Artifact Suppression Research Articles

Origin coordinate influence on performance of temporally extended signal space separation in magnetoencephalography

ObjectiveTemporally extended signal space separation (tSSS) is a powerful method for artifact suppression in magnetoencephalography (MEG). Because tSSS first separates MEG signals coming from inside and outside a certain sphere, definition of the sphere origin is important. For this study, we explored the influence of origin choice on tSSS performance in spontaneous and evoked activity from epilepsy patients. MethodsInterictal epileptiform discharges (IEDs) and somatosensory evoked fields (SEFs) were processed with two tSSSs: one with the default origin of (0, 0, 40 mm) in the head coordinate, and the other with an individual origin estimated using each patient’s anatomical magnetic resonance imaging (MRI). Equivalent current dipoles (ECDs) were calculated for the data. The ECD location and quality of estimation were compared across conditions. ResultsMEG data from 21 patients revealed marginal differences in ECD location, but the estimation quality inferred from goodness of fit (GOF) and confidence volume (CV) was better for the tSSS with individual origins. This choice affected IEDs more than it affected SEFs. ConclusionsIndividual sphere model resulted in better GOF and CV. SignificanceApplication of tSSS using an individual origin would be more desirable when available. This parameter might influence spontaneous activity more strongly.

Enhance Image Quality with CAS-CNN: A Deep Convolutional Neural Network for Compression Artifact Suppression

Objective: The goal is to outdo clumsy filtering algorithms and prevailing deep learning approaches in terms of the image quality metrics, computational efficiency, and memory usage by attaining better performance. Methodology: Two-step approach has the methodology component including the formulation of the problem and review of the literature collections to detect known techniques. CAS-CNN's architecture is designed, and the entire experiment setup requires the preparation of an original and a compressed images dataset and also defining loss function for training and improving CAS-CNN parameters as a whole. Outputs of metrics are the PSNR, SSIM, MOS values, inference time, model size, training time, and hardware usage. Results and Discussion: In terms of competitiveness, CAS-CNN is always ahead of all the latest algorithm levels and achieves the best results by standard metrics such as PSNR and SSIM, thus it is the method that gives the most undistorted recreated images with a high quality. It has shown its ability to compete closely with traditional methods by providing better performance, as well as consuming fewer computing resources, which makes it closer to the practical reality of real time applications. The CAS-CNN's model creates efficiency that enables cutting-edge design and testing into the market timelier than the existing methods do. Small progress is seen despite its quick performance; but still, important issues like artificial data and restrictions on given conditions are noticed, which means that we must not stop searching for scaling and generalizing power. Conclusion: CAS-CNN is a next-level algorithm concerning removing distortion in compression, superior to prevailing techniques in terms of both effectiveness, efficiency, and accessibility. It holds an edge over other methods due to its high performance and efficiency, and as such, it is an applicable tool for a variety of situations where one must deal with limited resources in the need for high-quality images. More research is needed to account for the existing shortcomings and apply CAS-CNN to a range of different datasets, as well as the circumstances in the real world.

Adaptive automated sinogram normalization for ring artifacts suppression in CT

Ring artifacts pose a major barrier to obtaining precise reconstruction in computed tomography (CT). The presence of ring artifacts complicates the use of automatic means of processing CT reconstruction results, such as segmentation, correction of geometric shapes, alignment of reconstructed volumes. Although there are numerous efficient methods for suppressing ring artifacts, many of them appear to be manual. Along with this, a large proportion of the automatic methods cope unsatisfactorily with the target task while requiring computational capacity. The current work introduces a projection data preprocessing method for suppressing ring artifacts that constitutes a compromise among the outlined aspects – automaticity, high efficiency and computational speed. Derived as the automation of the classical sinogram normalization method, the proposed method specific advantages consist in adaptability in relation to the filtered sinograms and the edge-preservation property proven within the experiments on both synthetic and real CT data. Concerning the challenging open-access data, the method has performed superior quality comparable to that of the advanced methods: it has demonstrated 70.4% ring artifacts suppression percentage (RASP) quality metric. In application to our real laboratory CT data, the proposed method allowed us to gain significant refinement of the reconstruction quality which has not been surpassed by a range of compared manual ring artifacts suppression methods.

Evaluation of low-dose computed tomography reconstruction using spatial-radon domain total generalized variation regularization

The x-ray radiation dose in computed tomography (CT) examination has been a major concern for patients. Lowing the tube current and exposure time in data acquisition is a straightforward and cost-effective strategy to reduce the x-ray radiation dose. However, this will inevitably increase the noise fluctuations in measured projection data, and the corresponding CT image quality will be severely degraded if noise suppression is not performed during image reconstruction. To reconstruct high-quality low-dose CT image, we present a spatial-radon domain total generalized variation (SRDTGV) regularization for statistical iterative reconstruction based on penalized weighted least-squares (PWLS) principle, which is called PWLS-SRDTGV for simplicity. The presented PWLS-SRDTGV model can simultaneously reconstruct high-quality CT image in space domain and its corresponding projection in radon domain. An efficient split Bregman algorithm was applied to minimize the cost function of the proposed reconstruction model. Qualitative and quantitative studies were performed to evaluate the effectiveness of the PWLS-SRDTGV image reconstruction algorithm using a digital 3D XCAT phantom and an anthropomorphic torso phantom. The experimental results demonstrate that PWLS-SRDTGV algorithm achieves notable gains in noise reduction, streak artifact suppression, and edge preservation compared with competing reconstruction approaches.

Comparing 2-dimensional versus 3-dimensional MR myelography for cerebrospinal fluid leak detection

PurposeWe compared cerebrospinal fluid (CSF) leak conspicuity and image quality as visualized using 3D versus 2D magnetic resonance (MR) myelography in patients with spinal CSF leaks. MethodsEighteen patients underwent spinal MR imaging at 3 Tesla. Three board-certified radiologists independently evaluated CSF leak conspicuity and image quality on a 4-point scale; the latter assessed by scoring fat suppression, venous visualization, and severity of CSF flow artifacts. Additionally, the evaluators ranked the overall performances of 2D versus 3D MR myelography upon completing side-by-side comparisons of CSF leak conspicuity. Inter-reader agreement was determined using the Gwet’s AC1. ResultsThe quality of 3D MR myelography images was significantly better than that of 2D MR myelography with respect to CSF leak conspicuity (mean scores: 3.3 vs. 1.9, p < 0.0001) and severity of CSF flow artifacts on the axial view (mean scores: 1.0 vs. 2.5, p = 0.0001). Inter-reader agreement was moderate to almost perfect for 2D MR myelography (AC1 = 0.55–1.00), and almost perfect for 3D MR myelography (AC1 = 0.85–1.00). Moreover, 3D MR myelography was judged to be superior to 2D acquisition in 78 %, 83 %, and 83 % of the samples per readers 1, 2 and 3, respectively; the inter-reader agreement was almost perfect (AC1: reader 1 vs. 2; 0.98, reader 2 vs. 3; 0.96, reader 3 vs. 1; 0.98). ConclusionCSF leaks are more conspicuous when using 3D MR myelography than when using its 2D counterpart; therefore, the former is more reliable for identifying such leaks.

Microwave Imaging: Locating Bone Fractures using Patch Antenna of ISM Band.

The human skeletal system relies heavily on the integrity of bones, which provide structural support and safeguard vital organs. Accurate detection is paramount for effective diagnosis. Conventional methods for identifying fractures manually are not only time-consuming but also susceptible to errors. The proposed methodology hinges on a patch antenna operating at 2.4 GHz and a bone phantom housing a simulated fracture, where the antenna is scanned. The collected signals are then processed with Delay-and-Sum (DAS), and Delay-Multiply-and-Sum (DMAS) reconstruction algorithms. The resulting images offer visual insights into the location of fractures. Through experimentation, the efficacy of the images varies considerably in terms of their capacity for noise and artifact suppression. While DAS exhibits reasonable effectiveness, it suppresses noise and artifacts comprehensively. In contrast, DMAS offers clearer and more precise images of bone fractures. In summary, the research introduces a cost-effective and non-invasive strategy for detecting bone fractures. By involving a patch antenna at 2.4 GHz, along with image reconstruction algorithms like DMAS and DAS, one can effectively visualize the location of bone fractures. The experimental results highlight the superiority of DMAS over DAS in terms of contrast resolution, making it a highly promising avenue for fracture detection.

Adapted Beamforming: A Robust and Flexible Approach for Removing Various Types of Artifacts from TMS-EEG Data.

Electroencephalogram (EEG) recorded as response to transcranial magnetic stimulation (TMS) can be highly informative of cortical reactivity and connectivity. Reliable EEG interpretation requires artifact removal as the TMS-evoked EEG can contain high-amplitude artifacts. Several methods have been proposed to uncover clean neuronal EEG responses. In practice, determining which method to select for different types of artifacts is often difficult. Here, we used a unified data cleaning framework based on beamforming to improve the algorithm selection and adaptation to the recorded signals. Beamforming properties are well understood, so they can be used to yield customized methods for EEG cleaning based on prior knowledge of the artifacts and the data. The beamforming implementations also cover, but are not limited to, the popular TMS-EEG cleaning methods: independent component analysis(ICA), signal-space projection (SSP), signal-space-projection-source-informed-reconstruction method (SSP-SIR), the source-estimate-utilizing noise-discarding algorithm (SOUND), data-driven Wiener filter (DDWiener), and the multiple-source approach. In addition to these established methods, beamforming provides a flexible way to derive novel artifact suppression algorithms by considering the properties of the recorded data. With simulated and measured TMS-EEG data, we show how to adapt the beamforming-based cleaning to different data and artifact types, namely TMS-evoked muscle artifacts, ocular artifacts, TMS-related peripheral responses, and channel noise. Importantly, beamforming implementations are fast to execute: We demonstrate how the SOUND algorithm becomes orders of magnitudes faster via beamforming. Overall, the beamforming-based spatial filtering framework can greatly enhance the selection, adaptability, and speed of EEG artifact removal.

Clean PDI-1 SQ: Suppression of HSQC artifacts in 2D proton-detected INADEQUATE spectra by pulse sequence redesign

Proton-detected INADEQUATE NMR experiments are widely used for structure elucidation of small molecules, in particular the implementations that display 13C single-quantum rather than double-quantum frequencies in the indirect dimension of 2D spectra. But unfortunately, such spectra in addition to the desired 1H–13C two-bond correlations also contain HSQC artifacts of comparable magnitude. The redesigned versatile experiment presented in this paper requires no compromise based on different 13C multiplicities and suppresses the HSQC artifacts that are a source of possible spectral misinterpretation. Demonstration of the new method is shown by applications to typical small molecules of different complexity.

A dense and U-shaped transformer with dual-domain multi-loss function for sparse-view CT reconstruction.

CT image reconstruction from sparse-view projections is an important imaging configuration for low-dose CT, as it can reduce radiation dose. However, the CT images reconstructed from sparse-view projections by traditional analytic algorithms suffer from severe sparse artifacts. Therefore, it is of great value to develop advanced methods to suppress these artifacts. In this work, we aim to use a deep learning (DL)-based method to suppress sparse artifacts. Inspired by the good performance of DenseNet and Transformer architecture in computer vision tasks, we propose a Dense U-shaped Transformer (D-U-Transformer) to suppress sparse artifacts. This architecture exploits the advantages of densely connected convolutions in capturing local context and Transformer in modelling long-range dependencies, and applies channel attention to fusion features. Moreover, we design a dual-domain multi-loss function with learned weights for the optimization of the model to further improve image quality. Experimental results of our proposed D-U-Transformer yield performance improvements on the well-known Mayo Clinic LDCT dataset over several representative DL-based models in terms of artifact suppression and image feature preservation. Extensive internal ablation experiments demonstrate the effectiveness of the components in the proposed model for sparse-view computed tomography (SVCT) reconstruction. The proposed method can effectively suppress sparse artifacts and achieve high-precision SVCT reconstruction, thus promoting clinical CT scanning towards low-dose radiation and high-quality imaging. The findings of this work can be applied to denoising and artifact removal tasks in CT and other medical images.

Swarm Intelligence-Based Improved Adaptive Chirp Mode Decomposition Algorithm for Suppression of Ocular Artifacts from EEG Signal

Swarm Intelligence-Based Improved Adaptive Chirp Mode Decomposition Algorithm for Suppression of Ocular Artifacts from EEG Signal

Full-Wavefield Migration Using an Imaging Condition of Global Normalization Multi-Order Wavefields: Application to a Synthetic Dataset

In marine seismic exploration, seismic signals comprise primaries that undergo first-order scattering, as well as multiples resulting from multi-order scattering events. Surface-related multiples involve multi-order scattering at the free surface interface between seawater and air and exhibit a smaller reflection angle and broader illumination compared to primaries. Internal multiples, originating from multi-order scattering among stratified layers, provide additional illumination compensation beneath the reflecting interface. However, in conventional primary migration, different-order wavefields may result in crosstalk artifacts. To address this issue, we developed a least-squares migration (LSM) method based on the multi-order wavefield global normalization condition. This methodology investigates the illumination effects and crosstalk artifacts associated with different-order surface-related and internal multiples, and then modifies the global normalization condition by incorporating an illumination compensation perspective. Virtual sources, represented by surface-related multiples and internal multiples, are integrated into the source compensation term, ultimately yielding a multi-order wavefield normalization condition. This normalization condition is subsequently combined with least-squares full-wavefield migration (LSFWM). Numerical experiments demonstrate that the normalization condition of multi-order wavefields can resolve the problem of weak deep imaging energy and promote the suppression of multiple crosstalk artifacts in the least-squares algorithm.

Artifact suppression for breast specimen imaging in micro CBCT using deep learning

BackgroundCone-beam computed tomography (CBCT) has been introduced for breast-specimen imaging to identify a free resection margin of abnormal tissues in breast conservation. As well-known, typical micro CT consumes long acquisition and computation times. One simple solution to reduce the acquisition scan time is to decrease of the number of projections, but this method generates streak artifacts on breast specimen images. Furthermore, the presence of a metallic-needle marker on a breast specimen causes metal artifacts that are prominently visible in the images. In this work, we propose a deep learning-based approach for suppressing both streak and metal artifacts in CBCT.MethodsIn this work, sinogram datasets acquired from CBCT and a small number of projections containing metal objects were used. The sinogram was first modified by removing metal objects and up sampling in the angular direction. Then, the modified sinogram was initialized by linear interpolation and synthesized by a modified neural network model based on a U-Net structure. To obtain the reconstructed images, the synthesized sinogram was reconstructed using the traditional filtered backprojection (FBP) approach. The remaining residual artifacts on the images were further handled by another neural network model, ResU-Net. The corresponding denoised image was combined with the extracted metal objects in the same data positions to produce the final results.ResultsThe image quality of the reconstructed images from the proposed method was improved better than the images from the conventional FBP, iterative reconstruction (IR), sinogram with linear interpolation, denoise with ResU-Net, sinogram with U-Net. The proposed method yielded 3.6 times higher contrast-to-noise ratio, 1.3 times higher peak signal-to-noise ratio, and 1.4 times higher structural similarity index (SSIM) than the traditional technique. Soft tissues around the marker on the images showed good improvement, and the mainly severe artifacts on the images were significantly reduced and regulated by the proposed. method.ConclusionsOur proposed method performs well reducing streak and metal artifacts in the CBCT reconstructed images, thus improving the overall breast specimen images. This would be beneficial for clinical use.

An Unsupervised Deep Learning Framework for Retrospective Gating of Catheter-Based Cardiac Imaging

Motion artifacts are a major challenge in the in vivo application of catheter-based cardiac imaging modalities. Gating is a critical tool for suppressing motion artifacts. Electrocardiogram (ECG) gating requires a trigger device or synchronous ECG recordings for retrospective analysis. Existing retrospective software gating methods extract gating signals through separate steps based on changes in vessel morphology or image features, which require a high computational cost and are prone to error accumulation. In this paper, we report on an end-to-end unsupervised learning framework for retrospective image-based gating (IBG) of catheter-based intracoronary images, named IBG Network. It establishes a direct mapping from a continuously acquired image sequence to a gated subsequence. The network was trained on clinical data sets in an unsupervised manner, addressing the difficulty of obtaining the gold standard in deep learning-based motion suppression techniques. Experimental results of in vivo intravascular ultrasound and optical coherence tomography sequences show that the proposed method has better performance in terms of motion artifact suppression and processing efficiency compared with the state-of-the-art nonlearning signal-based and IBG methods.

Artifact suppression and improved signal-to-noise ratio by phase-locked multiplexed coherent imaging.

Laser additive manufacturing (AM) promises direct metal 3D printing, but is held back by defects and process instabilities, giving rise to a need for in situ process monitoring. Inline coherent imaging (ICI) has proven effective for in situ, direct measurements of vapor depression depth and shape in AM and laser welding but struggles to track turbulent interfaces due to poor coupling back into a single-mode fiber and the presence of artifacts. By z-domain multiplexing, we achieve phase-sensitive image consolidation, automatically attenuating autocorrelation artifacts and improving interface tracking rates by 58% in signal-starved applications.

Optimized Excitation in Microwave-induced Thermoacoustic Imaging for Artifact Suppression.

Microwave-induced thermoacoustic imaging (M-TAI) allows the visualization of macroscopic and microscopic structures of bio-tissues. However, it suffers from severe inherent artifacts that might misguide the subsequent diagnostics and treatments of diseases. To overcome this limitation, we propose an optimized excitation strategy. In detail, the strategy integrates dynamically compound specific absorption rate (SAR) and co-planar configuration of polarization state, incident wave vector and imaging plane. Starting from the theoretical analysis, we interpret the underlying mechanism supporting the superiority of the optimized excitation strategy to achieve an effect equivalent to homogenizing the deposited electromagnetic energy in bio-tissues. The following numerical simulations demonstrate that the strategy enables better preservation of the conductivity weighting of samples while increasing Pearson correlation coefficient. Furthermore, the in vitro and in vivo M-TAI experiments validate the effectiveness and robustness of this optimized excitation strategy in artifact suppression, allowing the simultaneous identification of both boundary and inside fine structures within bio-tissues. All the results suggest that the optimized excitation strategy can be expanded to diverse scenarios, inspiring more suitable strategies that remarkably suppress the inherent artifacts in M-TAI.

Research on the Innovation of Teaching Mode of Orienteering Sports Courses in Colleges and Universities Based on Mobile Intelligent Terminals

Abstract This paper studied motion state recognition algorithms using acceleration signals (ACC). At the same time, research is also being conducted on ECG motion artifact suppression algorithms and respiration rate detection algorithms. Based on this, we designed a mobile intelligent terminal sports app that allows users to view physiological signals and sports status in real time at any time. In college and university orienteering classes, the mobile intelligent terminal movement app is used to teach the sport. The results of comparing the experimental group and the control group in the orienteering course show that the mobile intelligent terminal orienteering teaching makes a significant difference in the men’s physical quality, except for the seated forward body flexion (P&lt;0.05). There were significant differences in the women’s group in terms of 50 m running (P = 0.013) and 800 m (P = 0.042). The experiment improved the dimensions of the Orienteering Campus Short Distance Test, Learning Motivation Indicator, and Emotional Management.

SeNAS-Net: Self-Supervised Noise and Artifact Suppression Network for Material Decomposition in Spectral CT

SeNAS-Net: Self-Supervised Noise and Artifact Suppression Network for Material Decomposition in Spectral CT

Enhanced Micro-Doppler Feature Extraction Using Adaptive Short-Time Kernel-Based Sparse Time-Frequency Distribution

The extraction of the micro-Doppler (m-D) feature based on time-frequency distribution (TFD) is of great significance for target detection and identification. To improve the feature extraction performance, numerous TFDs have been developed, with the majority falling under Cohen’s class. Nevertheless, these TFDs basically face a trade-off between artifact suppression and energy concentration. The main reason is that each Cohen’s class TFD is constructed by applying the two-dimensional Fourier transform to a kerneled ambiguity function directly, while existing kernels generally attenuate artifacts at the expense of losing valuable information. In this paper, a TFD reconstruction method employing an adaptive short-time kernel (ASTK) is developed in the framework of sparse representation (SR) theory to overcome this trade-off and enhance the m-D feature. Firstly, the task of the optimal kernel is explained from the viewpoint of the instantaneous auto-correlation function (IAF). Secondly, based on the quasi-linear frequency modulation feature of most m-D signals during short-time periods, the distribution rule of the short-time IAF (STIAF) in the ambiguity plane is concluded. Guided by this rule, an ASTK that can effectively remove unwanted artifacts with the least information loss is designed. Finally, an SR-based reconstruction procedure is conducted on the kerneled STIAF to generate an artifact-free TFD with high energy concentration, which can effectively enhance the m-D feature. Experiments using both simulated and real-world m-D signals demonstrate the effectiveness of the proposed method.

Ocular and muscle artifacts suppression from EEG through MEMD and statistics

Ocular and muscle artifacts suppression from EEG through MEMD and statistics

Fast and robust quantification of uncertainty in non-linear diffusion MRI models

Diffusion MRI (dMRI) allows for non-invasive investigation of brain tissue microstructure. By fitting a model to the dMRI signal, various quantitative measures can be derived from the data, such as fractional anisotropy, neurite density and axonal radii maps. We investigate the Fisher Information Matrix (FIM) and uncertainty propagation as a generally applicable method for quantifying the parameter uncertainties in linear and non-linear diffusion MRI models. In direct comparison with Markov Chain Monte Carlo (MCMC) sampling, the FIM produces similar uncertainty estimates at much lower computational cost. Using acquired and simulated data, we then list several characteristics that influence the parameter variances, including data complexity and signal-to-noise ratio. For practical purposes we investigate a possible use of uncertainty estimates in decreasing intra-group variance in group statistics by uncertainty-weighted group estimates. This has potential use cases for detection and suppression of imaging artifacts.