Diffusion-weighted imaging at 7 Tesla & qti


Head of Department:
Prof. Dr. med. Michael Uder

Diffusion-weighted imaging at 7 Tesla and multi-dimensional diffusion-weighting

Diffusion tensor imaging (DTI) is commonly used to study the effects of diffusion anisotropy [1]. However, DTI entangles the influence of anisotropy and orientation coherence. This makes it impossible to differentiate e.g. between environments with crossing anisotropic diffusion or inherently isotropic diffusion [2,3]. Recent advances in multidimensional diffusion encoding allow differentiating between these cases, and create maps of the microscopic diffusion anisotropy, independent of orientation coherence. In general, higher order b-tensors are combined with a linear diffusion encoding to obtain the necessary information [4,5].

We explore the use of time-varying diffusion gradient profiles to create multidimensional diffusion weighting (see Fig. 1). By developing custom sequences and optimizing the diffusion gradients profiles numerically (as in [6]) we obtain high resolution diffusion anisotropy maps (see Fig. 2). We also work on optimizing the acquisition technique [7]. The University Hospital Erlangen offers the opportunity to carry out research on state-of-the-art 7 Tesla and 3 Tesla systems, where we investigate the influence of the field strength on the diffusion encoded signal. Additionally, we collaborate closely with the neuroradiology and neurology department to explore potential applications for microstructural diffusion imaging in the context of brain pathologies like cancer or epilepsy.


[1] Laun F, Fritzsche KH, Kuder TA, Stieltjes B.
Introduction to the basic principles and techniques of diffusion-weighted imaging
Radiologe. 2011 Mar;51(3):170-9.

[2] Fritzsche KH, Laun FB, Meinzer HP, Stieltjes B.
Opportunities and pitfalls in the quantification of fiber integrity: what can we gain from Q-ball imaging?
Neuroimage. 2010 May 15;51(1):242-51.

[3] Müller L, Wetscherek A, Kuder TA, Laun FB.
Eddy current compensated double diffusion encoded (DDE) MRI.
Magn Reson Med. 2017 Jan;77(1):328-335.

[4] Westin CF, Knutsson H, Pasternak O, Szczepankiewicz F, Özarslan E, van Westen D, Mattisson C, Bogren M, O'Donnell LJ, Kubicki M, Topgaard D, Nilsson M.
Q-space trajectory imaging for multidimensional diffusion MRI of the human brain.
Neuroimage. 2016 Jul 15;135:345-62.

[5] Lasic S, Szczepankiewicz F, Eriksson S, Nilsson M, Topgaard D.
Microanisotropy imaging: quantification of microscopic diffusion anisotropy and orientational order parameter by diffusion MRI with magic-angle spinning of the q-vector.
Front. Phys. 2014; 2(11)

[6] Sjölund J, Szczepankiewicz F, Nilsson M, Topgaard D, Westin CF, Knutsson H.
Constrained optimization of gradient waveforms for generalized diffusion encoding, J. Magn. Res. 2015; 261:157-168  

[7] Martin J, Endt S, Wetscherek A, Kuder TA, Doerfler A, Uder M, Hensel B, Laun FB.
Contrast-to-noise ratio analysis of microscopic diffusion anisotropy indices in q-space trajectory imaging.
Z. Med. Physik. 2019; pii: S0939-3889(18)30127-2. 

Figure 1. Multidimensional diffusion weighting. By combining measurements with different b-tensors such as linear (top row), planar (middle row), and spherical (bottom row), it becomes possible to disentangle the influence of anisotropy and orientation coherence on the diffusion weighted signal. Time-varying diffusion gradient profiles (right side) are used to create the higher order b-tensors.
Figure 2. High-resolution diffusion anisotropy maps acquired at 7 Tesla. Maps of the direction-encoded fractional anisotropy (cFA, left) and microscopic anisotropy (uFA, right) with a voxel size of 1.5 mm³ isotropic were acquired with numerically optimized diffusion gradient profiles.
Prof. Dr. rer. nat. Armin Nagel
phone: +49 9131 85-25900
DECT: +49 9131 85-45618
e-mail: armin.nagel@uk-erlangen.de
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Prof. Dr. rer. nat. Frederik B. Laun
phone: +49 9131 85-26268
DECT: +49 9131 85-45622
e-mail: frederik.laun@uk-erlangen.de
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