Rotation-invariant microstructure mapping revisited

Microstructure imaging with diffusion MRI relies on non-linear fitting of a biophysical tissue model to the data, often at low signal-to-noise ratio. In this work, we derive a new rotation-invariant feature set for microstructure mapping based on a rank-1 decomposition of the multi-shell diffusion MRI signal in spherical harmonics. Simulations show that using this feature set avoids non-central-χ bias that is present at low SNR in parameter estimation based on conventional rotation-invariants. Results in human brain imaging data acquired with free waveform diffusion encoding show robust parameter estimates across white matter.