Here, we present an extension to our previously published structural ultrahigh resolution T1-weighted magnetic resonance imaging (MRI) dataset with an isotropic resolution of 250 µm, consisting of multiple additional ultrahigh resolution contrasts. Included are up to 150 µm Time-of-Flight angiography, an updated 250 µm structural T1-weighted reconstruction, 330 µm quantitative susceptibility mapping, up to 450 µm structural T2-weighted imaging, 700 µm T1-weighted back-to-back scans, 800 µm diffusion tensor imaging, one hour continuous resting-state functional MRI with an isotropic spatial resolution of 1.8 mm as well as more than 120 other structural T1-weighted volumes together with multiple corresponding proton density weighted acquisitions collected over ten years. All data are from the same participant and were acquired on the same 7 T scanner. The repository contains the unprocessed data as well as (pre-)processing results. The data were acquired in multiple studies with individual goals. This is a unique and comprehensive collection comprising a “human phantom” dataset. Therefore, we compiled, processed, and structured the data, making them publicly available for further investigation.

Purpose To 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. Methods The 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. Results Using 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. Conclusions Since 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.

G-ratio weighted imaging is a non-invasive, in-vivo MRI-based technique that aims at estimating an aggregated measure of relative myelination of axons across the entire brain white matter. The MR g-ratio and its constituents (axonal and myelin volume fraction) are more specific to the tissue microstructure than conventional MRI metrics targeting either the myelin or axonal compartment. To calculate the MR g-ratio, an MRI-based myelin-mapping technique is combined with an axon-sensitive MR technique (such as diffusion MRI). Correction for radio-frequency transmit (B1+) field inhomogeneities is crucial for myelin mapping techniques such as magnetization transfer saturation. Here we assessed the effect of B1+ correction on g-ratio weighted imaging. To this end, the B1+ field was measured and the B1+ corrected MR g-ratio was used as the reference in a Bland-Altman analysis. We found a substantial bias (≈-89%) and error (≈37%) relative to the dynamic range of g-ratio values in the white matter if the B1+ correction was not applied. Moreover, we tested the efficiency of a data-driven B1+ correction approach that was applied retrospectively without additional reference measurements. We found that it reduced the bias and error in the MR g-ratio by a factor of three. The data-driven correction is readily available in the open-source hMRI toolbox (www.hmri.info) which is embedded in the statistical parameter mapping (SPM) framework.

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