT) and therefore treatment planning dose calculations. The goal was to perform a quantitative image quality analysis of our Artifact Management for Proton Planning (AMPP) algorithm which used gantry tilts for managing metal artefacts on Head and Neck (HN) CT scans and major vendors’ commercial approaches.Materials and methodsMetal artefact reduction (MAR) algorithms were evaluated using an anthropomorphic phantom with a removable jaw for the acquisition of images with and without (baseline) metal artifacts. AMPP made use of two angled CT scans to generate one artifact-reduced image set. The MAR algorithms from four vendors were applied to the images with artefacts and the analysis was performed with respective baselines. Planar HU difference maps and volumetric HU differences were analyzed.ResultsAMPP algorithm outperformed all vendors’ commercial approaches in the elimination of artefacts in the oropharyngeal region, showing the lowest percent of pixels outside +− 20 HU criteria, 4%; whereas those in the MAR-corrected images ranged from 26% to 67%. In the region of interest within the affected slices, the commercial MAR algorithms showed inconsistent performance, whereas the AMPP algorithm performed consistently well throughout the phantom’s posterior region.ConclusionsA novel MAR algorithm was evaluated and compared to four commercial algorithms using an anthropomorphic phantom. Unanimously, the analysis showed the AMPP algorithm outperformed vendors’ commercial approaches, showing the potential to be broadly implemented, improve visualizations in patient anatomy and provide accurate HU information.

Evaluation of radiation treatment planning by computed tomography metal artifact reduction algorithm

Introduction: The objective of this study was to evaluate the usefulness of Computed Tomography(CT) images acquired through repeated subtraction reconstruction algorithms to reduce metal artifacts in CT Treatment Planning System(TPS).Materials and methods: Origin images of Gammex phantom and Rando phantom and non orthopedic metal artifact reduction(O-MAR) images were obtained after high density implantation. O-MAR applied images were also obtained. For evaluation of images, regions of interest(ROI) were set at five tissue rods and three points directly affected by artifacts in Gammex phantom. CT number and noise were compared and analyzed. To investigate dose change according to CT number change of the surrounding artifact, a three virtual cylindrical target volume was formed on Rando phantom CT images. The average dose was then compared and analyzed.Results: CT number difference according to the application of O-MAR showed significant difference among lung and bone rod and 3 ROI directly affected. Noise difference according to O-MAR application was significantly different in rod except for bone rod. In the treatment plan using Rando phantom, non-O-MAR and O-MAR images showed -4.3 ~ 1.9% and -0.4 ~ 2.3% dose differences, respectively.Conclusion: Applying a metal artifact reduction reconstruction algorithm can reduce image distortion due to high density implantation, improve image quality, and correct CT number.

Dosimetric impact of commercial CT metal artifact reduction algorithms and a novel in-house algorithm for proton therapy of head and neck cancer.

To compare the dosimetric impact of all major commercial vendors' metal artifact reduction (MAR) algorithms to one another, as well as to a novel in-house technique (AMPP) using an anthropomorphic head phantom. The phantom was an Alderson phantom, modified to allow for artifact-filled and baseline (no artifacts) computed tomography (CT) scans using teeth capsules made with metal amalgams or bone-equivalent materials. It also included a cylindrical insert that was accessible from the bottom of the neck and designed to introduce soft tissue features into the phantom that were used in the analysis. The phantom was scanned with the metal teeth in place using each respective vendor's MAR algorithm: OMAR (Philips), iMAR (Siemens), SEMAR (Canon), and SmartMAR (GE); the AMPP algorithm was designed in-house. Uncorrected and baseline (bone-equivalent teeth) image sets were also acquired using a Siemens scanner. Proton spot scanning treatment plans were designed on the baseline image set for five targets in the phantom. Once optimized, the proton beams were copied onto the different artifact-corrected image sets, with no reoptimization of the beams' parameters, to evaluate dose distribution differences in the different MAR-corrected and -uncorrected image sets. Dose distribution differences were evaluated by comparing dose-volume histogram (DVH) metrics, including planning target volume D95 and clinical target volume D99 coverages, V100, D0.03cc, and heterogeneity indexes, along with a qualitative and water equivalent thickness (WET) analysis. Uncorrected CT metal artifacts and commercial MAR algorithms negatively impacted the proton dose distributions of all five target shapes and locations in an inconsistent manner, sometimes overdosing by as much as 11.1% (D0.03) or underdosing by as much as 11.7% (V100) the planning target volumes. The AMPP-corrected images, however, provided dose distributions that consistently agreed with the baseline dose distribution. The dosimetry results also suggest that the commercial MAR algorithms' performances varied more with target location and less with target shape. Once relocated further from the metal, the target showed dose distributions that agreed more with the baseline for all commercial solutions, improving the overdosing by as much as 6%, implying inadequate HU correction from commercial MAR algorithms. In comparison to the baseline, HU profile shapes were considerably altered by commercial algorithms and reference values showed differences that represent stopping power percentage differences of 2.7-10%. The AMPP algorithm plans showed the smallest WET differences with the baseline (0.06cm on average), while the commercial image sets created differences that ranged from 0.11 to 0.54cm. Computed tomography metal artifacts negatively impacted proton dose distributions on all five targets analyzed. The commercial MAR solutions performed inconsistently throughout all targets compared to the metal-free baseline. A lack of CTV coverage and an increased number of hotspots were observed throughout all commercial solutions. Dose distribution errors were related to the proximity to the artifacts, demonstrating the inability of commercial techniques to adequately correct severe artifacts. In contrast, AMPP consistently showed dose distributions that best matched the baseline, likely because it makes use of accurate HU information, as opposed to interpolated data like commercial algorithms.

CT metal artifact reduction algorithms: Toward a framework for objective performance assessment.

PurposeAlthough several metal artifact reduction (MAR) algorithms for computed tomography (CT) scanning are commercially available, no quantitative, rigorous, and reproducible method exists for assessing their performance. The lack of assessment methods poses a challenge to regulators, consumers, and industry. We explored a phantom‐based framework for assessing an important aspect of MAR performance: how applying MAR in the presence of metal affects model observer performance at a low‐contrast detectability (LCD) task This work is, to our knowledge, the first model observer–based framework for the evaluation of MAR algorithms in the published literature.MethodsWe designed a numerical head phantom with metal implants. In order to incorporate an element of randomness, the phantom included a rotatable inset with an inhomogeneous background. We generated simulated projection data for the phantom. We applied two variants of a simple MAR algorithm, sinogram inpainting, to the projection data, that we reconstructed using filtered backprojection. To assess how MAR affected observer performance, we examined the detectability of a signal at the center of a region of interest (ROI) by a channelized Hotelling observer (CHO). As a figure of merit, we used the area under the ROC curve (AUC).ResultsWe used simulation to test our framework on two variants of the MAR technique of sinogram inpainting. We found that our method was able to resolve the difference in two different MAR algorithms’ effect on LCD task performance, as well as the difference in task performances when MAR was applied, vs not.ConclusionWe laid out a phantom‐based framework for objective assessment of how MAR impacts low‐contrast detectability, that we tested on two MAR algorithms. Our results demonstrate the importance of testing MAR performance over a range of object and imaging parameters, since applying MAR does not always improve the quality of an image for a given diagnostic task. Our framework is an initial step toward developing a more comprehensive objective assessment method for MAR, which would require developing additional phantoms and methods specific to various clinical applications of MAR, and increasing study efficiency.