Here, we present an extension to our previously published structural ultrahigh resolution T
-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 T
-weighted reconstruction, 330 µm quantitative susceptibility mapping, up to 450 µm structural T
-weighted imaging, 700 µm T
-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 T
-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.
The effective transverse relaxation rate (R
2
*) is sensitive to the microstructure of the human brain like the g-ratio which characterises the relative myelination of axons. However, the ...fibre-orientation dependence of R
2
* degrades its reproducibility and any microstructural derivative measure. To estimate its orientation-independent part (R
2,iso
*) from single multi-echo gradient-recalled-echo (meGRE) measurements at arbitrary orientations, a second-order polynomial in time model (hereafter M2) can be used. Its linear time-dependent parameter,
β
1
, can be biophysically related to R
2,iso
* when neglecting the myelin water (MW) signal in the hollow cylinder fibre model (HCFM). Here, we examined the performance of M2 using experimental and simulated data with variable g-ratio and fibre dispersion. We found that the fitted
β
1
can estimate R
2,iso
* using meGRE with long maximum-echo time (TE
max
≈ 54 ms), but not accurately captures its microscopic dependence on the g-ratio (error 84%). We proposed a new heuristic expression for
β
1
that reduced the error to 12% for
ex vivo
compartmental R
2
values. Using the new expression, we could estimate an MW fraction of 0.14 for fibres with negligible dispersion in a fixed human optic chiasm for the
ex vivo
compartmental R
2
values but not for the
in vivo
values. M2 and the HCFM-based simulations failed to explain the measured R
2
*-orientation-dependence around the magic angle for a typical
in vivo
meGRE protocol (with TE
max
≈ 18 ms). In conclusion, further validation and the development of movement-robust
in vivo
meGRE protocols with TE
max
≈ 54 ms are required before M2 can be used to estimate R
2,iso
* in subjects.
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.
Cover Image Papazoglou, Sebastian; Ashtarayeh, Mohammad; Oeschger, Jan Malte ...
NMR in biomedicine,
March 2024, Volume:
37, Issue:
3
Journal Article
Peer reviewed
The cover image is based on the Research Article Insights and improvements in correspondence between axonal volume fraction measured with diffusion‐weighted MRI and electron microscopy by Sebastian ...Papazoglou et al., https://doi.org/10.1002/nbm.5070.
Cover Image Papazoglou, Sebastian; Ashtarayeh, Mohammad; Oeschger, Jan Malte ...
NMR in biomedicine,
03/2024, Volume:
37, Issue:
3
Journal Article
Peer reviewed
The cover image is based on the Research Article Insights and improvements in correspondence between axonal volume fraction measured with diffusion‐weighted MRI and electron microscopy by Sebastian ...Papazoglou et al., https://doi.org/10.1002/nbm.5070.
Biophysical diffusion‐weighted imaging (DWI) models are increasingly used in neuroscience to estimate the axonal water fraction (
fAW), which in turn is key for noninvasive estimation of the axonal ...volume fraction (
fA). These models require thorough validation by comparison with a reference method, for example, electron microscopy (EM). While EM studies often neglect the unmyelinated axons and solely report the fraction of myelinated axons, in DWI both myelinated and unmyelinated axons contribute to the DWI signal. However, DWI models often include simplifications, for example, the neglect of differences in the compartmental relaxation times or fixed diffusivities, which in turn might affect the estimation of
fAW. We investigate whether linear calibration parameters (scaling and offset) can improve the comparability between EM‐ and DWI‐based metrics of
fA. To this end, we (a) used six DWI models based on the so‐called standard model of white matter (WM), including two models with fixed compartmental diffusivities (e.g., neurite orientation dispersion and density imaging, NODDI) and four models that fitted the compartmental diffusivities (e.g., white matter tract integrity, WMTI), and (b) used a multimodal data set including ex vivo diffusion DWI and EM data in mice with a broad dynamic range of fibre volume metrics. We demonstrated that the offset is associated with the volume fraction of unmyelinated axons and the scaling factor is associated with different compartmental
T2 and can substantially enhance the comparability between EM‐ and DWI‐based metrics of
fA. We found that DWI models that fitted compartmental diffusivities provided the most accurate estimates of the EM‐based
fA. Finally, we introduced a more efficient hybrid calibration approach, where only the offset is estimated but the scaling is fixed to a theoretically predicted value. Using this approach, a similar one‐to‐one correspondence to EM was achieved for WMTI. The method presented can pave the way for use of validated DWI‐based models in clinical research and neuroscience.
We show for five biophysical diffusion weighted imaging (DWI) models, including WMTI and NODDI, that linear calibration with an offset ($U_2$) associated with pool of unmyelinated axons, and a scaling ($s$) associated with the different compartmental relaxation times, substantially improves the one‐to‐one correspondence between the axonal volume fraction from DWI and the volume fraction of myelinated axons from electron microscopy (EM). Furthermore, we show that the best one‐to‐one correspondence is achieved for DWI models fitting compartmental diffusivities.
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.
Purpose
To propose and validate an efficient method, based on a biophysically motivated signal model, for removing the orientation‐dependent part of
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
. The estimated parameters were compared to the classical, mono‐exponential decay model for
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
, it was compared to the established phenomenological method for separating
into orientation‐dependent and ‐independent parts.
Results
Using the phenomenological method on the classical signal model, the well‐known separation of
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
using only a single GRE measurement.
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