Artifact Reduction Research Articles

DeepRetroMoCo: deep neural network-based retrospective motion correction algorithm for spinal cord functional MRI.

There are distinct challenges in the preprocessing of spinal cord fMRI data, particularly concerning the mitigation of voluntary or involuntary movement artifacts during image acquisition. Despite the notable progress in data processing techniques for movement detection and correction, applying motion correction algorithms developed for the brain cortex to the brainstem and spinal cord remains a challenging endeavor. In this study, we employed a deep learning-based convolutional neural network (CNN) named DeepRetroMoCo, trained using an unsupervised learning algorithm. Our goal was to detect and rectify motion artifacts in axial T2*-weighted spinal cord data. The training dataset consisted of spinal cord fMRI data from 27 participants, comprising 135 runs for training and 81 runs for testing. To evaluate the efficacy of DeepRetroMoCo, we compared its performance against the sct_fmri_moco method implemented in the spinal cord toolbox. We assessed the motion-corrected images using two metrics: the average temporal signal-to-noise ratio (tSNR) and Delta Variation Signal (DVARS) for both raw and motion-corrected data. Notably, the average tSNR in the cervical cord was significantly higher when DeepRetroMoCo was utilized for motion correction, compared to the sct_fmri_moco method. Additionally, the average DVARS values were lower in images corrected by DeepRetroMoCo, indicating a superior reduction in motion artifacts. Moreover, DeepRetroMoCo exhibited a significantly shorter processing time compared to sct_fmri_moco. Our findings strongly support the notion that DeepRetroMoCo represents a substantial improvement in motion correction procedures for fMRI data acquired from the cervical spinal cord. This novel deep learning-based approach showcases enhanced performance, offering a promising solution to address the challenges posed by motion artifacts in spinal cord fMRI data.

Cochlear Implant Artifacts Removal in EEG-Based Objective Auditory Rehabilitation Assessment.

Cochlear implant (CI) is a neural prosthesis that can restore hearing for patients with severe to profound hearing loss. Observed variability in auditory rehabilitation outcomes following cochlear implantation may be due to cerebral reorganization. Electroencephalography (EEG), favored for its CI compatibility and non-invasiveness, has become a staple in clinical objective assessments of cerebral plasticity post-implantation. However, the electrical activity of CI distorts neural responses, and EEG susceptibility to these artifacts presents significant challenges in obtaining reliable neural responses. Despite the use of various artifact removal techniques in previous studies, the automatic identification and reduction of CI artifacts while minimizing information loss or damage remains a pressing issue in objectively assessing advanced auditory functions in CI recipients. To address this problem, we propose an approach that combines machine learning algorithms-specifically, Support Vector Machines (SVM)-along with Independent Component Analysis (ICA) and Ensemble Empirical Mode Decomposition (EEMD) to automatically detect and minimize electrical artifacts in EEG data. The innovation of this research is the automatic detection of CI artifacts using the temporal properties of EEG signals. By applying EEMD and ICA, we can process and remove the identified CI artifacts from the affected EEG channels, yielding a refined signal. Comparative analysis in the temporal, frequency, and spatial domains suggests that the corrected EEG recordings of CI recipients closely align with those of peers with normal hearing, signifying the restoration of reliable neural responses across the entire scalp while eliminating CI artifacts.

Dosimetric significance of manual density overrides in oropharyngeal cancer

Kilovoltage computed tomography plays a crucial role in radiotherapy planning. However, the presence of high-density metallic objects can introduce streaking artifacts in CT scans, resulting in inaccurate dose calculations by the treatment planning software. Previous studies have explored manual density overrides and artifact reduction algorithms individually to enhance dose calculation accuracy, but their combined application on patient plans within a treatment planning system remains unexplored. This research aims to assess the necessity of manual density overrides when an artifact reduction algorithm is already employed to address dental artifacts in oropharyngeal cancer treatment plans. A total of 20 previously treated volumetric modulated arc therapy plans were collected, and manual density overrides were removed followed by plan recalculation. Dosimetric parameters were then compared between the original and modified plans. Statistical analysis revealed several dosimetric parameters for the planning target volume (PTV), clinical target volume (CTV), and oral cavity that exhibited statistically significant differences upon removing the manual density override. However, these differences were found to be small in absolute terms. No other organs evaluated demonstrated statistically significant differences in dose. The most significant disparity observed was an 8.26 cGy increase in mean dose to the CTV, which represents only 0.12% of the prescription dose. Based on these findings, it can be concluded that manual density overrides are likely unnecessary when an artifact reduction algorithm is employed in oropharyngeal cancer cases.

Bi-Constraints Diffusion: A Conditional Diffusion Model with Degradation Guidance for Metal Artifact Reduction.

In recent years, score-based diffusion models have emerged as effective tools for estimating score functions from empirical data distributions, particularly in integrating implicit priors with inverse problems like CT reconstruction. However, score-based diffusion models are rarely explored in challenging tasks such as metal artifact reduction (MAR). In this paper, we introduce the BiConstraints Diffusion Model for Metal Artifact Reduction (BCDMAR), an innovative approach that enhances iterative reconstruction with a conditional diffusion model for MAR. This method employs a metal artifact degradation operator in place of the traditional metal-excluded projection operator in the data-fidelity term, thereby preserving structure details around metal regions. However, scorebased diffusion models tend to be susceptible to grayscale shifts and unreliable structures, making it challenging to reach an optimal solution. To address this, we utilize a precorrected image as a prior constraint, guiding the generation of the score-based diffusion model. By iteratively applying the score-based diffusion model and the data-fidelity step in each sampling iteration, BCDMAR effectively maintains reliable tissue representation around metal regions and produces highly consistent structures in non-metal regions. Through extensive experiments focused on metal artifact reduction tasks, BCDMAR demonstrates superior performance over other state-of-the-art unsupervised and supervised methods, both quantitatively and in terms of visual results.

Adaptive and Iterative Learning With Multi-Perspective Regularizations for Metal Artifact Reduction.

Metal artifact reduction (MAR) is important for clinical diagnosis with CT images. The existing state-of-the-art deep learning methods usually suppress metal artifacts in sinogram or image domains or both. However, their performance is limited by the inherent characteristics of the two domains, i.e., the errors introduced by local manipulations in the sinogram domain would propagate throughout the whole image during backprojection and lead to serious secondary artifacts, while it is difficult to distinguish artifacts from actual image features in the image domain. To alleviate these limitations, this study analyzes the desirable properties of wavelet transform in-depth and proposes to perform MAR in the wavelet domain. First, wavelet transform yields components that possess spatial correspondence with the image, thereby preventing the spread of local errors to avoid secondary artifacts. Second, using wavelet transform could facilitate identification of artifacts from image since metal artifacts are mainly high-frequency signals. Taking these advantages of the wavelet transform, this paper decomposes an image into multiple wavelet components and introduces multi-perspective regularizations into the proposed MAR model. To improve the transparency and validity of the model, all the modules in the proposed MAR model are designed to reflect their mathematical meanings. In addition, an adaptive wavelet module is also utilized to enhance the flexibility of the model. To optimize the model, an iterative algorithm is developed. The evaluation on both synthetic and real clinical datasets consistently confirms the superior performance of the proposed method over the competing methods.

Perceptual Quality Improvement in Videoconferencing Using Keyframes-Based GAN

In the latest years, videoconferencing has taken a fundamental role in interpersonal relations, both for personal and business purposes. Lossy video compression algorithms are the enabling technology for videoconferencing, as they reduce the bandwidth required for real-time video streaming. However, lossy video compression decreases the perceived visual quality. Thus, many techniques for reducing compression artifacts and improving video visual quality have been proposed in recent years. In this work, we propose a novel GAN-based method for compression artifacts reduction in videoconferencing. Given that, in this context, the speaker is typically in front of the camera and remains the same for the entire duration of the transmission, we can maintain a set of reference keyframes of the person from the higher-quality I-frames that are transmitted within the video stream and exploit them to guide the visual quality improvement; a novel aspect of this approach is the update policy that maintains and updates a compact and effective set of reference keyframes. First, we extract multi-scale features from the compressed and reference frames. Then, our architecture combines these features in a progressive manner according to facial landmarks. This allows the restoration of the high-frequency details lost after the video compression. Experiments show that the proposed approach improves visual quality and generates photo-realistic results even with high compression rates. Code and pre-trained networks are publicly available at https://github.com/LorenzoAgnolucci/Keyframes-GAN.

Explicit Abnormality Extraction for Unsupervised Motion Artifact Reduction in Magnetic Resonance Imaging.

Motion artifacts compromise the quality of magnetic resonance imaging (MRI) and pose challenges to achieving diagnostic outcomes and image-guided therapies. In recent years, supervised deep learning approaches have emerged as successful solutions for motion artifact reduction (MAR). One disadvantage of these methods is their dependency on acquiring paired sets of motion artifact-corrupted (MA-corrupted) and motion artifact-free (MA-free) MR images for training purposes. Obtaining such image pairs is difficult and therefore limits the application of supervised training. In this paper, we propose a novel UNsupervised Abnormality Extraction Network (UNAEN) to alleviate this problem. Our network is capable of working with unpaired MA-corrupted and MA-free images. It converts the MA-corrupted images to MA-reduced images by extracting abnormalities from the MA-corrupted images using a proposed artifact extractor, which intercepts the residual artifact maps from the MA-corrupted MR images explicitly, and a reconstructor to restore the original input from the MA-reduced images. The performance of UNAEN was assessed by experimenting with various publicly available MRI datasets and comparing them with state-of-the-art methods. The quantitative evaluation demonstrates the superiority of UNAEN over alternative MAR methods and visually exhibits fewer residual artifacts. Our results substantiate the potential of UNAEN as a promising solution applicable in real-world clinical environments, with the capability to enhance diagnostic accuracy and facilitate image-guided therapies. Our codes are publicly available at https://github.com/YuSheng-Zhou/UNAEN.

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.

Effect of auto-adaptive metal artifact reduction (aMAR) program in cone-beam computed tomography on assessing pre-implant bone levels.

This research aimed to introduce an auto-adaptive metal artifact reduction (aMAR) algorithm in cone-beam computed tomography (CBCT) to assess the levels of the pre-implant alveolar crest. Dental implants as a treatment modality for edentulous patients consist of a titanium alloy, which creates a metal artifact, resulting in a dark dental structure in the CBCT scans. Metallic artifacts are limiting factors for the precise detection in CBCT images. These are related to the dark areas around materials and metallic structures (e.g., restorations, implants, and endodontic instruments). To overcome this problem, the metal artifact reduction (MAR) program has been recommended as a post-procedure stage for CBCT image reconstruction. Recent developments offer CBCT scanners with an aMAR option with a greater dynamic range to help overcome the challenges of peri-implant bone evaluation to reach accurate dental diagnoses.

Skin Conductance Response Artifact Reduction: Leveraging Accelerometer Noise Reference and Deep Breath Detection

Skin Conductance Response Artifact Reduction: Leveraging Accelerometer Noise Reference and Deep Breath Detection

A Wavelet-Based Approach for Motion Artifact Reduction in Ambulatory Seismocardiography.

Wearable sensing has become a vital approach to cardiac health monitoring, and seismocardiography (SCG) is emerging as a promising technology in this field. However, the applicability of SCG is hindered by motion artifacts, including those encountered in practice of which the strongest source is walking. This holds back the translation of SCG to clinical settings. We therefore investigated techniques to enhance the quality of SCG signals in the presence of motion artifacts. To simulate ambulant recordings, we corrupted a clean SCG dataset with real-walking-vibrational noise. We decomposed the signal using several empirical-mode-decomposition methods and the maximum overlap discrete wavelet transform (MODWT). By combining MODWT, time-frequency masking, and nonnegative matrix factorization, we developed a novel algorithm which leveraged the vertical axis accelerometer to reduce walking vibrations in dorsoventral SCG. The accuracy and applicability of our method was verified using heart rate estimation. We used an interactive selection approach to improve estimation accuracy. The best decomposition method for reduction of motion artifact noise was the MODWT. Our algorithm improved heart rate estimation from 0.1 to 0.8 r-squared at -15 dB signal-to-noise ratio (SNR). Our method reduces motion artifacts in SCG signals up to a SNR of -19 dB without requiring any external assistance from electrocardiography (ECG). Such a standalone solution is directly applicable to the usage of SCG in daily life, as a content-rich replacement for other wearables in clinical settings, and other continuous monitoring scenarios. In applications with higher noise levels, ECG may be incorporated to further enhance SCG and extend its usable range. This work addresses the challenges posed by motion artifacts, enabling SCG to offer reliable cardiovascular insights in more difficult scenarios, and thereby facilitating wearable monitoring in daily life and the clinic.

Combination of metal artifact reduction and sharpening filter application for horizontal root fracture diagnosis in teeth adjacent to a zirconia implant.

This study examined the influence of metal artifact reduction (MAR), the application of sharpening filters, and their combination on the diagnosis of horizontal root fracture (HRF) in teeth adjacent to a zirconia implant on cone-beam computed tomography (CBCT) examinations. Nineteen single-rooted teeth (9 with HRF and 10 without) were individually positioned in the right central incisor socket of a dry human maxilla. A zirconia implant was placed adjacent to each tooth. Imaging was performed using an OP300 Maxio CBCT (Instrumentarium, Tuusula, Finland) unit with the following settings: a current of 8 mA, both MAR modes (enabled and disabled), a 5×5 cm field of view, a voxel size of 0.085 mm, and a peak kilovoltage of 90 kVp. Four oral and maxillofacial radiologists independently evaluated the CBCT scans under both MAR conditions and across 3 levels of sharpening filter application (none, Sharpen 1×, and Sharpen 2×). Diagnostic metrics were calculated and compared using 2-way analysis of variance (α=5%). The weighted kappa test was used to assess intra- and inter-examiner reliability in the diagnosis of HRF. MAR tool activation, sharpening filter use, and their combination did not significantly impact the area under the receiver operating characteristic curve, sensitivity, or specificity of HRF diagnosis (P>0.05). Intra- and inter-examiner agreement ranged from fair to substantial. The diagnosis of HRF in a tooth adjacent to a zirconia implant is not affected by the activation of MAR, the application of a sharpening filter, or the combination of these tools.

Denoising of 4D-STEM Dataset using Pix2Pix GAN Algorithm and Artifact Reduction Strategy

Denoising of 4D-STEM Dataset using Pix2Pix GAN Algorithm and Artifact Reduction Strategy

Effects of Subject Position on Metal Artifact Reduction of a Reverse Shoulder Prosthesis Using Computed Tomography

To validate the effects of subject position on single energy metal artifact reduction (SEMAR) of a reverse shoulder prosthesis using computed tomography (CT). A water phantom with a reverse shoulder prosthesis was scanned at four positions on the XY plane of the CT gantry (on-center, 50 mm, 100 mm, and 150 mm from on-center in the negative direction of the X axis, respectively). We obtained images with and without SEMAR. The artifact index (AI) was measured via physical assessment. Scheffé's (Ura) paired comparison methods were performed with the amount of metal artifact by ten radiological technologists via visual assessment. The AI was significantly reduced when using SEMAR. As the phantom moved away from the on-center position, the AI increased, and metal artifacts increased in Scheffé's methods. SEMAR reduces metal artifacts of a reverse shoulder prosthesis, but metal artifacts may increase as the subject position moves away from the on-center position.

Vertical root fracture diagnosis in teeth with metallic posts: Impact of metal artifact reduction and sharpening filters.

This study examined the influence of a metal artifact reduction (MAR) tool, sharpening filters, and their combination on the diagnosis of vertical root fracture (VRF) in teeth with metallic posts using cone-beam computed tomography (CBCT). Twenty single-rooted human premolars - 9 with VRF and 11 without - were individually placed in a human mandible. A metallic post composed of a cobalt-chromium alloy was inserted into the root canal of each tooth. CBCT scans were then acquired under the following parameters: 8 mA, a 5×5 cm field of view, a voxel size of 0.085 mm, 90 kVp, and with MAR either enabled or disabled. Five oral and maxillofacial radiologists independently evaluated the CBCT exams under each MAR mode and across 3 sharpening filter conditions: no filter, Sharpen 1×, and Sharpen 2×. The diagnostic performance was quantified by the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. These metrics were compared using 2-way analysis of variance with a significance level of α=5%. Intra- and inter-examiner agreement were assessed using the weighted kappa test. Neither MAR nor the application of sharpening filters significantly impacted AUC or specificity (P>0.05). However, sensitivity increased when MAR was combined with Sharpen 1× and Sharpen 2× (P=0.015). The intra-examiner agreement ranged from fair to substantial (0.34-0.66), while the inter-examiner agreement ranged from fair to moderate (0.27-0.41). MAR in conjunction with sharpening filters improved VRF detection; therefore, their combined use is recommended in cases of suspected VRF.

Potential impact of metal crowns at varying distances from a carious lesion on its detection on cone-beam computed tomography scans with several protocols.

This study evaluated the impact of artifacts generated by metal crowns on the detection of proximal caries lesions in teeth at various distances using cone-beam computed tomography (CBCT). Additionally, the diagnostic impacts of tube current and metal artifact reduction (MAR) were investigated. Thirty teeth were arranged within 10 phantoms, each containing 1 first premolar, 1 second premolar, and 1 second molar. A sound first molar (for the control group) or a tooth with a metal crown was placed. Of the 60 proximal surfaces evaluated, 15 were sound and 45 exhibited enamel caries. CBCT scans were acquired using an OP300 Maxio unit (Instrumentarium, Tuusula, Finland), while varying the tube current (4, 8, or 12.5 mA) and enabling or disabling MAR. Five observers assessed mesial and distal surfaces using a 5-point scale. Multi-way analysis of variance was employed for data comparison, with P<0.05 indicating statistical significance. The area under the curve (AUC) varied from 0.40 to 0.60 (sensitivity: 0.28-0.45, specificity: 0.44-0.80). The diagnostic accuracy was not significantly affected by the presence of a metal crown, milliamperage, or MAR (P>0.05). However, the overall AUC and specificity were significantly lower for surfaces near a crown (P<0.05). CBCT-based caries detection was not influenced by the presence of a metal crown, variations in milliamperage, or MAR activation. However, the diagnostic accuracy was low and was further diminished for surfaces near a crown. Consequently, CBCT is not recommended for the detection of incipient caries lesions.

Enlarged Motion-Aware and Frequency-Aware Network for Compressed Video Artifact Reduction

Enlarged Motion-Aware and Frequency-Aware Network for Compressed Video Artifact Reduction

Development of Residual Dense U-Net (RDU-Net)-Based Metal Artefacts Reduction Technique Using Spectral Photon Counting CT

Development of Residual Dense U-Net (RDU-Net)-Based Metal Artefacts Reduction Technique Using Spectral Photon Counting CT

Impact of the spatial orientation of the patient's head, metal artifact reduction, and tube current on cone-beam computed tomography artifact expression adjacent to a dental implant: A laboratory study using a simulated surgical guide.

The aim of this study was to evaluate image artifacts in the vicinity of dental implants in cone-beam computed tomography (CBCT) scans obtained with different spatial orientations, tube current levels, and metal artifact reduction algorithm (MAR) conditions. One dental implant and 2 tubes filled with a radiopaque solution were placed in the posterior region of a mandible using a surgical guide to ensure parallel alignment. CBCT scans were acquired with the mandible in 2 spatial orientations in relation to the X-ray projection plane (standard and modified) at 3 tube current levels: 5, 8, and 11 mA. CBCT scans were repeated without the implant and were reconstructed with and without MAR. The mean voxel and noise values of each tube were obtained and compared using multi-way analysis of variance and the Tukey test (α=0.05). Mean voxel values were significantly higher and noise values were significantly lower in the modified orientation than in the standard orientation (P<0.05). MAR activation and tube current levels did not show significant differences in most cases of the modified spatial orientation and in the absence of the dental implant (P>0.05). Modifying the spatial orientation of the head increased brightness and reduced spatial orientation noise in adjacent regions of a dental implant, with no influence from the tube current level and MAR.

F2IFlow for CT Metal Artifact Reduction

F2IFlow for CT Metal Artifact Reduction