Artifact Reduction Research Articles

Dual-energy head cone-beam CT using a dual-layer flat-panel detector: Hybrid material decomposition and a feasibility study.

Flat panel detector (FPD) based cone-beam computed tomography (CT) has made tremendous progress in the last two decades, with many new and advanced medical and industrial applications keeping emerging from diagnostic imaging and image guidance for radiotherapy and interventional surgery. The current cone-beam CT (CBCT), however, is still suboptimal for head CT scan which requires a high standard of image quality. While the dual-layer FPD technology is under extensive development and is promising to further advance CBCT from qualitative anatomic imaging to quantitative dual-energy CT, its potential of enabling head CBCT applications has not yet been fullyinvestigated. The relatively moderate energy separation from the dual-layer FPD and the overall low signal level especially at the bottom-layer detector, could raise significant challenges in performing high-quality dual-energy material decomposition (MD). In this work, we propose a hybrid, physics and model guided, MD algorithm that attempts to fully use the detected x-ray signals and prior-knowledge behind head CBCT using dual-layer FPD. Firstly, a regular projection-domain MD is performed as initial results of our approach and for comparison as conventional method. Secondly, based on the combined projection, a dual-layer multi-material spectral correction (dMMSC) is applied to generate beam hardening free images. Thirdly, the dMMSC corrected projections are adopted as a physics-model based guidance to generate a hybrid MD. A set of physics experiments including fan-beam scan and cone-beam scan using a head phantom and a Gammex Multi-Energy CT phantom are conducted to validate our proposedapproach. The combined reconstruction could reduce noise by about 10% with no visible resolution degradation. The fan-beam studies on the Gammex phantom demonstrated an improved MD performance, with the averaged iodine quantification error for the 5-15 mg/ml iodine inserts reduced from about 5.6% to 3.0% by the hybrid method. On fan-beam scan of the head phantom, our proposed hybrid MD could significantly reduce the streak artifacts, with CT number nonuniformity (NU) in the selected regions of interest (ROIs) reduced from 23 Hounsfield Units (HU) to 4.2 HU, and the corresponding noise suppressed from 31 to 6.5 HU. For cone-beam scan, after scatter correction (SC) and cone-beam artifact reduction (CBAR), our approach can also significantly improve image quality, with CT number NU in the selected ROI reduced from 24.2 to 6.6 HU and the noise level suppressed from 22.1 to 8.2HU. Our proposed physics and model guided hybrid MD for dual-layer FPD based head CBCT can significantly improve the robustness of MD and suppress the low-signal artifact. This preliminary feasibility study also demonstrated that the dual-layer FPD is promising to enable head CBCT spectralimaging.

Zirconia implants interfere with the evaluation of peri-implant bone defects in cone beam computed tomography (CBCT) images even with artifact reduction, a pilot study.

Three-dimensional cone beam computed tomography (CBCT) imaging can be considered, especially in patients with complicated peri-implantitis (PI). Artifacts induced by dense materials are the drawback of CBCT imaging and the peri-implant bone condition may not be assessed reliably because the artifacts are present in the same area. This pilot study investigates the performance of the artifact reduction algorithm (ARA) of the Planmeca Viso G7 CBCT device (Planmeca, Helsinki, Finland) with three different implant materials and imaging parameters. Three pairs of dental implants consisting of titanium, zirconia, and fiber reinforced composite (FRC) were set into a pig mandible. A vertical defect simulating peri-implantitis bone loss was made on the buccal side of one of each implant. The defect was identified and measured by two observers and compared to the actual dimensions. In addition, the bone structure and the marginal cortex visibility between the implants were estimated visually. The bone defect and its dimensions with the zirconia implant could not be identified in any image with or without the metal artifact reduction algorithm. The bone defect of titanium and FRC implants were identified with all three imaging parameters or even without ARA. The interobserver agreement between the two observers was almost perfect for all categories analyzed. Peri-implantitis defect of the zirconia implant and the peri-implant bone structure of the zirconia implants cannot be recognized reliably with any ARA levels, or any imaging parameters used with the Planmeca Viso G7. The need for ARA when imaging the peri-implant bone condition of the titanium and FRC implants may be unnecessary.

Monte Carlo model of a prototype flat-panel detector for multi-energy applications in radiotherapy.

The incorporation of multi-energy capabilities into radiotherapy flat-panel detectors offers advantages including enhanced soft tissue visualization by reduction of signal from overlapping anatomy such as bone in 2D image projections; creation of virtual monoenergetic images for 3D contrast enhancement, metal artefact reduction and direct acquisition of relative electron density. A novel dual-layer on-board imager offering dual energy processing capabilities is being designed. As opposed to other dual-energy implementation techniques which require separate acquisition with two different x-ray spectra, the dual-layer detector design enables simultaneous acquisition of high and low energy images with a single exposure. A computational framework is required to optimize the design parameters and evaluate detector performance for specific clinical applications. In this study, we report on the development of a Monte Carlo (MC) model of the imager including model validation. The stack-up of the dual-layer imager (DLI) was implemented in GEANT4 Application for Tomographic Emission (GATE). The DLI model has an active area of 43×43 cm2 , with top and bottom Cesium Iodide (CsI) scintillators of 600 and 800μm thickness, respectively. Measurement of spatial resolution and imaging of dedicated multi-material dual-energy (DE) phantoms were used to validate the model. The modulation transfer function (MTF) of the detector was calculated for a 120 kVp x-ray spectrum using a 0.5mm thick tantalum edge rotated by 2.5o . For imaging validation, the DE phantom was imaged using a 140 kVp x-ray spectrum. For both validation simulations, corresponding measurements were done using an initial prototype of the imager. Agreement between simulations and measurement was assessed using normalized root mean square error (NRMSE) and 1D profile difference for the MTF and phantom images respectively. Further comparison between measurement and simulation was made using virtual monoenergetic images (VMIs) generated from basis material images derived using precomputed look-up tables. The MTF of the bottom layer of the dual-layer model shows values decreasing more quickly with spatial frequency, compared to the top layer, due to the thicker bottom scintillator thickness and scatter from the top layer. A comparison with measurement shows NRMSE of 0.013 and 0.015 as well as identical MTF50 of 0.8 mm1 and 1.0 mm1 for the top and bottom layer respectively. For the DE imaging of the DE-phantom, although a maximum deviation of 3.3% is observed for the 10mm aluminum and Teflon inserts at the top layer, the agreement for all other inserts is less than 2.2% of the measured value at both layers. Material decomposition of simulated scatter-free DE images gives an average accuracy in PMMA and aluminum composition of 4.9% and 10.3% for 11-30mm PMMA and 1-10mm aluminum objects respectively. A comparison of decomposed values using scatter containing measured and simulated DE images shows good agreement within statistical uncertainty. Validation using both MTF and phantom imaging shows good agreement between simulation and measurements. With the present configuration of the digital prototype, the model can generate material decomposed images and virtual monoenergetic images.

Evaluation of a new method for metal artifact reducing in computed tomographic images

Background: The common streak artifacts in computed tomographic (CT) images result from metal implants in patients. Metal artifact suppresses and obstructs diagnosis or misdiagnoses as it occurred in ten percent of the patients’ tomographic images. Objectives: To develop the method for metal artifact reduction in CT images using MATLAB software and implement it in phantoms with the metal artifact as well as in patients with the metal artifact in the head and neck region. Materials and methods: The new method of metal artifact reduction in CT images using MATLAB software. The homogeneous polymethylmethacrylate (PMMA) phantom, the Alderson Rando phantom, and patients with a metal implant in the head and neck region were scanned by the Philips Brilliance Big Bore CT system. Commercial orthopedic metal artifact reduction (OMAR) software and a new method software were applied to the CT images of phantoms and patients. The quantitative analysis of image quality on a metal artifact of the head and neck region was evaluated in the percent noise. The qualitative analysis in clinical imaging was evaluated in scoring by two radiologists with the same experience. Results: In the Alderson Rando phantom, the new algorithm indicated higher efficiency in metal artifact reduction than OMAR software. In contrast, for the patient at head and neck CT images with metal artifact reduction, OMAR, and the new method showed comparable results. The new method suppressed the artifact in homogeneous PMMA, Alderson Rando phantoms, and patients with a metal implant in the head and neck region with approximately 40%, 40%, and 60% percentage of noise reduction, respectively. The qualitative analysis by two radiologists showed comparable results of OMAR and the new method. Conclusion: The efficiency of metal artifact reduction of the new method is better than no correction and OMAR in homogeneous PMMA phantom and Alderson Rando phantom. However, the efficiency of OMAR is better than the new method, and no correction regarding the percent noise.

Evaluation of Artifact Appearance and Burden in Pediatric Brain Tumor MR Imaging with Compressed Sensing in Comparison to Conventional Parallel Imaging Acceleration.

Clinical magnetic resonance imaging (MRI) aims for the highest possible image quality, while balancing the need for acceptable examination time, reasonable signal-to-noise ratio (SNR), and lowest artifact burden. With a recently introduced imaging acceleration technique, compressed sensing, the acquisition speed and image quality of pediatric brain tumor exams can be improved. However, little attention has been paid to its impact on method-related artifacts in pediatric brain MRI. This study assessed the overall artifact burden and artifact appearances in a standardized pediatric brain tumor MRI by comparing conventional parallel imaging acceleration with compressed sensing. This showed that compressed sensing resulted in fewer physiological artifacts in the FLAIR sequence, and a reduction in technical artifacts in the 3D T1 TFE sequences. Only a slight difference was noted in the T2 TSE sequence. A relatively new range of artifacts, which are likely technique-related, was noted in the 3D T1 TFE sequences. In conclusion, by equipping a basic pediatric brain tumor protocol for 3T MRI with compressed sensing, the overall burden of common artifacts can be reduced. However, attention should be paid to novel compressed-sensing-specific artifacts.

87 Accurate Visualization of Stents Through Artifact Reduction with Photon-Counting Detector CT

87 Accurate Visualization of Stents Through Artifact Reduction with Photon-Counting Detector CT

241 Reduction of Metal Artifacts in 7T MRI for Pre-Clinical Diffusing Alpha-Emitting Radiation Therapy Rectal Studies

241 Reduction of Metal Artifacts in 7T MRI for Pre-Clinical Diffusing Alpha-Emitting Radiation Therapy Rectal Studies

Treatment of pelvic giant cell tumor by wide resection with patient-specific bone-cutting guide and reconstruction with 3D-printed personalized implant

BackgroundThis study reports our experience in the treatment of aggressive pelvic GCT through wide resection assisted with patient-specific bone-cutting guides (PSBCGs) and subsequent reconstruction with 3D-printed personalized implants (3DPIs), aiming to present the operative technique of this method and evaluate its clinical efficacy.MethodsWe retrospectively analyzed seven patients who underwent wide resection of pelvic GCT followed by reconstruction with 3DPIs from August 2019 to February 2021. There were two males and five females, with a mean age of 43 years. PSBCGs and 3DPIs were prepared using 3D-printing technology. The operational outcomes, local recurrence, radiological results, and any associated complications of this technique were assessed. And the functional outcomes were assessed according to the Musculoskeletal Tumor Society (MSTS) 93 functional score.ResultsThe mean follow-up time was 35.3 months (range 28–45 months). There was no intraoperative complication. Negative surgical margins were achieved in all patients. Postoperative pelvic radiographs showed that 3DPIs matched the shape and size of the bone defect. The anterior–posterior, inlet, and outlet pelvic radiograph demonstrated precise reconstruction consistent with the surgical planning. In addition, tomosynthesis‐Shimadzu metal artifact reduction technology (T-SMART) showed good osseointegration at an average of three months after surgery (range 2–4 months). There was no local recurrence or tumor metastasis. The average MSTS score was 24.4 (range 23–27) at the last follow-up. Delayed wound healing was observed in one patient, and the wounds healed after debridement. Prosthesis-related complications were not detected during the follow-up, such as aseptic loosening or structure failure.ConclusionsThe treatment of aggressive pelvic GCTs through wide resection assisted with PSBCGs and subsequent reconstruction with 3DPIs is a feasible method, which provides good clinical results and reasonable functional outcomes.

284 Impact of Novel Reconstruction and Metal Artifact Reduction Algorithms on Metal Electron Density Curve for KVCT Simulator

284 Impact of Novel Reconstruction and Metal Artifact Reduction Algorithms on Metal Electron Density Curve for KVCT Simulator

Artifacts in magnetic resonance imaging of the head and neck: Unwanted effects caused by implant-supported restorations fabricated with different alloys

Implant-supported restorations may cause artifacts in magnetic resonance imaging (MRI) of the head and neck area. However, the effect of different alloys remains unclear. The purpose of this in vitro study was to assess artifacts in head and neck MRI caused by implant-supported restorations with different alloys. Three dry mandibles were prepared to receive bilateral dental implants at the second premolar and second molar sites. Different alloy combinations were evaluated: titanium implants+cobalt chromium restorations; titanium-zirconium implants+cobalt chromium restorations; and zirconia implants+ceramic restorations. Specimens were imaged by using a 3-Tesla magnetic resonance scanner system (Achieva 3.0T TX; Philips GmbH) with a turbo-spin-echo sequence. Scan protocols were adjusted to optimize metal artifact reduction and shorten scan time. Artifact volumes were assessed and statistically analyzed by using the Kruskal-Wallis and Spearman tests (α=.05). A statistically significant difference was found among artifact volume caused by different materials (P=.002). The presence of titanium alloy was correlated with the artifact volume (r=-.87). Artifacts were greater for titanium and fewer for titanium-zirconium alloys, whereas zirconia implants found only minimal artifacts. The dimension of artifacts produced by implant-supported restorations varied according to the material.

BARRN: A Blind Image Compression Artifact Reduction Network for Industrial IoT Systems

BARRN: A Blind Image Compression Artifact Reduction Network for Industrial IoT Systems

Editorial Comment: Clinical Impact of Metal Artifact Reduction of Lower Extremity Arthroplasties at 3 T.

Editorial Comment: Clinical Impact of Metal Artifact Reduction of Lower Extremity Arthroplasties at 3 T.

Compressed Sensing SEMAC MRI of Hip, Knee, and Ankle Arthroplasty Implants: A 1.5-T and 3-T Intrapatient Performance Comparison for Diagnosing Periprosthetic Abnormalities.

Background. The utility of 3-T MRI for diagnosing joint disorders is established, but its performance for diagnosing abnormalities around arthroplasty implants is unclear. Objective. This study compared 1.5-T and 3-T compressed sensing slice encoding for metal artifact correction (CS-SEMAC) MRI for diagnosing periprosthetic abnormalities around hip, knee, and ankle arthroplasty implants. Methods. Forty-five participants (mean age ± SD, 71 ± 14 years; 26 women, 19 men) with symptomatic lower extremity arthroplasty (hip, knee, and ankle, 15 each) prospectively underwent consecutive 1.5- and 3-T MRI with intermediate-weighted (IW) and STIR CS-SEMAC sequences. Using a Likert scale, three radiologists evaluated presence or absence of periprosthetic abnormalities, including bone marrow edema-like signal, osteolysis, stress reaction/fracture, synovitis, tendon abnormalities and collections, image quality, and visibility of anatomic structures. Statistical analysis included nonparametric comparison and interchangeability testing. Results. For diagnosing periprosthetic abnormalities, 1.5-T and 3-T CS-SEMAC MRI were interchangeable. Across all three joints, 3 T produced lower noise than 1.5 T (median IW/STIR score at 3 T vs 1.5 T, 4/4 [range, 2-5/3-5] vs 3/3 [2-5/2-4]; p < .01 for both), sharper edges (4/4 [both 2-5] vs 3/3 [2-4/2-5]; p < .02/p < .05), and more effective metal artifact reduction (4/4 [3-5/2-5] vs 4/4 [both 3-5]; p < .02/p = .72). Agreement was moderate to substantial for image contrast (IW/STIR, 0.66/0.54 [95% CI, 0.41-0.91/0.29-0.80]; p = .58/p = .16) and joint capsule visualization (0.57/0.70 [0.32-0.81/0.51-0.89]; p = .16/p = .19). The bone-implant interface was more visible at 1.5 T (median IW/STIR score, 4/4 [range, both 2-5] at 1.5 T vs 3/3 [both 2-5] at 3 T; p = .08/p = .58), but periprosthetic tissues had superior visibility at 3 T (4/4 [both 3-5] at 3 T vs 4/4 [2-5/3-5] at 1.5 T; p = .07/p = .19). Conclusion. Optimized 1.5-T and 3-T CS-SEMAC MRI are interchangeable for diagnosing periprosthetic abnormalities, although metallic artifacts are larger at 3 T. Clinical Impact. With CS-SEMAC MRI, lower extremity arthroplasty implants can be scanned at 3 T rather than 1.5 T.

NeuXus open-source tool for real-time artifact reduction in simultaneous EEG-fMRI

The simultaneous acquisition of electroencephalography and functional magnetic resonance imaging (EEG-fMRI) allows the complementary study of the brain's electrophysiology and hemodynamics with high temporal and spatial resolution. One application with great potential is neurofeedback training of targeted brain activity, based on the real-time analysis of the EEG and/or fMRI signals. This depends on the ability to reduce in real time the severe artifacts affecting the EEG signal acquired with fMRI, mainly the gradient and pulse artifacts. A few methods have been proposed for this purpose, but they are either slow, hardware-dependent, publicly unavailable, or proprietary software. Here, we present a fully open-source and publicly available tool for real-time EEG artifact reduction in simultaneous EEG-fMRI recordings that is fast and applicable to any hardware. Our tool is integrated in the Python toolbox NeuXus for real-time EEG processing and adapts to a real-time scenario well-established artifact average subtraction methods combined with a long short-term memory network for R peak detection. We benchmarked NeuXus on three different datasets, in terms of artifact power reduction and background signal preservation in resting state, alpha-band power reactivity to eyes closure, and event-related desynchronization during motor imagery. We showed that NeuXus performed at least as well as the only available real-time tool for conventional hardware setups (BrainVision's RecView) and a well-established offline tool (EEGLAB's FMRIB plugin). We also demonstrated NeuXus’ real-time ability by reporting execution times under 250 ms. In conclusion, we present and validate the first fully open-source and hardware-independent solution for real-time artifact reduction in simultaneous EEG-fMRI studies.

Reduction of Distortion Artifacts in Brain MRI Using a Field Map-based Correction Technique in Diffusion-weighted Imaging

PurposeThe aim of this study was to evaluate the image quality and feasibility of a field map-based technique to correct for susceptibility-induced geometric distortions which are typical for diffusion EPI brain imaging.MethodsWe prospectively included 52 patients during clinical routine in this single-center study. All scans were performed on a 3T MRI. Patients’ indications for MRI mainly consisted of suspected stroke due to the clinical presentation. For the morphological comparison of the corrected and uncorrected EPI diffusion, three experienced radiologists assessed the image quality of the sequences in a blinded and randomized fashion using a Likert scale (1 being poor; 5 being excellent). To ensure comparability of the two methods, an additional quantitative analysis of the apparent diffusion coefficient (ADC) was performed.ResultsCorrected EPI diffusion was rated significantly superior in all the selected categories: overall level of artifacts (p < 0.001), degree of distortion at the frontal, temporal, occipital and brainstem levels (p < 0.001), conspicuousness of ischemic lesions (p < 0.001), image quality (p < 0.001), naturality (p < 0.001), contrast (p < 0.001), and diagnostic confidence (p < 0.001).ConclusionCorrected EPI diffusion offers a significant reduction of geometric distortion in all evaluated brain regions and an improved conspicuousness of ischemic lesions. Image quality, overall artifacts, naturality, contrast and diagnostic confidence were also rated superior in comparison to uncorrected EPI diffusion.

Supervised Transfer Learning for Multi Organs 3D Segmentation With Registration Tools for Metal Artifact Reduction in CT Images

Radiological diagnostics are undeniably crucial in everyday healthcare. CT scans can sometimes provide misleading clues and delay successive treatment due to artifacts caused by reflected radiation from metallic implants. This work successfully segments multiple organs containing metal implants and discards artifacts using a combination of non-rigid transformations, Scribbles-based segmentation, and a pre-trained auto segmentation model (DynaUnet -Pretrained-Model). The presented transfer learning model combined the benefits of an interactive environment and reduced computational and processing-time costs. The transfer learning model proved high auto segmentation performance for multi-organs with metal implants' presence by decreasing metal artefact's impact on the segmentation process and the achieved segmentation accuracies between 0.9998 for the spleen and 0.9829 for the stomach.

Improvement of Prior Image for Metal Artifact Reduction of Computed Tomography

Improvement of Prior Image for Metal Artifact Reduction of Computed Tomography

A correction algorithm for improved magnetic field monitoring with distal field probes.

A significant source of artifacts in MRI are field fluctuations. Field monitoring is a new technology that allows measurement of field dynamics during a scan via "field probes," which can be used to improve image reconstruction. Ideally, probes are located within the volume where gradients produce nominally linear field patterns. However, in some situations probes must be located far from isocenter where rapid field variation can arise, leading to erroneous field-monitoring characterizations and images. This work aimed to develop an algorithm that improves the robustness of field dynamics in these situations. The algorithm is split into three components. Component 1 calculates field dynamics one spatial order at a time, whereas the second implements a weighted least squares solution based on probe distance. Component 3 then calculates phase residuals and removes the residual phase for distant probes before recalculation. Two volunteers and a phantom were scanned on a 7T MRI using diffusion-weighted sequences, and field monitoring was performed. Image reconstructions were informed with field dynamics calculated conventionally, and with the correction algorithm, after which in vivo images were compared qualitatively and phantom image error was quantitatively assessed. The algorithm was able to correct corrupted field dynamics, resulting in image-quality improvements. Significant artifact reduction was observed when correcting higher-order fits. Stepwise fitting provided the most correction benefit, which was marginally improved when adding the other correction strategies. The proposed algorithm can mitigate effects of phase errors in field monitoring, providing improved characterization of field dynamics.

An improved dual-domain network for metal artifact reduction in CT images using aggregated contextual transformations

Objective. Metal artifact reduction (MAR) remains a challenging task due to the difficulty of removing artifacts while preserving anatomical details of the tissue. Although current dual-domain networks have shown promising performance in MAR, they heavily rely on the image domain, which can be too smooth and lose important information in the metal-affected area. To address this problem, we propose an improved dual domain network framework. Approach. We enhance sinogram completion performance by utilizing an aggregated contextual transformations network in the sinogram domain. Furthermore, we utilize a prior-projection-based linearized correction method to obtain images with beam-hardening artifacts removed, which are incorporated into the input of the image post-processing network to assist in training the image domain network. Finally, we train the sinogram domain network and the image domain network separately to their respective convergences. Main results. In experiments conducted on a simulated dataset, our method achieves the best average RMSE of 25.1, SSIM of 0.973, and PSNR of 42.1, respectively. Significance. The proposed method is capable of preserving tissue structures near metallic objects while eliminating metal artifacts from the reconstructed images. Related codes will be released at https://github.com/Corinna-China/AOTDudoNet

A new dental CBCT metal artifact reduction method based on a dual-domain processing framework

Objective. Cone beam computed tomography (CBCT) has been wildly used in clinical treatment of dental diseases. However, patients often have metallic implants in mouth, which will lead to severe metal artifacts in the reconstructed images. To reduce metal artifacts in dental CBCT images, which have a larger amount of data and a limited field of view compared to computed tomography images, a new dental CBCT metal artifact reduction method based on a projection correction and a convolutional neural network (CNN) based image post-processing model is proposed in this paper. Approach. The proposed method consists of three stages: (1) volume reconstruction and metal segmentation in the image domain, using the forward projection to get the metal masks in the projection domain; (2) linear interpolation in the projection domain and reconstruction to build a linear interpolation (LI) corrected volume; (3) take the LI corrected volume as prior and perform the prior based beam hardening correction in the projection domain, and (4) combine the constructed projection corrected volume and LI-volume slice-by-slice in the image domain by two concatenated U-Net based models (CNN1 and CNN2). Simulated and clinical dental CBCT cases are used to evaluate the proposed method. The normalized root means square difference (NRMSD) and the structural similarity index (SSIM) are used for the quantitative evaluation of the method. Main results. The proposed method outperforms the frequency domain fusion method (FS-MAR) and a state-of-art CNN based method on the simulated dataset and yields the best NRMSD and SSIM of 4.0196 and 0.9924, respectively. Visual results on both simulated and clinical images also illustrate that the proposed method can effectively reduce metal artifacts. Significance. This study demonstrated that the proposed dual-domain processing framework is suitable for metal artifact reduction in dental CBCT images.