Jelle Veraart^{1,2}, Ying-Chia Lin^{1,2}, Tiejun E. Zhao^{3}, and Steven H. Baete^{1,2}

Combination of diffusion weighted MRI with orthogonal measures such as T$$$_2$$$-weighting has been proposed to overcome the fit degeneracy found in microstructure modeling of diffusion signals. However, the repetition of diffusion measurements at different TE leads to unacceptably long acquisition times, hindering clinical applicability of this approach. Here, we propose an accelerated acquisition using a multi spin echo diffusion and T$$$_2$$$-weighted sequence which samples each diffusion weighting at several TEs with a CPMG read-out train after the standard monopolar diffusion encoding spin echo. In the current configuration this speeds the acquisition up by a factor of 2.5x.

Here, we propose an
accelerated fused T$$$_2$$$-relaxometry-diffusometry acquisition. In our
approach we acquire for each diffusion weighting multiple full 2D EPI $$$k$$$-spaces
for each of several TE in a multi spin echo sequence (Fig 1). This translates
to a 2.5x reduction in scan time. In this work we demonstrate the feasibility
of accelerating T$$$_2$$$-relaxometry-diffusometry by using a multi spin echo
sequence *in vivo* in a clinical 3T
scanner.

Data: Diffusion-relaxometry datasets were collected
using a product spin echo (SE-EPI) and the proposed custom-made multi spin echo
(MSE-EPI, Fig 1) diffusion sequence. In both sequences, 30 isotropically
distributed directions were acquired for each of $$$b\,=\,500,1000,2000,3000$$$
and $$$4000\,\mathrm{s/mm^2}$$$ and TE$$$\,=\,71, 107, 143$$$ and $$$179\,\mathrm{ms}$$$.
Due to scanner limitations, the $$$b\,=\,4000\,\mathrm{s/mm^2}$$$ could not be
acquired for the shortest TE with the SE-EPI sequence. Datasets were acquired
of a healthy volunteer (female, 24y/o) on a 3T clinical
scanner (MAGNETOM Prisma, Siemens, Erlangen) using a 20-channel head coil (TR$$$\,=\,4000\,\mathrm{ms}$$$, 2.5$$$\,\mathrm{mm}$$$
isotropic resolution, FoV$$$\,=\,210\,\mathrm{mm}$$$, 36 slices, multiband
acceleration of 2, GRAPPA 2, PF 6/8) in a single scan session (SE-EPI: 32:49$$$\,\mathrm{min}$$$,
MSE-EPI: 13:02$$$\,\mathrm{min}$$$). Images were denoised [15], intensity corrected (N4), corrected
for susceptibility, eddy currents and subject motion using $$$\mathrm{eddy}$$$ [16] and registered to the first
diffusion image using $$$\mathrm{flirt}$$$ [16].
Tissue segmentation (MRtrix3, $$$\mathrm{5ttgen}\,{fsl}$$$)
was performed on an MPRAGE image (1mm isotropic resolution, TR/TE$$$\,=\,2300/2.87\,\mathrm{ms}$$$).
A single T$$$_2$$$-DKI-compartment was fitted to the data using an
unconstrained linear least square estimator in Matlab (Mathworks). Note that
the diffusion time is constant for the different TE with the MSE-EPI, whilst it
increases with TE for the SE-EPI.

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[3] Jespersen, S. N., Kroenke, C. D., Ostergaard, L., Ackerman, J. J., Yablonskiy, D. A.. Modeling dendrite density from magnetic resonance diffusion measurements. Neuroimage 2007; 34 (4), 1473–1486.

[4] Jelescu IO, Budde MD. Design and validation of diffusion MRI models of white matter. Front Phys 2017;28.

[5] Jelescu IO, Veraart J, Fieremans E, Novikov D. Degeneracy in model parameter estimation for multi-compartmental diffusion in neuronal tissue. NMR in biomedicine 2016;29:33-47.

[6] Fieremans E, Burcaw LM, Lee H-H, Lemberskiy G, Veraart J, Novikov D. In vivo observation and biophysical interpretation of time-dependent diffusion in human white matter. NeuroImage 2016;129:414-427.

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[10] Pizzolato M, Canales-Rodriguez E, Daducci A, Thiran J. Multimodal microstructure imaging: joint T2-relaxometry and diffusometry to estimate myelin, intracellular, extracellular, and cerebrospinal fluid properties. Proc Intl Soc Mag Reson Med; 2018; Paris, France. p. 3118.

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Figure 1: Sequence diagram of a Multi Spin Echo EPI (MSE-EPI) diffusion
sequence with echo times $$$\,=\,67,
102, 137$$$ and $$$179\,\mathrm{ms}$$$ (top) and expected
signal decay due to T$$$_2$$$-relaxation and diffusion (bottom). By reading out
each echo in a CPMG train after the standard spin echo diffusion encoding, a range
of TEs can be sampled at once, thus encoding a diffusion contrast at several TE
in a single echo train. This accelerates the combined
T$$$_2$$$-relaxometry-diffusometry measurement.

Figure 2: Transversal slices of a Diffusion-T$$$_2$$$-relaxometry
measurement of a healthy human brain using a) a standard single spin echo
diffusion sequence (32:49$$$\,\mathrm{min}$$$)
and b) the accelerated MSE-EPI sequence (13:02$$$\,\mathrm{min}$$$) at 4 TE and 5 $$$b$$$-values. Individual slices are
amplified for contrast as indicated with the factor indicated at the right top
of each slice.

Figure 3: Maps of mean diffusivity (MD),
fractional anisotropy (FA), mean kurtosis (MK) and T$$$_2$$$ fitted to a
Diffusion-T$$$_2$$$-relaxometry measurement of a healthy human brain using a) a
standard single spin echo diffusion sequence and b) the accelerated MSE-EPI
sequence. Notwithstanding a slight misalignment of the slices, a high degree of
similarity is seen.

Figure 4: Comparison of mean diffusivity (MD),
fractional anisotropy (FA), mean kurtosis (MK) and T$$$_2$$$ fitted to
measurements using a repeated standard single spin echo and the multi spin echo
sequence. Voxels from grey matter (gm), white matter (wm) and cerebrospinal
fluid (csf) are colored separately.

Figure 5: Scatterplots of T$$$_2$$$ relative to mean
diffusivity (MD), fractional anisotropy (FA) and mean kurtosis (MK). Voxels
from grey matter (gm), white matter (wm) and cerebrospinal fluid (csf) are colored
separately.