Denoising of volumetric magnetic resonance imaging using multi-channel three-dimensional convolutional neural network with applications on fast spin echo acquisitions.

Background: Three-dimensional (3D) magnetic resonance imaging (MRI) can be acquired with a high spatial resolution with flexibility being reformatted into arbitrary planes, but at the cost of reduced signal-to-noise ratio. Deep-learning methods are promising for denoising in MRI. However, the existing 3D denoising convolutional neural networks (CNNs) rely on either a multi-channel two-dimensional (2D) network or a single-channel 3D network with limited ability to extract high dimensional features. We aim to develop a deep learning approach based on multi-channel 3D convolution to utilize inherent noise information embedded in multiple number of excitation (NEX) acquisition for denoising 3D fast spin echo (FSE) MRI. Methods: A multi-channel 3D CNN is developed for denoising multi-NEX 3D FSE magnetic resonance (MR) images based on the feature extraction of 3D noise distributions embedded in 2-NEX 3D MRI. The performance of the proposed approach was compared to several state-of-the-art MRI denoising methods on both synthetic and real knee data using 2D and 3D metrics of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). Results: The proposed method achieved improved denoising performance compared to the current state-of-the-art denoising methods in both slice-by-slice 2D and volumetric 3D metrics of PSNR and SSIM. Conclusions: A multi-channel 3D CNN is developed for denoising of multi-NEX 3D FSE MR images. The superior performance of the proposed multi-channel 3D CNN in denoising multi-NEX 3D MRI demonstrates its potential in tasks that require the extraction of high-dimensional features.

Forecasting air pollutant concentration using a novel spatiotemporal deep learning model based on clustering, feature selection and empirical wavelet transform.

Accurate forecasting of air pollutant concentration is of great importance since it is an essential part of the early warning system. However, it still remains a challenge due to the limited information of emission source and high uncertainties of the dynamic processes. In order to improve the accuracy of air pollutant concentration forecast, this study proposes a novel hybrid model using clustering, feature selection, real-time decomposition by empirical wavelet transform, and deep learning neural network. First, all air pollutant time series are decomposed by empirical wavelet transform based on real-time decomposition, and subsets of output data are constructed by combining corresponding decomposed components. Second, each subset of output data is classified into several clusters by clustering algorithm, and then appropriate inputs are selected by feature selection method. Third, a deep learning-based predictor, which uses three dimensional convolutional neural network and bidirectional long short-term memory neural network, is applied to predict decomposition components of each cluster. Last, air pollutant concentration forecast for each monitoring station is obtained by reconstructing predicted values of all the decomposition components. PM2.5 concentration data of Beijing, China is used to validate and test our model. Results show that the proposed model outperforms other models used in this study. In our model, mean absolute percentage error for 1, 6, 10 h ahead PM2.5 concentration prediction is 4.03%, 6.87%, and 8.98%, respectively. These outcomes demonstrate that the proposed hybrid model is a powerful tool to provide highly accurate forecast for air pollutant concentration.