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Multi-parametric liver tissue characterization using MR Fingerprinting: simultaneous T1, T2, T2* and fat fraction mapping

Research output: Contribution to journalArticlepeer-review

Original languageEnglish
Article numbermrm.28311
JournalMagnetic Resonance in Medicine
Accepted/In press16 Apr 2020


  • 9pLiverMRF_MainDocument

    9pLiverMRF_MainDocumentR1_forPURE_SV.pdf, 1.78 MB, application/pdf

    Uploaded date:17 Apr 2020

    Version:Accepted author manuscript

    Licence:CC BY

King's Authors


Purpose: Quantitative T1, T2, T2* and fat fraction (FF) maps are promising imaging biomarkers for the assessment of liver disease, however these are usually acquired in sequential scans. Here we propose an extended Magnetic Resonance Fingerprinting (MRF) framework enabling simultaneous liver T1, T2, T2* and FF mapping from a single ~14s breath-hold scan. Methods: A gradient echo (GRE) liver MRF sequence with nine readouts per TR, low flip angles (5-15o), varying magnetisation preparation and golden angle radial trajectory is acquired at 1.5T to encode T1, T2, T2* and FF simultaneously. The 9-echo time-series are reconstructed using a low-rank tensor constrained reconstruction and used to fit T2*, B0 and to separate the water and fat signals. Water and fat specific T1, T2 and M0 are obtained through dictionary matching, whereas FF estimation is extracted from the M0 maps. The framework was evaluated in a standardized T1/T2 phantom, a water-fat phantom and 12 subjects in comparison to reference methods. Preliminary clinical feasibility is shown in 4 patients. Results: The proposed water T1, water T2, T2* and FF maps in phantoms showed high coefficients of determination (r2>0.97) relative to reference methods. Measured liver MRF values in vivo (mean ±standard deviation) for T1, T2, T2* and FF were 671±60ms, 43.2±6.8ms, 29±6.6ms and 3.2±2.6% with biases of 92ms, -7.1ms, -1.4ms and 0.63% when compared to conventional methods. Conclusion: A 9-echo liver MRF sequence allows for quantitative multi-parametric liver tissue characterization in a single breath-hold scan of ~14s. Future work will aim to validate the proposed approach in patients with liver disease.

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