Jason Ostenson1, Bruce M Damon2, E Brian Welch3. 1. Vanderbilt University Institute of Imaging Science, Nashville, TN 37232, United States of America; Chemical and Physical Biology Program, Vanderbilt University, United States of America. 2. Vanderbilt University Institute of Imaging Science, Nashville, TN 37232, United States of America; Chemical and Physical Biology Program, Vanderbilt University, United States of America; Dept. of Radiology and Radiological Sciences, Vanderbilt University, United States of America; Dept. of Biomedical Engineering, Vanderbilt University, United States of America; Dept. of Molecular Physiology and Biophysics, Vanderbilt University, United States of America. Electronic address: bruce.damon@vanderbilt.edu. 3. Vanderbilt University Institute of Imaging Science, Nashville, TN 37232, United States of America; Dept. of Radiology and Radiological Sciences, Vanderbilt University, United States of America; Dept. of Biomedical Engineering, Vanderbilt University, United States of America.
Abstract
PURPOSE: MR fingerprinting (MRF) sequences permit efficient T1 and T2 estimation in cranial and extracranial regions, but these areas may include substantial fat signals that bias T1 and T2 estimates. MRI fat signal fraction estimation is also a topic of active research in itself, but may be complicated by B0 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 T1 and T2, and accounts for B0 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 B0 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 T1s and T2s, 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 T1 and T2 estimates due to fat signal relative to other MRF sequences by several hundred ms. The B0 correction improved the FSF, T1, and T2 estimation compared to those estimates without correction. CONCLUSION: The proposed method improves MRF water T1 and T2 estimation in the presence of fat and provides accurate FSF estimation with inline B0 correction.
PURPOSE: MR fingerprinting (MRF) sequences permit efficient T1 and T2 estimation in cranial and extracranial regions, but these areas may include substantial fat signals that bias T1 and T2 estimates. MRI fat signal fraction estimation is also a topic of active research in itself, but may be complicated by B0 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 T1 and T2, and accounts for B0 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 B0 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 T1s and T2s, 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 T1 and T2 estimates due to fat signal relative to other MRF sequences by several hundred ms. The B0 correction improved the FSF, T1, and T2 estimation compared to those estimates without correction. CONCLUSION: The proposed method improves MRF water T1 and T2 estimation in the presence of fat and provides accurate FSF estimation with inline B0 correction.
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