Purpose The goal of this work is to propose a motion robust reconstruction method for diffusion‐weighted MRI that resolves shot‐to‐shot phase mismatches without using phase estimation. Methods Assuming that shot‐to‐shot phase variations are slowly varying, spatial‐shot matrices can be formed using a local group of pixels to form columns, in which each column is from a different shot (excitation). A convex model with a locally low‐rank constraint on the spatial‐shot matrices is proposed. In vivo brain and breast experiments were performed to evaluate the performance of the proposed method. Results The proposed method shows significant benefits when the motion is severe, such as for breast imaging. Furthermore, the resulting images can be used for reliable phase estimation in the context of phase‐estimation‐based methods to achieve even higher image quality. Conclusion We introduced the shot–locally low‐rank method, a reconstruction technique for multishot diffusion‐weighted MRI without explicit phase estimation. In addition, its motion robustness can be beneficial to neuroimaging and body imaging.

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PurposeTo resolve the motion‐induced phase variations in multi‐shot multi‐direction diffusion‐weighted imaging (DWI) by applying regularization to magnitude images.Theory and MethodsA nonlinear model was developed to estimate phase and magnitude images separately. A locally low‐rank regularization (LLR) term was applied to the magnitude images from all diffusion‐encoding directions to exploit the spatial and angular correlation. In vivo experiments with different resolutions and b‐values were performed to validate the proposed method.ResultsThe proposed method significantly reduces the noise level compared to the conventional reconstruction method and achieves submillimeter (0.8mm and 0.9mm isotropic resolutions) DWI with a b‐value of 1,000  and 1‐mm isotropic DWI with a b‐value of 2,000  without modification of the sequence.ConclusionsA joint reconstruction method with spatial‐angular LLR regularization on magnitude images substantially improves multi‐direction DWI reconstruction, simultaneously removes motion‐induced phase artifacts, and denoises images.

Purpose To accelerate and improve multishot diffusion‐weighted MRI reconstruction using deep learning. Methods An unrolled pipeline containing recurrences of model‐based gradient updates and neural networks was introduced for accelerating multishot DWI reconstruction with shot‐to‐shot phase correction. The network was trained to predict results of jointly reconstructed multidirection data using single‐direction data as input. In vivo brain and breast experiments were performed for evaluation. Results The proposed method achieves a reconstruction time of 0.1 second per image, over 100‐fold faster than a shot locally low‐rank reconstruction. The resultant image quality is comparable to the target from the joint reconstruction with a peak signal‐to‐noise ratio of 35.3 dB, a normalized root‐mean‐square error of 0.0177, and a structural similarity index of 0.944. The proposed method also improves upon the locally low‐rank reconstruction (2.9 dB higher peak signal‐to‐noise ratio, 29% lower normalized root‐mean‐square error, and 0.037 higher structural similarity index). With training data from the brain, this method also generalizes well to breast diffusion‐weighted imaging, and fine‐tuning further reduces aliasing artifacts. Conclusion A proposed data‐driven approach enables almost real‐time reconstruction with improved image quality, which improves the feasibility of multishot DWI in a wide range of clinical and neuroscientific studies.

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We propose a novel investment decision strategy (IDS) based on deep learning. The performance of many IDSs is affected by stock similarity. Most existing stock similarity measurements have the problems: (a) The linear nature of many measurements cannot capture nonlinear stock dynamics; (b) The estimation of many similarity metrics (e.g. covariance) needs very long period historic data (e.g. 3K days) which cannot represent current market effectively; (c) They cannot capture translation-invariance. To solve these problems, we apply Convolutional AutoEncoder to learn a stock representation, based on which we propose a novel portfolio construction strategy by: (i) using the deeply learned representation and modularity optimisation to cluster stocks and identify diverse sectors, (ii) picking stocks within each cluster according to their Sharpe ratio (Sharpe 1994). Overall this strategy provides low-risk high-return portfolios. We use the Financial Times Stock Exchange 100 Index (FTSE 100) data for evaluation. Results show our portfolio outperforms FTSE 100 index and many well known funds in terms of total return in 2000 trading days.

The Gaofen-3 (GF-3) data processor was developed as a workstation-based GF-3 synthetic aperture radar (SAR) data processing system. The processor consists of two vital subsystems of the GF-3 ground segment, which are referred to as data ingesting subsystem (DIS) and product generation subsystem (PGS). The primary purpose of DIS is to record and catalogue GF-3 raw data with a transferring format, and PGS is to produce slant range or geocoded imagery from the signal data. This paper presents a brief introduction of the GF-3 data processor, including descriptions of the system architecture, the processing algorithms and its output format.

Azimuth multichannel (AMC) synthetic aperture radar (SAR), which contains multiple receiving antennas along the azimuth, can prevent the minimum antenna area constraint and provide high-resolution and wide-swath (HRWS) SAR images. Channel calibration and along-track baseline estimation are important topics in an AMC SAR system, since they have a great impact on image quality. Based on the signal model for stationary target of AMC SAR, this paper first analyses the influence of the along-track baseline and channel imbalances on SAR images by simulation. Then, a novel method to simultaneously estimate the along-track baseline, phase imbalance and range sample time imbalance (RSTI) based on the azimuth cross-correlation in the two-dimensional frequency domain is addressed. In addition, with the help of simulations and real data acquired by Gaofen-3 (GF-3), the effectiveness of this method is verified by comparing with some existing methods. Finally, this paper analyzes the estimation accuracy of this method under different scenarios and signal-to-noise ratios (SNRs), and points out the direction for future research.