MR fingerprinting with simultaneous T, T, and fat signal fraction estimation with integrated B correction reduces bias in water T and T estimates.

Ostenson J, Damon BM, Welch EB
Magn Reson Imaging. 2019 60: 7-19

PMID: 30910696 · PMCID: PMC6581466 · DOI:10.1016/j.mri.2019.03.017

PURPOSE - MR fingerprinting (MRF) sequences permit efficient T and T estimation in cranial and extracranial regions, but these areas may include substantial fat signals that bias T and T estimates. MRI fat signal fraction estimation is also a topic of active research in itself, but may be complicated by B heterogeneity and blurring during spiral k-space acquisitions, which are commonly used for MRF. An MRF method is proposed that separates fat and water signals, estimates water T and T, and accounts for B effects with spiral blurring correction, in a single sequence.

THEORY AND METHODS - A k-space-based fat-water separation method is further extended to unbalanced steady-state free precession MRF with swept echo time. Repeated application of this k-space fat-water separation to demodulated forms of the measured data allows a B map and correction to be approximated. The method is compared with MRF without fat separation across a broad range of fat signal fractions (FSFs), water Ts and Ts, and under heterogeneous static fields in simulations, phantoms, and in vivo.

RESULTS - The proposed method's FSF estimates had a concordance correlation coefficient of 0.990 with conventional measurements, and reduced biases in the T and T estimates due to fat signal relative to other MRF sequences by several hundred ms. The B correction improved the FSF, T, and T estimation compared to those estimates without correction.

CONCLUSION - The proposed method improves MRF water T and T estimation in the presence of fat and provides accurate FSF estimation with inline B correction.

Copyright © 2019 Elsevier Inc. All rights reserved.

MeSH Terms (17)

Abdomen Adipose Tissue Algorithms Bias Computer Simulation Fourier Analysis Head Humans Image Processing, Computer-Assisted Knee Magnetic Resonance Imaging Models, Statistical Muscle, Skeletal Phantoms, Imaging Reproducibility of Results Signal Processing, Computer-Assisted Water

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