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Extracting Gold Standard Relaxation Times and Field Map Estimates from the Balanced SSFP Frequency Profile by Neural Network Fitting

MPS-Authors
/persons/resource/persons216029

Heule,  R
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84187

Scheffler,  K
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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http://indexsmart.mirasmart.com/ISMRM2019/PDFfiles/4534.html
(Abstract)

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Citation

Heule, R., & Scheffler, K. (2019). Extracting Gold Standard Relaxation Times and Field Map Estimates from the Balanced SSFP Frequency Profile by Neural Network Fitting. Poster presented at 27th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM 2019), Montréal, QC, Canada.


Cite as: https://hdl.handle.net/21.11116/0000-0003-96F4-0
Abstract
It has been observed that the balanced steady-state free precession (bSSFP) frequency profile exhibits asymmetries if the intra-voxel frequency content is inhomogeneous and asymmetric. Recent attempts to calculate T1 and T2 values of human brain tissues from the measured bSSFP profile fail to account for anisotropies in the tissue microenvironment and are thus subject to a considerable bias, in particular for white matter. To eliminate this bias, a feedforward neural network is trained with the bSSFP profile as input and a multi-parametric output (i.e., T1, T2, B1, ?B0) using gold standard relaxation times and reference field maps as ground truth.