Creatine(Cr)-weighted chemical-exchange-saturation-transfer(CEST) MRI is being developed to detect alteration in Cr concentration during modulation as well as Cr-associated disorders. Cr-weighted CEST contrast show dependence on saturation parameters and overlap from other effects in brain. The conventional asymmetry approach results in a mixed contrast, which is less specific to Cr. By using appropriate saturation parameters, contamination from some of the metabolites/molecules can be reduced; however, it is difficult to suppress the contamination completely as shown by simulation studies. Proposed protocol and improved z-spectral fitting approach can be used for computing Cr-weighted CEST contrast with reduced contamination in human brain at 7T.
MRI of brain of healthy human subjects(three) were performed at 7T scanner(Siemens). The pulse sequence used in the current study was reported previously11. MRI data for B0 map, B1 map, Z-spectra or CEST were acquired. Details of MRI parameters are listed in the Table-1. CEST data at two time points were acquired for reproducibility. Bloch McConnell equation based simulations were carried out to obtain optimum saturation parameters for Cr-weighted CEST with reduced contamination from other CEST effects(Glutamate-weighted, APT-weighted, Myoinositol-weighted)11. CEST contrast was computed using CESTasy and modified Z-spectra fitting approaches based upon Lorentzian functions. CEST data were corrected for B0 and B1 field inhomogeneity. For estimating CEST effects from individual components, scaled Z-spectrum(Zsc) data was fitted using superposition of Lorentzian functions corresponding to multiple pools (Eq[1]).
$$$f(\triangle\omega)=100\times\sum_1^N\frac{{A_{n}}}{1+4\times\frac{\triangle\omega-\triangle\omega_{n}}{\sigma_{n}}} \space\space\space ...[1] $$$
where Δω is the frequency-offset from the water resonance, An, Δωn and σn are the amplitude, frequency-offset and linewidth of the CEST peak for the nth proton pool, respectively. In this study, two Z-spectra fitting approaches(Model-1 and Model-2) were used. In Model-1, the Zsc was fitted with the sum of five Lorentzian functions. For brain data following 5 pools were considered: MT, DS, rNOE, Cr-weighted CEST and APT-weighted/Glu. Model-2 is an improved version of Model-1. In Model-2, instead of fitting Zsc directly, we first used limited data(±12 to ±20ppm) with negligible contribution from DS, for computing MT component. This fitted MT component was removed from the Zsc and remaining part was fitted using superposition of 4-pool Lorentizian function. For reproducibility, coefficient-of-variation(COV) was computed for multiple ROIs in brain.
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