Test-retest reliability, concurrent validity, and sensitivity to change of mobility assessments in older adults with urinary incontinence.

Diffusion magnetic resonance imaging (dMRI) is a non-invasive neuroimaging technique that probes the microstructural properties of biological tissues by quantifying the Brownian motion of water molecules. This technique offers significant potential for investigating subtle alterations in tissue architecture associated with various neurological and pathological conditions. To extract meaningful tissue microstructural information, dMRI data are fit to biophysical models that explain the influence of cellular features, such as axons and myelin sheaths, on water diffusion, thereby enabling the inference of micrometer-scale geometrical features from millimeter-resolution dMRI data and offering valuable insights into tissue integrity and function. Biophysical models characterize tissue microstructure in greater detail by incorporating distinct compartments for intra-axonal, extra-axonal, and cerebrospinal fluid (CSF) spaces. However, the accuracy of these models strongly depends on the specific parameters selected during dMRI acquisition (e.g., pulse sequence, diffusion time, gradient strength). The key challenge lies in identifying the optimal scanning parameters to ensure precise estimation of model outputs. In addition, accurate parameter estimation typically requires acquiring multiple dMRI images under varying scan settings, which can substantially increase scan duration. In-vivo studies and clinical applications, however, are constrained by strict limits on total scan time due to concerns such as participant comfort and motion artifacts. As a result, optimizing dMRI acquisition protocols becomes essential to maximize the information gained within a feasible timeframe, while adhering to scanner hardware limitations. This work focuses on optimizing dMRI acquisition protocols for estimating complex, multi-compartment tissue biophysical models under constraints of scan time and scanner hardware, aiming to maximize information content within practical acquisition settings. We address a fundamental question: how can dMRI experiments be designed to capture the most informative signal within clinically feasible timeframes, while leveraging advanced biophysical models to reveal tissue geometrical features linked to disease, development, and trauma?