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A protocol for evaluating ultrasmall superparamagnetic particles of iron oxide (USPIO) uptake and elimination in cerebral small vessel disease patients was developed and piloted. B₁-insensitive R₁ measurement was evaluated in vitro. Twelve participants with history of minor stroke were scanned at 3-T MRI including structural imaging, and R₁ and R₂* mapping. Participants were scanned (i) before and (ii) after USPIO (ferumoxytol) infusion, and again at (iii) 24⁻30 h and (iv) one month. Absolute and blood-normalised changes in R₁ and R₂* were measured in white matter (WM), deep grey matter (GM), white matter hyperintensity (WMH) and stroke lesion regions. R₁ measurements were accurate across a wide range of values. R₁ (p < 0.05) and R₂* (p < 0.01) mapping detected increases in relaxation rate in all tissues immediately post-USPIO and at 24⁻30 h. R₂* returned to baseline at one month. Blood-normalised R₁ and R₂* changes post-infusion and at 24⁻30 h were similar, and were greater in GM versus WM (p < 0.001). Narrower distributions were seen with R₂* than for R₁ mapping. R₁ and R₂* changes were correlated at 24⁻30 h (p < 0.01). MRI relaxometry permits quantitative evaluation of USPIO uptake; R₂* appears to be more sensitive to USPIO than R₁. Our data are explained by intravascular uptake alone, yielding estimates of cerebral blood volume, and did not support parenchymal uptake. Ferumoxytol appears to be eliminated at 1 month. The approach should be valuable in future studies to quantify both blood-pool USPIO and parenchymal uptake associated with inflammatory cells or blood-brain barrier leak.
Pubmed ID: 30759756
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Software tools for global and local image registration. The algorithm used for global registration is based on a block matching approach enabling robust registration (outliers rejection). The local registration implementation uses a cubic B-Spline parametrisation (Free-Form Deformation). All registration algorithms are based on symmetric approaches where forward and backward transformations can be optimised concurrently. NiftyReg has been implemented for both CPU and GPU (through the use of CUDA).
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