Biology subjects

Oeschger, J. M.

Publications and source records attributed to Oeschger, J. M..

2 recordsLinked to original sources

bioRxiv2022

Axisymmetric diffusion kurtosis imaging with Rician bias correction: A simulation study

PurposeTo compare the estimation accuracy of axisymmetric diffusion kurtosis imaging (DKI) and standard DKI in combination with Rician bias correction (RBC) under the influence of noise. MethodsAxisymmetric DKI is more robust against noise-induced variation in the measured signal than standard DKI because of its reduced parameter space. However, its susceptibility to Rician noise bias at low signal-to-noise ratios (SNRs) is unknown. Here, we investigate two main questions: first, does Rician bias correction improve estimation accuracy of axisymmetric DKI?; second, is the estimation accuracy of axisymmetric DKI increased compared to standard DKI? Estimation accuracy was investigated on the five axisymmetric DKI tensor metrics (AxTM): the parallel and perpendicular diffusivity and kurtosis and the mean kurtosis, using a simulation study based on synthetic and in-vivo data. ResultsWe found that RBC was most effective for increasing accuracy of the parallel AxTM in highly to moderately aligned white matter. For the perpendicular AxTM, axisymmetric DKI without RBC performed slightly better than with RBC. However, the combination of axisymmetric DKI with RBC was the overall best performing algorithm across all five AxTM and the axisymmetric DKI framework itself substantially improved accuracy in tissues with low fiber alignment. ConclusionThe combination of axisymmetric DKI with RBC facilitates accurate DKI parameter estimation at unprecedented low SNRs ({approx} 15), possibly making it a valuable tool for neuroscience and clinical research studies where scan time is a limited resource. The tools used in this paper are publicly available in the open-source ACID toolbox for SPM.

neuroscience↗

bioRxiv2021

A representative reference for MRI-based human axon radius assessment using light microscopy

Non-invasive assessment of axon radii via MRI bears great potential for clinical and neuroscience research as it is a main determinant of the neuronal conduction velocity. However, there is a lack of representative histological reference data on the scale of the cross-section of MRI voxels for validating the MRI-visible, effective radius (reff). Because the current gold standard stems from neuroanatomical studies designed to estimate the frequency-weighted arithmetic mean radius (rarith) on small ensembles of axons, it is unsuited to estimate the tail-weighted reff. We propose CNN-based segmentation on high-resolution, large-scale light microscopy (lsLM) data to generate a representative reference for reff. In a human corpus callosum, we assessed estimation accuracy and bias of rarith and reff. Furthermore, we investigated whether mapping anatomy-related variation of rarith and reff is confounded by low-frequency variation of the image intensity, e.g., due to staining heterogeneity. Finally, we analyzed the potential error due to outstandingly large axons in reff. Compared to rarith, reff was estimated with higher accuracy (normalized-root-mean-square-error of reff: 7.2 %; rarith: 21.5 %) and lower bias (normalized-mean-bias-error of reff: -1.7 %; rarith: 16 %). While rarith was confounded by variation of the image intensity, variation of reff seemed anatomy-related. The largest axons contributed between 0.9 % and 3 % to reff. In conclusion, the proposed method accurately estimates reff at MRI voxel resolution across a human corpus callosum sample. Further investigations are required to assess generalization to brain areas with different axon radii ensembles.

neuroscience↗