Water–fat Dixon cardiac magnetic resonance fingerprinting

  • Olivier Jaubert
    School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom
  • Gastão Cruz
    School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom
  • Aurélien Bustin
    School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom
  • Torben Schneider
    Philips Healthcare Guilford United Kingdom
  • Begoña Lavin
    School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom
  • Peter Koken
    Philips Research Europe Hamburg Germany
  • Reza Hajhosseiny
    School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom
  • Mariya Doneva
    Philips Research Europe Hamburg Germany
  • Daniel Rueckert
    Department of Computing Imperial College London London United Kingdom
  • René M. Botnar
    School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom
  • Claudia Prieto
    School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom

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<jats:sec><jats:title>Purpose</jats:title><jats:p>Cardiac magnetic resonance fingerprinting (cMRF) has been recently introduced to simultaneously provide T<jats:sub>1</jats:sub>, T<jats:sub>2</jats:sub>, and M<jats:sub>0</jats:sub> maps. Here, we develop a 3‐point Dixon‐cMRF approach to enable simultaneous water specific T<jats:sub>1</jats:sub>, T<jats:sub>2</jats:sub>, and M<jats:sub>0</jats:sub> mapping of the heart and fat fraction (FF) estimation in a single breath‐hold scan.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>Dixon‐cMRF is achieved by combining cMRF with several innovations that were previously introduced for other applications, including a 3‐echo GRE acquisition with golden angle radial readout and a high‐dimensional low‐rank tensor constrained reconstruction to recover the highly undersampled time series images for each echo. Water–fat separation of the Dixon‐cMRF time series is performed to allow for water‐ and fat‐specific T<jats:sub>1</jats:sub>, T<jats:sub>2</jats:sub>, and M<jats:sub>0</jats:sub> estimation, whereas FF estimation is extracted from the M<jats:sub>0</jats:sub> maps. Dixon‐cMRF was evaluated in a standardized T<jats:sub>1</jats:sub>–T<jats:sub>2</jats:sub> phantom, in a water–fat phantom, and in healthy subjects in comparison to current clinical standards: MOLLI, SASHA, T<jats:sub>2</jats:sub>‐GRASE, and 6‐point Dixon proton density FF (PDFF) mapping.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Dixon‐cMRF water T<jats:sub>1</jats:sub> and T<jats:sub>2</jats:sub> maps showed good agreement with reference T<jats:sub>1</jats:sub> and T<jats:sub>2</jats:sub> mapping techniques (R<jats:sup>2</jats:sup> > 0.99 and maximum normalized RMSE ~5%) in a standardized phantom. Good agreement was also observed between Dixon‐cMRF FF and reference PDFF (R<jats:sup>2</jats:sup> > 0.99) and between Dixon‐cMRF water T<jats:sub>1</jats:sub> and T<jats:sub>2</jats:sub> and water selective T<jats:sub>1</jats:sub> and T<jats:sub>2</jats:sub> maps (R<jats:sup>2</jats:sup> > 0.99) in a water–fat phantom. In vivo Dixon‐cMRF water T<jats:sub>1</jats:sub> values were in good agreement with MOLLI and water T<jats:sub>2</jats:sub> values were slightly underestimated when compared to T<jats:sub>2</jats:sub>‐GRASE. Average myocardium septal T<jats:sub>1</jats:sub> values were 1129 ± 38 ms, 1026 ± 28 ms, and 1045 ± 32 ms for SASHA, MOLLI, and the proposed water Dixon‐cMRF. Average T<jats:sub>2</jats:sub> values were 51.7 ± 2.2 ms and 42.8 ± 2.6 ms for T<jats:sub>2</jats:sub>‐GRASE and water Dixon‐cMRF, respectively. Dixon‐cMRF FF maps showed good agreement with in vivo PDFF measurements (R<jats:sup>2</jats:sup> > 0.98) and average FF in the septum was measured at 1.3%.</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>The proposed Dixon‐cMRF allows to simultaneously quantify myocardial water T<jats:sub>1</jats:sub>, water T<jats:sub>2</jats:sub>, and FF in a single breath‐hold scan, enabling multi‐parametric T<jats:sub>1</jats:sub>, T<jats:sub>2</jats:sub>, and fat characterization. Moreover, reduced T<jats:sub>1</jats:sub> and T<jats:sub>2</jats:sub> quantification bias caused by water–fat partial volume was demonstrated in phantom experiments.</jats:p></jats:sec>

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