Application of Laplacian-based Methods to Multi-echo Phase Data for Accurate Susceptibility Mapping
Emma Biondetti1, David L. Thomas2, and Karin Shmueli1

1Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom, 2Leonard Wolfson Experimental Neurology Centre, UCL Institute of Neurology, University College London, London, United Kingdom

### Synopsis

In Susceptibility Mapping (SM) using multi-echo gradient-echo phase data, unwrapping and/or background-field removal is often performed using Laplacian-based methods. However, SM pipelines in the literature have applied these methods at different stages. Here, using simulated and acquired images, we compared the performance of three pipelines that apply Laplacian-based methods at different stages. We showed that Laplacian-based methods alter the linearity of the phase over time. We demonstrated that only a processing pipeline that takes this into account, i.e. by fitting the multi-echo data over time to correctly estimate a field map before applying Laplacian-based methods, gives accurate susceptibility values.

### Introduction

In Susceptibility Mapping (SM), Laplacian-based techniques, e.g. SHARP1,2, have been used to perform unwrapping and/or background-field removal of multi-echo gradient-echo phase data. The unwrapped gradient-echo phase at time $t$ and location $\bf r$ is $$\phi(t, {\bf r}) = \gamma \cdot \Delta B({\bf r}) \cdot t + \phi_0(0,{\bf r}) \quad (1),$$ where $\gamma$ is the proton gyromagnetic ratio, $\phi_0$ the $t = 0$ phase offset and $\Delta B$ the total field variation from local ($\Delta B_{loc}$) and background ($\Delta B_{bg}$) sources. In SM, multi-echo acquisitions are preferable because they allow fitting to Equation (1) to give $\phi_0$ and increase the accuracy of $\Delta B$ estimates3.

Several studies4-6 have used Laplacian unwrapping at each echo time (TE) as an initial step. Assuming $\phi_0 = 0$, a field map has then been calculated by scaling the Laplacian-unwrapped phase according to (1) at each TE and averaging the results. Similarly, other studies7,8 have used simultaneous Laplacian unwrapping and background-field removal at each TE followed by scaling according to (1), again assuming $\phi_0 = 0$. However, averaging the processed phase images assumes that Laplacian-based methods preserve the linear phase-time dependence (1). In contrast, others2,9 have fitted the unwrapped phase over TEs to calculate $\Delta B$ (and $\phi_0 = 0$) and have then removed $\Delta B_{bg}$ from the fitted $\Delta B$ using a Laplacian-based technique.

### Purpose

Here, we applied three processing pipelines2,4-9 to phase images of a numerical phantom and a healthy volunteer. We investigated the effect of using Laplacian-based methods at different stages of the SM pipeline on the phase-time linearity (1) and the accuracy of $\chi$ estimation.

### Methods

Laplacian-based phase unwrapping (Lap-Unw) or background-field removal (Lap-Bg) were implemented with SHARP1,2 and a 3x3x3 Laplacian kernel2. For Lap-Unw, non-eroded brain mask (FSL BET10 for the volunteer) and threshold11 $\sigma = 10^{-10}$ were used. For Lap-Bg, the same brain mask with 2-voxel12 erosion and threshold2 $\sigma = 0.05$ were used.

Phantom

Wrapped phase ($\phi_0 = 0$) was simulated at five echoes (TE1/ΔTE = 10/10 ms) from a ground-truth susceptibility distribution13,14 (Zubal phantom15, Figure 1), using a Fourier-based forward model16. Background-field free field maps were then calculated using three distinct pipelines:

- Avg-Unw: Lap-Unw1 on the phase at each TE; field map calculation:5 $$\Delta B = \frac{\sum_{echo = 1}^5 \phi_{echo} (TE_{echo}, {\bf r})}{\gamma \sum_{echo = 1}^5 TE_{echo}} \qquad (2)$$

then Lap-Bg1 on $\Delta B$.

- Avg-Bg: Lap-Bg2 on the phase at each TE; field map calculation:7 $$\Delta B = \frac{1}{5} \sum_{echo = 1}^5 \frac{\phi(TE_{echo}, {\bf r})}{\gamma \cdot TE_{echo}} \qquad (3).$$

- Fit: linear fit of the simulated (non-wrapped) phase over TEs; Lap-Bg2 on the fitted $\Delta B$2,9.

$\chi$ maps were calculated (TKD17 with correction for underestimation2 and $\delta = 2/3$) to assess the effect of each pipeline on the $\chi$ values. $\chi$ mean, standard deviation (SD) and Root Mean Square Error (RMSE) were calculated in all the regions of interest (ROIs) shown in Figure 1.

Volunteer

3D gradient-echo brain images of a healthy volunteer were acquired on a Philips Achieva 3T scanner with a 32-channel head coil, 1-mm isotropic resolution, 7 echoes (TE1/ΔTE = 3.7/6.9 ms), TR = 50 ms, SENSE factor = 2 and flip angle = 10°.

The effect of Lap-Unw and Lap-Bg was tested. For comparison, the phase at each TE was also unwrapped with PRELUDE18. The mean and SD of the processed phase were calculated in three ROIs (Figure 3a) drawn on the fifth-echo magnitude image.

### Results and Discussion

Laplacian-based processing altered the linearity of the phase-time relationship (1) in both the numerical phantom (Figure 2) and the volunteer (Figure 3). Such alterations of (1) were expected, because SHARP2 involves non-linear operations. These findings suggest that Laplacian unwrapping does not only unwrap the phase but also removes some $\Delta B_{bg}$ from $\Delta B$, even with a very small $\sigma$.

In the phantom, scaling and averaging the SHARP-processed phase caused inaccuracies in the estimated field maps (Figure 4) and, therefore, errors in $\chi_{Avg-Unw}$ and $\chi_{Avg-Bg}$ versus $\chi_{Fit}$ (Figure 5). Unlike $\chi_{Fit}$, $\chi_{Avg-Unw}$ and $\chi_{Avg-Bg}$ underestimated $\chi_{True}$ in the caudate nucleus and globus pallidus, whereas all mean $\chi$ estimates were similar in the thalamus and white matter. $\chi_{Fit}$ had the lowest SDs or the smallest RMSE values in all ROIs except the globus pallidus, in which, however, $\chi_{Fit}$ had the lowest RMSE as a percentage of $\chi_{Fit}$.

### Conclusions

We demonstrated that Laplacian-based techniques alter the phase-time linearity (1). We also showed that Fit, therefore, gave the most accurate $\chi$ results, suggesting that Fit, or analogous pipelines13 that fit the phase over multiple echoes, should be used before applying Laplacian-based methods.

### Acknowledgements

We would like to acknowledge our healthy volunteers and Dr Marios Yiannakas for his assistance with MRI scanning.

Emma Biondetti is supported by the Engineering and Physical Sciences Research Council (EP/M506448/1).

Dr David L. Thomas is supported by the UCL Leonard Wolfson Experimental Neurology Centre (PR/ylr/18575).

### References

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### Figures

Figure 1. Susceptibility phantom and $\chi$ values. $\chi$ values were taken from Ref. 13 for white matter, caudate nucleus, putamen, globus pallidus, thalamus, superior sagittal sinus and other brain regions and from Ref. 14 for air/non-brain regions.

Figure 2. Phantom: effect of Avg-Unw (b) and Avg-Bg (c) on the phase simulated from the ground-truth $\chi$ distribution (a). Mean and SD of the phase in four ROIs (caudate nucleus, thalamus, globus pallidus and white matter) are plotted against TEs.

Figure 3. Healthy volunteer: effect of PRELUDE unwrapping (b), Lap-Unw (c) and Lap-Bg (d) on the measured phase. Mean and SD of the phase in three ROIs drawn on the fifth-echo magnitude image (a) are plotted against TEs.

Figure 4. Phantom: transverse and coronal slices of field maps calculated with Avg-Unw (a), Avg-Bg (b) and Fit (c).

Figure 5. Phantom: transverse and coronal slices of $\chi$ maps calculated with Avg-Unw (b), Avg-Bg (c) and Fit (d) versus the true $\chi$ (a).

The table (e) shows the true and calculated $\chi$ [ppm] (mean ± SD) and the RMSEs [ppm] for each method in all ROIs.

Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
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