Purpose
To compare the estimation accuracy of axisymmetric diffusion kurtosis imaging (DKI) and standard DKI in combination with Rician bias correction (RBC).
Methods
Axisymmetric 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 (SNR) is unknown. Here, we investigate two main questions: first, does RBC improve estimation accuracy of axisymmetric DKI?; second, is 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 mean of the kurtosis tensor, using a noise simulation study based on synthetic data of tissues with varying fiber alignment and in‐vivo data focusing on white matter.
Results
RBC mainly increased accuracy for the parallel AxTM in tissues with highly to moderately aligned fibers. 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 in white matter and axisymmetric DKI itself substantially improved accuracy in axisymmetric tissues with low fiber alignment.
Conclusion
Combining axisymmetric DKI with RBC facilitates accurate DKI parameter estimation at unprecedented low SNRs (≈15$$ \approx 15 $$) in white matter, possibly making it a valuable tool for neuroscience and clinical research studies where scan time is a limited resource. The tools used here are available in the open‐source ACID toolbox for SPM.
•A pipeline for automated estimation of ensemble mean axon radii at MRI voxel scale•Incorporation of 4 human white matter brain tissue samples for training and testing•Accurate mapping of the ...MRI-visible radius in the presence of staining heterogeneity•Includes 2 to 4 orders of magnitude more axons than the current gold standard (EM)•The pipeline enables validation of biophysical, MRI-based radius estimation models
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 at 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 bulk-determined 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 error due to outstandingly large axons in reff. Compared to rarith, reff was estimated with higher accuracy (maximum normalized-root-mean-square-error of reff: 8.5 %; rarith: 19.5 %) and lower bias (maximum absolute normalized-mean-bias-error of reff: 4.8 %; rarith: 13.4 %). While rarith was confounded by variation of the image intensity, variation of reff seemed anatomy-related. The largest axons contributed between 0.8 % and 2.9 % to reff. In conclusion, the proposed method is a step towards representatively estimating reff at MRI voxel resolution. Further investigations are required to assess generalization to other brains and brain areas with different axon radii distributions.
The purpose of the study is to identify differences between axisymmetric diffusion kurtosis imaging (DKI) and standard DKI, their consequences for biophysical parameter estimates, and the protocol ...choice influence on parameter estimation.
Noise-free and noisy, synthetic diffusion MRI human brain data is simulated using standard DKI for a standard and the fast "199" acquisition protocol. First the noise-free "baseline" difference between both DKI models is estimated and the influence of fiber complexity is investigated. Noisy data is used to establish the signal-to-noise ratio at which the baseline difference exceeds noise variability. The influence of protocol choices and denoising is investigated. The five axisymmetric DKI tensor metrics (AxTM), the parallel and perpendicular diffusivity and kurtosis and mean of the kurtosis tensor are used to compare both DKI models. Additionally, the baseline difference is also estimated for the five parameters of the WMTI-Watson model.
The parallel and perpendicular kurtosis and all of the WMTI-Watson parameters had large baseline differences. Using a Westin or FA mask reduced the number of voxels with large baseline difference, that is, by selecting voxels with less complex fibers. For the noisy data, precision was worsened by the fast "199" protocol but adaptive denoising can help counteract these effects.
For the diffusivities and mean of the kurtosis tensor, axisymmetric DKI with a standard protocol delivers similar results as standard DKI. Fiber complexity is one main driver of the baseline differences. Using the "199" protocol worsens precision in noisy data but adaptive denoising mitigates these effects.
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.
Abstract 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.
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