Biology subjects

Tabelow, K.

Publications and source records attributed to Tabelow, K..

4 recordsLinked to original sources

bioRxiv2023

ACID: A Comprehensive Toolbox for Image Processing and Modeling of Brain, Spinal Cord, and Post-mortem Diffusion MRI Data

Diffusion MRI (dMRI) has become a crucial imaging technique in the field of neuroscience, with a growing number of clinical applications. Although most studies still focus on the brain, there is a growing interest in utilizing dMRI to investigate the healthy or injured spinal cord. The past decade has also seen the development of biophysical models that link MR-based diffusion measures to underlying microscopic tissue characteristics, which necessitates validation through ex vivo dMRI measurements. Building upon 13 years of research and development, we present an open-source, MATLAB-based academic software toolkit dubbed ACID: A Comprehensive Toolbox for Image Processing and Modeling of Brain, Spinal Cord, and Ex Vivo Diffusion MRI Data. ACID is an extension to the Statistical Parametric Mapping (SPM) software, designed to process and model dMRI data of the brain, spinal cord, and ex vivo specimens by incorporating state-of-the-art artifact correction tools, diffusion and kurtosis tensor imaging, and biophysical models that enable the estimation of microstructural properties in white matter. Additionally, the software includes an array of linear and non-linear fitting algorithms for accurate diffusion parameter estimation. By adhering to the Brain Imaging Data Structure (BIDS) data organization principles, ACID facilitates standardized analysis, ensures compatibility with other BIDS-compliant software, and aligns with the growing availability of large databases utilizing the BIDS format. Furthermore, being integrated into the popular SPM framework, ACID benefits from a wide range of segmentation, spatial processing, and statistical analysis tools as well as a large and growing number of SPM extensions. As such, this comprehensive toolbox covers the entire processing chain from raw DICOM data to group-level statistics, all within a single software package.

neuroscience↗

bioRxiv2023

Investigating apparent differences between standard DKI and axisymmetric DKI and its consequences for biophysical parameter estimates

PurposeIdentify differences between the acquisition-time efficient axisymmetric diffusion kurtosis imaging (DKI) model and standard DKI and their consequences on biophysical parameter estimates using standard DKI parameters as the ground truth. MethodsNoise-free, synthetic diffusion MRI (dMRI) human brain data are generated using standard DKI and fitted with axisymmetric DKI and standard DKI. Then, the five axisymmetric DKI tensor metrics (AxTM), the parallel and perpendicular diffusivity and kurtosis and mean of the kurtosis tensor, attainable with both DKI models are computed. Next, the five biophysical parameters axon water fraction and dispersion, extra axonal parallel and perpendicular diffusivity and intra axonal parallel diffusivity are estimated from the AxTM using the WMTI-Watson model. Finally, the number of substantially differing voxels (SDV), defined as voxels where estimation results of both DKI models differ more than 5%, is calculated for the AxTM and the biophysical parameters. ResultsFor the AxTM, the number of SDV was biggest for the parallel (26%) and perpendicular (51%) kurtosis while the other three AxTM had very few SDV (less than 5%). The biophysical parameters had much more SDV than the AxTM from which they were computed, ranging from 29% to 50%. ConclusionAxisymmetric DKI is a viable alternative to standard DKI in studies focusing on effects based on the parallel and perpendicular diffusion and mean of the kurtosis tensor. However, our findings urge caution when using axisymmetric DKI to investigate effects based on the parallel and perpendicular kurtosis or use it to estimate the biophysical parameters.

biophysics↗

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↗

bioRxiv2022

Error quantification in multi-parameter mapping facilitates robust estimation and enhanced group level sensitivity

Multi-Parameter Mapping (MPM) is a comprehensive quantitative neuroimaging protocol that enables estimation of four physical parameters (longitudinal and effective transverse relaxation rates R1 and [Formula], proton density PD, and magnetization transfer saturation MTsat) that are sensitive to microstructural tissue properties such as iron and myelin content. Their capability to reveal microstructural brain differences, however, is tightly bound to controlling random noise and artefacts (e.g. caused by head motion) in the signal. Here, we introduced a method to estimate the local error of PD, R1 and MTsat maps that captures both noise and artefacts on a routine basis without requiring additional data. To investigate the methods sensitivity to random noise, we calculated the model-based signal-to-noise ratio (mSNR) and showed in measurements and simulations that it correlated linearly with an experimental raw-image-based SNR map. We found that the mSNR varied with MPM protocols, magnetic field strength (3T vs. 7T) and MPM parameters: it halved from PD to R1 and decreased from PD to MTsat by a factor of 3-4. Exploring the artefact-sensitivity of the error maps, we generated robust MPM parameters using two successive acquisitions of each contrast and the acquisition-specific errors to down-weight erroneous regions. The resulting robust MPM parameters showed reduced variability at the group level as compared to their single-repeat or averaged counterparts. The error and mSNR maps may better inform power-calculations by accounting for local data quality variations across measurements. Code to compute the mSNR maps and robustly combined MPM maps is available in the open-source hMRI toolbox.

neuroscience↗