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

Abstract

Purpose To demonstrate the feasibility of multidimensional diffusion MRI to probe and quantify microscopic fractional anisotropy (µFA) in human kidneys in vivo. Methods Linear tensor encoded (LTE) and spherical tensor encoded (STE) renal diffusion MRI scans were performed in 10 healthy volunteers. Respiratory triggering and image registration were used to minimize motion artefacts during the acquisition. Kidney cortex–medulla were semi‐automatically segmented based on fractional anisotropy (FA) values. A model‐free analysis of LTE and STE signal dependence on b‐value in the renal cortex and medulla was performed. Subsequently, µFA was estimated using a single‐shell approach. Finally, a comparison of conventional FA and µFA is shown. Results The hallmark effect of µFA (divergence of LTE and STE signal with increasing b‐value) was observed in all subjects. A statistically significant difference between LTE and STE signal was found in the cortex and medulla, starting from b = 750 s/mm2 and b = 500 s/mm2, respectively. This difference was maximal at the highest b‐value sampled (b = 1000 s/mm2) which suggests that relatively high b‐values are required for µFA mapping in the kidney compared to conventional FA. Cortical and medullary µFA were, respectively, 0.53 ± 0.09 and 0.65 ± 0.05, both respectively higher than conventional FA (0.19 ± 0.02 and 0.40 ± 0.02). Conclusion The feasibility of combining LTE and STE diffusion MRI to probe and quantify µFA in human kidneys is demonstrated for the first time. By doing so, we show that novel microstructure information—not accessible by conventional diffusion encoding—can be probed by multidimensional diffusion MRI. We also identify relevant technical limitations that warrant further development of the technique for body MRI.

Comparison of PGSE and STEAM DTI acquisitions with varying diffusion times for probing anisotropic structures in human kidneys

“…The cortical ADC values ranged from (2.23 ± 0.15) × 10 −3 mm 2 /s to (2.53 ± 0.05) × 10 −3 mm 2 /s, whereas the medulla ADC values were between (2.10 ± 0.15) × 10 −3 mm 2 /s and (2.49 ± 0.22) × 10 −3 mm 2 /s. The FA values of the renal cortex were significantly lower than the medulla FA in all sequences ( P < 0.001), which is consistent with the literature . Moreover, the ADC values obtained in the cortical ROIs were significantly higher compared to those calculated in the medullary ROIs only in the PGSE sequence with the short diffusion time ( P < 0.05).…”

“…Consistent with findings from previous studies, the medulla FA values measured in all sequences were significantly higher than those obtained in the renal cortex, whereas the ADC values of the medulla were significantly lower than of the cortex only in PGSE with short Δ . The high diffusion anisotropy of the medulla is related to centripedically oriented anatomic structures of the kidney .…”

“…Assuming D = 1.8 × 10 −3 mm 2 /s and ∆ = 37 ms, we obtain a two‐dimensional diffusion length L d ≈ of about 16 µm, which enables sensitivity to the radial length scale of the thin segment of loop of Henle occuring within the inner medulla . Similarly, L d ≈ 30 µm at a diffusion time of 125 ms amplifies sensitivity to the distal and proximal tubules that are found in the outer medulla and cortex …”

Motion‐compensated gradient waveforms for tensor‐valued diffusion encoding by constrained numerical optimization

“…We expect that this waveform design will improve the feasibility and quality of microstructure imaging that relies on tensor-valued encoding in organs that require special attention to ballistic motion, such as cardiac, liver, and kidney imaging. 5,6,12,13,51 In this work, we explored the numerical effects of flow and acceleration over a wide range of values, and established the magnitude at which the higher moments, jerk, and snap become relevant. However, it remains the responsibility of the user to determine the appropriate level and order of motion compensation to use in gradient waveform optimization, and to account for organ-specific challenges in the remainder of the experimental design.…”

“…For example, the relatively slow and incoherent movement of blood in capillaries has a measurable impact on the diffusion‐weighted signal at low b‐values and carries information about the vasculature and can be mistaken for fast diffusion, or so called “pseudo diffusion.” 1 Other sources of motion include cardiac and pulmonary motion. These influence diffusion measurements in the brain by arterial pulsation 2,3 and by gross movement of tissue, such as in chest, cardiac and kidney imaging, 4‐6 or from vibrations induced by the diffusion encoding itself 7‐9 …”