Abstract
PurposeTo propose and validate an efficient method, based on a biophysically motivated signal model, for removing the orientation‐dependent part of R2* using a single gradient‐recalled echo (GRE) measurement.MethodsThe proposed method utilized a temporal second‐order approximation of the hollow‐cylinder‐fiber model, in which the parameter describing the linear signal decay corresponded to the orientation‐independent part of R2*. The estimated parameters were compared to the classical, mono‐exponential decay model for R2* in a sample of an ex vivo human optic chiasm (OC). The OC was measured at 16 distinct orientations relative to the external magnetic field using GRE at 7T. To show that the proposed signal model can remove the orientation dependence of R2*, it was compared to the established phenomenological method for separating R2* into orientation‐dependent and ‐independent parts.ResultsUsing the phenomenological method on the classical signal model, the well‐known separation of R2* into orientation‐dependent and ‐independent parts was verified. For the proposed model, no significant orientation dependence in the linear signal decay parameter was observed.ConclusionsSince the proposed second‐order model features orientation‐dependent and ‐independent components at distinct temporal orders, it can be used to remove the orientation dependence of R2* using only a single GRE measurement.
Highlights
Quantitative MRI measures in the human brain are typically sensitive to multiple microstructural features at once, e.g., myelin and iron content.[1,2] The combination of complementary qMRI measures with biophysical models can help to disentangle these different contributions and to increase the specificity of qMRI with respect to distinct microstructural properties.[3]
gradient‐recalled echo (GRE)‐MRI is interesting for qMRI since both its magnitude, from which the apparent transverse relaxation rate R∗2 may be estimated, and its phase, which represents the basis of quantitative susceptibility mapping,[4,5,6] are sensitive to microstructure
For the linear model Equation 1, the apparent transverse relaxation rate α1, which corresponded to the classical R∗2 clearly showed dependence on θ (Figure 3A and D)
Summary
Quantitative MRI (qMRI) measures in the human brain are typically sensitive to multiple microstructural features at once, e.g., myelin and iron content.[1,2] The combination of complementary qMRI measures with biophysical models can help to disentangle these different contributions and to increase the specificity of qMRI with respect to distinct microstructural properties.[3]. Where, according to the predictions of the HCFM, β1 is orientation‐independent, whereas β2 is orientation‐dependent, following a sin[4] θ function
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