| Literature DB >> 32611990 |
Tomoko Maekawa1,2, Kouhei Kamiya1,2,3, Katsutoshi Murata4, Thorsten Feiweier5, Masaaki Hori1,3, Shigeki Aoki1.
Abstract
The microstructural underpinnings of reduced diffusivity in transient splenial lesion remain unclear. Here, we report findings from oscillating gradient spin-echo (OGSE) diffusion imaging in a case of transient splenial lesion. Compared with normal-appearing white matter, the splenial lesion exhibited greater differences between diffusion time t = 6.5 and 35.2 ms, indicating microstructural changes occurring within the corresponding length scale. We also conducted 2D Monte-Carlo simulation. The results suggested that emergence of small and non-exchanging compartment, as often imagined in intramyelinic edema, does not fit well with the in vivo observation. Simulations with axonal swelling and microglial infiltration yielded results closer to the in vivo observations. The present report exemplifies the importance of controlling t for more specific radiological image interpretations.Entities:
Keywords: OGSE; diffusion time; microstructure; simulation; transient splenial lesion
Mesh:
Year: 2020 PMID: 32611990 PMCID: PMC8203477 DOI: 10.2463/mrms.bc.2020-0046
Source DB: PubMed Journal: Magn Reson Med Sci ISSN: 1347-3182 Impact factor: 2.471
Fig. 1(a) DWIs (top) and D maps (bottom) for PGSE (left) and OGSE (right). (b) Measured diffusivities (mean ± 1 SD) in the splenial lesion and NAWM. Above the bar chart are the relative ratios of diffusivity reduction [(DNAWM − Dlesion)/DNAWM]. The lesion exhibited reduced diffusivity on both OGSE and PGSE. The reduction was weaker in OGSE. DWIs, diffusion-weighted images; NAWM, normal-appearing white matter; OGSE, oscillating gradient spin-echo; PGSE, pulsed gradient spin-echo.
Fig. 2Microgeometry used for the simulation (baseline state). Axons were modeled as randomly packed cylinders (circles). Signal contribution from myelin (black) was ignored due to its short T2. For the baseline state, we used a uniform g-ratio of 0.7, and axonal fraction (including myelin) was set at 0.6. For all simulations, the field of view (50 × 50 μm2) and the number of axons in the field were held constant. We assumed intrinsic diffusivity as D0 ≡ D| = 2.0 μm2/ms for all compartments, uniform particle density and relaxation time, and negligible intercompartmental exchange. Each simulation is comprised of 5 × 104; particles and a time step of 5 × 10−5 ms. D(t) was computed directly from particle displacement (i.e., diffusivities that would be observed with PGSE in a narrow pulse limit).
Fig. 3The three pathological scenarios: axonal swelling (left), microglial infiltration (middle), and intramyelinic edema (right). The top row shows the microgeometries. For axonal swelling, the inner diameters of axons were increased by a factor of up to 1.2, while the myelin volume was kept constant. Microglias were modeled as circles (radius, 5 μm), and the fraction of microglias (red circles) was varied from 0 (baseline) to 0.2. Intramyelinic edema was modeled as small holes (radius, 0.13–0.48 μm) within the myelin sheath. The proportion of intramyelinic holes (red circles) was varied from 0 to 0.15. The second row shows the total diffusivities within a voxel; the bottom three rows show the diffusivity in each compartment [intra-axonal space, extracellular space, and pathological compartment (microglias or intramyelinic edema)]. The shaded area corresponds to the range of clinical data (t = 6.5–35.2 ms). Microglial infiltration led to greater difference from the baseline state at t = 35.2 ms than at t = 6.5 ms, in agreement with the in vivo observation. Similar trend was seen for axonal swelling, though the change between t = 6.5 and 35.2 ms was smaller. Intramyelinic edema did not alter the magnitude of difference from the baseline state between t = 6.5 and 35.2 ms.