Comprehensive diffusion MRI dataset for in vivo human brain microstructure mapping using 300 mT/m gradients

Tian, Qiyuan; Fan, Qiuyun; Witzel, Thomas; Polackal, Maya N.; Ohringer, Ned A.; Ngamsombat, Chanon; Russo, Andrew W.; Machado, Natalya; Brewer, Kristina; Wang, Fuyixue; Setsompop, Kawin; Polimeni, Jon̈athan R.; Keil, Boris; Wald, Lawrence L.; Rosen, Bruce R.; Klawiter, Eric C.; Nummenmaa, Aapo; Huang, Susie Y.

doi:10.1038/s41597-021-01092-6

Cited by 26 publications

(32 citation statements)

References 94 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…first measured from background voxels and assumed constant across the brain. There are various methods to remove the Rician noise bias that include but are not limited to a) utilising real-valued rather than magnitude images [40, 39], b) approximating the Rician signal as Gaussian via e.g. the Koay inversion technique [37] and, as was not explicitly done in this study, c) applying a Rician noise model during optimisation.…”

Section: Discussionmentioning

confidence: 99%

“…The data used in this study is openly available via http://www.humanconnectomeproject.org/ and [40]. At https://git.fmrib.ox.ac.uk/amyh/noddi-axialdiffusivity we provide a cuDIMOT implementation of NODDI [34] where the assumed diffusivities d ║ and d iso are user-defined at runtime and MATLAB scripts to implement the modified NODDI model.…”

Section: Discussionmentioning

confidence: 99%

“…Diffusion MRI data were previously acquired, reconstructed and pre-processed according to Tian et al [40] and Fan et al [39]. Briefly, complex diffusion data were acquired on the 3T MGH Connectom scanner (Siemens Healthcare, Erlangen, Germany) using gradients up to 300mT/m [41,42] and a pulsed gradient, spin echo EPI sequence [43]: T R/T E = 4000/77 ms, 2mm isotropic resolution, 17 b-values from 0 to 17.8 ms/µm 2 , two different diffusion times δ/∆ = 8/19 ms and δ/∆ = 8/49 ms.…”

Section: In Vivo Human Datamentioning

confidence: 99%

See 2 more Smart Citations

Estimating axial diffusivity in the NODDI model

Howard

Lange

Mollink

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

By analysing the diffusion MRI signal, we can infer information about the microscopic structure of the brain. Two parameters of interest - the intra-axonal axial diffusivity and fibre orientation dispersion - are potential biomarkers for very different aspects of the white matter microstructure, yet they are difficult to disentangle. The parameters covary such that, if one is not accurately accounted for, the other will be biased. In this work we use high b-value data to isolate the signal from the intra-axonal compartment and resolve any degeneracies with the extra-axonal compartment. In the high b-value regime, we then use a model of dispersed sticks to estimate the intra-axonal axial diffusivity and fibre orientation distribution on a voxelwise basis. Our results in in vivo, human data show an intra-axonal axial diffusivity of ~ 2.3 – 3 μm2/ms, where 3 μm2/ms is the diffusivity of free water at 37°C. The intra-axonal axial diffusivity is seen to vary considerably across the white matter. For example, in the corpus callosum we find high values in the genu and splenium, and lower values in the midbody. Furthermore, the axial diffusivity and orientation dispersion appear negatively correlated, behaviour which we show is consistent with the presence of fibre undulations but not consistent with a degeneracy between fanning fibres and axial diffusivity. Finally, we demonstrate that the parameter maps output from Neurite Orientation Dispersion and Density Imaging (NODDI) change substantially when the assumed axial diffusivity was increased from 1.7 to 2.5 or 3 μm2/ms.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: In Vivo Human Datamentioning

confidence: 99%

See 1 more Smart Citation

Estimating axial diffusivity in the NODDI model

Howard

Lange

Mollink

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…To this end, we consider different fibre configularions: one, two and three fibre bundles consisting of parallel cylinders with the same diameter distributions (Gamma distribution with mean = 5.33 μm, std = 3.00 μm) with separate fibre bundles crossing at right angles (in the case of two and three fibres), as well as spherical pores (Gaussian distribution with mean = 7μm, std = 0.5μm), and we simulate the signals using the optimised sequences from simulation 1. Then, for each measurement, we consider Rician noise at SNR levels of 20 and 50 in the non-diffusion weighted images, typical for for clinical and pre-clinical acquisitions 70,71, as well as noise free signals, i.e. SNR ∞.…”

Section: Methodsmentioning

confidence: 99%

Optimisation and Pre-clinical Demonstration of Temporal Diffusion Ratio for Imaging Restricted Diffusion

Warner

Palombo

Cruz

et al. 2022

Preprint

View full text Add to dashboard Cite

Temporal Diffusion Ratio (TDR) is a recently proposed dMRI technique (Dell'Acqua, 2019) which provides contrast between areas with restricted diffusion and areas either without restricted diffusion or with length scales too small for characterisation. Hence, it has a potential for mapping pore sizes, in particular large axon diameters or other cellular structures. TDR employs the signal from two dMRI acquisitions obtained with the same, large, b-value but with different diffusion times and gradient settings. TDR is advantageous as it employs standard acquisition sequences, does not make any assumptions on the underlying tissue structure and does not require any model fitting, avoiding issues related to model degeneracy. This work for the first time optimises the TDR diffusion sequences in simulation for a range of different tissues and scanner constraints. We extend the original work (which considers substrates containing cylinders) by additionally considering the TDR signal obtained from spherical structures, representing cell soma in tissue. Our results show that contrasting an acquisition with short gradient duration and short diffusion time with an acquisition with long gradient duration and long diffusion time improves the TDR contrast for a wide range of pore configurations. Additionally, in the presence of Rician noise, computing TDR from a subset (50% or fewer) of the acquired diffusion gradients rather than the entire shell as proposed originally further improves the contrast. In the last part of the work the results are demonstrated experimentally on rat spinal cord. In line with simulations, the experimental data shows that optimised TDR improves the contrast compared to non-optimised TDR. Furthermore, we find a strong correlation between TDR and histology measurements of axon diameter. In conclusion, we find that TDR has great potential and is a very promising alternative (or potentially complement) to model-based approaches for mapping pore sizes and restricted diffusion in general.

show abstract

“…The LMM algorithm is available in the article's supplementary material. MRI datasets that can be used for LMM analysis have been made publicly available through the figshare repository (Tian et al, 2022 ): https://springernature.figshare.com/collections/Comprehensive_diffusion_MRI_dataset_for_in_vivo_human_brain_microstructure_mapping_using_300_mT_m_gradients/5315474 .…”

Section: Data Availability Statementmentioning

confidence: 99%

Linear multi‐scale modeling of diffusion MRI data: A framework for characterization of oriented structures across length scales

Wichtmann

Fan

Eskandarian

et al. 2022

Human Brain Mapping

Self Cite

View full text Add to dashboard Cite

Diffusion‐weighted magnetic resonance imaging (DW‐MRI) has evolved to provide increasingly sophisticated investigations of the human brain's structural connectome in vivo. Restriction spectrum imaging (RSI) is a method that reconstructs the orientation distribution of diffusion within tissues over a range of length scales. In its original formulation, RSI represented the signal as consisting of a spectrum of Gaussian diffusion response functions. Recent technological advances have enabled the use of ultra‐high b ‐values on human MRI scanners, providing higher sensitivity to intracellular water diffusion in the living human brain. To capture the complex diffusion time dependence of the signal within restricted water compartments, we expand upon the RSI approach to represent restricted water compartments with non‐Gaussian response functions, in an extended analysis framework called linear multi‐scale modeling (LMM). The LMM approach is designed to resolve length scale and orientation‐specific information with greater specificity to tissue microstructure in the restricted and hindered compartments, while retaining the advantages of the RSI approach in its implementation as a linear inverse problem. Using multi‐shell, multi‐diffusion time DW‐MRI data acquired with a state‐of‐the‐art 3 T MRI scanner equipped with 300 mT/m gradients, we demonstrate the ability of the LMM approach to distinguish different anatomical structures in the human brain and the potential to advance mapping of the human connectome through joint estimation of the fiber orientation distributions and compartment size characteristics.

show abstract

Comprehensive diffusion MRI dataset for in vivo human brain microstructure mapping using 300 mT/m gradients

Cited by 26 publications

References 94 publications

Estimating axial diffusivity in the NODDI model

Estimating axial diffusivity in the NODDI model

Optimisation and Pre-clinical Demonstration of Temporal Diffusion Ratio for Imaging Restricted Diffusion

Linear multi‐scale modeling of diffusion MRI data: A framework for characterization of oriented structures across length scales

Contact Info

Product

Resources

About