Abstract
Microstructure imaging techniques based on tensor-valued diffusion encoding have gained popularity within the MRI research community. Unlike conventional diffusion encoding—applied along a single direction in each shot—tensor-valued encoding employs diffusion encoding along multiple directions within a single preparation of the signal. The benefit is that such encoding may probe tissue features that are not accessible by conventional encoding. For example, diffusional variance decomposition (DIVIDE) takes advantage of tensor-valued encoding to probe microscopic diffusion anisotropy independent of orientation coherence. The drawback is that tensor-valued encoding generally requires gradient waveforms that are more demanding on hardware; it has therefore been used primarily in MRI systems with relatively high performance. The purpose of this work was to explore tensor-valued diffusion encoding on clinical MRI systems with varying performance to test its technical feasibility within the context of DIVIDE. We performed whole-brain imaging with linear and spherical b-tensor encoding at field strengths between 1.5 and 7 T, and at maximal gradient amplitudes between 45 and 80 mT/m. Asymmetric gradient waveforms were optimized numerically to yield b-values up to 2 ms/μm2. Technical feasibility was assessed in terms of the repeatability, SNR, and quality of DIVIDE parameter maps. Variable system performance resulted in echo times between 83 to 115 ms and total acquisition times of 6 to 9 minutes when using 80 signal samples and resolution 2×2×4 mm3. As expected, the repeatability, signal-to-noise ratio and parameter map quality depended on hardware performance. We conclude that tensor-valued encoding is feasible for a wide range of MRI systems—even at 1.5 T with maximal gradient waveform amplitudes of 33 mT/m—and baseline experimental design and quality parameters for all included configurations. This demonstrates that tissue features, beyond those accessible by conventional diffusion encoding, can be explored on a wide range of MRI systems.
Highlights
Diffusion magnetic resonance imaging enables non-invasive imaging of tissue microstructure
SNR was relatively high for configuration D, but its parameter maps exhibited contrast that differed from other configurations
We have demonstrated that tensor-valued diffusion encoding with high b-values is technically feasible across a wide range of different MRI scanners and configurations, and we have shown the range of results that can be expected in terms of diffusional variance decomposition (DIVIDE) parameter maps
Summary
Diffusion magnetic resonance imaging (dMRI) enables non-invasive imaging of tissue microstructure. The vast majority of dMRI techniques rely on the conventional Stejskal-Tanner experiment [1] that employs a pair of pulsed field gradients to yield diffusion encoding along a single direction for each preparation of the signal, so called ‘linear’ encoding. Methods such as diffusion tensor imaging (DTI) [2] yield voxel-scale average parameters which are sensitive to alterations of the tissue microstructure in both healthy development and disease [3]. Markedly different tissue archetypes, such as elongated cell structures that are randomly oriented and isotropic tissues with varying cell density, may be indistinguishable regardless of the modelling approach [8, 9]
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