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High Efficient Reconstruction of Single-Shot Magnetic Resonance \(T_{2}\) Mapping Through Overlapping Echo Detachment and DenseNet

  • Chao Wang
  • Yawen Wu
  • Xinghao Ding
  • Yue Huang
  • Congbo Cai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11306)

Abstract

Rapid and quantitative magnetic resonance \(T_{2}\) imaging plays an important role in medical imaging field. However, the existing quantitative \(T_{2}\) mapping method are usually time-consuming and sensitive to motion artifacts. Recently, a novel single-shot quantitative parameter mapping method based on overlapped-echo detachment technique has been proposed by us, but an efficient reconstruction algorithm is necessary. In this paper, a multi-stage DenseNet was utilized to reconstruct single-shot \(T_{2}\) mapping efficiently. The contributions of the paper mainly include the following aspects. First, an end-to-end neural network is proposed, which can directly obtain the reconstructed images without any secondary processing. Second, DenseNet was introduced into the reconstruction network to better reuse the features. Third, a weighted Euclidean loss function is proposed, which can be better used for image reconstruction.

Keywords

Magnetic resonance imaging (MRI) Single-shot \(T_{2}\) mapping Reconstruction Deep learning DenseNet 

Notes

Acknowledgement

This work was supported by National Natural Science Foundation of China; Grant numbers: 81671674 and 61571382.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Chao Wang
    • 1
  • Yawen Wu
    • 1
  • Xinghao Ding
    • 1
  • Yue Huang
    • 1
  • Congbo Cai
    • 1
    • 2
  1. 1.Fujian Key Laboratory of Sensing and Computing for Smart City, School of Information Science and EngineeringXiamen UniversityXiamenChina
  2. 2.Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronics ScienceXiamen UniversityXiamenChina

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