
PurposeTo quantify the variations of the power‐law dependences on diffusion time t or gradient frequency of extracellular water diffusion measured by diffusion MRI (dMRI).MethodsModel cellular systems containing only extracellular water were used to investigate the dependence of , the extracellular diffusion coefficient. Computer simulations used a randomly packed tissue model with realistic intracellular volume fractions and cell sizes. DMRI measurements were performed on samples consisting of liposomes containing heavy water(D2O, deuterium oxide) dispersed in regular water (H2O). was obtained over a broad range (∼1–1000 ms) and then fit power‐law equations and .ResultsBoth simulated and experimental results suggest that no single power‐law adequately describes the behavior of over the range of diffusion times of most interest in practical dMRI. Previous theoretical predictions are accurate over only limited ranges; for example, is valid only for short times, whereas or is valid only for long times but cannot describe other ranges simultaneously. For the specific range of 5–70 ms used in typical human dMRI measurements, matches the data well empirically.ConclusionThe optimal power‐law fit of extracellular diffusion varies with diffusion time. The dependency obtained at short or long limits cannot be applied to typical dMRI measurements in human cancer or liver. It is essential to determine the appropriate diffusion time range when modeling extracellular diffusion in dMRI‐based quantitative microstructural imaging.
Diffusion, Diffusion Magnetic Resonance Imaging, Neoplasms, Humans, Computer Simulation, Models, Biological
Diffusion, Diffusion Magnetic Resonance Imaging, Neoplasms, Humans, Computer Simulation, Models, Biological
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 14 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
