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Journal Article

Mitigating the impact of flip angle and orientation dependence in single compartment R2* estimates via 2-pool modeling


Mohammadi,  Siawoosh
Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Germany;
Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Milotta, G., Corbin, N., Lambert, C., Lutti, A., Mohammadi, S., & Callaghan, M. F. (2023). Mitigating the impact of flip angle and orientation dependence in single compartment R2* estimates via 2-pool modeling. Magnetic Resonance in Medicine, 89(1), 128-143. doi:10.1002/mrm.29428.

Cite as: https://hdl.handle.net/21.11116/0000-000B-2DC6-4
Purpose: The effective transverse relaxation rate ( R∗2 ) 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 fiber 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; therefore, neuroscientific studies commonly make the simplifying assumption of a mono-exponential decay obtaining a single R∗2 estimate per voxel. In this work, we investigated how the multi-compartment nature of tissue microstructure affects single compartment R∗2 estimates.

Methods: We used 2-pool (myelin and non-myelin water) simulations to characterize the bias in single compartment R∗2 estimates. Based on our numeric observations, we introduced a linear model that partitions R∗2 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: R∗2 increased with myelin water fraction and residency time leading to a linear dependence on α. We observed excellent agreement between our numeric and empirical results. Furthermore, the α-independent component of the proposed linear model was robust to the choice of α and reduced dependence on fiber orientation, although 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 R∗2 estimates.