Purpose To improve hyperpolarized 13C (HP‐13C) MRI by image denoising with a new approach, patch‐based higher‐order singular value decomposition (HOSVD). Methods The benefit of using a patch‐based HOSVD method to denoise dynamic HP‐13C MR imaging data was investigated. Image quality and the accuracy of quantitative analyses following denoising were evaluated first using simulated data of [1‐13C]pyruvate and its metabolic product, [1‐13C]lactate, and compared the results to a global HOSVD method. The patch‐based HOSVD method was then applied to healthy volunteer HP [1‐13C]pyruvate EPI studies. Voxel‐wise kinetic modeling was performed on both non‐denoised and denoised data to compare the number of voxels quantifiable based on SNR criteria and fitting error. Results Simulation results demonstrated an 8‐fold increase in the calculated SNR of [1‐13C]pyruvate and [1‐13C]lactate with the patch‐based HOSVD denoising. The voxel‐wise quantification of kPL (pyruvate‐to‐lactate conversion rate) showed a 9‐fold decrease in standard errors for the fitted kPL after denoising. The patch‐based denoising performed superior to the global denoising in recovering kPL information. In volunteer data sets, [1‐13C]lactate and [13C]bicarbonate signals became distinguishable from noise across captured time points with over a 5‐fold apparent SNR gain. This resulted in >3‐fold increase in the number of voxels quantifiable for mapping kPB (pyruvate‐to‐bicarbonate conversion rate) and whole brain coverage for mapping kPL. Conclusions Sensitivity enhancement provided by this denoising significantly improved quantification of metabolite dynamics and could benefit future studies by improving image quality, enabling higher spatial resolution, and facilitating the extraction of metabolic information for clinical research.
This paper proposes a novel horizontal visibility graph entropy (HVGE) approach to evaluate EEG signals from alcoholic subjects and controlled drinkers and compare with a sample entropy (SaE) method. Firstly, HVGEs and SaEs are extracted from 1,200 recordings of biomedical signals, respectively. A statistical analysis method is employed to choose the optimal channels to identify the abnormalities in alcoholics. Five group channels are selected and forwarded to a K-Nearest Neighbour (K-NN) and a support vector machine (SVM) to conduct classification, respectively. The experimental results show that the HVGEs associated with left hemisphere, 1, 3 and FC5 electrodes, of alcoholics are significantly abnormal. The accuracy of classification with 10-fold cross-validation is 87.5 with about three HVGE features. By using just optimal 13-dimension HVGE features, the accuracy is 95.8 . In contrast, SaE features associated cannot identify the left hemisphere disorder for alcoholism and the maximum classification ratio based on SaE is just 95.2 even using all channel signals. These results demonstrate that the HVGE method is a promising approach for alcoholism identification by EEG signals.Electronic supplementary materialThe online version of this article (doi:10.1007/s40708-014-0003-x) contains supplementary material, which is available to authorized users.
Abstract-This report presents results from the Video Person Recognition Evaluation held in conjunction with the 11th
Existing traditional and ConvNet-based methods for light field depth estimation mainly work on the narrow-baseline scenario. This paper explores the feasibility and capability of ConvNets to estimate depth in another promising scenario: wide-baseline light fields. Due to the deficiency of training samples, a large-scale and diverse synthetic wide-baseline dataset with labelled data is introduced for depth prediction tasks. Considering the practical goal for real-world applications, we design an end-to-end trained lightweight convolutional network to infer depths from light fields, called LLF-Net. The proposed LLF-Net is built by incorporating a cost volume which allows variable angular light field inputs and an attention module that enables to recover details at occlusion areas. Evaluations are made on the synthetic and real-world wide-baseline light fields, and experimental results show that the proposed network achieves the best performance when compared to recent stateof-the-art methods. We also evaluate our LLF-Net on narrowbaseline datasets, and it consequently improves the performance of previous methods.
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