Bias field correction of MPRAGE by an external reference - The poor man's MP2RAGE

Purpose: To implement and evaluate a sequential approach to obtain semi-quantitative T1-weighted MPRAGE images, unbiased by B1 inhomogeneities at 7T.Methods: In the reference gradient echo used for normalization of the MPRAGE image, flipangle (αGE) and acquisition voxel size (Vref) was varied to optimize tissue contrast and acquisition time (Tacq). The finalized protocol was implemented at three different resolutions and the reproducibility was evaluated. Maps of T1 were derived based on the normalized MPRAGE through forward signal modelling.Results: A good compromise between tissue contrast and SNR was reached at αGE=3°. Areduction of the reference GE Tacq by a factor of 4, at the cost of negligible bias, was obtained by increasing Vref with a factor of 8 relative the MPRAGE resolution. The coefficient-ofvariation in segmented WM was 9±5% after normalization, compared to 24±12% before. The T1 maps showed no obvious bias and had reasonable values with regard to literature, especially after optional B1 correction through separate flip angle mapping.Conclusion: A non-interleaved acquisition for normalization of MPRAGE offers a simplealternative to MP2RAGE to obtain semi-quantitative purely T1-weighted images. Theseimages can be converted to T1 maps analogously to the established MP2RAGE approach. Scan time can be reduced by increasing Vref which has a miniscule effect on image quality

Correction of FLASH-based MT saturation in human brain for residual bias of B1-inhomogeneity at 3T

Background: Magnetization transfer (MT) saturation reflects the additional saturation of the MRI signal imposed by an MT pulse and is largely driven by the saturation of the bound pool. This reduction of the bound polarization by the MT pulse is less efficient than predicted by the differential B1-square law of absorption. Thus, B1 inhomogeneities lead to a residual bias in the MT saturation maps. We derive a heuristic correction to reduce this bias for a widely used multi-parameter mapping protocol at 3T. Methods: The amplitude of the MT pulse was varied via the nominal flip angle to mimic variations in B1. The MT saturation's dependence on the actual flip angle features a linear correction term, which was determined separately for gray and white matter. Results: The deviation of MT saturation from differential B1-square law is well described by a linear decrease with the actual flip angle of the MT pulse. This decrease showed no significant differences between gray and white matter. Thus, the post hoc correction does not need to take different tissue types into account. Bias-corrected MT saturation maps appeared more symmetric and highlighted highly myelinated tracts. Discussion:Our correction involves a calibration that is specific for the MT pulse. While it can also be used to rescale nominal flip angles, different MT pulses and/or protocols will require individual calibration. Conclusion: The suggested B1 correction of the MT maps can be applied post hoc using an independently acquired flip angle map

Resampling Strategies to Estimate Mean Concentrations from Low SNR In Vivo MR Spectra

A resampling method for quantitative MRS is presented that retains absolute concentrations. Averaging the spectra from a subset improved the concentrationestimates via increased SNR. Using short-TE STEAM spectra from human thalamus, the influence of subset size was systematically studied and verifiedagainst conventional bootstrap re-sampling. Even small subsets provided considerably better signal-to-noise ratio and reliability. The standard deviation orconfidence intervals can be derived by correcting the variance over all subset estimates for the inherent redundancy. Subsets of about half the group size arerecommended, because of their large number and the almost normal distribution of mean concentration estimates

Identification of Signal Bias in the Variable Flip Angle Method by Linear Display of the Algebraic Ernst Equation

A novel linear parameterization for the variable flip angle method for longitudinal relaxation time T1 quantification from spoiled steady state MRI is derived from the half angle tangent transform, τ, of the flip angle. Plotting the signal S at coordinates x = Sτ and y = S/τ, respectively, establishes a line that renders signal amplitude and relaxation term separately as y-intercept and slope. This representation allows for estimation of the respective parameter from the experimental data. A comprehensive analysis of noise propagation is performed. Numerical results for efficient optimization of longitudinal relaxation time and proton density mapping experiments are derived. Appropriate scaling allows for a linear presentation of data that are acquired at different short pulse repetition times, TR << T1 thus increasing flexibility in the data acquisition by removing the limitation of a single pulse repetition time. Signal bias, like due to slice-selective excitation or imperfect spoiling, can be readily identified by systematic deviations from the linear plot. The method is illustrated and validated by 3T experiments on phantoms and human brain. Magn Reson Med, 2011. © 2011 Wiley-Liss, Inc

Radiofrequency bias correction of magnetization prepared rapid gradient echo MRI at 7.0 Tesla using an external reference in a sequential protocol

At field strengths of 7 T and above, T1-weighted imaging of human brain suffers increasingly from radiofrequency (RF) B1 inhomogeneities. The well-known MP2RAGE (magnetization prepared two rapid acquisition gradient echoes) sequence provides a solution but may not be readily available for all MR systems. Here, we describe the implementation and evaluation of a sequential protocol to obtain normalized magnetization prepared rapid gradient echo (MPRAGE) images at 0.7,0.8, or 0.9-mm isotropic spatial resolution. Optimization focused on the reference gradient-recalled echo (GRE) that was used for normalization of the MPRAGE. A good compromise between white-gray matter contrast and the signal-to-noise ratio (SNR) was reached at a flip angle of 3° and total scan time was reduced by increasing the reference voxel size by a factor of 8 relative to the MPRAGE resolution. The average intra-subject coefficient-of-variation (CV) in segmented white matter (WM) was 7.9 ±3.3% after normalization, compared to 20 ±8.4% before. The corresponding inter-subject average CV in WM as 7.6 ±7.6% and 13 ±7.8%. Maps of T1 derived from forward signal modelling showed no obvious bias after correction by a separately acquired flip angle map. To conclude, a non-interleaved acquisition for normalization of MPRAGE offers a simple alternative to MP2RAGE to obtain semi-quantitative purely T1-weighted images. These images can be converted to T1 maps, analogously to the established MP2RAGE approach. Scan time can be reduced by increasing the reference voxel size which has only a miniscule effect on image quality

From in situ to ex vivo: the effect of autolysis and fixation on quantitative MRI markers for myelin

Ex vivo histology remains the gold standard against which MRI biophysical models, e.g. the MR g-ratio which characterises the fraction of a fibre’s diameter that is myelinated, are evaluated. The MR g-ratio model requires a measure of myelin density, for which magnetization transfer saturation (MT) has been used as a biomarker. However, changes occurring post mortem, e.g. autolysis, temperature changes and fixation, significantly alter the MRI signal. Here we investigate how these changes impact MT. We found that MT decreased post mortem but greatlyincreased upon fixation. These effects are similar to reported changes of other established MRI myelin-markers

Non-negative least squares fitting of multi-exponential T2 decay data: Are we able to accurately measure the fraction of myelin water?

The ability to determine the myelin water fraction (MWF) in vivo is essential to assessments of neurodevelopmental myelination and myelin damage in neurodegenerative diseases. The analysis of multi-exponential T2 decay data relies on the non-negative-least-squares (NNLS) fitting, which may be sensitive to the chosen fitting parameters. We performed simulations to explore the outcomes of NNLS under different parameter selection. The lowest allowed T2 was found to have the largest effect on correctly estimating the T2 of different water pools as well as the MWF. Lower refocusing FAs led to further underestimation of the MWF

Characterising the temporal evolution of fixation in human post mortem brain via linear relaxometry modelling – a marker of cross-linking?

MRI-based biophysical models are typically validated by comparison to ex-vivo histology of fixed tissue. The fixation process itself and the accompanied autolysis processes strongly modify tissue composition, and lead to MR signal changes, making the validation of biophysical models for in vivo MRI particularly challenging. To better understand the temporal evolution of the fixation process within the whole brain and its influence on MRI parameters, we monitor the temporal evolution of the fixation process of a whole human post-mortem brain using the linear relaxometry model across 15 time-points comprised of one unfixed, in-situ MRI scan and 14 ex-vivo MRI scans at different stages of the fixation process (days 1-93)

The Influence of Radio-Frequency Transmit Field Inhomogeneities on the Accuracy of G-ratio Weighted Imaging

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

hMRI - A toolbox for quantitative MRI in neuroscience and clinical research

Neuroscience and clinical researchers are increasingly interested in quantitative magnetic resonance imaging (qMRI) due to its sensitivity to micro-structural properties of brain tissue such as axon, myelin, iron and water concentration. We introduce the hMRI-toolbox, an open-source, easy-to-use tool available on GitHub, for qMRI data handling and processing, presented together with a tutorial and example dataset. This toolbox allows the estimation of high-quality multi-parameter qMRI maps (longitudinal and effective transverse relaxation rates and , proton density and magnetisation transfer saturation) that can be used for quantitative parameter analysis and accurate delineation of subcortical brain structures. The qMRI maps generated by the toolbox are key input parameters for biophysical models designed to estimate tissue microstructure properties such as the MR g-ratio and to derive standard and novel MRI biomarkers. Thus, the current version of the toolbox is a first step towards in vivo histology using MRI (hMRI) and is being extended further in this direction. Embedded in the Statistical Parametric Mapping (SPM) framework, it benefits from the extensive range of established SPM tools for high-accuracy spatial registration and statistical inferences and can be readily combined with existing SPM toolboxes for estimating diffusion MRI parameter maps. From a user's perspective, the hMRI-toolbox is an efficient, robust and simple framework for investigating qMRI data in neuroscience and clinical research