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Co-registered EEG and functional MRI (EEG/fMRI) is a potential clinical tool for planning invasive EEG in patients with epilepsy. In addition, the analysis of EEG/fMRI data provides a fundamental insight into the precise physiological meaning of both fMRI and EEG data. Routine application of EEG/fMRI for localization of epileptic sources is hampered by large artefacts in the EEG, caused by switching of scanner gradients and heartbeat effects. Residuals of the ballistocardiogram (BCG) artefacts are similarly shaped as epileptic spikes, and may therefore cause false identification of spikes. In this study, new ideas and methods are presented to remove gradient artefacts and to reduce BCG artefacts of different shapes that mutually overlap in time. Gradient artefacts can be removed efficiently by subtracting an average artefact template when the EEG sampling frequency and EEG low-pass filtering are sufficient in relation to MR gradient switching (Gonçalves et al., 2007). When this is not the case, the gradient artefacts repeat themselves at time intervals that depend on the remainder between the fMRI repetition time and the closest multiple of the EEG acquisition time. These repetitions are deterministic, but difficult to predict due to the limited precision by which these timings are known. Therefore, we propose to estimate gradient artefact repetitions using a clustering algorithm, combined with selective averaging. Clustering of the gradient artefacts yields cleaner EEG for data recorded during scanning of a 3T scanner when using a sampling frequency of 2048Hz. It even gives clean EEG when the EEG is sampled with only 256Hz. Current BCG artefacts-reduction algorithms based on average template subtraction have the intrinsic limitation that they fail to deal properly with artefacts that overlap in time. To eliminate this constraint, the precise timings of artefact overlaps were modelled and represented in a sparse matrix. Next, the artefacts were disentangled with a least squares procedure. The relevance of this approach is illustrated by determining the BCG artefacts in a data set consisting of 29 healthy subjects recorded in a 1.5T scanner and 15 patients with epilepsy recorded in a 3T scanner. Analysis of the relationship between artefact amplitude, duration and heartbeat interval shows that in 22% (1.5T data) to 30% (3T data) of the cases BCG artefacts show an overlap. The BCG artefacts of the EEG/fMRI data recorded on the 1.5T scanner show a small negative correlation between HBI and BCG amplitude. In conclusion, the proposed methodology provides a substantial improvement of the quality of the EEG signal without excessive computer power or additional hardware than standard EEG-compatible equipment. ► With gradient artefact clustering templates are created to correct EEG/fMRI. ► With gradient artefact clustering clean EEG can be obtained at low sampling frequency. ► BCG artefacts overlap in time 20 to 30 % of the cases. ► Overlapping BCG artefacts are effectively removed from the data.

Although the total variational model can significantly suppress noise and make blurred images sharper, it will unavoidably produce blocky artifacts. In this paper, a new hybrid model based on overlapping group sparsity (OGS) was proposed to effectively prevent the above problems, which combines the advantages of the fractional-order total variational and the non-convex regularizer. The overlapping group sparse fractional-order total variational (OGS-FOTV) regular term can restore complex features in images while reducing noise. Meanwhile, the non-convex regularization term based on the overlapping group sparsity on hyper-Laplacian (OGS-HL) prior can usefully control the staircase artifacts and better protect image edges. To tackle the above denoising model, we employ the alternating direction method of multipliers to decompose our model into several sub-problems and solve them one by one. For images affected by different levels of white Gaussian noise, we conducted a contrast experiment with other advanced methods. The results show that the new model is superior to other related models in denoising.

Surface electromyography (sEMG) signal classification has many applications such as human-machine interaction, diagnosis of kinesiological studies, and neuromuscular diseases. However, these signals are complicated because of different artifacts added to the sEMG signal during recording. In this study, a multi-stage classification technique is proposed for the identification of distinct movements of the lower limbs using sEMG signals acquired from leg muscles of healthy knee and abnormal knee subjects. This investigation involves 11 subjects with a knee abnormality and 11 without knee abnormality for three distinct activities viz. walking, leg extension from sitting position (sitting), and flexion of the leg (standing). Discrete wavelet denoising to fourth level decomposition has been implemented for the artifact reduction and the signal has been segmented using overlapping windowing technique. A study of four different architectures of 1D convolutional neural network models is undertaken for the prediction of lower limb activities and the final prediction is achieved via a voting mechanism of all four model results. The performance parameters of CNN models have been calculated for three different cases: (1) healthy subjects (2) subjects with knee abnormality (3) Pooled data (combination of abnormal knee and healthy knee subjects) using nested threefold cross-validation. It has been found that the voting mechanism yields an average classification accuracy as 99.35%, 97.63%, and 97.14% for healthy subjects, knee abnormal subjects, and pooled data, respectively. The result validates that the proposed voting-based 1D CNN model is efficient and useful in lower limb activity recognition using the sEMG signal.

Total variation model is a popular image restoration method due to its capability to preserve edges. However, it is easy to produce staircase artifacts. In this paper, we propose a non-convex and non-separable regularization term combining the overlapping group sparsity (OGS) and the minimax-concave penalty, which is obtained by subtracting the minimax-concave of the OGS term from the OGS term. The OGS term in the new regularization term can remove the staircase artifacts and preserve the image structure. The minimax-concave penalty can promote the sparsity of image in gradient domain and improve the estimation of the high-frequency components. We prove and set the range of the regularization parameter to ensure that the total cost function of the proposed model is convex. In order to solve the proposed model, we develop an adaptive alternating direction method of multipliers algorithm to tune the regularization parameter adaptively. In the experiments, we compare the proposed model with other state-of-the-art models in image denoising and image deblurring. The experimental results show the effectiveness of the proposed model in image restoration.

Objectives To evaluate the ability of proton-density with fat-suppression BLADE (proprietary name for periodically rotated overlapping parallel lines with enhanced reconstruction in MR systems from Siemens Healthcare, PDFS BLADE) and turbo inversion recovery magnitude-BLADE (TIRM BLADE) sequences to reduce motion and pulsation artifacts in shoulder magnetic resonance examinations. Materials and methods Forty-one consecutive patients who had been routinely scanned for shoulder examination participated in the study. The following pairs of sequences with and without BLADE were compared: (a) Oblique coronal proton-density sequence with fat saturation of 25 patients and (b) oblique sagittal T2 TIRM-weighed sequence of 20 patients. Qualitative analysis was performed by two experienced radiologists. Image motion and pulsation artifacts were also evaluated. Results In oblique coronal PDFS BLADE sequences, motion artifacts have been significantly eliminated, even in five cases of non-diagnostic value with conventional imaging. Similarly, in oblique sagittal T2 TIRM BLADE sequences, image quality has been improved, even in six cases of non-diagnostic value with conventional imaging. Furthermore, flow artifacts have been improved in more than 80% of all the cases. Conclusions The use of BLADE sequences is recommended in shoulder imaging, especially in uncooperative patients because it effectively eliminates motion and pulsation artifacts.

As one of the most significant issues in imaging science, image denoising plays a major role in plenty of image processing applications. Due to the ill-posed nature of image denoising, total variation regularization is widely used in image denoising problems for its capability to suppress noise and preserve image edges. Nevertheless, traditional total variation will inevitably yield undesirable staircase artifacts when applied to recorded images. Inspired by the success of ℓq norm minimization and overlapping group sparsity in image denoising, and the effective staircase artifacts removal by fractional total variation, the hybrid model which combines the fractional order total variation with overlapping group sparsity and higher order total variation with ℓq norm is developed in this paper to restore images corrupted by Gaussian noise. An efficient algorithm based on the parallel linear alternating direction method of multipliers is developed for solving the corresponding model and the numerical experiments demonstrate the effectiveness of the proposed approach against several state-of-the-art methods, in terms of peak signal-to-noise ratio and structure similarity index measure values.

Background Radial k-space sampling techniques have been shown to reduce motion artifacts in adult abdominal MRI. Objective To compare a T2-weighted radial k-space sampling MRI pulse sequence (BLADE) with standard respiratory-triggered T2-weighted turbo spin echo (TSE) in pediatric abdominal imaging. Materials and methods Axial BLADE and respiratory-triggered turbo spin echo sequences were performed without fat suppression in 32 abdominal MR examinations in children. We retrospectively assessed overall image quality, the presence of respiratory, peristaltic and radial artifact, and lesion conspicuity. We evaluated signal uniformity of each sequence. Results BLADE showed improved overall image quality (3.35 ± 0.85 vs. 2.59 ± 0.59, P  < 0.001), reduced respiratory motion artifact (0.51 ± 0.56 vs. 1.89 ± 0.68, P  < 0.001), and improved lesion conspicuity (3.54 ± 0.88 vs. 2.92 ± 0.77, P  = 0.006) compared to respiratory triggering turbo spin-echo (TSE) sequences. The bowel motion artifact scores were similar for both sequences (1.65 ± 0.77 vs. 1.79 ± 0.74, P  = 0.691). BLADE introduced a radial artifact that was not observed on the respiratory triggering-TSE images (1.10 ± 0.85 vs. 0, P  < 0.001). BLADE was associated with diminished signal variation compared with respiratory triggering-TSE in the liver, spleen and air ( P <  0.001). Conclusion The radial k-space sampling technique improved the quality and reduced respiratory motion artifacts in young children compared with conventional respiratory-triggered turbo spin-echo sequences.

In this paper, we present the design of a partial overlapping block using exact Legendre moment computation (POBRELM) for gray-level image reconstruction. We address and solve the problem of artifact caused by independent reconstruction of the block affecting the visual image quality. This new approach takes advantage of only partial information of the neighbors of each block in spite of using global overlapping. Processing less neighborhood area during the exact computation of Legendre moments leads to significant improvement in the processing time. Simulation results show that the proposed method achieves not only considerable enhancement in terms of reconstruction error, but also drastic reduction in computation time. This makes our method attractive for real-time applications.