High Efficient Reconstruction of Single-shot T2 Mapping from OverLapping-Echo Detachment Planar Imaging Based on Deep Residual Network

TL;DR

This paper introduces a deep residual neural network that efficiently reconstructs T2 mapping from single-shot OLED MRI images, enabling real-time imaging with high accuracy using simulated training data.

Contribution

The study presents a novel ResNet-based method trained on simulated data that generalizes well to real human brain MRI, significantly improving reconstruction speed and accuracy.

Findings

Outperforms traditional echo-detachment methods in accuracy.

Achieves T2 mapping within tens of milliseconds.

Generalizes from simulated to real human brain data effectively.

Abstract

Purpose: An end-to-end deep convolutional neural network (CNN) based on deep residual network (ResNet) was proposed to efficiently reconstruct reliable T2 mapping from single-shot OverLapping-Echo Detachment (OLED) planar imaging. Methods: The training dataset was obtained from simulations carried out on SPROM software developed by our group. The relationship between the original OLED image containing two echo signals and the corresponded T2 mapping was learned by ResNet training. After the ResNet was trained, it was applied to reconstruct the T2 mapping from simulation and in vivo human brain data. Results: Though the ResNet was trained entirely on simulated data, the trained network was generalized well to real human brain data. The results from simulation and in vivo human brain experiments show that the proposed method significantly outperformed the echo-detachment-based method. Reliable T2 mapping was achieved within tens of milliseconds after the network had been trained while the echo-detachment-based OLED reconstruction method took minutes. Conclusion: The proposed method will greatly facilitate real-time dynamic and quantitative MR imaging via OLED sequence, and ResNet has the potential to reconstruct images from complex MRI sequence efficiently.