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  High-resolution neural network-driven mapping of multiple diffusion metrics leveraging asymmetries in the balanced steady-state free precession frequency profile

Birk, F., Glang, F., Loktyushin, A., Birkl, C., Ehses, P., Scheffler, K., et al. (2022). High-resolution neural network-driven mapping of multiple diffusion metrics leveraging asymmetries in the balanced steady-state free precession frequency profile. NMR in Biomedicine, 35(6). doi:10.1002/nbm.4669.

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Birk, F1, Author           
Glang, F1, Author           
Loktyushin, A1, Author           
Birkl, C, Author
Ehses, P, Author           
Scheffler, K1, Author           
Heule, R1, Author           
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1Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497796              

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 Abstract: We propose to utilize the rich information content about microstructural tissue properties entangled in asymmetric balanced steady-state free precession (bSSFP) profiles to estimate multiple diffusion metrics simultaneously by neural network (NN) parameter quantification. A 12-point bSSFP phase-cycling scheme with high-resolution whole-brain coverage is employed at 3 and 9.4 T for NN input. Low-resolution target diffusion data are derived based on diffusion-weighted spin-echo echo-planar-imaging (SE-EPI) scans, that is, mean, axial, and radial diffusivity (MD, AD, and RD), fractional anisotropy (FA), as well as the spherical coordinates (azimuth Φ and inclination ϴ) of the principal diffusion eigenvector. A feedforward NN is trained with incorporated probabilistic uncertainty estimation. The NN predictions yielded highly reliable results in white matter (WM) and gray matter structures for MD. The quantification of FA, AD, and RD was overall in good agreement with the reference but the dependence of these parameters on WM anisotropy was somewhat biased (e.g. in corpus callosum). The inclination ϴ was well predicted for anisotropic WM structures, while the azimuth Φ was overall poorly predicted. The findings were highly consistent across both field strengths. Application of the optimized NN to high-resolution input data provided whole-brain maps with rich structural details. In conclusion, the proposed NN-driven approach showed potential to provide distortion-free high-resolution whole-brain maps of multiple diffusion metrics at high to ultrahigh field strengths in clinically relevant scan times.

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 Dates: 2021-122022-06
 Publication Status: Published in print
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 Identifiers: DOI: 10.1002/nbm.4669
eDoc: e4669
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Title: NMR in Biomedicine
Source Genre: Journal
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Publ. Info: London : Heyden & Son
Pages: 16 Volume / Issue: 35 (6) Sequence Number: - Start / End Page: - Identifier: ISSN: 0952-3480
CoNE: https://pure.mpg.de/cone/journals/resource/954925574973