4.4 Article

Varying undersampling directions for accelerating multiple acquisition magnetic resonance imaging

期刊

NMR IN BIOMEDICINE
卷 35, 期 4, 页码 -

出版社

WILEY
DOI: 10.1002/nbm.4572

关键词

balanced steady-state free precession; compressed sensing; deep learning; multicontrast MRI; phase-encoding dimension; sampling pattern

资金

  1. National Research Foundation of Korea [NRF-2020R1A4A1018714, NRF-2020R1A2C2008949]
  2. Korea Medical Device Development Fund [9991006735]
  3. National Research Foundation of Korea [2020R1A2C2008949] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

向作者/读者索取更多资源

The proposed new sampling strategy showed superior performance in various types of MRI imaging, regardless of sampling pattern or datasets, particularly in multicontrast MR imaging and multiple PC-bSSFP imaging.
In this study, we propose a new sampling strategy for efficiently accelerating multiple acquisition MRI. The new sampling strategy is to obtain data along different phase-encoding directions across multiple acquisitions. The proposed sampling strategy was evaluated in multicontrast MR imaging (T1, T2, proton density) and multiple phase-cycled (PC) balanced steady-state free precession (bSSFP) imaging by using convolutional neural networks with central and random sampling patterns. In vivo MRI acquisitions as well as a public database were used to test the concept. Based on both visual inspection and quantitative analysis, the proposed sampling strategy showed better performance than sampling along the same phase-encoding direction in both multicontrast MR imaging and multiple PC-bSSFP imaging, regardless of sampling pattern (central, random) or datasets (public, retrospective and prospective in vivo). For the prospective in vivo applications, acceleration was performed by sampling along different phase-encoding directions at the time of acquisition with a conventional rectangular field of view, which demonstrated the advantage of the proposed sampling strategy in the real environment. Preliminary trials on compressed sensing (CS) also demonstrated improvement of CS with the proposed idea. Sampling along different phase-encoding directions across multiple acquisitions is advantageous for accelerating multiacquisition MRI, irrespective of sampling pattern or datasets, with further improvement through transfer learning.

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