Biology subjects

Fritz, F. J.

Publications and source records attributed to Fritz, F. J..

4 recordsLinked to original sources

bioRxiv2026

Longitudinal whole-human-brain quantitative MRI study on autolysis, fixation, rehydration, and shrinkage effects

Postmortem MRI links in vivo MRI contrast to microstructure measured with ex vivo histology, yet fixation systematically alters relevant physical MRI parameters. We quantified these effects by longitudinally characterizing multi-parametric mapping (MPM) measures - longitudinal (R1) and effective transverse (R2*) relaxation rates, normalized proton density proxy (NA), and magnetization transfer saturation (MTsat) - and tissue-volume change in five whole human brains scanned unfixed ex vivo in situ, during formaldehyde immersion fixation, and, when available, after transfer to phosphate buffered saline solution. We included an independent in vivo cohort of 25 younger healthy participants to provide a qualitative in vivo reference. The largest changes were found for R1 during fixation relative to in situ values (up to 351% in dGM), followed by R2* (up to 60% in WM). NA was largely unchanged during fixation. Although MTsat decreased by up to 25% in gray matter from in vivo to in situ, it largely preserved tissue contrast during fixation and PBS immersion, making it an ideal candidate for longitudinal registration across tissue conditions. Phenomenological models characterized fixation-induced parameter changes and relative tissue-volume change. R1 and fixation-associated volume loss evolved under broadly similar timescales, consistent with a contribution from fixation-related water loss or redistribution. This densely sampled dataset and analyses provide a resource for linking in vivo MRI to postmortem fixed-tissue MRI and ex vivo histology under a standardized immersion-fixation protocol.

neuroscience↗

bioRxiv2022

Deciphering the fibre-orientation independent component of R2* (R2,iso*) in the human brain with a single multi-echo gradient-recalled-echo measurement under varying microstructural conditions

The effective transverse relaxation rate (R2*) is sensitive to the microstructure of the human brain, e.g. the g-ratio characterising the relative myelination of axons. However, R2* depends on the orientation of the fibres relative to the main magnetic field degrading its reproducibility and that of any microstructural derivative measure. To decipher its orientation-independent part (R2,iso*), a second-order polynomial in time (M2) can be applied to single multi-echo gradient-recalled-echo (meGRE) measurements at arbitrary orientation. The linear-time dependent parameter, {beta}1, of M2 can be biophysically related to R2,iso* when neglecting the signal from the myelin water (MW) in the hollow cylinder fibre model (HCFM). Here, we examined the effectiveness of M2 using experimental and simulated data with variable g-ratio and fibre dispersion. We showed that the fitted {beta}1 effectively estimates R2,iso*when using meGRE with long maximum echo time (TEmax {approx} 54 ms) but its microscopic dependence on the g-ratio was not accurately captured. This error was reduced to less than 12% when accounting for the MW contribution in a newly introduced biophysical expression for {beta}1. We further used this new expression to estimate the MW fraction (0.14) and g-ratio (0.79) in a human optic chiasm. However, the proposed method failed to estimate R2,iso* for a typical in-vivo meGRE protocol (TEmax {approx} 18 ms). At this TEmax and around the magic angle, the HCFM-based simulations failed to explain the R2*-orientation-dependence. In conclusion, estimation of R2,iso* with M2 in vivo requires meGRE protocols with very long TEmax {approx} 54 ms.

neuroscience↗

bioRxiv2020

MESMERISED: Super-accelerated 7 T STEAM imaging for quantitative multi-contrast and diffusion MRI

There is an increasing interest in quantitative imaging of T1, T2 and diffusion contrast in the brain due to greater robustness against bias fields and artifacts, as well as better biophysical interpretability in terms of microstructure. However, acquisition time constraints are a challenge, particularly when multiple quantitative contrasts are desired and when extensive sampling of diffusion directions, high b-values or long diffusion times are needed for multi-compartment microstructure modeling. Although ultra-high fields of 7 T and above have desirable properties for many MR modalities, the shortening T2 and the high specific absorption rate (SAR) of inversion and refocusing pulses bring great challenges to quantitative T1, T2 and diffusion imaging. Here, we present the MESMERISED sequence (Multiplexed Echo Shifted Multiband Excited and Recalled Imaging of STEAM Encoded Diffusion). MESMERISED removes the dead time in Stimulated Echo Acquisition Mode (STEAM) imaging by an echo-shifting mechanism. The echo-shift (ES) factor is independent of multiband (MB) acceleration and allows for very high multiplicative (ESxMB) acceleration factors, particularly under moderate and long mixing times. This results in super-acceleration and high time efficiency at 7 T for quantitative T1 and diffusion imaging, while also retaining the capacity to perform quantitative T2 and B1 mapping. We demonstrate the super-acceleration of MESMERISED for whole-brain T1 relaxometry with total acceleration factors up to 36 at 1.8 mm isotropic resolution, and up to 54 at 1.25 mm resolution qT1 imaging, corresponding to a 6x and 9x speedup, respectively, compared to MB-only accelerated acquisitions. We then demonstrate highly efficient diffusion MRI with high b-values and long diffusion times in two separate cases. First, we show that super-accelerated multi-shell diffusion acquisitions with 370 whole-brain diffusion volumes over 8 b-value shells up to b = 7000 s/mm2 can be generated at 2 mm isotropic in under 8 minutes, a data rate of almost a volume per second, or at 1.8 mm isotropic in under 11 minutes, achieving up to 3.4x speedup compared to MB-only. A comparison of b = 7000 s/mm2 MESMERISED against standard MB pulsed gradient spin echo (PGSE) diffusion imaging shows 70% higher SNR efficiency and greater effectiveness in supporting complex diffusion signal modeling. Second, we demonstrate time-efficient sampling of different diffusion times with 1.8 mm isotropic diffusion data acquired at four diffusion times up to 290 ms, which supports both Diffusion Tensor Imaging (DTI) and Diffusion Kurtosis Imaging (DKI) at each diffusion time. Finally, we demonstrate how adding quantitative T2 and B1+ mapping to super-accelerated qT1 and diffusion imaging enables efficient quantitative multi-contrast mapping with the same MESMERISED sequence and the same readout train. MESMERISED extends possibilities to efficiently probe T1, T2 and diffusion contrast for multi-component modeling of tissue microstructure.

neuroscience↗

bioRxiv2019

Fast quantification of uncertainty in non-linear diffusion MRI models for artifact detection and more power in group studies

Diffusion MRI (dMRI) allows for non-invasive investigation of brain tissue microstructure. By fitting a model to the dMRI signal, various quantitative measures can be derived from the data, such as fractional anisotropy, neurite density and axonal radii maps. The uncertainty in these dMRI measures is often ignored, while previous work in functional MRI has shown that incorporating uncertainty estimates can lead to group statistics with a higher statistical power. We propose the Fisher Information Matrix (FIM) as a generally applicable method for quantifying the parameter uncertainties in non-linear diffusion MRI models. In direct comparison with Markov Chain Monte Carlo sampling, the FIM produces similar uncertainty estimates at lower computational cost. Using acquired and simulated data, we then list several characteristics that influence the parameter variances, like data complexity and signal-to-noise ratio. In individual subjects, the parameter standard deviations can help in detecting white matter artifacts as patches of relatively large standard deviations. In group statistics, we recommend using the parameter standard deviations by means of variance weighted averaging. Doing so can reduce the overall variance in group statistics and reduce the effect of data artifacts without discarding data from the analysis. Both these effects can lead to a higher statistical power in group studies.

neuroscience↗