Mitigating the impact of flip angle and orientation dependence in single compartment R2* estimates via 2-pool modeling
Giorgia Milotta, Nadège Corbin, Christian Lambert, Antoine Lutti, Siawoosh Mohammadi, Martina F. Callaghan
Abstract
Purpose
The effective transverse relaxation rate (R2*) is influenced by biological features that make it a useful means of probing brain microstructure. However, confounding factors such as dependence on flip angle (α) and fibre orientation with respect to the main field (θ) complicate interpretation. The α- and θ-dependence stem from the existence of multiple sub-voxel micro-environments, e.g. myelin and non-myelin water compartments. Ordinarily, it is challenging to quantify these sub-compartments, thus neuroscientific studies commonly make the simplifying assumption of a mono-exponential decay obtaining a single R2* estimate per voxel. In this work, we investigated how the multi-compartment nature of tissue microstructure affects single compartment R2* estimates.
Methods
We used two pool (myelin and non-myelin water) simulations to characterize the bias in single compartment R2* estimates. Based on our numerical observations we introduced a linear model that partitions R2* into α-dependent and α-independent components, and validated this in vivo at 7T. We investigated the dependence of both components on the sub-compartment properties and assessed their robustness, orientation dependence and reproducibility empirically.
Results
R2* increased with myelin water fraction and residency time leading to a linear dependence on α. We observed excellent agreement between our numerical and empirical results. Furthermore, the α-independent component of the proposed linear model was robust to the choice of α and reduced dependence on fibre orientation, though it suffered from marginally higher noise sensitivity.
Conclusion
We have demonstrated and validated a simple approach that mitigates flip angle and orientation biases in single-compartment R2* estimates.