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Multi-Echo Segmented Diffusion-Weighted MRI for Ex-Vivo Whole-Brain Measurements with 300mT/m Gradients
Cornelius Eichner1, Toralf Mildner1, Michael Paquette1, Torsten Schlumm1, Catherine Crockford2, Roman Wittig2, Evgeniya Kirilina1, Carsten Jäger1, Harald E. Möller1, Nikolaus Weiskopf1, Angela Friederici1, and Alfred Anwander1

1Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, 2Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany

### Synopsis

We provide a novel method to increase SNR of segmented diffusion-weighted EPI acquisitions. Multiple gradient echoes were acquired after each diffusion-preparation and combined in an SNR-optimized way using weightings from quantitative T2* maps. The combination of diffusion-weighted echoes yielded an SNR-gain of 58% compared to single-echo dMRI data with an increase in the segmented readout duration by only 23.1 ms. The multi-echo diffusion MRI acquisition and combination were employed to acquire high-quality ex-vivo diffusion-weighted MRI data from a wild chimpanzee brain.

### Introduction

Typical challenges of ex-vivo diffusion MRI (dMRI) acquisitions include low diffusivity in fixed tissue requiring strong diffusion-weightings, signal drop from trapped air, image distortions and increased echo-times due to strong diffusion-weighting requirements. Highly segmented EPI (sEPI) acquisitions can be employed to counteract these challenges by shortening echo-times and reducing image distortions in ex-vivo dMRI1,2. The short sEPI readout trains can easily be repeated to acquire multiple gradient-echoes (GRE) with a negligible increase in acquisition time. However, such Multi-Echo (ME) acquisitions are not employed in dMRI, due to T2* signal decay between echoes. In functional MRI, T2* relaxation maps are employed for SNR optimal echo-combination3. In this work, an ME-sEPI Stejskal-Tanner dMRI sequence4 was developed to achieve high-quality dMRI acquisition for ex-vivo imaging of a wild chimpanzee brain on a human-scale scanner (Figure 1A). Individual echoes were combined using T2*-dependent weighting.

### Methods

MRI data were acquired from the brain of a 6-year-old juvenile wild female chimpanzee from Taï National Forest (Ivory Coast). The animal died from natural cause without human interference. The brain was extracted on site by a veterinarian four hours after death and immersion-fixed with 4% paraformaldehyde. Further preparations included the removal of superficial vessels, washing out paraformaldehyde in phosphate-buffered saline and placement in Fomblin. ME-dMRI data were acquired using a 3T Connectom System (Siemens Healthineers, Erlangen, Germany) with maximum gradient strength of 300 mT/m and a flexible 23-channel surface coil5. With a total left-right brain extent of 85 mm (100 mm including container), measurement on a small-bore scanner was not possible. ME-dMRI-sEPI and ME-GRE-sEPI datasets were acquired with matched resolution and acceleration (parameters in Table 1). The ME acquisition increased the readout by only 23.1 ms, which is neglectable compared to the diffusion-preparation plus readout of a single-echo only.

MP-PCA denoising6 was employed prior to echo-combination. A T2* map was calculated by fitting an exponential model to the ME-GRE data. A voxel-wise weighting-factor $w_i$ for each echo $S_i$ was calculated based on echo-time and T2*. For optimal SNR, the individual ME-dMRI echoes were combined using weighted averaging: $$S_{comb}=\sum_{i=1}^{n} w_i S_i=\sum_{i=1}^{n}\frac{\exp{-\frac{TE(i)}{T_2^*}}}{\sum_{j=1}^{n}\exp{-\frac{TE(j)}{T_2^*}}}S_i$$

The T2*-dependence of $w_i$ yielded a voxel-specific SNR-gain compared to single-echo dMRI. To estimate the SNR-gain, the noise was approximated as Gaussian with similar variance, for all echoes. The SNR-gain was then computed as a weighted sum of Gaussian random variables.

$$\text{SNR}_{comb}=\frac{S_{comb}}{\sigma_{comb}}=\frac{S_{comb}}{\sqrt{\sum_i^nw_i^2}\sigma}$$

Deterministic diffusion tensor tracking and visualization of the processed dataset was performed using the software package brainGL [https://github.com/braingl].

### Results

The ME-dMRI results from b=0 and one selected diffusion direction with b=3000s/mm2 are displayed in Figure 1B. The dMRI acquisition did not suffer from visible image distortions. A representative slice of the T2* reconstruction is displayed in Figure 2A. Weighting-factors $w_i$ for different regions of interest are displayed in Figure 2B. During echo-combination, voxels with reduced T2* received stronger weighting from earlier echoes compared to voxels with higher T2*. Figure 3 displays the spatial dependence of SNR-gain of ME combination compared to single-echo dMRI. The distribution shows an average SNR-gain of 58%, which is equivalent to 2.5 independent averages (Figure 3A). In the limiting case of neglectable T2* decay, the combination would have converged to a non-weighted average with a theoretical maximum SNR-gain factor of 2 (i.e $\sqrt{n_{\text{avg}}}$). The fiber tracking results of the dMRI data are displayed in Figure 4.

### Discussion

In this work, we employed an ME-dMRI sequence to acquire high-quality dMRI data of a fixed ex-vivo wild chimpanzee brain. We leveraged short readout trains to reduce image distortion and acquire multiple echoes for each diffusion-weighting. The ME-data were combined using a weighted average based on the underlying T2* map, yielding an overall SNR-gain of 58%. With an additional readout duration of only 23.1 ms per segment, this SNR-gain was achieved at a very small cost. We expect this method to further boost image quality and SNR of both segmented and single shot dMRI. Furthermore, ME-dMRI can potentially also provide information about microstructural T2*, by filtering restricted tissue compartments using diffusion-weighting. Future work on multiple echo-combination is required to investigate the extent to which individual diffusion-weighted echoes are affected by the compartmental T2* decay of the underlying microstructure.

### Acknowledgements

CE is supported by the SPP2041 program "Computational Connectomics" of the German Research Foundation (DFG)

### References

1. Miller K. et. al, Diffusion imaging of whole, post-mortem human brains on a clinical MRI scanner. NeuroImage, 2011
2. McNab JA. et. al, The Human Connectome Project and beyond: initial applications of 300 mT/m gradients. NeuroImage, 2013
3. Kundu P. et. al, Differentiating BOLD and non-BOLD signals in fMRI time series using multi-echo EPI. NeuroImage, 2012
4. Stejskal EO. and Tanner JE., Spin Diffusion Measurements: Spin Echoes in the Presence of a Time‐Dependent Field Gradient. J. Chem. Phys, 1965
5. Frass-Kriegl R. et al. Flexible 23-channel coil array for high-resolution magnetic resonance imaging at 3 Tesla. Plos One, 2015
6. Veraart J. et. al, Diffusion MRI noise mapping using random matrix theory. Magn Reson Med, 2016

### Figures

Figure 1: A - Diagram of the developed segmented ME-dMRI pulse sequence. The first echo is acquired at the echo-time of the spin echo. The following echoes follow a T2* decay with different characteristics than the first echo. B - Exemplary brain data with and without diffusion-weighting. The T2* decay of the echoes is clearly visible. The data are displayed in arbitrary signal units.

Figure 2: A - T2* map calculated from the multi-echo GRE sequence. T2* is influenced by both tissue and magnetic susceptibility from trapped air. Specific ROIs are highlighted to show T2* differences from grey matter (red), white matter (blue) and trapped air (green) B - Voxel-specific echo-weighting for ROIs from A. During echo-combination, areas with reduced T2* get a stronger weighting of earlier echoes to counteract signal decay.

Figure 3: A - Whole volume distribution of SNR-gain from ME acquisition compared to standard single-echo dMRI. The distribution shows a mean SNR-gain of 58%. B - Map of the factor for SNR-gain from ME dMRI. The colormap ranges from one (i.e., no effect on SNR) to two (i.e., theoretical maximum from averaging four signals). The effectiveness of ME dMRI is influenced by both tissue type and fields disturbances such as air.

Figure 4: A - Fractional anisotropy reconstruction from single-echo and multi-echo dMRI data. The increased SNR of ME dMRI (bottom) reflects in a visibly smoother reconstruction of fractional anisotropy. Please note, that contrast loss in inferior direction is due to the steep coil intensity profile of the employed surface coil. B - Whole brain transparent visualization of deterministic tractography from ME-dMRI data.

Table 1 - Summary of sequence parameters for ME-dMRI and ME-GRE sEPI sequences

Proc. Intl. Soc. Mag. Reson. Med. 27 (2019)
3510