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.
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This work was supported by National Natural Science Foundation of China; Grant numbers: 81671674 and 61571382.
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Wang, C., Wu, Y., Ding, X., Huang, Y., Cai, C. (2018). High Efficient Reconstruction of Single-Shot Magnetic Resonance \(T_{2}\) Mapping Through Overlapping Echo Detachment and DenseNet. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11306. Springer, Cham. https://doi.org/10.1007/978-3-030-04224-0_35
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