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An iterative sparse deconvolution method for simultaneous multicolor 19 F‐MRI of multiple contrast agents
Author(s) -
Schoormans Jasper,
Calcagno Claudia,
Daal Mariah R.R.,
Wüst Rob C.I.,
Faries Christopher,
Maier Alexander,
Teunissen Abraham J.P.,
Naidu Sonum,
SanchezGaytan Brenda L.,
Nederveen Aart J.,
Fayad Zahi A.,
Mulder Willem J.M.,
Coolen Bram F.,
Strijkers Gustav J.
Publication year - 2020
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.27926
Subject(s) - deconvolution , imaging phantom , robustness (evolution) , artifact (error) , signal (programming language) , noise (video) , biological system , signal to noise ratio (imaging) , scanner , chemistry , nuclear magnetic resonance , algorithm , computer science , physics , artificial intelligence , optics , image (mathematics) , biochemistry , biology , gene , programming language
Purpose 19 F‐MRI is gaining widespread interest for cell tracking and quantification of immune and inflammatory cells in vivo. Different fluorinated compounds can be discriminated based on their characteristic MR spectra, allowing in vivo imaging of multiple 19 F compounds simultaneously, so‐called multicolor 19 F‐MRI. We introduce a method for multicolor 19 F‐MRI using an iterative sparse deconvolution method to separate different 19 F compounds and remove chemical shift artifacts arising from multiple resonances. Methods The method employs cycling of the readout gradient direction to alternate the spatial orientation of the off‐resonance chemical shift artifacts, which are subsequently removed by iterative sparse deconvolution. Noise robustness and separation was investigated by numerical simulations. Mixtures of fluorinated oils (PFCE and PFOB) were measured on a 7T MR scanner to identify the relation between 19 F signal intensity and compound concentration. The method was validated in a mouse model after intramuscular injection of fluorine probes, as well as after intravascular injection. Results Numerical simulations show efficient separation of 19 F compounds, even at low signal‐to‐noise ratio. Reliable chemical shift artifact removal and separation of PFCE and PFOB signals was achieved in phantoms and in vivo. Signal intensities correlated excellently to the relative 19 F compound concentrations (r −2 = 0.966/0.990 for PFOB/PFCE). Conclusions The method requires minimal sequence adaptation and is therefore easily implemented on different MRI systems. Simulations, phantom experiments, and in‐vivo measurements in mice showed effective separation and removal of chemical shift artifacts below noise level. We foresee applicability for simultaneous in‐vivo imaging of 19 F‐containing fluorine probes or for detection of 19 F‐labeled cell populations.