Abstract #1109

Sub-millimeter MR Fingerprinting using deep-learning-based spatially-constrained tissue quantification

Zhenghan Fang^1,2,3, Yong Chen^1,2, Weili Lin^1,2, and Dinggang Shen^1,2

¹Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States, ²Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States, ³Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States

MRF is a relatively new quantitative MR imaging technique which can provide rapid and simultaneous quantification of multiple tissue properties. However, high-resolution MRF, particularly at sub-millimeter levels, is technically challenging and often requires extended scan time. In this study, a rapid high-resolution MRF technique was developed using a deep-learning-based spatially-constrained tissue quantification method. The experimental results from in vivo brain data demonstrate that high-quality T₁ and T₂ quantification with 0.8-mm resolution can be achieved in 15 sec per slice.

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