Tensor-valued diffusion encoding for diffusional variance decomposition (DIVIDE): Technical feasibility in clinical MRI systems

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/μm 2 . 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 mm 3 . 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.

Maxwell‐compensated design of asymmetric gradient waveforms for tensor‐valued diffusion encoding

“…As expected, the differences in MD and FA were relatively small (see Supporting Information Figure ), but gross parameter differences were observed in the µFA, MK A (overestimated by 0.3 and 0.8, respectively), and MK I (underestimated by 0.8). The underestimation of MK I was severe enough to cause large regions in the parenchyma to exhibit negative values, which are not expected from the current experiments . The measured and predicted differences show a remarkable similarity, indicating that the difference between signal and QTI parameters based on DDE‐PTE and MCW‐PTE is indeed primarily because of the prominent concomitant gradients created by DDE‐PTE.…”

In vivo demonstration of microscopic anisotropy in the human kidney using multidimensional diffusion MRI

“…The number of encoding directions was chosen such that a rotation invariant powder signal could be obtained with the acquisition protocol. The minimum number of directions necessary to fulfil this requirement was estimated by following a previously proposed simulation framework . Most renal DTI studies have found FA in the renal medulla to be (1) lower than 0.5, and (2) higher than cortical FA (see above).…”

“…Most renal DTI studies have found FA in the renal medulla to be (1) lower than 0.5, and (2) higher than cortical FA (see above). Therefore, assuming a maximum FA of 0.5 (see Results for details of our FA estimates in this data set), ~11 directions yield a rotation invariant powder signal for b × MD < 3, where MD = mean diffusivity. As such, considering the highest b‐value used in this study (1000 s/mm 2 ), ~11 directions yield a rotation invariant powder signal for a MD of 3 × 10 −3 mm 2 /s (diffusion coefficient of water at body temperature), which is above the typically observed MD in the renal cortex and medulla (also verified in our data, see Results).…”

“…The number of encoding directions was therefore set to 12 for all LTE and STE acquisitions. Encoding waveforms were optimized numerically assuming a maximum gradient amplitude of 75 mT/m and slew rate of 100 T/m/s, heat dissipation factor η = 0.6, and using the Euclidean norm to obtain rotatable waveforms (Figure ). The LTE waveform was generated from the STE waveform such that the magnitude of the q ‐vector remained constant .…”

Searching for the neurite density with diffusion MRI: Challenges for biophysical modeling

“…The techniques for data analysis described in this section were employed after arithmetic averaging of the signal across diffusion‐encoding directions, so‐called “powder averaging” (Callaghan, Jolley, & Lelievre, ; Jespersen, Lundell, Sønderby, & Dyrby, ; Lasič et al, ). Provided data is acquired with a sufficient number of directions (Szczepankiewicz, Westin, Ståhlberg, Lätt, & Nilsson, ), powder averaging yields a signal whose orientation‐invariant aspects of diffusion are preserved but with an orientation distribution that mimics complete dispersion of anisotropic structures.…”