Double diffusion encoding and applications for biomedical imaging

Henriques, Rafael Neto; Palombo, Marco; Jespersen, Sune Nørhøj; Shemesh, Noam; Lundell, Henrik; Ianuş, Andrada

doi:10.1016/j.jneumeth.2020.108989

Cited by 33 publications

(30 citation statements)

References 243 publications

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“…On the other hand, kurtosis increases in more abrupt damaged microstructural tissues (e.g., ischemia, traumatic brain injury) can occur due to cytogenic and vasogenic effects (Hui et al, 2012 ; Zhuo et al, 2012 ). To increase the specificity of kurtosis, several studies have used advanced diffusion encoding sequences to decouple different kurtosis sources (e.g., Szczepankiewicz et al, 2016 , 2020 ; Westin et al, 2016 ; Topgaard, 2017 ; Henriques et al, 2020a , b ). Due to the high potential of these techniques, procedures for reconstructing diffusion-weighted data acquired from advanced diffusion sequences are currently being incorporated in DIPY.…”

Section: Discussionmentioning

confidence: 99%

Diffusional Kurtosis Imaging in the Diffusion Imaging in Python Project

Henriques

Correia

Marrale

et al. 2021

Front. Hum. Neurosci.

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Diffusion-weighted magnetic resonance imaging (dMRI) measurements and models provide information about brain connectivity and are sensitive to the physical properties of tissue microstructure. Diffusional Kurtosis Imaging (DKI) quantifies the degree of non-Gaussian diffusion in biological tissue from dMRI. These estimates are of interest because they were shown to be more sensitive to microstructural alterations in health and diseases than measures based on the total anisotropy of diffusion which are highly confounded by tissue dispersion and fiber crossings. In this work, we implemented DKI in the Diffusion in Python (DIPY) project—a large collaborative open-source project which aims to provide well-tested, well-documented and comprehensive implementation of different dMRI techniques. We demonstrate the functionality of our methods in numerical simulations with known ground truth parameters and in openly available datasets. A particular strength of our DKI implementations is that it pursues several extensions of the model that connect it explicitly with microstructural models and the reconstruction of 3D white matter fiber bundles (tractography). For instance, our implementations include DKI-based microstructural models that allow the estimation of biophysical parameters, such as axonal water fraction. Moreover, we illustrate how DKI provides more general characterization of non-Gaussian diffusion compatible with complex white matter fiber architectures and gray matter, and we include a novel mean kurtosis index that is invariant to the confounding effects due to tissue dispersion. In summary, DKI in DIPY provides a well-tested, well-documented and comprehensive reference implementation for DKI. It provides a platform for wider use of DKI in research on brain disorders and in cognitive neuroscience.

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Section: Discussionmentioning

confidence: 99%

Diffusional Kurtosis Imaging in the Diffusion Imaging in Python Project

Henriques

Correia

Marrale

et al. 2021

Front. Hum. Neurosci.

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“…Several methods have been suggested to separate anisotropic diffusion on the microscopic scale, reflecting the presence of thin fibers such as axons and astrocytic processes, from macroscopic anisotropy, which reflects co-alignment of fibers across the acquisition volume. These methods include double diffusion encoding (DDE) experiments ( Shemesh et al., 2016 , Henriques et al., 2020 ), related tensor valued encoding methods in multiple directions with tailored gradient waveforms Topgaard (2017) , and analysis of the non-monoexponential attenuation of disordered samples or the so-called “powder average” of diffusion measurements in multiple directions with respect to the b value in conventional diffusion experiments ( Callaghan et al., 1979 , Kroenke et al., 2004 , Palombo et al., 2017 , Lundell et al., 2020 , Kaden et al., 2016 , (Veraart et al., 2019) ). Microscopic axial (D // ) and transverse (D ┴ ) diffusivities as well as the related microscopic anisotropy (µFA) can be calculated from these measurements, and these reflect the diffusion properties within cytomorphological units on the length scale of the diffusion process, regardless of their mutual orientation on larger length scales ( Fig.…”

Section: Introductionmentioning

confidence: 99%

Compartmental diffusion and microstructural properties of human brain gray and white matter studied with double diffusion encoding magnetic resonance spectroscopy of metabolites and water

et al. 2021

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Highlights Compartment and cell-specific microscopic anisotropy are measured with DDES. The intracellular space is highly anisotropic in white (WM) and gray matter (GM). The extracellular space in GM is isotropic, while that of WM is highly anisotropic. Water and metabolites intracellular mean diffusivities are lower in GM than in WM. Intracellular tortuosity derived from water and tNAA is higher in GM than WM.

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“…In the majority of SDE acquisitions δ and Δ are fixed and G is varied to change the b-value, although varying the gradient duration and diffusion time can provide additional orthogonal measurements (D. S. Novikov et al 2019). Over the last decade, diffusion sequences which further vary the gradient waveform within one measurement, such as double diffusion encoding (DDE) (Mitra 1995;Henriques et al 2020) or b-tensor encoding approaches (Lasic et al 2014;C. F. Westin et al 2016), have been gaining interest as they can further improve the specificity of the measurements towards the underlying tissue microstructure.…”

Section: Introductionmentioning

confidence: 99%

On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge

Luca

Ianuş

Leemans

et al. 2021

Preprint

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Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. Most predictions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings.

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Double diffusion encoding and applications for biomedical imaging

Cited by 33 publications

References 243 publications

Diffusional Kurtosis Imaging in the Diffusion Imaging in Python Project

Diffusional Kurtosis Imaging in the Diffusion Imaging in Python Project

Compartmental diffusion and microstructural properties of human brain gray and white matter studied with double diffusion encoding magnetic resonance spectroscopy of metabolites and water

On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge

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