Dongyeob Han^{1}, Taehwa Hong^{1}, and Dong-Hyun Kim^{1}

A high resolution (0.5x0.5x1mm^{3}) 3D MRF method was
proposed using a hybrid radial cartesian-EPI acquisition with both segmented & interleaved EPI strategy. For the reconstruction, k-space SVD compression and CG-SENSE were applied. An in vivo brain results were presented.

Figure 1 shows the propose pulse sequence diagram and the flip angle,
TR patterns. A hybrid radial cartesian-EPI trajectory [7] was applied in 3D MRF
acquisition to minimize the scan time while obtaining under-isotropic
resolution (0.5x0.5x1mm^{3}). Furthermore, to mitigate many artifacts caused
by the EPI, both segmented & interleaved EPI acquisition though the k_{z}-axis
were used. Total 120 slices were acquired and 120 k_{z} lines (=N_{z})
were segmented to 6 segments (=N_{seg}). Therefore, each segment has 20
k_{z} lines and then interleaved EPI acquisition was applied with
acceleration factor 5 (k_{z} EPI blip has 5Δk_{z} momentum shown in Fig 1a). Therefore, only 4 k_{z}
lines were acquired during one TR as shown in Fig 1a. For the next TR, the
slice encoding gradient was increased as 1 Δk_{z} momentum, and then same segmented & interleaved EPI acquisition
was applied while radial spoke rotation with golden angle along the k_{xy}
direction [8]. The momentum of the slice encoding gradients were increased until
5 consequtive TRs (Δk_{z}*(acceleration
factor-1)=4Δk_{z}). After
then, the partition number was started from the bottom of the k_{z}
segment. This manner was applied until 600 TRs (=N_{t}), then waiting
time (5 sec) was applied. Then repeated 8 times (=N_{spks}) to fill the
k_{xy} plane. A graphical example with reduced N_{z}=40 and N_{seg}=2
were presented in Figure 2.

Brain data of healthy volunteer was acquired using 20ch head coil, 3T Siemens Prisma
scanner with IRB approval. Scan parameters : resolution : 0.5x0.5x1mm^{3},
FOV : 192x192x120 mm^{3}, N_{spks}=8 (number of radial spokes, inplane
acceleration factor was 72 with respect to Nyquist limit), N_{z}=120
(number of slices), N_{seg}=6, R_{EPI}=5 (EPI reduction factor),
N_{t}=600 (MRF time points). Total
scan time=12min 10sec. Dictionary was generated with T_{1}=[50:10:2000,
2050:50:3500] ms and T_{2}=[20:1:250,260:10:350] ms using Bloch simulation. The
dictionary was compressed using SVD [9].

For the reconstruction, the acquired data *S* was compressed using SVD to $$$\widetilde{S}$$$ in
k-space domain [9]. After then, the following problem was solved using CG-SENSE
[10].

$$\widetilde{x}(n)=\arg \min_{\widetilde{x}(n)}\sum_c\parallel G\cdot F \cdot E_c \cdot \widetilde{x}(n) - \widetilde{S}(n) \parallel_2^2 , n=1, 2, ... ,N_{sv}$$

*G* is the gridding matrix, *F* is the Fourier matrix
and *E _{c}* is the coil sensitivity maps.

The B_{1}^{+} field inhomogeneity affects
estimation accuracy in MRF [13,14]. Therefore, acquiring additional B_{1}^{+} map and
including B_{1} effect in dictionary might be needed. Bloch-Siegert with spiral acquisition,
used in previous study [5], is an effective way to acquire 3D B_{1}^{+} map within
short scan time.

Waiting time used in this study was set to be 5sec. This waiting time is quite long compared to other 3D MRF studies [5,6]. Therefore, if we reduce the waiting time to 2sec, the total scan time was also reduced to 9min 46sec from 12min 10sec.

Changing the number of segments and reduction factor of EPI will affect the quality of quantitative maps and scan time. Thus, further optimization and analysis would be required and its our future work.

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[12] The Berkeley Advanced Reconstruction Toolbox (BART) toolbox (https://mrirecon.github.io/bart/) 2015.

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