Dongmyung Shin^{1}, Se-Hong Oh^{2}, Doohee Lee^{1}, Jingu Lee^{1}, and Jongho Lee^{1}

In this work, we demonstrated that a modified gradient-echo sampling of FID and echo (mGESFIDE) sequence was able to produce multiple contrast images (R_{2}, R_{2}', R_{2}*, local field map, QSM, positive and negative susceptibility maps) in 5 minutes of scan time. We developed a new algorithm for improved R_{2} and R_{2}' maps by considering RF slice profiles in the excitation and refocusing RF pulses. Additionally, we developed a new method that generated a high-quality local field map by utilizing all echoes of mGESFIDE.

**[Pulse sequence]** Data was collected at 3T using a 2D mGESFIDE sequence (Fig. 1). Total 20 echoes were acquired before (3:3:21 ms, 7 echoes) and after the refocusing RF pulse (31:3:67 ms, 13 echoes). The scan parameters were as follows: TR = 3000 ms, voxel size = 1x1 mm^{2}, number of slices = 40 and scan time = 5:36 minutes. For comparison, 2D multi-echo SE and GRE were acquired (SE: 10 echoes (10:10:100 ms), TR = 3500 ms; GRE: 7 echoes (3.06:4.59:30.6 ms), TR = 2400 ms; Other parameters were the same). The total scan time for SE and GRE was 14:55 minutes.

**[R2 and R2' estimation]** Overall data processing pipeline is summarized in Figure 2. A novel R_{2} and R_{2}' estimation method incorporated the excitation and refocusing RF slice profiles in the model which was expressed as follows:$$S(TE)=\begin{cases}\sum_zsin\space\alpha(z)\cdot exp(-(R_2 + R_2')\cdot TE) & TE<TE_{ref}/2\\\sum_zsin\space\alpha(z)\cdot(1-cos\space\beta(z))\cdot exp(-R_2\cdot TE)\cdot exp(-R_2'\cdot|TE-TE_{ref}|) & TE>TE_{ref}/2\end{cases}\space\space\space\space\space\space[Eq. 1]$$ where α and β are excitation and refocusing flip angles calculated by RF slice profile sampled with 200 points along the z-direction denoted by z [6], S is a generated signal, TE is echo time, and TE_{ref} is refocusing echo time (=52 ms). To estimate R_{2} and R_{2}', a dictionary was filled with generated signals over a wide range of R_{2} and R_{2}' values (R_{2} = 0:0.1:40 Hz and R_{2}' = 0:0.1:30 Hz). This dictionary was searched for the best matching parameters for mGESFIDE data. The results of this dictionary matching method were compared with those from conventional GESFIDE processing which does not incorporate slice profile information [3]. The additional comparison was performed with the results from GRE and SE.

**[Local field estimation]** For a reliable estimation of the field map in mGESIFIDE, a new method utilizing all echoes of mGESFIDE was developed. The minimization problem is given as follows:$$argmin_{f,\phi_0,\phi_1}\sum_{TE_j<TE_{ref}/2}||S(TE_j)-A(TE_j)\cdot exp(i\cdot(f\cdot TE_j+\phi_{0}))||^2_2+\sum_{TE_k>TE_{ref}/2}||S(TE_k)-A(TE_k)\cdot exp(i\cdot(f\cdot TE_k+\phi_{1}))||^2_2\space\space\space\space\space\space[Eq. 2]$$where f represents the frequency shift of a voxel which is the same before and after the refocusing RF pulse, Φ_{0} and Φ_{1} are different phase offsets before and after the refocusing RF pulse, S is the complex signal data, A is the signal magnitude. The cost function was linearized and updated iteratively [7]. From f, local field was estimated [8]. For comparison, complex multi-echo data from GRE were processed for a local field map. QSM maps were generated from the local field maps using MEDI [9].

**[Susceptibility source separation] **Since our sequence provides both R_{2}' and local field maps, one can perform susceptibility sources separation to generate positive and negative susceptibility source maps (χ_{pos} and χ_{neg}) as suggested in [5]. The results were compared with the maps generated using R_{2}' and local field maps from the SE and GRE.

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