Publications

Preprint

Two observables of one wall

Two observables of one wall: how surface relaxivity can bias the diffusion intra-axonal fraction and the myelin water fraction. Rutger H.J. Fick (2026). arXiv:2607.09401 [physics.med-ph].

Surface relaxivity and time-dependent diffusion are two readouts of the same wall collisions on one substrate: the transverse rate a microstructure model fits is a bulk rate plus a surface rate ρ·(S/V), and because intra- and extra-axonal water carry different S/V, their T2 differ — so any compartment estimate normalised by a TE-weighted b=0 is biased. The paper derives closed forms for the interior (Brownstein–Tarr) and exterior (Novikov–Burcaw) surface rates over myelinated cylinders, validates them with wall-counting Monte Carlo, and quantifies the resulting bias on the diffusion intra-axonal signal fraction f_intra and on the myelin water fraction (MWF). The surface relaxivity & MWF page is the reproducible walkthrough, and every figure regenerates from the engines here.

The original dmipy toolbox

Dmipy (2019)

The Dmipy Toolbox: Diffusion MRI Multi-Compartment Modeling and Microstructure Recovery Made Easy. Rutger H.J. Fick, Demian Wassermann, Rachid Deriche (2019). Frontiers in Neuroinformatics 13:64. DOI:10.3389/fninf.2019.00064.

The paper behind the original 2019 toolbox — the modular multi-compartment model-design grammar that the analytical inverse (dmipy-fit) carries forward. If you use the fitting framework, please cite this alongside the specific models you compose (dmipy-fit's citation graph generates the full reference list automatically).