Models of diffusion signal decay in magnetic resonance imaging: Capturing complexity

摘要: Diffusion-weighted MRI is a key diagnostic component of clinical medicine. Radiologists use models the effects diffusion weighting to connect decay signal intensity in tissues with macroscopic (diffusion tensor imaging) and microscopic (q-space structures. This multi-scale problem has stimulated creation many spanning phenomenon cells, tissues, organs. Such can be heuristic or based on simulations, stochastic processes, histological structure physical physiological constraints. The goal this paper provide an overview these approaches by considering several different classes mathematical (linear, nonlinear, integer, fractional orders) that can, have some cases, been used fit attenuation complex biological such as brain white gray matter. focus here not solving Bloch-Torrey equation or fitting curves data, but choices (Gaussian, anomalous, isotropic, anisotropic) one often make when beginning analysis data. It hoped presentation, while oversimplified, will students new investigators who seek introduction model selection characterization. Examples are given illustrate relationship between functional form rate and, case Stejskal-Tanner gradient pulse sequence, expected decay. Model limitations noted connections more advanced treatments modeling methods provided.