EPI phase error correction with deep learning (PEC-DL) at 7 T

Unsupervised deep learning model for correcting Nyquist ghosts of single-shot spatiotemporal encoding

PURPOSE

To design an unsupervised deep learning (DL) model for correcting Nyquist ghosts of single-shot spatiotemporal encoding (SPEN) and evaluate the model for real MRI applications.

METHODS

The proposed method consists of three main components: (1) an unsupervised network that combines Residual Encoder and Restricted Subspace Mapping (RERSM-net) and is trained to generate a phase-difference map based on the even and odd SPEN images; (2) a spin physical forward model to obtain the corrected image with the learned phase difference map; and (3) cycle-consistency loss that is explored for training the RERSM-net.

RESULTS

The proposed RERSM-net could effectively generate smooth phase difference maps and correct Nyquist ghosts of single-shot SPEN. Both simulation and real in vivo MRI experiments demonstrated that our method outperforms the state-of-the-art SPEN Nyquist ghost correction method. Furthermore, the ablation experiments of generating phase-difference maps show the advantages of the proposed unsupervised model.

CONCLUSION

The proposed method can effectively correct Nyquist ghosts for the single-shot SPEN sequence.

Research on Apparel Retail Sales Forecasting Based on xDeepFM-LSTM Combined Forecasting Model

Accurate sales forecasting can provide a scientific basis for the management decisions of enterprises. We proposed the xDeepFM-LSTM combined forecasting model for the characteristics of sales data of apparel retail enterprises. We first used the Extreme Deep Factorization Machine (xDeepFM) model to explore the correlation between the sales influencing features as much as possible, and then modeled the sales prediction. Next, we used the Long Short-Term Memory (LSTM) model for residual correction to improve the accuracy of the prediction model. We then designed and implemented comparison experiments between the combined xDeepFM-LSTM forecasting model and other forecasting models. The experimental results show that the forecasting performance of xDeepFM-LSTM is significantly better than other forecasting models. Compared with the xDeepFM forecasting model, the combined forecasting model has a higher optimization rate, which provides a scientific basis for apparel companies to make adjustments to adjust their demand plans.