A unified filtering method for estimating asymmetric orientation distribution functions

Numerous filtering methods have been proposed for estimating asymmetric orientation distribution functions (ODFs) for diffusion magnetic resonance imaging (dMRI). It can be hard to make sense of all these different methods, which share similar features and result in similar outputs. In this work, we disentangle these many filtering methods proposed in the past and combine them into a novel, unified filtering equation. We also propose a self -supervised data -driven approach for calibrating the filtering parameter values. Our equation is implemented in an open -source GPU-accelerated python software to facilitate its integration into any existing dMRI processing pipeline. Our method is applied on multi -shell multi -tissue fiber ODFs from the Human Connectome Project dataset (1.25 mm3 native resolution) and on single -shell single -tissue fiber ODFs from the Bilingualism and the Brain dataset (2.0 mm3 isotropic resolution) to evaluate the occurrence of asymmetric patterns on different spatial resolutions, representing cutting -edge and "clinical"research data. Asymmetry measures such as the asymmetric index (ASI) and our novel number of fiber directions (NuFiD) are then used to explain the behaviour of our method in these images. The contributions of this work are: (i) the disentanglement and unification of filtering methods for estimating asymmetric ODFs; (ii) a calibration method for automatically fixing the parameters governing the filtering; (iii) an open -source, efficient implementation of our unified filtering method for estimating asymmetric ODFs; (iv) a novel number of fiber directions (NuFiD) index for explaining asymmetric fiber configurations; and (v) a novel template of asymmetries, revealing that our filtering method estimates asymmetric configurations in at least 50% of the brain voxels (similar to 31% of the white matter and similar to 63% of the gray matter).