TY - JOUR
T1 - Double diffusion encoding and applications for biomedical imaging
AU - Henriques, Rafael N.
AU - Palombo, Marco
AU - Jespersen, Sune N.
AU - Shemesh, Noam
AU - Lundell, Henrik
AU - Ianuş, Andrada
N1 - Funding Information:
The authors would like to acknowledge the following sources of financial support. AI is supported by European Union's Horizon 2020 research and innovation programme under the Marie Sk?odowska-Curie grant agreement No 101003390 and Champalimaud Centre for the Unknown, Lisbon (Portugal); RNH and NS are supported by European Research Council (ERC) (agreement No. 679058); MP is supported by Engineering and Physical Sciences Research Council (EPSRC EP/N018702/1) and UKRI Future Leaders FellowshipMR/T020296/1; SJ is supported by Danish National Research Foundation (CFIN), and the Danish Ministry of Science, Innovation, and Education (MINDLab); HL is supported by H2020 European Research Council, Grant/Award Number: 804746; Danish Council for Independent Research, Grant/Award Number: 4093-00280B.
Funding Information:
The authors would like to acknowledge the following sources of financial support. AI is supported by E uropean Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 101003390 and C hampalimaud Centre for the Unknown, Lisbon (Portugal); RNH and NS are supported by E uropean Research Council (ERC) (agreement No. 679058) ; MP is supported by E ngineering and Physical Sciences Research Council (EPSRC EP/N018702/1) and U KRI Future Leaders Fellowship MR/T020296/1; SJ is supported by D anish National Research Foundation (CFIN) , and the D anish Ministry of Science, Innovation, and Education (MINDLab) ; HL is supported by H 2020 European Research Council, Grant/Award Number: 804746; D anish Council for Independent Research , Grant/Award Number: 4093-00280B.
Publisher Copyright:
© 2020 Elsevier B.V.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/1/15
Y1 - 2021/1/15
N2 - Diffusion Magnetic Resonance Imaging (dMRI) is one of the most important contemporary non-invasive modalities for probing tissue structure at the microscopic scale. The majority of dMRI techniques employ standard single diffusion encoding (SDE) measurements, covering different sequence parameter ranges depending on the complexity of the method. Although many signal representations and biophysical models have been proposed for SDE data, they are intrinsically limited by a lack of specificity. Advanced dMRI methods have been proposed to provide additional microstructural information beyond what can be inferred from SDE. These enhanced contrasts can play important roles in characterizing biological tissues, for instance upon diseases (e.g. neurodegenerative, cancer, stroke), aging, learning, and development. In this review we focus on double diffusion encoding (DDE), which stands out among other advanced acquisitions for its versatility, ability to probe more specific diffusion correlations, and feasibility for preclinical and clinical applications. Various DDE methodologies have been employed to probe compartment sizes (Section 3), decouple the effects of microscopic diffusion anisotropy from orientation dispersion (Section 4), probe displacement correlations, study exchange, or suppress fast diffusing compartments (Section 6). DDE measurements can also be used to improve the robustness of biophysical models (Section 5) and study intra-cellular diffusion via magnetic resonance spectroscopy of metabolites (Section 7). This review discusses all these topics as well as important practical aspects related to the implementation and contrast in preclinical and clinical settings (Section 9) and aims to provide the readers a guide for deciding on the right DDE acquisition for their specific application.
AB - Diffusion Magnetic Resonance Imaging (dMRI) is one of the most important contemporary non-invasive modalities for probing tissue structure at the microscopic scale. The majority of dMRI techniques employ standard single diffusion encoding (SDE) measurements, covering different sequence parameter ranges depending on the complexity of the method. Although many signal representations and biophysical models have been proposed for SDE data, they are intrinsically limited by a lack of specificity. Advanced dMRI methods have been proposed to provide additional microstructural information beyond what can be inferred from SDE. These enhanced contrasts can play important roles in characterizing biological tissues, for instance upon diseases (e.g. neurodegenerative, cancer, stroke), aging, learning, and development. In this review we focus on double diffusion encoding (DDE), which stands out among other advanced acquisitions for its versatility, ability to probe more specific diffusion correlations, and feasibility for preclinical and clinical applications. Various DDE methodologies have been employed to probe compartment sizes (Section 3), decouple the effects of microscopic diffusion anisotropy from orientation dispersion (Section 4), probe displacement correlations, study exchange, or suppress fast diffusing compartments (Section 6). DDE measurements can also be used to improve the robustness of biophysical models (Section 5) and study intra-cellular diffusion via magnetic resonance spectroscopy of metabolites (Section 7). This review discusses all these topics as well as important practical aspects related to the implementation and contrast in preclinical and clinical settings (Section 9) and aims to provide the readers a guide for deciding on the right DDE acquisition for their specific application.
KW - diffusion correlation tensor
KW - Diffusion MRI
KW - double diffusion encoding
KW - exchange
KW - magnetic resonance spectroscopy
KW - microscopic anisotropy
KW - tissue microstructure
KW - Magnetic Resonance Spectroscopy
KW - Brain/diagnostic imaging
KW - Magnetic Resonance Imaging
KW - Anisotropy
KW - Diffusion Magnetic Resonance Imaging
KW - Diffusion
UR - https://www.scopus.com/pages/publications/85097157825
U2 - 10.1016/j.jneumeth.2020.108989
DO - 10.1016/j.jneumeth.2020.108989
M3 - Review
C2 - 33144100
AN - SCOPUS:85097157825
SN - 0165-0270
VL - 348
SP - 108989
JO - Journal of Neuroscience Methods
JF - Journal of Neuroscience Methods
M1 - 108989
ER -