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Deep Learning for Fast and Spatially-Constrained Tissue Quantification from Highly-Undersampled Data in Magnetic Resonance Fingerprinting (MRF)

  • Zhenghan Fang
  • Yong Chen
  • Mingxia Liu
  • Yiqiang Zhan
  • Weili Lin
  • Dinggang Shen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11046)

Abstract

Magnetic resonance fingerprinting (MRF) is a novel quantitative imaging technique that allows simultaneous measurements of multiple important tissue properties in human body, e.g., T1 and T2 relaxation times. While MRF has demonstrated better scan efficiency as compared to conventional quantitative imaging techniques, further acceleration is desired, especially for certain subjects such as infants and young children. However, the conventional MRF framework only uses a simple template matching algorithm to quantify tissue properties, without considering the underlying spatial association among pixels in MRF signals. In this work, we aim to accelerate MRF acquisition by developing a new post-processing method that allows accurate quantification of tissue properties with fewer sampling data. Moreover, to improve the accuracy in quantification, the MRF signals from multiple surrounding pixels are used together to better estimate tissue properties at the central target pixel, which was simply done with the signal only from the target pixel in the original template matching method. In particular, a deep learning model, i.e., U-Net, is used to learn the mapping from the MRF signal evolutions to the tissue property map. To further reduce the network size of U-Net, principal component analysis (PCA) is used to reduce the dimensionality of the input signals. Based on in vivo brain data, our method can achieve accurate quantification for both T1 and T2 by using only 25% time points, which are four times of acceleration in data acquisition compared to the original template matching method.

Keywords

Magnetic resonance fingerprinting Relaxation times Deep learning 

References

  1. 1.
    Larsson, H.B.W., et al.: Assessment of demyelination, edema, and gliosis by in vivo determination of T1 and T2 in the brain of patients with acute attack of multiple sclerosis. Magn. Reson. Med. 11(3), 337–348 (1989)CrossRefGoogle Scholar
  2. 2.
    Usman, A.A., et al.: Cardiac magnetic resonance T2 mapping in the monitoring and follow-up of acute cardiac transplant rejection clinical perspective: a pilot study. Circ. Cardiovasc. Imaging 5(6), 782–790 (2012)CrossRefGoogle Scholar
  3. 3.
    Payne, A.R., et al.: Bright-blood T2-weighted MRI has high diagnostic accuracy for myocardial hemorrhage in myocardial infarction clinical perspective: a preclinical validation study in swine. Circ. Cardiovasc. Imaging 4(6), 738–745 (2011)CrossRefGoogle Scholar
  4. 4.
    Van Heeswijk, R.B., et al.: Free-breathing 3T magnetic resonance T2-mapping of the heart. JACC: Cardiovasc. Imaging 5(12), 1231–1239 (2012)Google Scholar
  5. 5.
    Coppo, S., et al.: Overview of magnetic resonance fingerprinting. In: MAGNETOM Flash, 65 (2016)Google Scholar
  6. 6.
    Ma, D., et al.: Magnetic resonance fingerprinting. Nature 495, 187–192 (2013)CrossRefGoogle Scholar
  7. 7.
    Ronneberger, O., et al.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham (2015)Google Scholar
  8. 8.
    Cohen, O., et al.: Deep learning for rapid sparse MR fingerprinting reconstruction. arXiv preprint arXiv:1710.05267 (2017)
  9. 9.
    Hoppe, E., et al.: Deep learning for magnetic resonance fingerprinting: a new approach for predicting quantitative parameter values from time series. Stud. Health Technol. Inf. 243, 202–206 (2017)Google Scholar
  10. 10.
    Mcgivney, D.F., et al.: SVD compression for magnetic resonance fingerprinting in the time domain. IEEE Trans. Med. Imaging 33(12), 2311 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Zhenghan Fang
    • 1
  • Yong Chen
    • 1
  • Mingxia Liu
    • 1
  • Yiqiang Zhan
    • 2
  • Weili Lin
    • 1
  • Dinggang Shen
    • 1
  1. 1.Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA
  2. 2.Institute for Medical Imaging TechnologySchool of Biomedical Engineering, Shanghai Jiao Tong UniversityShanghaiChina

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